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Liu Y, Lv Z, Zhou S, Fu Z, Wang Y, Yi L, Li X, Wang Y, Hu S, Zhou Z, Chen Y. A smartwatch sphygmomanometer-based model for predicting short-term new-onset hypertension in individuals with high-normal blood pressure: a cohort study. Clin Exp Hypertens 2024; 46:2304023. [PMID: 38346228 DOI: 10.1080/10641963.2024.2304023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 01/03/2024] [Indexed: 02/15/2024]
Abstract
OBJECTIVES The objective was to utilize a smartwatch sphygmomanometer to predict new-onset hypertension within a short-term follow-up among individuals with high-normal blood pressure (HNBP). METHODS This study consisted of 3180 participants in the training set and 1000 participants in the validation set. Participants underwent both ambulatory blood pressure monitoring (ABPM) and home blood pressure monitoring (HBPM) using a smartwatch sphygmomanometer. Multivariable Cox regressions were used to analyze cumulative events. A nomogram was constructed to predict new-onset hypertension. Discrimination and calibration were assessed using the C-index and calibration curve, respectively. RESULTS Among the 3180 individuals with HNBP in the training set, 693 (21.8%) developed new-onset hypertension within a 6-month period. The nomogram for predicting new-onset hypertension had a C-index of 0.854 (95% CI, 0.843-0.867). The calibration curve demonstrated good agreement between the nomogram's predicted probabilities and actual observations for short-term new-onset hypertension. In the validate dataset, during the 6-month follow-up, the nomogram had a good C-index of 0.917 (95% CI, 0.904-0.930) and a good calibration curve. As the score increased, the risk of new-onset hypertension significantly increased, with an HR of 8.415 (95% CI: 5.153-13.744, p = .000) for the middle-score vs. low-score groups and 86.824 (95% CI: 55.071-136.885, p = .000) for the high-score vs. low-score group. CONCLUSIONS This study provides evidence for the use of smartwatch sphygmomanometer to monitor blood pressure in individuals at high risk of developing new-onset hypertension in the near future. TRIAL REGISTRATION ChiCTR2200057354.
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Affiliation(s)
- Yuqi Liu
- Department of Cardiology, the Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
- Department of Cardiology, the First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Zhonghua Lv
- Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, China
| | - Shanshan Zhou
- Department of Cardiology, the Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
- Department of Cardiology, the First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Zihao Fu
- Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, China
| | - Yifei Wang
- Medical data center, Chinese PLA General Hospital, Beijing, China
| | - Li Yi
- Department of Cardiology, the Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Xiaolong Li
- Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, China
| | - Ying Wang
- Department of Cardiology, the Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
- Department of Cardiology, the First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Shunying Hu
- Department of Cardiology, the Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
- Department of Cardiology, the First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Zhirui Zhou
- Radiation Oncology Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yundai Chen
- Department of Cardiology, the Sixth Medical Centre, Chinese PLA General Hospital, Beijing, China
- Department of Cardiology, the First Medical Centre, Chinese PLA General Hospital, Beijing, China
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Su QY, Chen WJ, Zheng YJ, Shi W, Gong FC, Huang SW, Yang ZT, Qu HP, Mao EQ, Wang RL, Zhu DM, Zhao G, Chen W, Wang S, Wang Q, Zhu CQ, Yuan G, Chen EZ, Chen Y. Development and external validation of a nomogram for the early prediction of acute kidney injury in septic patients: a multicenter retrospective clinical study. Ren Fail 2024; 46:2310081. [PMID: 38321925 PMCID: PMC10851832 DOI: 10.1080/0886022x.2024.2310081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/21/2024] [Indexed: 02/08/2024] Open
Abstract
Background and purpose: Acute kidney injury (AKI) is a common serious complication in sepsis patients with a high mortality rate. This study aimed to develop and validate a predictive model for sepsis associated acute kidney injury (SA-AKI). Methods: In our study, we retrospectively constructed a development cohort comprising 733 septic patients admitted to eight Grade-A tertiary hospitals in Shanghai from January 2021 to October 2022. Additionally, we established an external validation cohort consisting of 336 septic patients admitted to our hospital from January 2017 to December 2019. Risk predictors were selected by LASSO regression, and a corresponding nomogram was constructed. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) and clinical impact curves (CIC) in both internal and external validation. Results: AKI incidence was 53.2% in the development cohort and 48.2% in the external validation cohort. The model included five independent indicators: chronic kidney disease stages 1 to 3, blood urea nitrogen, procalcitonin, D-dimer and creatine kinase isoenzyme. The AUC of the model in the development and validation cohorts was 0.914 (95% CI, 0.894-0.934) and 0.923 (95% CI, 0.895-0.952), respectively. The calibration plot, DCA, and CIC demonstrated the model's favorable clinical applicability. Conclusion: We developed and validated a robust nomogram model, which might identify patients at risk of SA-AKI and promising for clinical applications.
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Affiliation(s)
- Qin-Yue Su
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wen-Jie Chen
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan-Jun Zheng
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wen Shi
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fang-Chen Gong
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shun-Wei Huang
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhi-tao Yang
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hong-Ping Qu
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - En-Qiang Mao
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rui-Lan Wang
- Department of Emergency Medicine, Shanghai First People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Du-Ming Zhu
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Gang Zhao
- Department of Emergency Medicine, Shanghai Sixth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Chen
- Department of Critical Care Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Sheng Wang
- Department of Critical Care Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Qian Wang
- Department of Emergency Medicine, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chang-Qing Zhu
- Department of Emergency Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Gao Yuan
- Department of Critical Care Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Er-Zhen Chen
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Chen
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Yang Q, Xiang Y, Ma G, Cao M, Fang Y, Xu W, Li L, Li Q, Feng Y, Yang Q. A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease. Ren Fail 2024; 46:2317450. [PMID: 38419596 PMCID: PMC10906131 DOI: 10.1080/0886022x.2024.2317450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/06/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND The high prevalence of mild cognitive impairment (MCI) in non-dialysis individuals with chronic kidney disease (CKD) impacts their prognosis and quality of life. OBJECTIVE This study aims to investigate the variables associated with MCI in non-dialysis outpatient patients with CKD and to construct and verify a nomogram prediction model. METHODS 416 participants selected from two hospitals in Chengdu, between January 2023 and June 2023. They were categorized into two groups: the MCI group (n = 210) and the non-MCI (n = 206). Univariate and multivariate binary logistic regression analyses were employed to identify independent influences (candidate predictor variables). Subsequently, regression models was constructed, and a nomogram was drawn. The restricted cubic spline diagram was drawn to further analyze the relationship between the continuous numerical variables and MCI. Internally validated using a bootstrap resampling procedure. RESULTS Among 416 patients, 210 (50.9%) had MCI. Logistic regression analysis revealed that age, educational level, occupational status, use of smartphones, sleep disorder, and hemoglobin were independent influencing factors of MCI (all p<.05). The model's area under the curve was 0.926,95% CI (0.902, 0.951), which was a good discriminatory measure; the Calibration curve, the Hosmer-Lemeshow test, and the Clinical Decision Curve suggested that the model had good calibration and clinical benefit. Internal validation results showed the consistency index was 0.926, 95%CI (0.925, 0.927). CONCLUSION The nomogram prediction model demonstrates good performance and can be used for early screening and prediction of MCI in non-dialysis patients with CKD. It provides valuable reference for medical staff to formulate corresponding intervention strategies.
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Affiliation(s)
- Qin Yang
- School of Nursing, Chengdu Medical College, Chengdu, China
| | - Yuhe Xiang
- School of Nursing, Chengdu Medical College, Chengdu, China
| | - Guoting Ma
- Health Management Center, Sichuan Tai Kang Hospital, Chengdu, China
| | - Min Cao
- Department of Orthopedics, Sichuan Second Traditional Chinese Medicine Hospital, Chengdu, China
| | - Yixi Fang
- School of Nursing, Chengdu Medical College, Chengdu, China
| | - Wenbin Xu
- School of Nursing, Chengdu Medical College, Chengdu, China
| | - Lin Li
- School of Nursing, Chengdu Medical College, Chengdu, China
| | - Qin Li
- Department of Nephrology, First Affiliated Hospital of Chengdu Medical College, Chengdu, China
| | - Yu Feng
- School of Nursing, Chengdu Medical College, Chengdu, China
| | - Qian Yang
- School of Nursing, Chengdu Medical College, Chengdu, China
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Mu J, Zhong H, Jiang M, Wang J, Zhang S. Development of a nomogram for predicting myopia risk among school-age children: a case-control study. Ann Med 2024; 56:2331056. [PMID: 38507901 PMCID: PMC10956924 DOI: 10.1080/07853890.2024.2331056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 02/23/2024] [Indexed: 03/22/2024] Open
Abstract
OBJECTIVES To analyze the factors influencing myopia and construct a nomogram to forecast the risk of myopia among school-age children, providing a reference for identifying high-risk groups to aid prevention and control. METHODS This case-control study enrolled 3512 students from three primary schools in Shenzhen using random cluster sampling for a questionnaire survey, myopia screening and ocular biometric parameter measurement. Logistic regression was used to analyze the influencing factors of myopia, and a nomogram was constructed to forecast myopia risk. Bootstrap resampling was used to verify the practicability of the nomogram. RESULTS Older age (odds ratio[OR] = 1.164; 95% confidence interval [CI]: 1.111-1.219), female sex (OR = 2.405; 95% CI: 2.003-2.887), maternal myopia (OR = 1.331; 95% CI: 1.114-1.589), incorrect posture during reading and writing (OR = 1.283; 95% CI: 1.078-1.528) and axial length (OR = 7.708; 95% CI: 6.044-8.288) are risk factors for myopia, whereas an increase in corneal radius (OR = 0.036; 95% CI: 0.025-0.052) is a protective factor against myopia. The area under the receiver operating characteristic (ROC) curve of the nomogram was 0.857, and the net benefit was high when the risk threshold of the decision curve analyses (DCA) ranged from 0.20 to 1.00. The measured values were consistent with the prediction. CONCLUSION The nomogram was accurate in predicting the risk of myopia among schoolchildren. This study provides a reference for screening high-risk students and for individualized myopia prevention and control.
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Affiliation(s)
- Jingfeng Mu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Haoxi Zhong
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Mingjie Jiang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Jiantao Wang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Shaochong Zhang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
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Sun X, Liu C, Zhang C, Zhang Z. Nomogram for predicting postoperative ileus after radical cystectomy and urinary diversion: a retrospective single-center study. Ann Med 2024; 56:2329125. [PMID: 38498939 PMCID: PMC10949833 DOI: 10.1080/07853890.2024.2329125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/23/2024] [Indexed: 03/20/2024] Open
Abstract
OBJECTIVE To predict the incidence of postoperative ileus in bladder cancer patients after radical cystectomy. METHODS We retrospectively analyzed the perioperative data of 452 bladder cancer patients who underwent radical cystectomy with urinary diversion at the Second Hospital of Tianjin Medical University between 2016 and 2021. Univariate and multivariate logistic regression were used to identify the risk factors for postoperative ileus. Finally, a nomogram model was established and verified based on the independent risk factors. RESULTS Our study revealed that 96 patients (21.2%) developed postoperative ileus. Using multivariate logistic regression analysis, we found that the independent risk factors for postoperative ileus after radical cystectomy included age > 65.0 years, high or low body mass index, constipation, hypoalbuminemia, and operative time. We established a nomogram prediction model based on these independent risk factors. Validation by calibration curves, concordance index, and decision curve analysis showed a strong correlation between predicted and actual probabilities of occurrence. CONCLUSION Our nomogram prediction model provides surgeons with a simple tool to predict the incidence of postoperative ileus in bladder cancer patients undergoing radical cystectomy.
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Affiliation(s)
- Xiaoyu Sun
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Chang Liu
- Department of Urology, Renmin Hospital of Wuhan Economic and Technological Development Zone (Hannan), Wuhan, China
| | - Changwen Zhang
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Zhihong Zhang
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
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Zhang F, Han Y, Mao Y, Zheng G, Liu L, Li W. Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study. Ann Med 2024; 56:2337739. [PMID: 38574396 PMCID: PMC10997367 DOI: 10.1080/07853890.2024.2337739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 03/04/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND AND AIM This study aims to validate the efficacy of the conventional non-invasive score in predicting significant fibrosis in metabolic-associated fatty liver disease (MAFLD) and to develop a non-invasive prediction model for MAFLD. METHODS This cross-sectional study was conducted among 7701 participants with MAFLD from August 2018 to December 2023. All participants were divided into a training cohort and a validation cohort. The study compared different subgroups' demographic, anthropometric, and laboratory examination indicators and conducted logistic regression analysis to assess the correlation between independent variables and liver fibrosis. Nomograms were created using the logistic regression model. The predictive values of noninvasive models and nomograms were evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). RESULTS Four nomograms were developed for the quantitative analysis of significant liver fibrosis risk based on the multivariate logistic regression analysis results. The nomogram's area under ROC curves (AUC) was 0.710, 0.714, 0.748, and 0.715 in overall MAFLD, OW-MAFLD, Lean-MAFLD, and T2DM-MAFLD, respectively. The nomogram had a higher AUC in all MAFLD participants and OW-MAFLD than the other non-invasive scores. The DCA curve showed that the net benefit of each nomogram was higher than that of APRI and FIB-4. In the validation cohort, the AUCs of the nomograms were 0.722, 0.750, 0.719, and 0.705, respectively. CONCLUSION APRI, FIB-4, and NFS performed poorly predicting significant fibrosis in patients with MAFLD. The new model demonstrated improved diagnostic accuracy and clinical applicability in identifying significant fibrosis in MAFLD.
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Affiliation(s)
- Fan Zhang
- Department of Endocrinology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China
- Department of Clinical Nutrition, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China
| | - Yan Han
- Department of Endocrinology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China
- Department of Clinical Nutrition, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China
| | - Yonghua Mao
- Department of Endocrinology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China
| | - Guojun Zheng
- Clinical Laboratory, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China
| | - Longgen Liu
- Department of Liver Diseases, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China
| | - Wenjian Li
- Department of Urology, Changzhou Third People’s Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China
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Xu L, Cao F, Wang L, Liu W, Gao M, Zhang L, Hong F, Lin M. Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients. Ren Fail 2024; 46:2324071. [PMID: 38494197 PMCID: PMC10946267 DOI: 10.1080/0886022x.2024.2324071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024] Open
Abstract
INTRODUCTION The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm. METHODS We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. The performance was validated using fivefold cross-validation. The optimal ML algorithm was used to construct the models to predictive the risk of the HF and all-cause mortality. The prediction performance of ML methods and Cox regression was compared. RESULTS Over a median follow-up of 49 months. Two hundred and ninety-eight patients developed HF required hospitalization; 199 patients died during the follow-up. The RF model (AUC = 0.853) was the best performing model for predicting HF, and the XGBoost model (AUC = 0.871) was the best model for predicting mortality. Baseline moderate or severe renal disease, systolic blood pressure (SBP), body mass index (BMI), age, Charlson Comorbidity Index (CCI) score were strongly associated with HF hospitalization, whereas age, CCI score, creatinine, age, high-density lipoprotein cholesterol (HDL-C), total cholesterol, baseline estimated glomerular filtration rate (eGFR) were the most significant predictors of mortality. For all the above endpoints, the ML models demonstrated better discrimination than Cox regression. CONCLUSIONS We developed and validated a novel method to predict the risk factors of HF and all-cause mortality that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among PD patients.
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Affiliation(s)
- Liping Xu
- Department of Nephrology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China
| | - Fang Cao
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
- Department of Nursing, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Lian Wang
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Weihua Liu
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Meizhu Gao
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Li Zhang
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Fuyuan Hong
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Miao Lin
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
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Tu Y, Zhang J, Zhao M, He F. Nomogram establishment for short-term survival prediction in ICU patients with aplastic anemia based on the MIMIC-IV database. Hematology 2024; 29:2339778. [PMID: 38625693 DOI: 10.1080/16078454.2024.2339778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 03/27/2024] [Indexed: 04/17/2024] Open
Abstract
OBJECTIVE To establish an efficient nomogram model to predict short-term survival in ICU patients with aplastic anemia (AA). METHODS The data of AA patients in the MIMIC-IV database were obtained and randomly assigned to the training set and testing set in a ratio of 7:3. Independent prognosis factors were identified through univariate and multivariate Cox regression analyses. The variance inflation factor was calculated to detect the correlation between variables. A nomogram model was built based on independent prognostic factors and risk scores for factors were generated. Model performance was tested using C-index, receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and Kaplan-Meier curve. RESULTS A total of 1,963 AA patients were included. A nomogram model with 7 variables was built, including SAPS II, chronic pulmonary obstructive disease, body temperature, red cell distribution width, saturation of peripheral oxygen, age and mechanical ventilation. The C-indexes in the training set and testing set were 0.642 and 0.643 respectively, indicating certain accuracy of the model. ROC curve showed favorable classification performance of nomogram. The calibration curve reflected that its probabilistic prediction was reliable. DCA revealed good clinical practicability of the model. Moreover, the Kaplan-Meier curve showed that receiving mechanical ventilation could improve the survival status of AA patients in the short term but did not in the later period. CONCLUSION The nomogram model of the short-term survival rate of AA patients was built based on clinical characteristics, and early mechanical ventilation could help improve the short-term survival rate of patients.
