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Wang B, Xiong Y, Li R, Zhang S. Age-related nomogram revealed optimal therapeutic option for older patients with primary liver cancer: less is more. Aging (Albany NY) 2024; 16:9824-9845. [PMID: 38848143 PMCID: PMC11210251 DOI: 10.18632/aging.205901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 05/03/2024] [Indexed: 06/09/2024]
Abstract
BACKGROUND Age bias in therapeutic decisions for older patients with cancer exists. There is a clear need to individualize such decisions. METHODS Based on the Surveillance, Epidemiology and End Results (SEER) database, 5081 primary liver cancer (PLC) patients between 2010 and 2014 were identified and divided into <64, 64-74 and >74 years group. Each group was randomly divided into training and internal validation cohorts, and patients who were diagnosed between 2015 and 2016 were included as an external validation. The nomogram model predicting overall survival (OS) was generated and evaluated based on the Cox regression for the influencing factors in prognosis. The K-M analysis was used to compare the difference among different treatments. RESULTS KM analysis showed a significant difference for OS in three age groups (P < 0.001). At the same time, we also found different prognostic factors and their importance in different age groups. Therefore, we created three nomograms based on the results of Cox regression results for each age group. The c-index was 0.802, 0.766, 0.781 respectively. The calibration curve and ROC curve show that our model has a good predictive efficacy and the reliability was also confirmed in the internal and external validation set. An available online page was established to simplify and visualize our model (http://124.222.247.135/). The results of treatment analysis revealed that the optimal therapeutic option for PLCs was surgery alone. CONCLUSIONS The optimal therapeutic option for older PLCs was surgery alone. The generated dynamic nomogram in this study may be a useful tool for personalized clinical decisions.
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Affiliation(s)
- Bo Wang
- Department of Geriatric Digestive Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yongqiang Xiong
- Department of Geriatric Digestive Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Ren Li
- Department of Geriatric Digestive Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Shu Zhang
- Department of Geriatric Digestive Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Experimental Teaching Center for Clinical Skills, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
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Zheng F, Yin P, Yang L, Wang Y, Hao W, Hao Q, Chen X, Hong N. MRI-Based Machine Learning Fusion Models to Distinguish Encephalitis and Gliomas. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:653-665. [PMID: 38343248 PMCID: PMC11031538 DOI: 10.1007/s10278-023-00957-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 04/20/2024]
Abstract
This paper aims to compare the performance of the classical machine learning (CML) model and the deep learning (DL) model, and to assess the effectiveness of utilizing fusion radiomics from both CML and DL in distinguishing encephalitis from glioma in atypical cases. We analysed the axial FLAIR images of preoperative MRI in 116 patients pathologically confirmed as gliomas and clinically diagnosed with encephalitis. The 3 CML models (logistic regression (LR), support vector machine (SVM) and multi-layer perceptron (MLP)), 3 DL models (DenseNet 121, ResNet 50 and ResNet 18) and a deep learning radiomic (DLR) model were established, respectively. The area under the receiver operating curve (AUC) and sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and validation sets. In addition, a deep learning radiomic nomogram (DLRN) and a web calculator were designed as a tool to aid clinical decision-making. The best DL model (ResNet50) consistently outperformed the best CML model (LR). The DLR model had the best predictive performance, with AUC, sensitivity, specificity, accuracy, NPV and PPV of 0.879, 0.929, 0.800, 0.875, 0.867 and 0.889 in the validation sets, respectively. Calibration curve of DLR model shows good agreement between prediction and observation, and the decision curve analysis (DCA) indicated that the DLR model had higher overall net benefit than the other two models (ResNet50 and LR). Meanwhile, the DLRN and web calculator can provide dynamic assessments. Machine learning (ML) models have the potential to non-invasively differentiate between encephalitis and glioma in atypical cases. Furthermore, combining DL and CML techniques could enhance the performance of the ML models.