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Affiliation(s)
- Yan Tu
- Department of Hematology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, People's Republic of China
| | - Jingcheng Zhang
- Department of Hematology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, People's Republic of China
| | - Mingzhe Zhao
- Department of Hematology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, People's Republic of China
| | - Fang He
- Department of Hematology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, People's Republic of China
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Liu P, Chen YW, Liu C, Wu YT, Zhao WC, Zhu JY, An Y, Xia NX. Development and validation of a nomogram model for predicting the risk of gallstone recurrence after gallbladder-preserving surgery. Hepatobiliary Pancreat Dis Int 2024; 23:288-292. [PMID: 36443144 DOI: 10.1016/j.hbpd.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 10/25/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND The high incidence of gallstone recurrence was a major concern for laparoscopic gallbladder-preserving surgery. This study aimed to investigate the risk factors for gallstone recurrence after gallbladder-preserving surgery and to establish an individualized nomogram model to predict the risk of gallstone recurrence. METHODS The clinicopathological and follow-up data of 183 patients who were initially diagnosed with gallstones and treated with gallbladder-preserving surgery at our hospital from January 2012 to January 2019 were retrospectively collected. The independent predictive factors for gallstone recurrence following gallbladder-preserving surgery were identified by multivariate logistic regression analysis. A nomogram model for the prediction of gallstone recurrence was constructed based on the selected variables. The C-index, receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive power of the nomogram model for gallstone recurrence. RESULTS During the follow-up period, a total of 65 patients experienced gallstone recurrence, and the recurrence rate was 35.5%. Multivariate logistic regression analysis revealed that the course of gallstones > 2 years [odds ratio (OR) = 2.567, 95% confidence interval (CI): 1.270-5.187, P = 0.009], symptomatic gallstones (OR = 2.589, 95% CI: 1.059-6.329, P = 0.037), multiple gallstones (OR = 2.436, 95% CI: 1.133-5.237, P = 0.023), history of acute cholecystitis (OR = 2.778, 95% CI: 1.178-6.549, P = 0.020) and a greasy diet (OR = 2.319, 95% CI: 1.186-4.535, P = 0.014) were independent risk factors for gallstone recurrence after gallbladder-preserving surgery. A nomogram model for predicting the recurrence of gallstones was established based on the above five variables. The results showed that the C-index of the nomogram model was 0.692, suggesting it was valuable to predict gallstone recurrence. Moreover, the calibration curve showed good consistency between the predicted probability and actual probability. CONCLUSIONS The nomogram model for the prediction of gallstone recurrence might help clinicians develop a proper treatment strategy for patients with gallstones. Gallbladder-preserving surgery should be cautiously considered for patients with high recurrence risks.
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Affiliation(s)
- Peng Liu
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Yong-Wei Chen
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Che Liu
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Yin-Tao Wu
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Wen-Chao Zhao
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Jian-Yong Zhu
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Yang An
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China
| | - Nian-Xin Xia
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China; Institute of Hepatobiliary Surgery of Chinese PLA, Beijing 100853, China; Key Laboratory of Digital Hepetobiliary Surgery of Chinese PLA, Beijing 100853, China.
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10
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Jian YL, Jia S, Shi S, Shi Z, Zhao Y. A nomogram to predict the risk of cognitive impairment in patients with depressive disorder. Res Nurs Health 2024; 47:302-311. [PMID: 38149849 DOI: 10.1002/nur.22364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 12/03/2023] [Accepted: 12/13/2023] [Indexed: 12/28/2023]
Abstract
This study was to describe the cognitive function status in patients with depressive disorder and to construct a nomogram model to predict the risk factors of cognitive impairment in these patients. From October 2019 to February 2021, a total of 141 patients with depressive disorder completed the survey in two hospitals. The Montreal cognitive assessment (MoCA) was used with a cutoff score of 26 to differentiate cognitive impairment. Univariable and multivariable logistic regression analyses were conducted to identify independent risk factors. A nomogram was then constructed based on the results of the multivariable logistic regression analysis. The patients had an average MoCA score of 23.99 ± 3.02. The multivariable logistic regression analysis revealed that age (OR: 1.096, 95% CI: 1.042-1.153, p < 0.001), education (OR: 0.065, 95% CI: 0.016-0.263, p < 0.001), depression severity (OR: 1.878, 95% CI: 1.021-3.456, p = 0.043), and sleep quality (OR: 2.454, 95% CI: 1.400-4.301, p = 0.002) were independent risk factors for cognitive impairment in patients with depressive disorder. The area under receiver operating characteristic (ROC) curves was 0.868 (95% CI: 0.807-0.929), indicating good discriminability of the model. The calibration curve of the model and the Hosmer-Lemeshow test (p = 0.571) demonstrated a well-fitted model with high calibration. Age, education, depression severity, and sleep quality were found to be significant predictors of cognitive function. A nomogram model was developed to predict cognitive impairment in patients with depressive disorder, providing a solid foundation for clinical interventions.
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Affiliation(s)
| | - Shoumei Jia
- School of Nursing, Fudan University, Shanghai, China
| | - Shenxun Shi
- Department of Psychiatry, Fudan University Huashan Hospital, Shanghai, China
| | | | - Ying Zhao
- School of Nursing, Fudan University, Shanghai, China
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11
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Callegaro D, Barretta F, Raut CP, Johnston W, Strauss DC, Honoré C, Bonvalot S, Fairweather M, Rutkowski P, van Houdt WJ, Gladdy RA, Tirotta F, Tzanis D, Skoczylas J, Haas RL, Miceli R, Swallow CJ, Gronchi A. New Sarculator Prognostic Nomograms for Patients With Primary Retroperitoneal Sarcoma: Case Volume Does Matter. Ann Surg 2024; 279:857-865. [PMID: 37753660 DOI: 10.1097/sla.0000000000006098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
OBJECTIVE To update the current Sarculator retroperitoneal sarcoma (RPS) prognostic nomograms considering the improvement in patient prognosis and the case volume effect. BACKGROUND Survival of patients with primary RPS has been increasing over time, and the volume-outcome relationship has been well recognized. Nevertheless, the specific impact on prognostic nomograms is unknown. METHODS All consecutive adult patients with primary localized RPS treated at 8 European and North American sarcoma reference centers between 2010 and 2017 were included. Patients were divided into 2 groups: high-volume centers (HVC, ≥13 cases/year) and low-volume centers (LVC, <13 cases/year). Primary end points were overall survival (OS) and disease-free survival (DFS). Multivariable analyses for OS and DFS were performed. The nomograms were updated by recalibration. Nomograms performance was assessed in terms of discrimination (Harrell C index) and calibration (calibration plot). RESULTS The HVC and LVC groups comprised 857 and 244 patients, respectively. The median annual primary RPS case volume (interquartile range) was 24.0 in HVC (15.0-41.3) and 9.0 in LVC (1.8-10.3). Five-year OS was 71.4% (95% CI: 68.3%-74.7%) in the HVC cohort and 63.3% (56.8%-70.5%) in the LVC cohort ( P =0.012). Case volume was associated with both OS (LVC vs. HVC hazard ratio 1.40, 95% CI: 1.08-1.82, P =0.011) and DFS (hazard ratio 1.93, 95% CI: 1.57-2.37, P <0.001) at multivariable analyses. When applied to the study cohorts, the Sarculator nomograms showed good discrimination (Harrell C index between 0.68 and 0.73). The recalibrated nomograms showed good calibration in the HVC group, whereas the original nomograms showed good calibration in the LVC group. CONCLUSIONS New nomograms for patients with primary RPS treated with surgery at high-volume versus low-volume sarcoma reference centers are available in the Sarculator app.
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Affiliation(s)
- Dario Callegaro
- Department of Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Francesco Barretta
- Department of Biostatistics for Clinical Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Chandrajit P Raut
- Department of Surgery, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Wendy Johnston
- Department of Surgery, Mount Sinai Hospital, Toronto, ON, Canada
| | - Dirk C Strauss
- Sarcoma Unit, Department of Academic Surgery, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Charles Honoré
- Department of Surgery, Institut Gustave Roussy, Villejuif, France
| | | | - Mark Fairweather
- Department of Surgery, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA
| | - Piotr Rutkowski
- Department of Soft Tissue/Bone Sarcoma and Melanoma, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Winan J van Houdt
- Department of Surgical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Rebecca A Gladdy
- Department of Surgical Oncology, Princess Margaret Cancer Centre, Mount Sinai Hospital, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Fabio Tirotta
- Sarcoma Unit, Department of Academic Surgery, Royal Marsden NHS Foundation Trust, London, United Kingdom
- Department of Sarcoma and General Surgery, Midlands Abdominal and Retroperitoneal Sarcoma Unit, University Hospital Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | | | - Jacek Skoczylas
- Department of Soft Tissue/Bone Sarcoma and Melanoma, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Rick L Haas
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Rosalba Miceli
- Department of Biostatistics for Clinical Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Carol J Swallow
- Department of Surgical Oncology, Princess Margaret Cancer Centre, Mount Sinai Hospital, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Alessandro Gronchi
- Department of Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
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12
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Cui Y, Zhou Y, Gao Y, Ma X, Wang Y, Zhang X, Zhou T, Chen S, Lu L, Zhang Y, Chang X, Tong A, Li Y. Novel alternative tools for metastatic pheochromocytomas/paragangliomas prediction. J Endocrinol Invest 2024; 47:1191-1203. [PMID: 38206552 DOI: 10.1007/s40618-023-02239-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 11/02/2023] [Indexed: 01/12/2024]
Abstract
OBJECTIVE The existing prediction models for metastasis in pheochromocytomas/paragangliomas (PPGLs) showed high heterogeneity in different centers. Therefore, this study aimed to establish new prediction models integrating multiple variables based on different algorithms. DESIGN AND METHODS Data of patients with PPGLs undergoing surgical resection at the Peking Union Medical College Hospital from 2007 to 2022 were collected retrospectively. Patients were randomly divided into the training and testing sets in a ratio of 7:3. Subsequently, decision trees, random forest, and logistic models were constructed for metastasis prediction with the training set and Cox models for metastasis-free survival (MFS) prediction with the total population. Additionally, Ki-67 index and tumor size were transformed into categorical variables for adjusting models. The testing set was used to assess the discrimination and calibration of models and the optimal models were visualized as nomograms. Clinical characteristics and MFS were compared between patients with and without risk factors. RESULTS A total of 198 patients with 59 cases of metastasis were included and classified into the training set (n = 138) and testing set (n = 60). Among all models, the logistic regression model showed the best discrimination for metastasis prediction with an AUC of 0.891 (95% CI, 0.793-0.990), integrating SDHB germline mutations [OR: 96.72 (95% CI, 16.61-940.79)], S-100 (-) [OR: 11.22 (95% CI, 3.04-58.51)], ATRX (-) [OR: 8.42 (95% CI, 2.73-29.24)] and Ki-67 ≥ 3% [OR: 7.98 (95% CI, 2.27-32.24)] evaluated through immunohistochemistry (IHC), and tumor size ≥ 5 cm [OR: 4.59 (95% CI, 1.34-19.13)]. The multivariate Cox model including the above risk factors also showed a high C-index of 0.860 (95% CI, 0.810-0.911) in predicting MFS after surgery. Furthermore, patients with the above risk factors showed a significantly poorer MFS (P ≤ 0.001). CONCLUSIONS Models established in this study provided alternative and reliable tools for clinicians to predict PPGLs patients' metastasis and MFS. More importantly, this study revealed for the first time that IHC of ATRX could act as an independent predictor of metastasis in PPGLs.
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Affiliation(s)
- Y Cui
- Department of Endocrinology, Key Laboratory of Endocrinology, National Health Commission of the People's Republic of China, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Y Zhou
- Department of Endocrinology, Key Laboratory of Endocrinology, National Health Commission of the People's Republic of China, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Y Gao
- Department of Endocrinology, Key Laboratory of Endocrinology, National Health Commission of the People's Republic of China, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - X Ma
- Department of Endocrinology, Key Laboratory of Endocrinology, National Health Commission of the People's Republic of China, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Y Wang
- Department of Endocrinology, Key Laboratory of Endocrinology, National Health Commission of the People's Republic of China, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - X Zhang
- Department of Urology Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - T Zhou
- Department of Endocrinology, Key Laboratory of Endocrinology, National Health Commission of the People's Republic of China, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - S Chen
- Department of Endocrinology, Key Laboratory of Endocrinology, National Health Commission of the People's Republic of China, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - L Lu
- Department of Endocrinology, Key Laboratory of Endocrinology, National Health Commission of the People's Republic of China, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Y Zhang
- Medical Research Center, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - X Chang
- Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
| | - A Tong
- Department of Endocrinology, Key Laboratory of Endocrinology, National Health Commission of the People's Republic of China, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
| | - Y Li
- Department of Endocrinology, Key Laboratory of Endocrinology, National Health Commission of the People's Republic of China, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
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13
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Aksoy YA, Xu B, Viswanathan K, Ahadi MS, Al Ghuzlan A, Alzumaili B, Bani MA, Barletta JA, Chau N, Chou A, Clarkson A, Clifton-Bligh RJ, De Leo A, Dogan S, Ganly I, Ghossein R, Gild ML, Glover AR, Hadoux J, Lamartina L, Lubin DJ, Magliocca K, Najdawi F, Nigam A, Papachristos A, Repaci A, Robinson BG, Sheen A, Shi Q, Sidhu SB, Sioson L, Solaroli E, Sywak MS, Tallini G, Tsang V, Turchini J, Untch BR, Gill AJ, Fuchs TL. Novel prognostic nomogram for predicting recurrence-free survival in medullary thyroid carcinoma. Histopathology 2024; 84:947-959. [PMID: 38253940 DOI: 10.1111/his.15141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/18/2023] [Accepted: 01/02/2024] [Indexed: 01/24/2024]
Abstract
AIMS Recently, there have been attempts to improve prognostication and therefore better guide treatment for patients with medullary thyroid carcinoma (MTC). In 2022, the International MTC Grading System (IMTCGS) was developed and validated using a multi-institutional cohort of 327 patients. The aim of the current study was to build upon the findings of the IMTCGS to develop and validate a prognostic nomogram to predict recurrence-free survival (RFS) in MTC. METHODS AND RESULTS Data from 300 patients with MTC from five centres across the USA, Europe, and Australia were used to develop a prognostic nomogram that included the following variables: age, sex, AJCC stage, tumour size, mitotic count, necrosis, Ki67 index, lymphovascular invasion, microscopic extrathyroidal extension, and margin status. A process of 10-fold cross-validation was used to optimize the model's performance. To assess discrimination and calibration, the area-under-the-curve (AUC) of a receiver operating characteristic (ROC) curve, concordance-index (C-index), and dissimilarity index (D-index) were calculated. Finally, the model was externally validated using a separate cohort of 87 MTC patients. The model demonstrated very strong performance, with an AUC of 0.94, a C-index of 0.876, and a D-index of 19.06. When applied to the external validation cohort, the model had an AUC of 0.9. CONCLUSIONS Using well-established clinicopathological prognostic variables, we developed and externally validated a robust multivariate prediction model for RFS in patients with resected MTC. The model demonstrates excellent predictive capability and may help guide decisions on patient management. The nomogram is freely available online at https://nomograms.shinyapps.io/MTC_ML_DFS/.
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Affiliation(s)
- Yagiz A Aksoy
- Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia
- NSW Health Pathology, Department of Anatomical Pathology, Royal North Shore Hospital, St Leonards, NSW, Australia
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Bin Xu
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kartik Viswanathan
- Department of Pathology, Emory University Hospital Midtown, Atlanta, GA, USA
| | - Mahsa S Ahadi
- Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia
- NSW Health Pathology, Department of Anatomical Pathology, Royal North Shore Hospital, St Leonards, NSW, Australia
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Abir Al Ghuzlan
- Medical Pathology and Biology Department, Gustave Roussy Campus Cancer, Villejuif, France
| | - Bayan Alzumaili
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Mohamed-Amine Bani
- Medical Pathology and Biology Department, Gustave Roussy Campus Cancer, Villejuif, France
| | - Justine A Barletta
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nicole Chau
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Angela Chou
- Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia
- NSW Health Pathology, Department of Anatomical Pathology, Royal North Shore Hospital, St Leonards, NSW, Australia
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Adele Clarkson
- Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia
- NSW Health Pathology, Department of Anatomical Pathology, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Roderick J Clifton-Bligh
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
- University of Sydney Endocrine Surgery Unit, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Antonio De Leo
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna Medical Center, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Snjezana Dogan
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ian Ganly
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ronald Ghossein
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matti L Gild
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
- Department of Endocrinology, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Anthony R Glover
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
- University of Sydney Endocrine Surgery Unit, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Julien Hadoux
- Endocrine Oncology, Gustave Roussy Campus Cancer, Villejuif, France
| | - Livia Lamartina
- Endocrine Oncology, Gustave Roussy Campus Cancer, Villejuif, France
| | - Daniel J Lubin
- Department of Pathology, Emory University Hospital Midtown, Atlanta, GA, USA
| | - Kelly Magliocca
- Department of Pathology, Emory University Hospital Midtown, Atlanta, GA, USA
| | - Fedaa Najdawi
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Aradhya Nigam
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alex Papachristos
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
- University of Sydney Endocrine Surgery Unit, Royal North Shore Hospital, St Leonards, NSW, Australia
- Department of Endocrinology, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Andrea Repaci
- Division of Endocrinology and Diabetes Prevention and Care, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Bruce G Robinson
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
- University of Sydney Endocrine Surgery Unit, Royal North Shore Hospital, St Leonards, NSW, Australia
- Department of Endocrinology, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Amy Sheen
- Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia
- NSW Health Pathology, Department of Anatomical Pathology, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Qiuying Shi
- Department of Pathology, Emory University Hospital Midtown, Atlanta, GA, USA
| | - Stan B Sidhu
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
- University of Sydney Endocrine Surgery Unit, Royal North Shore Hospital, St Leonards, NSW, Australia
- Department of Endocrinology, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Loretta Sioson
- Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia
- NSW Health Pathology, Department of Anatomical Pathology, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Erica Solaroli
- Endocrinology Unit, Azienda USL di Bologna, Bologna, Italy
| | - Mark S Sywak
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
- University of Sydney Endocrine Surgery Unit, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Giovanni Tallini
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna Medical Center, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Venessa Tsang
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
- Department of Endocrinology, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - John Turchini
- Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
- Douglass Hanly Moir Pathology, Macquarie Park, New South Wales, Australia
| | - Brian R Untch
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anthony J Gill
- Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia
- NSW Health Pathology, Department of Anatomical Pathology, Royal North Shore Hospital, St Leonards, NSW, Australia
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Talia L Fuchs
- Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
- Douglass Hanly Moir Pathology, Macquarie Park, New South Wales, Australia
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Frego N, Contieri R, Fasulo V, Maffei D, Avolio PP, Arena P, Beatrici E, Sordelli F, De Carne F, Lazzeri M, Saita A, Hurle R, Buffi NM, Casale P, Lughezzani G. Development of a microultrasound-based nomogram to predict extra-prostatic extension in patients with prostate cancer undergoing robot-assisted radical prostatectomy. Urol Oncol 2024; 42:159.e9-159.e16. [PMID: 38423852 DOI: 10.1016/j.urolonc.2024.01.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/12/2024] [Accepted: 01/26/2024] [Indexed: 03/02/2024]
Abstract
OBJECTIVES To develop a microultrasound-based nomogram including clinicopathological parameters and microultrasound findings to predict the presence of extra-prostatic extension and guide the grade of nerve-sparing. MATERIAL AND METHODS All patients underwent microultrasound the day before robot-assisted radical prostatectomy. Variables significantly associated with extra-prostatic extension at univariable analysis were used to build the multivariable logistic model, and the regression coefficients were used to develop the nomogram. The model was subjected to 1000 bootstrap resamples for internal validation. The performance of the microultrasound-based model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). RESULTS Overall, 122/295 (41.4%) patients had a diagnosis of extra-prostatic extension on definitive pathology. Microultrasound correctly identify extra-prostatic extension in 84/122 (68.9%) cases showing a sensitivity and a specificity of 68.9% and 84.4%, with an AUC of 76.6%. After 1000 bootstrap resamples, the predictive accuracy of the microultrasound-based model was 85.9%. The calibration plot showed a satisfactory concordance between predicted probabilities and observed frequencies of extra-prostatic extension. The DCA showed a higher clinical net-benefit compared to the model including only clinical parameters. Considering a 4% cut-off, nerve-sparing was recommended in 173 (58.6%) patients and extra-prostatic extension was detected in 32 (18.5%) of them. CONCLUSION We developed a microultrasound-based nomogram for the prediction of extra-prostatic extension that could aid in the decision whether to preserve or not neurovascular bundles. External validation and a direct comparison with mpMRI-based nomogram is crucial to corroborate our results.
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Affiliation(s)
- Nicola Frego
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Roberto Contieri
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Vittorio Fasulo
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Davide Maffei
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Pier Paolo Avolio
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Paola Arena
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Edoardo Beatrici
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Federica Sordelli
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Fabio De Carne
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
| | - Massimo Lazzeri
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy
| | - Alberto Saita
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy
| | - Rodolfo Hurle
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy
| | - Nicolò Maria Buffi
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy.
| | - Paolo Casale
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy
| | - Giovanni Lughezzani
- Department of Urology, IRCCS - Humanitas Research Hospital, Milan, Italy; Department of Biomedical Science, Humanitas University, Milan, Italy
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15
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Shi S, Zhu X, Cheang I, Liao S, Yin T, Lu X, Yao W, Zhang H, Li X, Zhou Y. Development and validation of a diagnostic nomogram in pulmonary hypertension due to left heart disease. Heart Lung 2024; 65:11-18. [PMID: 38364358 DOI: 10.1016/j.hrtlng.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 01/19/2024] [Accepted: 01/23/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Pulmonary hypertension (pH) due to left heart disease (pH-LHD) is the most common form of pH in clinical practice. OBJECTIVES The purpose of the study is to develop a diagnostic nomogram predictive model combining conventional noninvasive examination and detection indicators. METHODS Our study retrospectively included 361 patients with left heart disease (LHD) who underwent right heart catheterization between 2013 and 2020. All patients were randomly divided into a training cohort (253, 70 %) and a validation cohort (108, 30 %). pH was defined as resting mean pulmonary arterial pressure (mPAP) ≥25 mmHg measured by RHC examination. Data dimension reduction and feature selection were used by Lasso regression model. The nomogram was constructed based on multivariable logistic regression. RESULTS A total of 175 patients with LHD were diagnosed with pH during their hospitalization, representing 48.5 % of the cohort. The mean age of the overall group was 55.6 years, with 76.7 % being male patients. Excessive resting heart rate, elevated New York Heart Association functional class, increased red blood cell distribution width, right ventricular end-diastolic diameter, and pulmonary artery systolic pressure measured by echocardiography were independently associated with the prevalence of pH-LHD. The inclusion of these 5 variables in the nomogram showed good discrimination (AUC = 0.866 [95 % CI, 0.820-0.911]) and optimal calibration (Hosmer-Lemeshow test, P = 0.791) for the validation cohort. CONCLUSIONS The noninvasive nomogram of pH-LHD developed in this study has excellent diagnostic value and clinical applicability, and can more accurately evaluate the presence risk of pH in patients with LHD.
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Affiliation(s)
- Shi Shi
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China; Department of Cardiology, Hai'an People's Hospital, Nantong 226600, China
| | - Xu Zhu
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Iokfai Cheang
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Shengen Liao
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Ting Yin
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Xinyi Lu
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Wenming Yao
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Haifeng Zhang
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China; Department of Cardiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215002, China
| | - Xinli Li
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
| | - Yanli Zhou
- National Key Laboratory for Innovation and Transformation of Luobing Theory. Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.
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Jiang ZP, Zhou ZT, Xie T, Zhou L. Letter to the Editor on: "A novel nomogram based on preoperative parameters to predict posthepatectomy liver failure in patients with hepatocellular carcinoma". Surgery 2024; 175:1464-1465. [PMID: 38245446 DOI: 10.1016/j.surg.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 12/05/2023] [Accepted: 12/08/2023] [Indexed: 01/22/2024]
Affiliation(s)
- Ze-Ping Jiang
- Department of Intensive Care Medicine, Chinese People's Liberation Army Naval Medical Center, Naval Medical University of PLA, Shanghai, China
| | - Zao-Tian Zhou
- Department of Intensive Care Medicine, Chinese People's Liberation Army Naval Medical Center, Naval Medical University of PLA, Shanghai, China
| | - Tian Xie
- Department of Intensive Care Medicine, Chinese People's Liberation Army Naval Medical Center, Naval Medical University of PLA, Shanghai, China
| | - Lan Zhou
- Department of Intensive Care Medicine, Chinese People's Liberation Army Naval Medical Center, Naval Medical University of PLA, Shanghai, China.
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Lin S, Song Z, Shen S. Response to Letter to the Editor regarding "A novel nomogram based on preoperative parameters to predict posthepatectomy liver failure in patients with hepatocellular carcinoma". Surgery 2024; 175:1465-1466. [PMID: 38418295 DOI: 10.1016/j.surg.2024.01.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 01/21/2024] [Indexed: 03/01/2024]
Affiliation(s)
- Shuirong Lin
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P.R. China
| | - Zimin Song
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P.R. China
| | - Shunli Shen
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P.R. China.
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Chen Y, Huang S, Luo B, Jiang J, Ren W, Zou K, Zhong X, Lü M, Tang X. Prediction and evaluation of a nomogram model for recurrent acute pancreatitis. Eur J Gastroenterol Hepatol 2024; 36:554-562. [PMID: 38407842 DOI: 10.1097/meg.0000000000002732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
OBJECTIVE The purpose of this study was to investigate the influencing factors for recurrent acute pancreatitis and construct the nomogram model to predict the risk of recurrent acute pancreatitis. METHODS Patients diagnosed with acute pancreatitis in the Affiliated Hospital of Southwest Medical University were enrolled. We collected these patients' basic information, laboratory data, imaging information. Using Logistic regression and least absolute shrinkage and selection operator regression to select risk factor for Cross-Validation Criterion. To create nomogram and validated by receiver operator characteristic curve, calibration curves and decision curve analysis. RESULTS A total of 533 patients with acute pancreatitis were included, including 99 recurrent acute pancreatitis patients. The average age of recurrent acute pancreatitis patients was 49.69 years old, and 67.7% of them were male. At the same time, in all recurrent acute pancreatitis patients, hypertriglyceridemic pancreatitis is the most important reason (54.5%). Regression analysis and least absolute shrinkage and selection operator regression showed that smoking history, acute necrotic collection, triglyceride, and alcohol etiology for acute pancreatitis were identified and entered into the nomogram. The area under the receiver operator characteristic curve of the training set was 0.747. The calibration curve showed the consistency between the nomogram model and the actual probability. CONCLUSION In conclusion, some high-risk factors like smoking history, acute necrotic collection, triglyceride, and alcohol etiology for acute pancreatitis may predict recurrent pancreatitis and their incorporation into a nomogram has high accuracy in predicting recurrence.
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Affiliation(s)
- Yuan Chen
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Shu Huang
- Department of Gastroenterology, Lianshui County People' Hospital
- Department of Gastroenterology, Lianshui People' Hospital of Kangda College Affiliated to Nanjing Medical University, Huaian, China
| | - Bei Luo
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Jiao Jiang
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Wensen Ren
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Kang Zou
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Xiaolin Zhong
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Muhan Lü
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
| | - Xiaowei Tang
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou
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Chang ZY, Gao WX, Zhang Y, Chen P, Zhao W, Wu D, Chen ZD, Gao YH, Liang WQ, Chen L, Xi HQ. Development and validation of a nomogram to predict postsurgical intra-abdominal infection in blunt abdominal trauma patients: A multicenter retrospective study. Surgery 2024; 175:1424-1431. [PMID: 38402039 DOI: 10.1016/j.surg.2024.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 12/23/2023] [Accepted: 01/13/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND Intra-abdominal infection is a common complication of blunt abdominal trauma. Early detection and intervention can reduce the incidence of intra-abdominal infection and improve patients' prognoses. This study aims to construct a clinical model predicting postsurgical intra-abdominal infection after blunt abdominal trauma. METHODS This study is a retrospective analysis of 553 patients with blunt abdominal trauma from the Department of General Surgery of 7 medical centers (2011-2021). A 7:3 ratio was used to assign patients to the derivation and validation cohorts. Patients were divided into 2 groups based on whether intra-abdominal infection occurred after blunt abdominal trauma. Multivariate logistic regression and least absolute shrinkage and selection operator regression were used to select variables to establish a nomogram. The nomogram was evaluated, and the validity of the model was further evaluated by the validation cohort. RESULTS A total of 113 were diagnosed with intra-abdominal infection (20.4%). Age, prehospital time, C-reactive protein, injury severity score, operation duration, intestinal injury, neutrophils, and antibiotic use were independent risk factors for intra-abdominal infection in blunt abdominal trauma patients (P < .05). The area under the receiver operating curve (area under the curve) of derivation cohort and validation cohort was 0.852 (95% confidence interval, 0.784-0.912) and 0.814 (95% confidence interval, 0.751-0.902). The P value for the Hosmer-Lemeshow test was .135 and .891 in the 2 cohorts. The calibration curve demonstrated that the nomogram had a high consistency between prediction and practical observation. The decision curve analysis also showed that the nomogram had a better potential for clinical application. To facilitate clinical application, we have developed an online at https://nomogramcgz.shinyapps.io/IAIrisk/. CONCLUSION The nomogram is helpful in predicting the risk of postoperative intra-abdominal infection in patients with blunt abdominal trauma and provides guidance for clinical decision-making and treatment.
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Affiliation(s)
- Zheng Y Chang
- Medical School of Chinese PLA, Beijing, China; Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Wen X Gao
- Medical School of Chinese PLA, Beijing, China; Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yue Zhang
- Medical School of Chinese PLA, Beijing, China; Department of Endocrinology, the First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Peng Chen
- Medical School of Chinese PLA, Beijing, China; Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Wen Zhao
- Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing, China; School of Medicine, Nankai University, Tianjin, China
| | - Di Wu
- Medical School of Chinese PLA, Beijing, China; Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Zhi D Chen
- Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yun H Gao
- Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Wen Q Liang
- Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Lin Chen
- Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing, China.
| | - Hong Q Xi
- Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing, China.
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Yu R, Lu G, Cheng B, Li J, Jiang Q, Lan X. Construction and validation of a novel NAD + metabolism-related risk model for prognostic prediction in osteosarcoma. J Orthop Res 2024; 42:1086-1103. [PMID: 38047487 DOI: 10.1002/jor.25757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/18/2023] [Accepted: 11/28/2023] [Indexed: 12/05/2023]
Abstract
Currently, the prognosis of osteosarcoma (OS) remains discouraging, especially in elderly/metastatic OS patients. By impairing the antitumor effect of immune cells, tumor immune microenvironment (TIME) provides an environment conducive to tumor proliferation, which highly requires accelerated nicotinamide adenine dinucleotide (NAD+) metabolism for energy. Recently, many genes involved in the sustained production of NAD+ in malignant tumors have been verified to be possible prognostic indicators and therapeutic targets. Therefore, the current study was to probe into the association of NAD+ metabolism-related genes with TIME, immunotherapeutic response, and prognosis in OS. All OS data for the study were acquired from TARGET and GEO databases. In bioinformatics analysis, we performed Cox analysis, consensus clustering, principal component analysis, t-distributed stochastic neighbor embedding, uniform manifold approximation and projection, gene set enrichment analysis, gene set variation analysis, Lasso analysis, survival and ROC curves, nomogram, immune-related analysis, drug sensitivity analysis, and single-cell RNA sequencing (scRNA-seq) analysis. Cell transfection assay, RT-qPCR, western blot analysis, as well as cell wound healing, migration, and invasion assays were performed in vitro. Bioinformatics analysis identified A&B clusters and six NAD+ metabolism-related differentially expressed genes, constructed risk model and nomogram, and performed immune-related analysis, drug susceptibility analysis, and scRNA-seq analysis to inform the clinical treatment framework. In vitro experiment revealed that CBS and INPP1 can promote migration, proliferation as well as invasion of OS cells through TGF-β1/Smad2/3 pathway. Based on bioinformatics analysis and in vitro validation, this study confirmed that NAD+ metabolism affects TIME to suggest the prognosis of OS.