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Affiliation(s)
- Fei Zheng
- Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, People's Republic of China
| | - Ping Yin
- Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, People's Republic of China
| | - Li Yang
- Imaging Department, Shanxi Province, Shanxi Provincial People's Hospital, Shanxi Medical University, No. 359 Heping North Road, Jiancaoping District, Taiyuan, People's Republic of China
| | - Yujian Wang
- Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, People's Republic of China
| | - Wenhan Hao
- Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, People's Republic of China
| | - Qi Hao
- Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, People's Republic of China
| | - Xuzhu Chen
- Department of Radiology, Fengtai District, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Beijing, People's Republic of China.
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, People's Republic of China.
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Guo J, He Q, Li Y. Machine learning-based prediction of vitamin D deficiency: NHANES 2001-2018. Front Endocrinol (Lausanne) 2024; 15:1327058. [PMID: 38449846 PMCID: PMC10916299 DOI: 10.3389/fendo.2024.1327058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/26/2024] [Indexed: 03/08/2024] Open
Abstract
Background Vitamin D deficiency is strongly associated with the development of several diseases. In the current context of a global pandemic of vitamin D deficiency, it is critical to identify people at high risk of vitamin D deficiency. There are no prediction tools for predicting the risk of vitamin D deficiency in the general community population, and this study aims to use machine learning to predict the risk of vitamin D deficiency using data that can be obtained through simple interviews in the community. Methods The National Health and Nutrition Examination Survey 2001-2018 dataset is used for the analysis which is randomly divided into training and validation sets in the ratio of 70:30. GBM, LR, NNet, RF, SVM, XGBoost methods are used to construct the models and their performance is evaluated. The best performed model was interpreted using the SHAP value and further development of the online web calculator. Results There were 62,919 participants enrolled in the study, and all participants included in the study were 2 years old and above, of which 20,204 (32.1%) participants had vitamin D deficiency. The models constructed by each method were evaluated using AUC as the primary evaluation statistic and ACC, PPV, NPV, SEN, SPE, F1 score, MCC, Kappa, and Brier score as secondary evaluation statistics. Finally, the XGBoost-based model has the best and near-perfect performance. The summary plot of SHAP values shows that the top three important features for this model are race, age, and BMI. An online web calculator based on this model can easily and quickly predict the risk of vitamin D deficiency. Conclusion In this study, the XGBoost-based prediction tool performs flawlessly and is highly accurate in predicting the risk of vitamin D deficiency in community populations.
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Affiliation(s)
- Jiale Guo
- Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Qionghan He
- Department of Infection, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Yehai Li
- Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China
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Zhou Y, Cao Y, Liu W, Wang L, Kuang Y, Zhou Y, Chen Q, Cheng Z, Huang H, Zhang W, Jiang X, Wang B, Ren C. Leveraging a gene signature associated with disulfidptosis identified by machine learning to forecast clinical outcomes, immunological heterogeneities, and potential therapeutic targets within lower-grade glioma. Front Immunol 2023; 14:1294459. [PMID: 38162649 PMCID: PMC10757341 DOI: 10.3389/fimmu.2023.1294459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024] Open
Abstract
Background Disulfidptosis, a newly defined type of programmed cell death, has emerged as a significant regulatory process in the development and advancement of malignant tumors, such as lower-grade glioma (LGG). Nevertheless, the precise biological mechanisms behind disulfidptosis in LGG are yet to be revealed, considering the limited research conducted in this field. Methods We obtained LGG data from the TCGA and CGGA databases and performed comprehensive weighted co-expression network analysis, single-sample gene set enrichment analysis, and transcriptome differential expression analyses. We discovered nine genes associated with disulfidptosis by employing machine learning methods like Cox regression, LASSO regression, and SVM-RFE. These were later used to build a predictive model for patients with LGG. To confirm the expression level, functional role, and impact on disulfidptosis of ABI3, the pivotal gene of the model, validation experiments were carried out in vitro. Results The developed prognostic model successfully categorized LGG patients into two distinct risk groups: high and low. There was a noticeable difference in the time the groups survived, which was statistically significant. The model's predictive accuracy was substantiated through two independent external validation cohorts. Additional evaluations of the immune microenvironment and the potential for immunotherapy indicated that this risk classification could function as a practical roadmap for LGG treatment using immune-based therapies. Cellular experiments demonstrated that suppressing the crucial ABI3 gene in the predictive model significantly reduced the migratory and invasive abilities of both SHG44 and U251 cell lines while also triggering cytoskeletal retraction and increased cell pseudopodia. Conclusion The research suggests that the prognostic pattern relying on genes linked to disulfidptosis can provide valuable insights into the clinical outcomes, tumor characteristics, and immune alterations in patients with LGG. This could pave the way for early interventions and suggests that ABI3 might be a potential therapeutic target for disulfidptosis.