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Affiliation(s)
- Ronghui Yu
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Gang Lu
- Department of Orthopedics, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Banghong Cheng
- Department of Cardiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Junhong Li
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qiqing Jiang
- Department of Orthopedics, Jiangxi Provincial Children's Hospital, Nanchang, Jiangxi, China
| | - Xia Lan
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
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Li M, Tang J, Pan X, Zhang D. Predicting the Survival Benefit of Radiotherapy in Elderly Breast Cancer Patients: A Population-Based Analysis. J Surg Res 2024; 297:26-40. [PMID: 38428261 DOI: 10.1016/j.jss.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/30/2023] [Accepted: 02/05/2024] [Indexed: 03/03/2024]
Abstract
INTRODUCTION This study aimed to establish two prediction tools predicting cancer-specific survival (CSS) and overall survival (OS) in elderly breast cancer patients with or without radiotherapy. METHODS Clinicopathological data of breast cancer patients aged more than 70 y from 2010 to 2018 were retrospectively collected from the Surveillance, Epidemiology, and End Results database. Patients were randomly divided into the training and validation cohorts at 7:3, and the Cox proportional risk model was used to construct the nomograms. The concordance index, the area under the receiver operating characteristic curve, and the calibration plot are used to evaluate the discrimination and accuracy of the nomograms. RESULTS One lakh twenty eight thousand two hundred twenty three elderly breast cancer patients were enrolled, including 57,915 who received radiotherapy. The Cox regression model was used to identify independent factors. These independent influencing factors are used to construct the prediction models. The calibration plots reflect the excellent consistency between the predicted and actual survival rates. The concordance index of nomograms for CSS and OS was more than 0.7 in both the radiotherapy group and the nonradiotherapy group, and similar results are also shown in area under the receiver operating characteristic curve. Decision curve analysis showed that the prognostication accuracy of the model was much higher than that of the traditional tumor, node, metastasis staging. CONCLUSIONS Radiotherapy can benefit elderly breast cancer patients significantly. The two prediction tools provide a personalized survival scale for evaluating the CSS and OS of elderly breast cancer patients, which can better provide clinicians with better-individualized management for these patients.
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Affiliation(s)
- Maoxian Li
- Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
| | - Jie Tang
- Department of Biostatistics and Epidemiology, Public Health School, Shenyang Medical College, Shenyang, China
| | - Xiudan Pan
- Department of Biostatistics and Epidemiology, Public Health School, Shenyang Medical College, Shenyang, China
| | - Dianlong Zhang
- Women and Children's Hospital, Qingdao University, Qingdao, China.
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Guo Q, Gao Y, Lin Y, Li W, Zhang Z, Mao Y, Xu X. A nomogram of preoperative indicators predicting lymph vascular space invasion in cervical cancer. Arch Gynecol Obstet 2024; 309:2079-2087. [PMID: 38358484 DOI: 10.1007/s00404-024-07385-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 01/08/2024] [Indexed: 02/16/2024]
Abstract
PURPOSE To develop predictive nomograms of lymph vascular space invasion (LVSI) in patients with early-stage cervical cancer. METHODS We identified 403 patients with cervical cancer from the Affiliated Hospital of Jiangnan University from January 2015 to December 2019. Patients were divided into the training set (n = 242) and the validation set (n = 161), with patients in the training set subdivided into LVSI (+) and LVSI (-) groups according to postoperative pathology. Preoperative hematologic indexes were compared between the two subgroups. Univariate and multivariate logistic regression analyses were used to analyze the independent risk factors for LVSI, from which a nomogram was constructed using the R package. RESULTS LVSI (+) was present in 94 out of 242 patients in the training set, accompanied by a significant increase in the preoperative squamous cell carcinoma antigen (SCC), white blood cells (WBC), neutrophil (NE), platelet (PLT), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic inflammation index (SII), and tumor size (P < 0.05). Univariate analysis showed that SCC, WBC, NE, NLR, PLR, SII, and tumor size were correlated with LVSI (P < 0.05), and multivariate analysis showed that tumor size, SCC, WBC, and NLR were independent risk factors for LVSI (P < 0.05). A nomogram was correspondingly established with good performance in predicting LVSI [training: ROC-AUC = 0.845 (95% CI: 0.731-0.843) and external validation: ROC-AUC = 0.704 (95% CI: 0.683-0.835)] and high accuracy (training: C-index = 0.787; external validation: C-index = 0.759). CONCLUSION The nomogram based on preoperative tumor size, SCC, WBC, and NLR had excellent accuracy and discriminative capability to assess the risk of LVSI in early-stage cervical cancer patients.
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Affiliation(s)
- Qu Guo
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Yufeng Gao
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
- Wuxi Medical College, Jiangnan University, Wuxi, China
| | - Yaying Lin
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
- Wuxi Medical College, Jiangnan University, Wuxi, China
| | - Weimin Li
- Ultrasonography Department, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Zhenyu Zhang
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Yurong Mao
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xizhong Xu
- Department of Gynecology, Affiliated Hospital of Jiangnan University, Wuxi, China.
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Zheng Y, Jiang P, Tu Y, Huang Y, Wang J, Gou S, Tian C, Yuan R. Incidence, risk factors, and a prognostic nomogram for distant metastasis in endometrial cancer: A SEER-based study. Int J Gynaecol Obstet 2024; 165:655-665. [PMID: 38010285 DOI: 10.1002/ijgo.15264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 10/26/2023] [Accepted: 11/08/2023] [Indexed: 11/29/2023]
Abstract
OBJECTIVE To evaluate the metastatic pattern, identify the risk factors, and establish a nomogram for predicting prognosis of endometrial cancer (EC) with distant metastasis. METHODS A retrospective cohort study of women diagnosed with EC was conducted according to the Surveillance, Epidemiology, and End Results (SEER) database during 2010-2017. Multivariate logistic analysis and Cox analysis were performed to identify the risk factors in promoting distant metastasis and predictors associated with overall survival (OS) in this particular subpopulation. A nomogram was then constructed and validated by the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis. RESULTS A total of 2799 cases of distant metastasis in EC patients were identified, with an overall incidence rate of 3.74% from 2010 to 2017. Black race, unmarried status, non-endometrioid histologic types, and grade IV were significant risk factors for distant metastasis in EC patients. Meanwhile, race, histology, grade, metastasis status, surgery, lymphadenectomy, and chemotherapy were identified as independent prognostic factors for OS. A nomogram to predict 1-, 3-, and 5-year OS was established, and presented favorable accuracy and clinical applicability. Patients were further divided into high- and low-risk groups according to the model. CONCLUSION The nomogram was developed as a highly accurate, individualized tool to better predict the prognosis of EC patients with distant metastasis, which would help clinicians to identify high-risk patients, and adjust and tailor their treatment strategies.
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Affiliation(s)
- Yunfeng Zheng
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Peng Jiang
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuan Tu
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuzhen Huang
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jinyu Wang
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Shikai Gou
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chenfan Tian
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Rui Yuan
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Miao G, Cai Z, He X, Yang J, Zhang Y, Ma A, Zhao X, Tan M. Development of a predictive nomogram for 28-day mortality risk in non-traumatic or post-traumatic subarachnoid hemorrhage patients. Neurol Sci 2024; 45:2149-2163. [PMID: 37994964 DOI: 10.1007/s10072-023-07199-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 11/08/2023] [Indexed: 11/24/2023]
Abstract
OBJECTIVE Subarachnoid hemorrhage (SAH) is associated with high rates of mortality and permanent disability. At present, there are few definite clinical tools to predict prognosis in SAH patients. The current study aims to develop and assess a predictive nomogram model for estimating the 28-day mortality risk in both non-traumatic or post-traumatic SAH patients. METHODS The MIMIC-III database was searched to select patients with SAH based on ICD-9 codes. Patients were separated into non-traumatic and post-traumatic SAH groups. Using LASSO regression analysis, we identified independent risk factors associated with 28-day mortality and incorporated them into nomogram models. The performance of each nomogram was assessed by calculating various metrics, including the area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). RESULTS The study included 999 patients with SAH, with 631 in the non-traumatic group and 368 in the post-traumatic group. Logistic regression analysis revealed critical independent risk factors for 28-day mortality in non-traumatic SAH patients, including gender, age, glucose, platelet, sodium, BUN, WBC, PTT, urine output, SpO2, and heart rate and age, glucose, PTT, urine output, and body temperature for post-traumatic SAH patients. The prognostic nomograms outperformed the commonly used SAPSII and APSIII systems, as evidenced by superior AUC, NRI, IDI, and DCA results. CONCLUSION The study identified independent risk factors associated with the 28-day mortality risk and developed predictive nomogram models for both non-traumatic and post-traumatic SAH patients. The nomogram holds promise in guiding prognosis improvement strategies for patients with SAH.
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Affiliation(s)
- Guiqiang Miao
- Department of Orthopedics, Foshan Fosun Chancheng Hospital, Foshan, 528010, China
| | - Zhenbin Cai
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Xin He
- Clinical Laboratory Center, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Jie Yang
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Yunlong Zhang
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Ao Ma
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Xiaodong Zhao
- Department of Orthopedics, Foshan Fosun Chancheng Hospital, Foshan, 528010, China.
| | - Minghui Tan
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
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Xie R, Mao Z, Xu X, Sun T. Epidemiological features and a survival nomogram for primary lymphoma of the male genital tract. Ann Hematol 2024; 103:1687-1695. [PMID: 38424302 DOI: 10.1007/s00277-024-05668-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 02/14/2024] [Indexed: 03/02/2024]
Abstract
Primary lymphoma of the male genital tract (PLMGT) is rare, and data on its epidemiology and prognosis are lacking. Our study aimed to estimate the incidence and develop a predictive nomogram for PLMGT. We pooled the incidence and survival data of PLMGT over the last 20 years from the Surveillance, Epidemiology, and End Results (SEER) database. Incidence rates were calculated by year of diagnosis, age, race, and histology. Independent prognostic factors selected by Cox regression analysis were used to develop a nomogram for predicting overall survival (OS). Our study enrolled 1312 patients with PLMGT. The overall incidence rate of PLMGT was 0.437/1,000,000 during 2000-2019. OS was associated with age, marital status, histological subtype, Ann Arbor stage, and therapeutic strategy, which were used to construct nomograms to predict 1-, 3-, and 5-year OS rates. Receiver operating characteristic curves, calibration plots, and decision curve analysis showed good performance of the nomogram. Based on the total score of each patient from the nomogram, the patients were clustered into three risk groups, and the risk stratification model was more successful in predicting clinical outcomes than the traditional Ann Arbor staging system. The incidence rate of PLMGT has remained relatively stable over the past two decades. For the OS of patients with PLMGT, we established a novel predictive nomogram involving all independent risk factors obtained from the SEER database and developed a corresponding risk classification system that showed better predictive performance than the Ann Arbor staging system.
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Affiliation(s)
- Rongli Xie
- Department of Hematology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Zekai Mao
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Xiaojun Xu
- Department of Hematology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Tiantian Sun
- Department of Hematology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China.
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Pan Y, Han X, Tu Y, Zhang P, Yu H, Bao Y. Nomogram for Predicting Remission of Metabolic Syndrome 1 Year after Sleeve Gastrectomy Surgery in Chinese Patients with Obesity. Obes Surg 2024; 34:1590-1599. [PMID: 38478194 DOI: 10.1007/s11695-024-07156-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 04/20/2024]
Abstract
PURPOSE Sleeve gastrectomy (SG) is a widely used and effective treatment for patients with obesity and comorbid metabolic abnormalities. No specialized tool is available to predict metabolic syndrome (MS) remission after SG. We presented a nomogram that evaluated the probability of MS remission in obese patients 1 year after SG. MATERIALS AND METHODS Patients with preoperative MS who underwent SG were enrolled in this retrospective study. They were divided into a training set and a validation set. Multivariate logistic regression analysis was performed to identify independent predictors of MS remission, and these predictors were included in the nomogram. Receiver operating characteristic curve was used to evaluate discrimination. Calibration was performed with the Hosmer-Lemeshow goodness-of-fit test. The net benefits of the nomogram were evaluated using decision curve analysis (DCA). RESULTS Three hundred and eighteen patients with a median age of 34.0 years were analyzed. A training set and a validation set with 159 individuals each were established. A combination of age, preoperative high-density lipoprotein cholesterol, elevated triglycerides and glycated hemoglobin level independently and accurately predicted MS remission. The nomogram included these factors. The discriminative ability was moderate in training and validation sets (Area under curve 0.800 and 0.727, respectively). The Hosmer-Lemeshow X2 value of the nomogram was 8.477 (P = 0.388) for the training set and 5.361 (P = 0.718) for the validation set, indicating good calibration. DCA showed the nomogram had clinical benefits in both datasets. CONCLUSION Our nomogram could accurately predict MS remission in Chinese patients with obesity 1 year after SG.
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Affiliation(s)
- Yunhui Pan
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai, 200233, China
| | - Xiaodong Han
- Department of General Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Yinfang Tu
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai, 200233, China
| | - Pin Zhang
- Department of General Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Haoyong Yu
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai, 200233, China
| | - Yuqian Bao
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Diabetes Institute, Shanghai, 200233, China.
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Li X, Gao YH, Yang Z, Ma Y, Liu C, Liu GC, Wang DB. [Establishment of a prognostic nomogram and discussion on optimal treatment for cervical adenocarcinoma:a retrospective study based on SEER database and Chinese single-center data]. Zhonghua Fu Chan Ke Za Zhi 2024; 59:307-319. [PMID: 38644277 DOI: 10.3760/cma.j.cn112141-20231101-00172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Objective: To establish and validate a predicting nomogram for cervical adenocarcinoma based on surveillance, epidemiology and end results (SEER) database and Chinese single-center data, and to explore the optimal treatment for cervical adenocarcinoma. Methods: This study selected 2 478 cervical adenocarcinoma patients from the SEER database as the training cohort, and 195 cervical adenocarcinoma patients from Cancer Hospital of Dalian University of Technology, Liaouing Cancer Hospital and Institute as an external validation cohort. Clinicopathological information and follow-up data of the two cohorts were collected. The radiotherapy group was defined as receiving comprehensive treatment based on concurrent chemoradiotherapy after initial diagnosis, while the surgery group was defined as receiving comprehensive treatment based on radical surgery. Log-rank test and cox regression were used to evaluate factors affecting the prognosis of cervical adenocarcinoma patients. A nomogram was drawn to predict the 3-year and 5-year overall survival rates of cervical adenocarcinoma patients, and then internal validation of the training cohort from SEER database and external validation of the hospital cohort were conducted. Results: (1) In the SEER database training cohort, there were 385 patients (15.54%, 385/2 478) in the radiotherapy group and 2 093 patients (84.46%, 2 093/2 478) in the surgery group. Overall survival time of the radiotherapy group was (55.8±51.3) months, while that of the surgery roup was (94.4±61.7) months, the difference between the two groups was statistically significant (χ2=256.44, P<0.001). Log-rank test showed that age, marital status, maximum of tumor diameters, pathological grade, International Federation of Gynecology and Obstetrics (FIGO) stage, and treatments were all significant factors affecting the overall survival time of cervical adenocarcinoma patients (all P<0.001). Multivariate Cox regression analysis showed that elder (>50 years old), single status, huge tumors (>4 cm), high pathological grades (G2, G3), and advanced FIGO stages (≥Ⅱa2 stage) were independent risk factors for the overall survival time of cervical adenocarcinoma patients (all P<0.05); compared with radiotherapy, surgery was a protective factor for the prognosis of cervical adenocarcinoma patients (HR=0.619, 95%CI: 0.494-0.777; P<0.001). Further analysis of locally advanced stage and Ⅲc stage of patients showed that surgery was a protective factor for the prognosis of cervical adenocarcinoma patients with a maximum tumor diameter >4 to <6 cm (HR=0.414, 95%CI: 0.182-0.942; P=0.036) in locally advanced stage and Ⅲc T1 to T2 stage (HR=0.473, 95%CI: 0.307-0.728; P=0.001). (2) The external validation cohort consisted of 39 patients (20.00%, 39/195) in the radiotherapy group and 156 patients (80.00%, 156/195) in the surgery group. The overall survival time of patients in the radiotherapy group was (51.7±34.3) months, while that of the surgery group was (63.1±26.6) months (χ2=28.41, P<0.001). Further analysis was conducted on locally advanced stage and Ⅲc stage patients, and multivariate Cox regression analysis was performed after propensity score matching, which showed that surgery was a protective factor for the prognosis of cervical adenocarcinoma patients with a maximum tumor diameter >4 to <6 cm in locally advanced stage (HR=0.141, 95%CI: 0.023-0.843; P=0.032) and Ⅲc T1 to T2 stage (HR=0.184, 95%CI: 0.036-0.947; P=0.043). (3) Establishment and internal and external validation of nomogram: based on the six factors screened out by the multivariate Cox regression model, the nomogram was developed to predict the prognosis of cervical adenocarcinoma patients. The consistency index of the internal and external validation were 0.801 and 0.766, respectively, and the calibration curves matched well with the ideal fitting line. Conclusions: The key to the treatment of cervical adenocarcinoma is to prioritize radical surgery for patients with conditions for radical tumor resection. Compared with concurrent chemoradiotherapy, patients with locally advanced stages (Ⅰb3, Ⅱa2), and Ⅲc (T1, T2) stages cervical adenocarcinoma could benefit from comprehensive treatment based on radical surgery. The nomogram of this study has been validated internally and externally, and show good survival prediction efficacy for cervical adenocarcinoma patients.
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Affiliation(s)
- X Li
- Department of Gynecology, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang 110801, China
| | - Y H Gao
- Department of Gynecology, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang 110801, China
| | - Z Yang
- Department of Gynecology, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang 110801, China
| | - Y Ma
- Department of Gynecology, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang 110801, China
| | - C Liu
- Department of Gynecology, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang 110801, China
| | - G C Liu
- Department of Gynecology, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang 110801, China
| | - D B Wang
- Department of Gynecology, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang 110801, China
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Cai X, Lin J, Liu L, Zheng J, Liu Q, Ji L, Sun Y. A novel TCGA-validated programmed cell-death-related signature of ovarian cancer. BMC Cancer 2024; 24:515. [PMID: 38654239 DOI: 10.1186/s12885-024-12245-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/10/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Ovarian cancer (OC) is a gynecological malignancy tumor with high recurrence and mortality rates. Programmed cell death (PCD) is an essential regulator in cancer metabolism, whose functions are still unknown in OC. Therefore, it is vital to determine the prognostic value and therapy response of PCD-related genes in OC. METHODS By mining The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Genecards databases, we constructed a prognostic PCD-related genes model and performed Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic (ROC) curve for its predictive ability. A nomogram was created via Cox regression. We validated our model in train and test sets. Quantitative real-time PCR (qRT-PCR) was applied to identify the expression of our model genes. Finally, we analyzed functional analysis, immune infiltration, genomic mutation, tumor mutational burden (TMB) and drug sensitivity of patients in low- and high-risk group based on median scores. RESULTS A ten-PCD-related gene signature including protein phosphatase 1 regulatory subunit 15 A (PPP1R15A), 8-oxoguanine-DNA glycosylase (OGG1), HECT and RLD domain containing E3 ubiquitin protein ligase family member 1 (HERC1), Caspase-2.(CASP2), Caspase activity and apoptosis inhibitor 1(CAAP1), RB transcriptional corepressor 1(RB1), Z-DNA binding protein 1 (ZBP1), CD3-epsilon (CD3E), Clathrin heavy chain like 1(CLTCL1), and CCAAT/enhancer-binding protein beta (CEBPB) was constructed. Risk score performed well with good area under curve (AUC) (AUC3 - year =0.728, AUC5 - year = 0.730). The nomogram based on risk score has good performance in predicting the prognosis of OC patients (AUC1 - year =0.781, AUC3 - year =0.759, AUC5 - year = 0.670). Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the erythroblastic leukemia viral oncogene homolog (ERBB) signaling pathway and focal adhesion were enriched in the high-risk group. Meanwhile, patients with high-risk scores had worse OS. In addition, patients with low-risk scores had higher immune-infiltrating cells and enhanced expression of checkpoints, programmed cell death 1 ligand 1 (PD-L1), indoleamine 2,3-dioxygenase 1 (IDO-1) and lymphocyte activation gene-3 (LAG3), and were more sensitive to A.443,654, GDC.0449, paclitaxel, gefitinib and cisplatin. Finally, qRT-PCR confirmed RB1, CAAP1, ZBP1, CEBPB and CLTCL1 over-expressed, while PPP1R15A, OGG1, CASP2, CD3E and HERC1 under-expressed in OC cell lines. CONCLUSION Our model could precisely predict the prognosis, immune status and drug sensitivity of OC patients.
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Affiliation(s)
- Xintong Cai
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China
| | - Jie Lin
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China
| | - Li Liu
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China
| | - Jianfeng Zheng
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China
| | - Qinying Liu
- Fujian Provincial Key Laboratory of Tumor Biotherapy, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China
| | - Liyan Ji
- Geneplus-Beijing Institute, Beijing, China
| | - Yang Sun
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China.
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Chen J, Huang Z, Luo H, Li G, Ding Z, Tian H, Tang S, Mo S, Xu J, Wu H, Dong F. Development and validation of nomograms using photoacoustic imaging and 2D ultrasound to predict breast nodule benignity and malignancy. Postgrad Med J 2024; 100:309-318. [PMID: 38275274 DOI: 10.1093/postmj/qgad146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/03/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024]
Abstract
BACKGROUND The application of photoacoustic imaging (PAI), utilizing laser-induced ultrasound, shows potential in assessing blood oxygenation in breast nodules. However, its effectiveness in distinguishing between malignant and benign nodules remains insufficiently explored. PURPOSE This study aims to develop nomogram models for predicting the benign or malignant nature of breast nodules using PAI. METHOD A prospective cohort study enrolled 369 breast nodules, subjecting them to PAI and ultrasound examination. The training and testing cohorts were randomly divided into two cohorts in a ratio of 3:1. Based on the source of the variables, three models were developed, Model 1: photoacoustic-BIRADS+BMI + blood oxygenation, Model 2: BIRADS+Shape+Intranodal blood (Doppler) + BMI, Model 3: photoacoustic-BIRADS+BIRADS+ Shape+Intranodal blood (Doppler) + BMI + blood oxygenation. Risk factors were identified through logistic regression, resulting in the creation of three predictive models. These models were evaluated using calibration curves, subject receiver operating characteristic (ROC), and decision curve analysis. RESULTS The area under the ROC curve for the training cohort was 0.91 (95% confidence interval, 95% CI: 0.88-0.95), 0.92 (95% CI: 0.89-0.95), and 0.97 (95% CI: 0.96-0.99) for Models 1-3, and the ROC curve for the testing cohort was 0.95 (95% CI: 0.91-0.98), 0.89 (95% CI: 0.83-0.96), and 0.97 (95% CI: 0.95-0.99) for Models 1-3. CONCLUSIONS The calibration curves demonstrate that the model's predictions agree with the actual values. Decision curve analysis suggests a good clinical application.
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Affiliation(s)
- Jing Chen
- Ultrasound Department, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong 518020, China
| | - Zhibin Huang
- Ultrasound Department, The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong 518020, China
| | - Hui Luo
- Ultrasound Department, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong 518020, China
| | - Guoqiu Li
- Ultrasound Department, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong 518020, China
| | - Zhimin Ding
- Ultrasound Department, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong 518020, China
| | - Hongtian Tian
- Ultrasound Department, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong 518020, China
| | - Shuzhen Tang
- Ultrasound Department, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong 518020, China
| | - Sijie Mo
- Ultrasound Department, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong 518020, China
| | - Jinfeng Xu
- Ultrasound Department, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong 518020, China
| | - Huaiyu Wu
- Ultrasound Department, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong 518020, China
| | - Fajin Dong
- Ultrasound Department, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong 518020, China
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Yu B, Cho J, Kang BH, Kim K, Kim DH, Chang SW, Jung PY, Heo Y, Kang WS. Nomogram for predicting in-hospital mortality in trauma patients undergoing resuscitative endovascular balloon occlusion of the aorta: a retrospective multicenter study. Sci Rep 2024; 14:9164. [PMID: 38644449 DOI: 10.1038/s41598-024-59861-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 04/16/2024] [Indexed: 04/23/2024] Open
Abstract
Recently, resuscitative endovascular balloon occlusion of the aorta (REBOA) had been introduced as an innovative procedure for severe hemorrhage in the abdomen or pelvis. We aimed to investigate risk factors associated with mortality after REBOA and construct a model for predicting mortality. This multicenter retrospective study collected data from 251 patients admitted at five regional trauma centers across South Korea from 2015 to 2022. The indications for REBOA included patients experiencing hypovolemic shock due to hemorrhage in the abdomen, pelvis, or lower extremities, and those who were non-responders (systolic blood pressure (SBP) < 90 mmHg) to initial fluid treatment. The primary and secondary outcomes were mortality due to exsanguination and overall mortality, respectively. After feature selection using the least absolute shrinkage and selection operator (LASSO) logistic regression model to minimize overfitting, a multivariate logistic regression (MLR) model and nomogram were constructed. In the MLR model using risk factors selected in the LASSO, five risk factors, including initial heart rate (adjusted odds ratio [aOR], 0.99; 95% confidence interval [CI], 0.98-1.00; p = 0.030), initial Glasgow coma scale (aOR, 0.86; 95% CI 0.80-0.93; p < 0.001), RBC transfusion within 4 h (unit, aOR, 1.12; 95% CI 1.07-1.17; p < 0.001), balloon occlusion type (reference: partial occlusion; total occlusion, aOR, 2.53; 95% CI 1.27-5.02; p = 0.008; partial + total occlusion, aOR, 2.04; 95% CI 0.71-5.86; p = 0.187), and post-REBOA systolic blood pressure (SBP) (aOR, 0.98; 95% CI 0.97-0.99; p < 0.001) were significantly associated with mortality due to exsanguination. The prediction model showed an area under curve, sensitivity, and specificity of 0.855, 73.2%, and 83.6%, respectively. Decision curve analysis showed that the predictive model had increased net benefits across a wide range of threshold probabilities. This study developed a novel intuitive nomogram for predicting mortality in patients undergoing REBOA. Our proposed model exhibited excellent performance and revealed that total occlusion was associated with poor outcomes, with post-REBOA SBP potentially being an effective surrogate measure.
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Affiliation(s)
- Byungchul Yu
- Traumatology, Gachon University College of Medicine, Department of Trauma Surgery, Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - Jayun Cho
- Department of Trauma Surgery, Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - Byung Hee Kang
- Division of Trauma Surgery, Department of Surgery, Ajou School of Medicine, Suwon, Republic of Korea
| | - Kyounghwan Kim
- Department of Trauma Surgery, Jeju Regional Trauma Center, Cheju Halla General Hospital, 65, Doryeong-ro, Jeju-si, Jeju-do, Republic of Korea
| | - Dong Hun Kim
- Division of Trauma Surgery, Department of Surgery, Dankook University College of Medicine, Cheonan, Republic of Korea
| | - Sung Wook Chang
- Department of Thoracic and Cardiovascular Surgery, Trauma Center, Dankook University Hospital, Cheonan, Republic of Korea
| | - Pil Young Jung
- Department of Trauma and Acute Care Surgery, Yonsei University Wonju Severance Christian Hospital, Wonju, Republic of Korea
| | - Yoonjung Heo
- Division of Trauma Surgery, Department of Surgery, Dankook University College of Medicine, Cheonan, Republic of Korea
- Department of Trauma Surgery, Trauma Center, Dankook University Hospital, Cheonan, Republic of Korea
| | - Wu Seong Kang
- Department of Trauma Surgery, Jeju Regional Trauma Center, Cheju Halla General Hospital, 65, Doryeong-ro, Jeju-si, Jeju-do, Republic of Korea.
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Li F, Liu L, Feng Q, Wang X, Liu F, Yang L, Miao L, Wang W, Ji G, Yu C. Prognostic and predictive value of tumor deposits in advanced signet ring cell colorectal cancer: SEER database analysis and multicenter validation. World J Surg Oncol 2024; 22:107. [PMID: 38644507 DOI: 10.1186/s12957-024-03362-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 03/11/2024] [Indexed: 04/23/2024] Open
Abstract
BACKGROUND Colorectal signet-ring cell carcinoma (SRCC) is a rare cancer with a bleak prognosis. The relationship between its clinicopathological features and survival remains incompletely elucidated. Tumor deposits (TD) have been utilized to guide the N staging in the 8th edition of American Joint Committee on Cancer (AJCC) staging manual, but their prognostic significance remains to be established in colorectal SRCC. PATIENTS AND METHODS The subjects of this study were patients with stage III/IV colorectal SRCC who underwent surgical treatment. The research comprised two cohorts: a training cohort and a validation cohort. The training cohort consisted of 631 qualified patients from the SEER database, while the validation cohort included 135 eligible patients from four independent hospitals in China. The study assessed the impact of TD on Cancer-Specific Survival (CSS) and Overall Survival (OS) using Kaplan-Meier survival curves and Cox regression models. Additionally, a prognostic nomogram model was constructed for further evaluation. RESULTS In both cohorts, TD-positive patients were typically in the stage IV and exhibited the presence of perineural invasion (PNI) (P < 0.05). Compared to the TD-negative group, the TD-positive group showed significantly poorer CSS (the training cohort: HR, 1.87; 95% CI, 1.52-2.31; the validation cohort: HR, 2.43; 95% CI, 1.55-3.81; all P values < 0.001). This association was significant in stage III but not in stage IV. In the multivariate model, after adjusting for covariates, TD maintained an independent prognostic value (P < 0.05). A nomogram model including TD, N stage, T stage, TNM stage, CEA, and chemotherapy was constructed. Through internal and external validation, the model demonstrated good calibration and accuracy. Further survival curve analysis based on individual scores from the model showed good discrimination. CONCLUSION TD positivity is an independent factor of poor prognosis in colorectal SRCC patients, and it is more effective to predict the prognosis of colorectal SRCC by building a model with TD and other clinically related variables.
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Affiliation(s)
- Fuchao Li
- Department of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, China
- Department of Geriatrics, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China
| | - Lei Liu
- Medical Centre for Digestive Diseases, The Second Affiliated Hospital of Nanjing Medical University, 121 Jiangjiayuan Road, Nanjing, 210046, China
- Department of Gastroenterology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, Jiangsu, 214200, China
| | - Qingzhao Feng
- Department of General Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, China
| | - Xiaohong Wang
- Department of Gastroenterology, Xuzhou Central Hospital, Xuzhou, Jiangsu Province, 221009, China
| | - Fang Liu
- Department of Gastroenterology, Xuzhou Central Hospital, Xuzhou, Jiangsu Province, 221009, China
| | - Li Yang
- Department of Geriatrics, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China
| | - Lin Miao
- Medical Centre for Digestive Diseases, The Second Affiliated Hospital of Nanjing Medical University, 121 Jiangjiayuan Road, Nanjing, 210046, China.
| | - Weiming Wang
- Department of Oncology, Yixing Hospital Affiliated to Medical College of Yangzhou University, Yixing, Jiangsu Province, 214200, China.
| | - Guozhong Ji
- Medical Centre for Digestive Diseases, The Second Affiliated Hospital of Nanjing Medical University, 121 Jiangjiayuan Road, Nanjing, 210046, China.
| | - Chenggong Yu
- Department of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, China.
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Lim A, Edderkaoui M, Zhang Y, Wang Q, Wang R, Pandol SJ, Ou Y. Designing a predictive Framework: Immune-Related Gene-Based nomogram and prognostic model for kidney renal papillary cell carcinoma. Int Immunopharmacol 2024; 131:111878. [PMID: 38493693 DOI: 10.1016/j.intimp.2024.111878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/12/2024] [Accepted: 03/13/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Kidney renal papillary cell carcinoma (KIRP) is frequently associated with an unfavorable prognosis for affected individuals. Unfortunately, there has been insufficient exploration in search for a reliable prognosis signature and predictive indicators to forecast outcomes for KIRP patients. AIM The aim of this study is to employ a comprehensive analysis of data for the identification of prognosis genes, leading to the development of a nomogram with strong predictive capabilities. The objective is to provide a valuable statistical tool that, when implemented in a clinical setting, can offer patients an early opportunity for treatment and enhance their chances of ultimate recovery from this life-threatening disease. METHODS Different packages in R were used to analyze RNA-seq data from the TCGA data portal. Multivariate Cox regression analysis and Kaplan-Meier analysis were also used to investigate the prognostic values of immune-related genes and construct the predictive model and nomogram. A p-value < 0.05 was considered to be significant. RESULTS A total of 368 immune-related genes and 60 TFs were identified as differentially expressed in KIRP tissues compared with normal tissues. Of the 368, 23 were found to be related to overall survival. GO and KEGG analysis suggested that these prognostic immune-related genes mainly participated in the ERK1 and ERK2 cascades, Rap1 signaling pathway, and the PI3K-Akt signaling pathway. 9 genes were identified from Cox regression to be statistically significant prognostic-related genes. Survival analysis showed that a model based on these 9 prognostic-related genes has high predictive performance. Immunohistochemistry results show that APOH, BIRC5, CCL19, and GRN were significantly increased in kidney cancer. B cells and CD4 + T cells were positively correlated with risk score model. CONCLUSION A prognostic model was successfully created based on 9 immune-related genes correlated with overall survival in KIRP. This work aims to provide some insight into therapeutic approaches and prognostic predictors of KIRP.