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Affiliation(s)
- Yao Zhou
- National Health Commission (NHC) Key Laboratory of Carcinogenesis, Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Yudong Cao
- National Health Commission (NHC) Key Laboratory of Carcinogenesis, Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Weidong Liu
- National Health Commission (NHC) Key Laboratory of Carcinogenesis, Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Lei Wang
- National Health Commission (NHC) Key Laboratory of Carcinogenesis, Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Yirui Kuang
- National Health Commission (NHC) Key Laboratory of Carcinogenesis, Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yi Zhou
- National Health Commission (NHC) Key Laboratory of Carcinogenesis, Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Quan Chen
- National Health Commission (NHC) Key Laboratory of Carcinogenesis, Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zeyu Cheng
- The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Haoxuan Huang
- National Health Commission (NHC) Key Laboratory of Carcinogenesis, Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wenlong Zhang
- National Health Commission (NHC) Key Laboratory of Carcinogenesis, Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xingjun Jiang
- National Health Commission (NHC) Key Laboratory of Carcinogenesis, Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Binbin Wang
- Department of Neurosurgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Caiping Ren
- National Health Commission (NHC) Key Laboratory of Carcinogenesis, Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, School of Basic Medical Science, Central South University, Changsha, Hunan, China
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Liu C, Liu X, Hu M, Mao Z, Zhou Y, Peng J, Geng X, Chi K, Hong Q, Cao D, Sun X, Zhang Z, Zhou F. A Simple Nomogram for Predicting Hospital Mortality of Patients Over 80 Years in ICU: An International Multicenter Retrospective Study. J Gerontol A Biol Sci Med Sci 2023; 78:1227-1233. [PMID: 37162208 DOI: 10.1093/gerona/glad124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Indexed: 05/11/2023] Open
Abstract
OBJECTIVES This study aimed to develop and validate an easy-to-use intensive care unit (ICU) illness scoring system to evaluate the in-hospital mortality for very old patients (VOPs, over 80 years old). METHODS We performed a multicenter retrospective study based on the electronic ICU (eICU) Collaborative Research Database (eICU-CRD), Medical Information Mart for Intensive Care Database (MIMIC-III CareVue and MIMIC-IV), and the Amsterdam University Medical Centers Database (AmsterdamUMCdb). Least Absolute Shrinkage and Selection Operator regression was applied to variables selection. The logistic regression algorithm was used to develop the risk score and a nomogram was further generated to explain the score. RESULTS We analyzed 23 704 VOPs, including 3 726 deaths (10 183 [13.5% mortality] from eICU-CRD [development set], 12 703 [17.2%] from the MIMIC, and 818 [20.8%] from the AmsterdamUMC [external validation sets]). Thirty-four variables were extracted on the first day of ICU admission, and 10 variables were finally chosen including Glasgow Coma Scale, shock index, respiratory rate, partial pressure of carbon dioxide, lactate, mechanical ventilation (yes vs no), oxygen saturation, Charlson Comorbidity Index, blood urea nitrogen, and urine output. The nomogram was developed based on the 10 variables (area under the receiver operating characteristic curve: training of 0.792, testing of 0.788, MIMIC of 0.764, and AmsterdamUMC of 0.808 [external validating]), which consistently outperformed the Sequential Organ Failure Assessment, acute physiology score III, and simplified acute physiology score II. CONCLUSIONS We developed and externally validated a nomogram for predicting mortality in VOPs based on 10 commonly measured variables on the first day of ICU admission. It could be a useful tool for clinicians to identify potentially high risks of VOPs.