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Affiliation(s)
- Adrian Lim
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Mouad Edderkaoui
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; University of California at Los Angeles, California
| | - Yi Zhang
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Qiang Wang
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Ruoxiang Wang
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Stephen J Pandol
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; University of California at Los Angeles, California
| | - Yan Ou
- Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
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Wang J, Wang H, Ding Y, Jiao X, Zhu J, Zhai Z. NET-related gene signature for predicting AML prognosis. Sci Rep 2024; 14:9115. [PMID: 38643300 PMCID: PMC11032381 DOI: 10.1038/s41598-024-59464-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 04/11/2024] [Indexed: 04/22/2024] Open
Abstract
Acute Myeloid Leukemia (AML) is a malignant blood cancer with a high mortality rate. Neutrophil extracellular traps (NETs) influence various tumor outcomes. However, NET-related genes (NRGs) in AML had not yet received much attention. This study focuses on the role of NRGs in AML and their interaction with the immunological microenvironment. The gene expression and clinical data of patients with AML were downloaded from the TCGA-LAML and GEO cohorts. We identified 148 NRGs through the published article. Univariate Cox regression was used to analyze the association of NRGs with overall survival (OS). The least absolute shrinkage and selection operator were utilized to assess the predictive efficacy of NRGs. Kaplan-Meier plots visualized survival estimates. ROC curves assessed the prognostic value of NRG-based features. A nomogram, integrating clinical information and prognostic scores of patients, was constructed using multivariate logistic regression and Cox proportional hazards regression models. Twenty-seven NRGs were found to significantly impact patient OS. Six NRGs-CFTR, ENO1, PARVB, DDIT4, MPO, LDLR-were notable for their strong predictive ability regarding patient survival. The ROC values for 1-, 3-, and 5-year survival rates were 0.794, 0.781, and 0.911, respectively. In the training set (TCGA-LAML), patients in the high NRG risk group showed a poorer prognosis (p < 0.001), which was validated in two external datasets (GSE71014 and GSE106291). The 6-NRG signature and corresponding nomograms exhibit superior predictive accuracy, offering insights for pre-immune response evaluation and guiding future immuno-oncology treatments and drug selection for AML patients.
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Affiliation(s)
- Jiajia Wang
- Department of Hematology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China
- Center of Hematology Research, Anhui Medical University, Hefei, 230601, Anhui, China
- Department of Hematology, Tongling People's Hospital, Tongling, 244000, Anhui, China
| | - Huiping Wang
- Department of Hematology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China
- Center of Hematology Research, Anhui Medical University, Hefei, 230601, Anhui, China
| | - Yangyang Ding
- Department of Hematology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China
- Center of Hematology Research, Anhui Medical University, Hefei, 230601, Anhui, China
| | - Xunyi Jiao
- Department of Hematology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China
- Center of Hematology Research, Anhui Medical University, Hefei, 230601, Anhui, China
| | - Jinli Zhu
- Department of Hematology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China
- Center of Hematology Research, Anhui Medical University, Hefei, 230601, Anhui, China
| | - Zhimin Zhai
- Department of Hematology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China.
- Center of Hematology Research, Anhui Medical University, Hefei, 230601, Anhui, China.
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Wan L, Fan Y, Wu T, Liu Y, Zhang R, Chen S, Zhao C, Xue Y. Endoplasmic reticulum stress-related genes as prognostic and immunogenic biomarkers in prostate cancer. Eur J Med Res 2024; 29:242. [PMID: 38643190 PMCID: PMC11031923 DOI: 10.1186/s40001-024-01818-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 03/28/2024] [Indexed: 04/22/2024] Open
Abstract
BACKGROUND The metastasis and aggressive nature of prostate cancer (PCa) has become a major malignancy related threat that concerns men's health. The efficacy of immune monotherapy against PCa is questionable due to its lymphocyte-suppressive nature. METHOD Endoplasmic reticulum stress- (ERS-) and PCa-prognosis-related genes were obtained from the Molecular Signatures Database and the Cancer Genome Atlas database. The expression, prognosis and immune infiltration values of key genes were explored by "survival R package", "rms", "xCELL algorithm", and univariate-multivariate Cox and LASSO regression analyses. The "consensus cluster plus R package" was used for cluster analysis. RESULT As ERS-related genes, ERLIN2 and CDK5RAP3 showed significant expressional, prognostic and clinic-pathologic values. They were defined as the key genes significantly correlated with immune infiltration and response. The nomogram was constructed with T-stage and primary treatment outcome, and the risk-prognostic model was constructed in the following way: Riskscore = (- 0.1918) * ERLIN2 + (0.5254) * CDK5RAP3. Subsequently, prognostic subgroups based on key genes classified the high-risk group as a pro-cancer subgroup that had lower mutation rates of critical genes (SPOP and MUC16), multiple low-expression immune-relevant molecules, and differences in macrophages (M1 and M2) expressions. Finally, ERLIN2 as an anti-oncogene and CDK5RAP3 as a pro-oncogene were further confirmed by cell phenotype assays and immunohistochemistry. CONCLUSION We identified ERLIN2 and CDK5RAP3 as ERS-related genes with important prognostic and immunologic values, and classified patients between high- and low-risk subgroups, which provided new prognostic markers, immunotherapeutic targets, and basis for prognostic assessments.
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Affiliation(s)
- Lilin Wan
- Southeast University, 87 Dingjia Bridge Hunan Road, Nanjing, 210009, China
- Department of Urology, Zhongda Hospital Southeast University, 87 Dingjia Bridge Hunan Road, Nanjing, 210009, China
| | - Yunxia Fan
- Department of Urology, Jintan Affiliated Hospital of Jiangsu University, No.500, Jintan Avenue, Jintan District, Changzhou, 213200, China
| | - Tiange Wu
- Southeast University, 87 Dingjia Bridge Hunan Road, Nanjing, 210009, China
- Department of Urology, Zhongda Hospital Southeast University, 87 Dingjia Bridge Hunan Road, Nanjing, 210009, China
| | - Yifan Liu
- Southeast University, 87 Dingjia Bridge Hunan Road, Nanjing, 210009, China
- Department of Urology, Zhongda Hospital Southeast University, 87 Dingjia Bridge Hunan Road, Nanjing, 210009, China
| | - Ruixin Zhang
- Southeast University, 87 Dingjia Bridge Hunan Road, Nanjing, 210009, China
| | - Saisai Chen
- Southeast University, 87 Dingjia Bridge Hunan Road, Nanjing, 210009, China.
- Department of Urology, Zhongda Hospital Southeast University, 87 Dingjia Bridge Hunan Road, Nanjing, 210009, China.
| | - Chenggui Zhao
- Department of Laboratory, Zhongda Hospital Southeast University, 87 Dingjia Bridge Hunan Road, Nanjing, 210009, China.
| | - Yifeng Xue
- Department of Urology, Jintan Affiliated Hospital of Jiangsu University, No.500, Jintan Avenue, Jintan District, Changzhou, 213200, China.
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Xu H, Yan R, Ye C, Li J, Ji G. Specific mortality in patients with diffuse large B-cell lymphoma: a retrospective analysis based on the surveillance, epidemiology, and end results database. Eur J Med Res 2024; 29:241. [PMID: 38643217 PMCID: PMC11031870 DOI: 10.1186/s40001-024-01833-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/06/2024] [Indexed: 04/22/2024] Open
Abstract
BACKGROUND The full potential of competing risk modeling approaches in the context of diffuse large B-cell lymphoma (DLBCL) patients has yet to be fully harnessed. This study aims to address this gap by developing a sophisticated competing risk model specifically designed to predict specific mortality in DLBCL patients. METHODS We extracted DLBCL patients' data from the SEER (Surveillance, Epidemiology, and End Results) database. To identify relevant variables, we conducted a two-step screening process using univariate and multivariate Fine and Gray regression analyses. Subsequently, a nomogram was constructed based on the results. The model's consistency index (C-index) was calculated to assess its performance. Additionally, calibration curves and receiver operator characteristic (ROC) curves were generated to validate the model's effectiveness. RESULTS This study enrolled a total of 24,402 patients. The feature selection analysis identified 13 variables that were statistically significant and therefore included in the model. The model validation results demonstrated that the area under the receiver operating characteristic (ROC) curve (AUC) for predicting 6-month, 1-year, and 3-year DLBCL-specific mortality was 0.748, 0.718, and 0.698, respectively, in the training cohort. In the validation cohort, the AUC values were 0.747, 0.721, and 0.697. The calibration curves indicated good consistency between the training and validation cohorts. CONCLUSION The most significant predictor of DLBCL-specific mortality is the age of the patient, followed by the Ann Arbor stage and the administration of chemotherapy. This predictive model has the potential to facilitate the identification of high-risk DLBCL patients by clinicians, ultimately leading to improved prognosis.
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Affiliation(s)
- Hui Xu
- Department of Hematology, Taixing People's Hospital, No. 98, Runtai South Road, Taixing, 225400, Jiangsu, China
| | - Rong Yan
- Taixing People's Hospital, Taixing, Jiangsu, China
| | - Chunmei Ye
- Department of Hematology, Taixing People's Hospital, No. 98, Runtai South Road, Taixing, 225400, Jiangsu, China
| | - Jun Li
- Department of Hematology, Taixing People's Hospital, No. 98, Runtai South Road, Taixing, 225400, Jiangsu, China
| | - Guo Ji
- Department of Hematology, Taixing People's Hospital, No. 98, Runtai South Road, Taixing, 225400, Jiangsu, China.
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Zeng H, Xue X, Chen D, Zheng B, Liang B, Que Z, Xu D, Wang X, Lin S. Conditional survival analysis and real-time prognosis prediction in stage III T3-T4 colon cancer patients after surgical resection: a SEER database analysis. Int J Colorectal Dis 2024; 39:54. [PMID: 38639915 PMCID: PMC11031473 DOI: 10.1007/s00384-024-04614-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/15/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Conditional survival (CS) takes into consideration the duration of survival post-surgery and can provide valuable additional insights. The aim of this study was to investigate the risk factors associated with reduced one-year postoperative conditional survival in patients diagnosed with stage III T3-T4 colon cancer and real-time prognosis prediction. Furthermore, we aim to develop pertinent nomograms and predictive models. METHODS Clinical data and survival outcomes of patients diagnosed with stage III T3-T4 colon cancer were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2010 to 2019. Patients were divided into training and validation cohorts at a ratio of 7:3. The training set consisted of a total of 11,386 patients for conditional overall survival (cOS) and 11,800 patients for conditional cancer-specific survival (cCSS), while the validation set comprised 4876 patients for cOS and 5055 patients for cCSS. Univariate and multivariate Cox regression analyses were employed to identify independent risk factors influencing one-year postoperative cOS and cCSS. Subsequently, predictive nomograms for cOS and cCSS at 2-year, 3-year, 4-year, and 5-year intervals were constructed based on the identified prognostic factors. The performance of these nomograms was rigorously assessed through metrics including the concordance index (C-index), calibration curves, and the area under curve (AUC) derived from the receiver operating characteristic (ROC) analysis. Clinical utility was further evaluated using decision curve analysis (DCA). RESULTS A total of 18,190 patients diagnosed with stage III T3-T4 colon cancer were included in this study. Independent risk factors for one-year postoperative cOS and cCSS included age, pT stage, pN stage, pretreatment carcinoembryonic antigen (CEA) levels, receipt of chemotherapy, perineural invasion (PNI), presence of tumor deposits, the number of harvested lymph nodes, and marital status. Sex and tumor site were significantly associated with one-year postoperative cOS, while radiation therapy was notably associated with one-year postoperative cCSS. In the training cohort, the developed nomogram demonstrated a C-index of 0.701 (95% CI, 0.711-0.691) for predicting one-year postoperative cOS and 0.701 (95% CI, 0.713-0.689) for one-year postoperative cCSS. Following validation, the C-index remained robust at 0.707 (95% CI, 0.721-0.693) for one-year postoperative cOS and 0.700 (95% CI, 0.716-0.684) for one-year postoperative cCSS. ROC and calibration curves provided evidence of the model's stability and reliability. Furthermore, DCA underscored the nomogram's superior clinical utility. CONCLUSIONS Our study developed nomograms and predictive models for postoperative stage III survival in T3-T4 colon cancer with the aim of accurately estimating conditional survival. Survival bias in our analyses may lead to overestimation of survival outcomes, which may limit the applicability of our findings.
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Affiliation(s)
- Hao Zeng
- Department of Gastroenterology and Anorectal Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China
| | - Xueyi Xue
- Department of Gastroenterology and Anorectal Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China
| | - Dongbo Chen
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China
| | - Biaohui Zheng
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China
| | - Baofeng Liang
- Department of Gastroenterology and Anorectal Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China
- Department of Surgery II, Shanghang County Hospital, Longyan City, Fujian Province, China
| | - Zhipeng Que
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China
| | - Dongbo Xu
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China
| | - Xiaojie Wang
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China.
| | - Shuangming Lin
- Department of Gastroenterology and Anorectal Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China.
- Department of Gastroenterology and Anorectal Surgery, Longyan First Hospital, Fujian Medical University, No. 105 Jiuyi North Road, Longyan, 364000, Fujian Province, China.
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Li M, Hu X, Li Y, Chen G, Ding CG, Tian X, Tian P, Xiang H, Pan X, Ding X, Xue W, Zheng J, Ding C. Development and validation of a novel nomogram model for predicting delayed graft function in deceased donor kidney transplantation based on pre-transplant biopsies. BMC Nephrol 2024; 25:138. [PMID: 38641807 PMCID: PMC11031976 DOI: 10.1186/s12882-024-03557-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 03/21/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Delayed graft function (DGF) is an important complication after kidney transplantation surgery. The present study aimed to develop and validate a nomogram for preoperative prediction of DGF on the basis of clinical and histological risk factors. METHODS The prediction model was constructed in a development cohort comprising 492 kidney transplant recipients from May 2018 to December 2019. Data regarding donor and recipient characteristics, pre-transplantation biopsy results, and machine perfusion parameters were collected, and univariate analysis was performed. The least absolute shrinkage and selection operator regression model was used for variable selection. The prediction model was developed by multivariate logistic regression analysis and presented as a nomogram. An external validation cohort comprising 105 transplantation cases from January 2020 to April 2020 was included in the analysis. RESULTS 266 donors were included in the development cohort, 458 kidneys (93.1%) were preserved by hypothermic machine perfusion (HMP), 96 (19.51%) of 492 recipients developed DGF. Twenty-eight variables measured before transplantation surgery were included in the LASSO regression model. The nomogram consisted of 12 variables from donor characteristics, pre-transplantation biopsy results and machine perfusion parameters. Internal and external validation showed good discrimination and calibration of the nomogram, with Area Under Curve (AUC) 0.83 (95%CI, 0.78-0.88) and 0.87 (95%CI, 0.80-0.94). Decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSION A DGF predicting nomogram was developed that incorporated donor characteristics, pre-transplantation biopsy results, and machine perfusion parameters. This nomogram can be conveniently used for preoperative individualized prediction of DGF in kidney transplant recipients.