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Affiliation(s)
- Chao Liu
- Department of Critical Care Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiaoli Liu
- Center for Artificial Intelligence in Medicine, The Chinese PLA General Hospital, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Mei Hu
- Department of Critical Care Medicine, PLA Strategic Support Force Characteristic Medical Center, Beijing, China
| | - Zhi Mao
- Department of Critical Care Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yibo Zhou
- Department of Critical Care Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jinyu Peng
- Department of Critical Care Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiaodong Geng
- Department of Nephrology, The First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing, China
| | - Kun Chi
- Department of Nephrology, The First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing, China
| | - Quan Hong
- Department of Nephrology, The First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing, China
| | - Desen Cao
- Department of Biomedical Engineering, The General Hospital of PLA, Beijing, China
| | - Xuefeng Sun
- Department of Nephrology, The First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing, China
| | - Zhengbo Zhang
- Center for Artificial Intelligence in Medicine, The Chinese PLA General Hospital, Beijing, China
| | - Feihu Zhou
- Department of Critical Care Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing, China
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Miao X, Ding L, Hu J, Zhu H, Zhao K, Lu J, Jiang X, Xu Q, Zhu S. A web-based calculator combining Geriatric Nutritional Risk Index (GNRI) and Tilburg Frailty Indicator (TFI) predicts postoperative complications among young elderly patients with gastric cancer. Geriatr Gerontol Int 2023; 23:205-212. [PMID: 36746414 DOI: 10.1111/ggi.14544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 12/26/2022] [Accepted: 01/10/2023] [Indexed: 02/08/2023]
Abstract
AIM Nutritional status and frailty are significant indicators reflecting physiological reserve. We sought to establish and validate a web-based calculator containing the Geriatric Nutritional Risk Index (GNRI) and the Tilburg Frailty Indicator (TFI) together with general clinical information to predict total complications among elderly patients with gastric cancer. METHODS This was a prospective cohort study of 582 elderly patients with gastric cancer in a tertiary hospital in China. Nutritional status and frailty were assessed by the GNRI and the TFI, respectively. The nomogram was built and further converted into a web-based calculator. The receiver operating characteristic analysis was performed to evaluate the discrimination of the nomogram. Calibration was assessed using the calibration curve and Hosmer-Lemeshow test via the bootstrap resampling procedure. The decision curve analyses (DCAs) were employed to quantify the net benefits of a certain threshold probability for assessing the clinical values. RESULTS The GNRI (odds ratio [OR], 0.921; 95% confidence interval [CI], 0.895-0.949; P < 0.001), the TFI (OR, 1.243; 95% CI, 1.113-1.386; P < 0.001), surgical approach (OR, 1.913; 95% CI, 1.073-3.408; P = 0.028) and comorbidity (OR = 1.599, 95%CI = 1.028-2.486, P = 0.037) were independently associated with total complications. The nomogram demonstrated good discrimination (area under the receiver operating characteristic curve: training cohort, 0.735; validation cohort, 0.777) and calibration (P = 0.135). The DCA curves of the nomogram also showed good positive net benefits. CONCLUSIONS The web-based calculator incorporating the GNRI, the TFI, surgical approach, and comorbidity could successfully predict total complications among elderly patients with gastric cancer with good accuracy in a convenient manner. Geriatr Gerontol Int 2023; 23: 205-212.