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Affiliation(s)
- Meihe Li
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Xiaojun Hu
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Yang Li
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Guozhen Chen
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Chen-Guang Ding
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Xiaohui Tian
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Puxun Tian
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Heli Xiang
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Xiaoming Pan
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Xiaoming Ding
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
| | - Wujun Xue
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China.
| | - Jin Zheng
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China.
| | - Chenguang Ding
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China
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Wang L, Yang Y, Zhao Q. Retrospective analysis of predictive factors for AVF dysfunction in patients undergoing MHD. Medicine (Baltimore) 2024; 103:e37737. [PMID: 38640314 PMCID: PMC11029975 DOI: 10.1097/md.0000000000037737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 02/03/2024] [Accepted: 03/06/2024] [Indexed: 04/21/2024] Open
Abstract
To construct an early clinical prediction model for AVF dysfunction in patients undergoing Maintenance Hemodialysis (MHD) and perform internal and external verifications. We retrospectively examined clinical data from 150 patients diagnosed with MHD at Hefei Third People's Hospital from January 2014 to June 2023. Depending on arteriovenous fistula (AVF) functionality, patients were categorized into dysfunctional (n = 62) and functional (n = 88) cohorts. Using the least absolute shrinkage and selection operator(LASSO) regression model, variables potentially influencing AVF functionality were filtered using selected variables that underwent multifactorial logistic regression analysis. The Nomogram model was constructed using the R software, and the Area Under Curve(AUC) value was calculated. The model's accuracy was appraised through the calibration curve and Hosmer-Lemeshow test, with the model undergoing internal validation using the bootstrap method. There were 11 factors exhibiting differences between the group of patients with AVF dysfunction and the group with normal AVF function, including age, sex, course of renal failure, diabetes, hyperlipidemia, Platelet count (PLT), Calcium (Ca), Phosphorus, D-dimer (D-D), Fibrinogen (Fib), and Anastomotic width. These identified factors are included as candidate predictive variables in the LASSO regression analysis. LASSO regression identified age, sex, diabetes, hyperlipidemia, anastomotic diameter, blood phosphorus, and serum D-D levels as 7 predictive factors. Unconditional binary logistic regression analysis revealed that advanced age (OR = 4.358, 95% CI: 1.454-13.062), diabetes (OR = 4.158, 95% CI: 1.243-13.907), hyperlipidemia (OR = 3.651, 95% CI: 1.066-12.499), D-D (OR = 1.311, 95% CI: 1.063-1.616), and hyperphosphatemia (OR = 4.986, 95% CI: 2.513-9.892) emerged as independent risk factors for AVF dysfunction in MHD patients. The AUC of the predictive model was 0.934 (95% CI: 0.897-0.971). The Hosmer-Lemeshow test showed high consistency between the model's predictive results and actual clinical observations (χ2 = 1.553, P = .092). Internal validation revealed an AUC of 0.911 (95% CI: 0.866-0.956), with the Calibration calibration curve nearing the ideal curve. Advanced age, coexisting diabetes, hyperlipidemia, blood D-D levels, and hyperphosphatemia are independent risk factors for AVF dysfunction in patients undergoing MHD.
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Affiliation(s)
- Liqin Wang
- Hemodialysis Center, Hefei Third Clinical College (Hefei Third People’s Hospital), Anhui Medical University, Hefei, China
| | - Yanna Yang
- Department of Nephrology, Hefei Third Clinical College (Hefei Third People’s Hospital), Anhui Medical University, Hefei, China
| | - Qianqian Zhao
- Hemodialysis Center, Hefei Third Clinical College (Hefei Third People’s Hospital), Anhui Medical University, Hefei, China
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Wang Q, Shen K, Fei B, Wei M, Ge X, Xie Z. Development and validation of a nomogram to predict cancer-specific survival of elderly patients with unresected gastric cancer who received chemotherapy. Sci Rep 2024; 14:9008. [PMID: 38637579 PMCID: PMC11026516 DOI: 10.1038/s41598-024-59516-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 04/11/2024] [Indexed: 04/20/2024] Open
Abstract
This investigation aimed to explore the prognostic factors in elderly patients with unresected gastric cancer (GC) who have received chemotherapy and to develop a nomogram for predicting their cancer-specific survival (CSS). Elderly gastric cancer patients who have received chemotherapy but no surgery in the Surveillance, Epidemiology, and End Results Database between 2004 and 2015 were included in this study. Cox analyses were conducted to identify prognostic factors, leading to the formulation of a nomogram. The nomogram was validated using receiver operating characteristic (ROC) and calibration curves. The findings elucidated six prognostic factors encompassing grade, histology, M stage, radiotherapy, tumor size, and T stage, culminating in the development of a nomogram. The ROC curve indicated that the area under curve of the nomogram used to predict CSS for 3, 4, and 5 years in the training queue as 0.689, 0.708, and 0.731, and in the validation queue, as 0.666, 0.693, and 0.708. The calibration curve indicated a high degree of consistency between actual and predicted CSS for 3, 4, and 5 years. This nomogram created to predict the CSS of elderly patients with unresected GC who have received chemotherapy could significantly enhance treatment accuracy.
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Affiliation(s)
- Qi Wang
- Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Kexin Shen
- Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Bingyuan Fei
- Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Mengqiang Wei
- Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Xinbin Ge
- Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Zhongshi Xie
- Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China.
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Gu Z, Yang C, Zhang K, Wu H. Development and validation of a nomogram for predicting sever cancer-related fatigue in patients with cervical cancer. BMC Cancer 2024; 24:492. [PMID: 38637740 PMCID: PMC11025233 DOI: 10.1186/s12885-024-12258-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 04/15/2024] [Indexed: 04/20/2024] Open
Abstract
OBJECTIVE Cancer-related fatigue (CRF) has been considered the biggest influencing factor for cancer patients after surgery. This study aimed to develop and validate a nomogram for severe cancer-related fatigue (CRF) patients with cervical cancer (CC). METHODS A cross-sectional study was conducted to develop and validate a nomogram (building set = 196; validation set = 88) in the Department of Obstetrics and Gynecology of a Class III hospital in Shenyang, Liaoning Province. We adopted the questionnaire method, including the Cancer Fatigue Scale (CFS), Medical Uncertainty in Illness Scale (MUIS), Medical Coping Modes Questionnaire (MCMQ), Multidimensional Scale of Perceived Social Support (MSPSS), and Sense of Coherence-13 (SOC-13). Binary logistic regression was used to test the risk factors of CRF. The R4.1.2 software was used to develop and validate the nomogram, including Bootstrap resampling method, the ability of Area Under Curve (AUC), Concordance Index (C-Index), Hosmer Lemeshow goodness of fit test, Receiver Operating Characteristic (ROC) curve, Calibration calibration curve, and Decision Curve Analysis curve (DCA). RESULTS The regression equation was Logit(P) = 1.276-0.947 Monthly income + 0.989 Long-term passive smoking - 0.952 Physical exercise + 1.512 Diagnosis type + 1.040 Coping style - 0.726 Perceived Social Support - 2.350 Sense of Coherence. The C-Index of the nomogram was 0.921 (95% CI: 0.877∼0.958). The ROC curve showed the sensitivity of the nomogram was 0.821, the specificity was 0.900, and the accuracy was 0.857. AUC was 0.916 (95% CI: 0.876∼0.957). The calibration showed that the predicted probability of the nomogram fitted well with the actual probability. The DCA curve showed when the prediction probability was greater than about 10%, the benefit of the nomogram was positive. The results in the validation group were similar. CONCLUSION This nomogram had good identifiability, accuracy and clinical practicality, and could be used as a prediction and evaluation tool for severe cases of clinical patients with CC.
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Affiliation(s)
- ZhiHui Gu
- Department of Social Medicine, School of Health Management, China Medical University, No.77 PuHe Road, Shenyang North New District, 110122, Shenyang, Liaoning, People's Republic of China
| | - ChenXin Yang
- Department of Social Medicine, School of Health Management, China Medical University, No.77 PuHe Road, Shenyang North New District, 110122, Shenyang, Liaoning, People's Republic of China
| | - Ke Zhang
- Department of Social Medicine, School of Health Management, China Medical University, No.77 PuHe Road, Shenyang North New District, 110122, Shenyang, Liaoning, People's Republic of China
| | - Hui Wu
- Department of Social Medicine, School of Health Management, China Medical University, No.77 PuHe Road, Shenyang North New District, 110122, Shenyang, Liaoning, People's Republic of China.
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Yan Y, An X, Ren H, Luo B, Jin S, Liu L, Di Y, Li T, Huang Y. Nomogram-based geometric and hemodynamic parameters for predicting the growth of small untreated intracranial aneurysms. Neurosurg Rev 2024; 47:169. [PMID: 38635054 DOI: 10.1007/s10143-024-02408-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/30/2024] [Accepted: 04/09/2024] [Indexed: 04/19/2024]
Abstract
Previous studies have shown that the growth status of intracranial aneurysms (IAs) predisposes to rupture. This study aimed to construct a nomogram for predicting the growth of small IAs based on geometric and hemodynamic parameters. We retrospectively collected the baseline and follow-up angiographic images (CTA/ MRA) of 96 small untreated saccular IAs, created patient-specific vascular models and performed computational fluid dynamics (CFD) simulations. Geometric and hemodynamic parameters were calculated. A stepwise Cox proportional hazards regression analysis was employed to construct a nomogram. IAs were stratified into low-, intermediate-, and high-risk groups based on the total points from the nomogram. Receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA) and Kaplan-Meier curves were evaluated for internal validation. In total, 30 untreated saccular IAs were grown (31.3%; 95%CI 21.8%-40.7%). The PHASES, ELAPSS, and UIATS performed poorly in distinguishing growth status. Hypertension (hazard ratio [HR] 4.26, 95%CI 1.61-11.28; P = 0.004), nonsphericity index (95%CI 4.10-25.26; P = 0.003), max relative residence time (HR 1.01, 95%CI 1.00-1.01; P = 0.032) were independently related to the growth status. A nomogram was constructed with the above predictors and achieved a satisfactory prediction in the validation cohort. The log-rank test showed significant discrimination among the Kaplan-Meier curves of different risk groups in the training and validation cohorts. A nomogram consisting of geometric and hemodynamic parameters presented an accurate prediction for the growth status of small IAs and achieved risk stratification. It showed higher predictive efficacy than the assessment tools.
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Affiliation(s)
- Yujia Yan
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China
| | - Xingwei An
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Hecheng Ren
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China
| | - Bin Luo
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China
| | - Song Jin
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Li Liu
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Yang Di
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Tingting Li
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Ying Huang
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China.
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Meng X, Hao F, Wang N, Qin P, Ju Z, Sun D. Log odds of positive lymph nodes (LODDS)-based novel nomogram for survival estimation in patients with invasive micropapillary carcinoma of the breast. BMC Med Res Methodol 2024; 24:90. [PMID: 38637725 PMCID: PMC11025266 DOI: 10.1186/s12874-024-02218-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 04/15/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Invasive micropapillary carcinoma (IMPC) of the breast is known for its high propensity for lymph node (LN) invasion. Inadequate LN dissection may compromise the precision of prognostic assessments. This study introduces a log odds of positive lymph nodes (LODDS) method to address this issue and develops a novel LODDS-based nomogram to provide accurate prognostic information. METHODS The study analyzed data from 1,901 patients with breast IMPC from the Surveillance, Epidemiology, and End Results database. It assessed the relationships between LODDS and the number of excised LN (eLN), positive LN (pLN), and the pLN ratio (pLNR), identifying an optimal threshold value using a restricted cubic spline method. Predictive factors were identified by the Cox least absolute shrinkage and selection operator (Cox-LASSO) regression and validated through multivariate Cox regression to construct a nomogram. The model's accuracy, discrimination, and utility were assessed. The study also explored the consequences of excluding LODDS from the nomogram and compared its effectiveness with the tumor-node-metastasis (TNM) staging system. RESULTS LODDS improved N status classification by identifying heterogeneity in patients with pLN ratios of 0% (pLN =0) or 100% (pLN =eLN) and setting -1.08 as the ideal cutoff. Five independent prognostic factors for breast cancer-specific survival (BCSS) were identified: tumor size, N status, LODDS, progesterone receptor status, and histological grade. The LODDS-based nomogram achieved a strong concordance index of 0.802 (95% CI: 0.741-0.863), surpassing both the version without LODDS and the conventional TNM staging in all tests. CONCLUSIONS For breast IMPC, LODDS served as an independent prognostic factor, its effectiveness unaffected by the anatomical LN count, enhancing the accuracy of N staging. The LODDS-based nomogram showed promise in offering more personalized prognostic information.
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Affiliation(s)
- Xiangdi Meng
- Department of Radiation Oncology, Weifang People's Hospital, No. 151 Guangwen Street, Kuiwen District, Weifang, 261041, Shandong, China
- Graduate School of Medicine, Gunma University, Maebashi, Japan
| | - Furong Hao
- Department of Radiation Oncology, Weifang People's Hospital, No. 151 Guangwen Street, Kuiwen District, Weifang, 261041, Shandong, China
| | - Nan Wang
- Department of Radiation Oncology, Weifang People's Hospital, No. 151 Guangwen Street, Kuiwen District, Weifang, 261041, Shandong, China
| | - Peiyan Qin
- Department of Radiation Oncology, Weifang People's Hospital, No. 151 Guangwen Street, Kuiwen District, Weifang, 261041, Shandong, China
| | - Zhuojun Ju
- Graduate School of Medicine, Gunma University, Maebashi, Japan
| | - Daqing Sun
- Department of Radiation Oncology, Weifang People's Hospital, No. 151 Guangwen Street, Kuiwen District, Weifang, 261041, Shandong, China.
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Miao L, Wu D, Zhao H, Xie A. TIMM17A overexpression in lung adenocarcinoma and its association with prognosis. Sci Rep 2024; 14:8840. [PMID: 38632467 PMCID: PMC11024209 DOI: 10.1038/s41598-024-59526-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 04/11/2024] [Indexed: 04/19/2024] Open
Abstract
Lung adenocarcinoma (LUAD), a leading cause of cancer-related mortality worldwide, demands a deeper understanding of its molecular mechanisms and the identification of reliable biomarkers for better diagnosis and targeted therapy. Leveraging data from the Cancer Genome Atlas (TCGA), the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the Human Protein Atlas (HPA), we investigated the mRNA and protein expression profiles of TIMM17A and assessed its prognostic significance through Kaplan-Meier survival curves and Cox regression analysis. Through Gene Set Enrichment Analysis, we explored the regulatory mechanisms of TIMM17A in LUAD progression and demonstrated its role in modulating the proliferative capacity of A549 cells, a type of LUAD cell, via in vitro experiments. Our results indicate that TIMM17A is significantly upregulated in LUAD tissues, correlating with clinical staging, lymph node metastasis, overall survival, and progression-free survival, thereby establishing it as a critical independent prognostic factor. The construction of a nomogram model further enhances our ability to predict patient outcomes. Knockdown of TIMM17A inhibited the growth of LUAD cells. The potential of TIMM17A as a biomarker and therapeutic target for LUAD presents a promising pathway for improving patient diagnosis and treatment strategies.
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Affiliation(s)
- Lili Miao
- Department of Respiration, YiZheng People's Hospital, YiZheng, Jiangsu, China
| | - Dejun Wu
- Department of Respiration, YiZheng People's Hospital, YiZheng, Jiangsu, China
| | - Hongyu Zhao
- Department of Respiration, YiZheng People's Hospital, YiZheng, Jiangsu, China
| | - Aiwei Xie
- Department of Nephrology, YiZheng People's Hospital, YiZheng, Jiangsu, China.
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Li Y, Wang JP, Zhu X. Construction of a nomogram for predicting compensated cirrhosis with Wilson's disease based on non-invasive indicators. BMC Med Imaging 2024; 24:90. [PMID: 38627672 PMCID: PMC11020316 DOI: 10.1186/s12880-024-01265-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 03/29/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Wilson's disease (WD) often leads to liver fibrosis and cirrhosis, and early diagnosis of WD cirrhosis is essential. Currently, there are few non-invasive prediction models for WD cirrhosis. The purpose of this study is to non-invasively predict the occurrence risk of compensated WD cirrhosis based on ultrasound imaging features and clinical characteristics. METHODS A retrospective analysis of the clinical characteristics and ultrasound examination data of 102 WD patients from November 2018 to November 2020 was conducted. According to the staging system for WD liver involvement, the patients were divided into a cirrhosis group (n = 43) and a non-cirrhosis group (n = 59). Multivariable logistic regression analysis was used to identify independent influencing factors for WD cirrhosis. A nomogram for predicting WD cirrhosis was constructed using R analysis software, and validation of the model's discrimination, calibration, and clinical applicability was completed. Due to the low incidence of WD and the small sample size, bootstrap internal sampling with 500 iterations was adopted for validation to prevent overfitting of the model. RESULTS Acoustic Radiation Force Impulse (ARFI), portal vein diameter (PVD), and serum albumin (ALB) are independent factors affecting WD cirrhosis. A nomogram for WD cirrhosis was constructed based on these factors. The area under the ROC curve (AUC) of the model's predictive ability is 0.927 (95% CI: 0.88-0.978). As demonstrated by 500 Bootstrap internal sampling validations, the model has high discrimination and calibration. Clinical decision curve analysis shows that the model has high clinical practical value. ROC curve analysis of the model's rationality indicates that the model's AUC is greater than the AUC of using ALB, ARFI, and PVD alone. CONCLUSION The nomogram model constructed based on ARFI, PVD, and ALB can serve as a non-invasive tool to effectively predict the risk of developing WD cirrhosis.
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Affiliation(s)
- Yan Li
- Department of Ultrasound, The first affiliated hospital of Anhui University of Traditional Chinese Medicine, MeiShan Road, 230031, Anhui, P.R. China.
| | - Jing Ping Wang
- Department of Ultrasound, The first affiliated hospital of Anhui University of Traditional Chinese Medicine, MeiShan Road, 230031, Anhui, P.R. China
| | - Xiaoli Zhu
- Department of Intervention, The First Affiliated Hospital of Soochow University, 899, The Pinghai Road, 215006, Jiangsu, P.R. China
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Tian M, Zhu X, Ren L, Zhou X, Gu L, Meng K, Tian Y, Cai H, Liu X, Ding J. HE4-based nomogram for predicting overall survival in patients with idiopathic pulmonary fibrosis: construction and validation. Eur J Med Res 2024; 29:238. [PMID: 38627872 PMCID: PMC11020350 DOI: 10.1186/s40001-024-01829-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 04/05/2024] [Indexed: 04/19/2024] Open
Abstract
Idiopathic pulmonary fibrosis (IPF) is a life-threatening interstitial lung disease. Identifying biomarkers for early diagnosis is of great clinical importance. The epididymis protein 4 (HE4) is important in the process of inflammation and fibrosis in the epididymis. Its prognostic value in IPF, however, has not been studied. The mRNA and protein levels of HE4 were used to determine the prognostic value in different patient cohorts. In this study, prognostic nomograms were generated based on the results of the cox regression analysis. We identified the HE4 protein level increased in IPF patients, but not the HE4 gene expression. The increased expression of HE4 correlated positively with a poor prognosis for patients with IPF. The HR and 95% CI were 2.62 (1.61-4.24) (p < 0.001) in the training set. We constructed a model based on the risk-score = 0.16222182 * HE4 + 0/0.37580659/1.05003609 (for GAP index 0-3/4-5/6-8) + (- 1.1183375). In both training and validation sets, high-risk patients had poor prognoses (HR: 3.49, 95%CI 2.10-5.80, p = 0.001) and higher likelihood of dying (HR: 6.00, 95%CI 2.04-17.67, p = 0.001). Analyses of calibration curves and decision curves suggest that the method is effective in predicting outcomes. Furthermore, a similar formulation was used in a protein-based model based on HE4 that also showed prognostic value when applied to IPF patients. Accordingly, HE4 is an independent poor prognosis factor, and it has the potential to predict IPF patient survival.
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Affiliation(s)
- Mi Tian
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Xiaohui Zhu
- Department of Respiratory, The Fourth Affiliated Hospital of Nanjing Medical University, 298 Nanpu Road, Nanjing, 211899, China
| | - Lijun Ren
- Department of Pulmonary and Critical Care Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Xuan Zhou
- Department of Respiratory, The Fourth Affiliated Hospital of Nanjing Medical University, 298 Nanpu Road, Nanjing, 211899, China
- Phase I Clinical Trials Unit, The Affiliated Drum Tower Hospital of Nanjing University Medical School, 359 Pu Zhu Middle Road, Nanjing, 210031, China
| | - Lina Gu
- Department of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China
| | - Kaifang Meng
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, People's Republic of China
| | - Yaqiong Tian
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Hourong Cai
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
| | - Xiaoqin Liu
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
| | - Jingjing Ding
- Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
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Tao W, Zhan S, Shen Y, Zhao T, Li F, Gao M, Yang T, Yu J. Nomogram for predicting early hypophosphatemia in term infants. BMC Pediatr 2024; 24:255. [PMID: 38627752 PMCID: PMC11020330 DOI: 10.1186/s12887-024-04737-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Physiological processes rely on phosphate, which is an essential component of adenosine triphosphate (ATP). Hypophosphatasia can affect nearly every organ system in the body. It is crucial to monitor newborns with risk factors for hypophosphatemia and provide them with the proper supplements. We aimed to evaluate the risk factors and develop a nomogram for early hypophosphatemia in term infants. METHODS We conducted a retrospective study involving 416 term infants measured serum phosphorus within three days of birth. The study included 82 term infants with hypophosphatemia (HP group) and 334 term infants without hypophosphatemia (NHP group). We collected data on the characteristics of mothers, newborn babies, and childbirth. Furthermore, univariate and multivariate logistic regression analyses were performed to identify independent risk factors for hypophosphatemia in term infants, and a nomogram was developed and validated based on the final independent risk factors. RESULTS According to our analysis, the multivariate logistic regression analysis showed that male, maternal diabetes, cesarean delivery, lower serum magnesium, and lower birth weight were independent risk factors for early hypophosphatemia in term infants. In addition, the C-index of the developed nomogram was 0.732 (95% CI = 0.668-0.796). Moreover, the calibration curve indicated good consistency between the hypophosphatemia diagnosis and the predicted probability, and a decision curve analysis (DCA) confirmed the clinical utility of the nomogram. CONCLUSIONS The analysis revealed that we successfully developed and validated a nomogram for predicting early hypophosphatemia in term infants.
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Affiliation(s)
- Wan Tao
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Shina Zhan
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Yingjie Shen
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Tianjiao Zhao
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Feitian Li
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Miao Gao
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Tingting Yang
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China
| | - Jinqian Yu
- Neonatal Center, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, No.1 Shunkang Road, Shunyi District Beijing, Beijing, 101300, China.
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Zhou X, Liu M, Zheng Z, Cao X, Lin Y, Xu Y, Li Y, Wang HC, Sun Q. Nomogram predicts survival and surgical benefits for patients with breast cancer with initial bone metastasis: A population-based study. Cancer 2024; 130:1464-1475. [PMID: 38198445 DOI: 10.1002/cncr.35166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 01/12/2024]
Abstract
BACKGROUND Primary stage IV breast cancer is associated with a poor prognosis. At present, the value of local surgical treatment for patients with stage IV breast cancer remains uncertain; therefore, treatment principles remain controversial. Because of the high heterogeneity of these patients, it is often difficult to evaluate their prognoses. As a result, this study aimed to establish a prognostic nomogram to evaluate the prognosis of patients with breast cancer experiencing primary bone metastasis. METHODS The clinical characteristics and follow-up data of patients with primary breast cancer and bone metastasis from 2010 to 2018 were collected from the Surveillance, Epidemiology, and End Results database and from 2013 to 2021 at the Peking Union Medical College Hospital. Patients were divided into training and validation groups. Multivariate Cox regression analysis was used to identify the independent prognostic variables for predicting cancer-specific survival (CSS). On the basis of these independent risk factors, a nomogram was developed and used calibration curves to evaluate its accuracy. Patients were divided into three risk groups according to their scores and surgery-related survival curves plotted using the log-rank test. RESULTS Overall, 6372 patients were included, with 6319 from the Surveillance, Epidemiology, and End Results database and 53 from the Peking Union Medical College Hospital Breast Surgery Department. Multivariate analysis showed that age, race, marital status, grade, tumor stage, estrogen receptor status, progesterone receptor status, human epidermal growth factor receptor 2 status, and burden of other metastatic lesions were all associated with CSS. Based on these results, a nomogram that predicted the 1-, 3-, and 5-year CSS rates in patients with primary breast cancer and bone metastasis (concordance index > 0.69) was developed. After dividing patients into low-risk, high-risk, or super-high-risk groups based on nomogram scoring criteria, survival analysis revealed that patients in the low- and high-risk groups had significant survival benefits from primary focal surgery. CONCLUSION Independent risk factors for primary breast cancer in patients with bone metastasis were analyzed and a nomogram established to predict CSS. The prognostic tool derived in this study can assist clinicians in predicting the survival and surgical benefits of these patients through scoring, thereby providing further guidance for treatment strategies.
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Affiliation(s)
- Xingtong Zhou
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Mohan Liu
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Zhibo Zheng
- Department of International Medical Services, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Xi Cao
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yan Lin
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Ying Xu
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yan Li
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Hayson Chenyu Wang
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Qiang Sun
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
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Zheng Q, Yan H, He Y, Wang J, Zhang N, Huo L, Liu Y, Wang L, Xu L, Fan Z. An ultrasound-based nomogram for predicting axillary node pathologic complete response after neoadjuvant chemotherapy in breast cancer: Modeling and external validation. Cancer 2024; 130:1513-1523. [PMID: 38427584 DOI: 10.1002/cncr.35248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 12/18/2023] [Accepted: 12/20/2023] [Indexed: 03/03/2024]
Abstract
INTRODUCTION The staging and treatment of axillary nodes in breast cancer have become a focus of research. For breast cancer patients with fine-needle aspiration-or core needle biopsy-confirmed positive nodes, axillary lymph node dissection (ALND) after neoadjuvant chemotherapy (NAC) is still a standard treatment. However, some patients achieve an axillary pathologic complete response (pCR) after NAC. In this study, the authors sought to construct a model to predict axillary pCR in patients with positive axillary lymph nodes (cN+) breast cancer. METHODS Data from patients with pathologically proven cN+ breast cancer treated with NAC followed by ALND between January 2010 and April 2019 at the Peking University Cancer Hospital were reviewed. Axillary lymph node status was assessed using ultrasonography before and after NAC. The patient cohort was assigned to the construction and internal validation cohorts according to admission time. A nomogram was constructed based on the significant factors associated with axillary pCR. The predictive performance of the model was externally validated using data from Peking University First Hospital. RESULTS This study included 953 and 267 patients from Peking University Cancer Hospital and Peking University First Hospital, respectively. In the construction cohort, 39.7% (238 of 600) of patients achieved axillary pCR after NAC. The result of multivariate logistic regression analysis showed that tumor grade, clinical nodal response, NAC regimen, tumor pCR, lymphovascular invasion, and tumor biologic subtype were significant independent predictors of ypN0 (p < 0.05). The areas under the receiver operating characteristic curves for the construction, validation, and independent testing cohorts were 0.87 (95% confidence interval [CI], 0.84-0.90), 0.83 (95% CI, 0.79-0.87), and 0.84 (0.79-0.89), respectively. CONCLUSIONS A nomogram was constructed to predict the pCR of axillary lymph nodes after NAC for breast cancer. Validation of both the internal and external cohorts achieved good predictive performance, indicating that the model has preliminary clinical application prospects.
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Affiliation(s)
- Qijun Zheng
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Huicui Yan
- Department of Thyroid and Breast Surgery, Peking University First Hospital, Beijing, China
| | - Yingjian He
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Jiwei Wang
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Nan Zhang
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Ling Huo
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yiqiang Liu
- Department of Pathology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Lize Wang
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Ling Xu
- Department of Thyroid and Breast Surgery, Peking University First Hospital, Beijing, China
| | - Zhaoqing Fan
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
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Wang J, He X, Mi Y, Chen YQ, Li J, Wang R. PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma. BMC Cancer 2024; 24:463. [PMID: 38614981 PMCID: PMC11016215 DOI: 10.1186/s12885-024-12183-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/26/2024] [Indexed: 04/15/2024] Open
Abstract
BACKGROUND Clear cell renal cell carcinoma (ccRCC) is associated with a high prevalence of cancer-related deaths. The survival rates of patients are significantly lower in late-stage ccRCC than in early-stage ccRCC, due to the spread and metastasis of late-stage ccRCC, surgery has not reached the goal of radical cure, and the effect of traditional radiotherapy and chemotherapy is poor. Thus, it is crucial to accurately assess the prognosis and provide personalized treatment at an early stage in ccRCC. This study aims to develop an efficient nomogram model for stratifying and predicting the survival of ccRCC patients based on tumor stage. METHODS We first analyzed the microarray expression data of ccRCC patients from the Gene Expression Omnibus (GEO) database and categorized them into two groups based on the disease stage (early and late stage). Subsequently, the GEO2R tool was applied to screen out the genes that were highly expressed in all GEO datasets. Finally, the clinicopathological data of the two patient groups were obtained from The Cancer Genome Atlas (TCGA) database, and the differences were compared between groups. Survival analysis was performed to evaluate the prognostic value of candidate genes (PSAT1, PRAME, and KDELR3) in ccRCC patients. Based on the screened gene PSAT1 and clinical parameters that were significantly associated with patient prognosis, we established a new nomogram model, which was further optimized to a single clinical variable-based model. The expression level of PSAT1 in ccRCC tissues was further verified by qRT-PCR, Western blotting, and immunohistochemical analysis. RESULTS The datasets GSE73731, GSE89563, and GSE150404 identified a total of 22, 89, and 120 over-expressed differentially expressed genes (DEGs), respectively. Among these profiles, there were three genes that appeared in all three datasets based on different stage groups. The overall survival (OS) of late-stage patients was significantly shorter than that of early-stage patients. Among the three candidate genes (PSAT1, PRAME, and KDELR3), PSAT1 was shown to be associated with the OS of patients with late-stage ccRCC. Multivariate Cox regression analysis showed that age, tumor grade, neoadjuvant therapy, and PSAT1 level were significantly associated with patient prognosis. The concordance indices were 0.758 and 0.725 for the 3-year and 5-year OS, respectively. The new model demonstrated superior discrimination and calibration compared with the single clinical variable model. The enhancer PSAT1 used in the new model was shown to be significantly overexpressed in tissues from patients with late-stage ccRCC, as demonstrated by the mRNA level, protein level, and pathological evaluation. CONCLUSION The new prognostic prediction nomogram model of PSAT1 and clinicopathological variables combined was thus established, which may provide a new direction for individualized treatment for different-stage ccRCC patients.
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Affiliation(s)
- Jun Wang
- Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, 210008, China
- Department of Urology, Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, 214122, China
| | - Xiaoming He
- Wuxi Maternal and Child Health Hospital, Wuxi School of Medicine, Jiangnan University, Jiangsu, 214002, China
| | - Yuanyuan Mi
- Department of Urology, Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, 214122, China
| | - Yong Q Chen
- Wuxi School of Medicine, Jiangnan University, Wuxi, 214122, China
| | - Jie Li
- Department of Urology, First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, 210008, China.
| | - Rong Wang
- Wuxi School of Medicine, Jiangnan University, Wuxi, 214122, China.
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50
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Yi X, Zhan H, Lyu J, Du J, Dai M, Zhao M, Zhang Y, Zhou C, Xu X, Fan Y, Li L, Dong B, Jiang X, Xiao Z, Zhou J, Zhao M, Zhang J, Fu Y, Chen T, Xu Y, Tian J, Liu Q, Zeng H. A chest CT-based nomogram for predicting survival in acute myeloid leukemia. BMC Cancer 2024; 24:458. [PMID: 38609917 PMCID: PMC11010287 DOI: 10.1186/s12885-024-12188-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND The identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML). METHODS 952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements. RESULTS Three CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P < 0.01). A CT-MSF nomogram showed a performance with AUCs of 0.717, 0.794, 0.796 and 0.792 for predicting the 1-, 2-, 3-, and 5-year overall survival (OS) probabilities in the validation cohort, which were significantly higher than the ELN risk model. Moreover, a new MSN stratification system (MSF nomogram plus ELN risk model) could stratify patients into new high, intermediate and low risk group. Patients with high MSN risk may benefit from intensive treatment (P = 0.0011). CONCLUSIONS In summary, the chest CT-MSF nomogram, integrating myosarcopenia, spleen_CTV, and SF_CTV features, could be used to predict prognosis of AML.
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Affiliation(s)
- Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha, China
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Central South University, Changsha, China
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
| | - Huien Zhan
- Department of Hematology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Juan Du
- Department of Hematology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Min Dai
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Min Zhao
- Department of Nuclear Medicine, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Yu Zhang
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Cheng Zhou
- Department of Hematology, Xiangya Hospital, Central South University, Changsha, China
| | - Xin Xu
- Department of Geriatrics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Yi Fan
- Department of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Lin Li
- Department of Hematology, Hunan Provincial People' Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Baoxia Dong
- Department of Hematology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai,, China
| | - Xinya Jiang
- Department of Hematology, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Department of Hematology, Xiangya Hospital, Central South University, Changsha, China
| | - Zeyu Xiao
- The Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jihao Zhou
- Department of Hematology, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Minyi Zhao
- Department of Hematology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Jian Zhang
- Department of Hematology, The Third Xiangya hospital, Central South University, Changsha, China
| | - Yan Fu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Tingting Chen
- Department of Hematology, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
| | - Yang Xu
- Department of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Qifa Liu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Hui Zeng
- Department of Hematology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
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