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Affiliation(s)
- Xueyi Miao
- School of Nursing, Nanjing Medical University, Nanjing, China
| | - Lingyu Ding
- Department of Colorectal Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jieman Hu
- School of Nursing, Nanjing Medical University, Nanjing, China
| | - Hanfei Zhu
- School of Nursing, Nanjing Medical University, Nanjing, China
| | - Kang Zhao
- School of Nursing, Nanjing Medical University, Nanjing, China
| | - Jinling Lu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoman Jiang
- School of Nursing, Nanjing Medical University, Nanjing, China
| | - Qin Xu
- School of Nursing, Nanjing Medical University, Nanjing, China
| | - Shuqin Zhu
- School of Nursing, Nanjing Medical University, Nanjing, China
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Kuo YT, Kuo LK, Chen CW, Yuan KC, Fu CH, Chiu CT, Yeh YC, Liu JH, Shih MC. Score-based prediction model for severe vitamin D deficiency in patients with critical illness: development and validation. Crit Care 2022; 26:394. [PMID: 36544226 PMCID: PMC9768894 DOI: 10.1186/s13054-022-04274-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Severe vitamin D deficiency (SVDD) dramatically increases the risks of mortality, infections, and many other diseases. Studies have reported higher prevalence of vitamin D deficiency in patients with critical illness than general population. This multicenter retrospective cohort study develops and validates a score-based model for predicting SVDD in patients with critical illness. METHODS A total of 662 patients with critical illness were enrolled between October 2017 and July 2020. SVDD was defined as a serum 25(OH)D level of < 12 ng/mL (or 30 nmol/L). The data were divided into a derivation cohort and a validation cohort on the basis of date of enrollment. Multivariable logistic regression (MLR) was performed on the derivation cohort to generate a predictive model for SVDD. Additionally, a score-based calculator (the SVDD score) was designed on the basis of the MLR model. The model's performance and calibration were tested using the validation cohort. RESULTS The prevalence of SVDD was 16.3% and 21.7% in the derivation and validation cohorts, respectively. The MLR model consisted of eight predictors that were then included in the SVDD score. The SVDD score had an area under the receiver operating characteristic curve of 0.848 [95% confidence interval (CI) 0.781-0.914] and an area under the precision recall curve of 0.619 (95% CI 0.577-0.669) in the validation cohort. CONCLUSIONS This study developed a simple score-based model for predicting SVDD in patients with critical illness. TRIAL REGISTRATION ClinicalTrials.gov protocol registration ID: NCT03639584. Date of registration: May 12, 2022.
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Affiliation(s)
- Yu-Ting Kuo
- grid.412094.a0000 0004 0572 7815Department of Anesthesiology, National Taiwan University Hospital, No. 7, Zhongshan S. Rd., Taipei City, 10002 Taiwan
| | - Li-Kuo Kuo
- grid.413593.90000 0004 0573 007XDivision of Critical Care Medicine, Mackay Memorial Hospital, No. 92, Sec. 2, Zhongshan N. Rd., Taipei City, Taiwan ,grid.452449.a0000 0004 1762 5613Department of Medicine, Mackay Medical College, No. 46, Sec. 3, Zhongzheng Rd., Sanzhi Dist., New Taipei City, Taiwan
| | - Chung-Wei Chen
- grid.414746.40000 0004 0604 4784Department of Surgical Intensive Care Unit, Far Eastern Memorial Hospital, No. 21, Sec. 2, Nanya S. Rd., Banciao Dist., New Taipei City, Taiwan
| | - Kuo-Ching Yuan
- grid.412897.10000 0004 0639 0994Department of Critical Care Medicine, Taipei Medical University Hospital, No. 252, Wuxing St, Taipei City, Taiwan
| | - Chun-Hsien Fu
- grid.412094.a0000 0004 0572 7815Department of Anesthesiology, National Taiwan University Hospital, No. 7, Zhongshan S. Rd., Taipei City, 10002 Taiwan ,grid.256105.50000 0004 1937 1063Department of Anesthesiology, Fu Jen Catholic University Hospital, No. 69, Guizi Road, New Taipei City, Taiwan
| | - Ching-Tang Chiu
- grid.412094.a0000 0004 0572 7815Department of Anesthesiology, National Taiwan University Hospital, No. 7, Zhongshan S. Rd., Taipei City, 10002 Taiwan
| | - Yu-Chang Yeh
- grid.412094.a0000 0004 0572 7815Department of Anesthesiology, National Taiwan University Hospital, No. 7, Zhongshan S. Rd., Taipei City, 10002 Taiwan
| | - Jen-Hao Liu
- grid.412094.a0000 0004 0572 7815Department of Anesthesiology, National Taiwan University Hospital, No. 7, Zhongshan S. Rd., Taipei City, 10002 Taiwan
| | - Ming-Chieh Shih
- grid.260567.00000 0000 8964 3950Department of Applied Mathematics, College of Science and Engineering, National Dong Hwa University, No. 1-12, Sec. 2, University Rd., Hualien County, 974 Taiwan
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