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Wang L, Yang L, Li T, Geng S. Development and Validation of Nomogram for the Prediction of Malignant Ventricular Arrhythmia Including Circulating Inflammatory Cells in Patients with Acute ST-Segment Elevation Myocardial Infarction. J Inflamm Res 2023; 16:3185-3196. [PMID: 37529768 PMCID: PMC10389081 DOI: 10.2147/jir.s420305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 07/24/2023] [Indexed: 08/03/2023] Open
Abstract
Background Malignant ventricular arrhythmia (MVA) can seriously affect the hemodynamic changes of the body. In this study, we developed and validated a nomogram to predict the in-hospital MVA risk in patients with STEMI after emergency PCI. Methods The multivariable logistic regression analysis included variables with a P<0.05 in the univariate logistic regression analysis and investigated the independent predictors affecting in-hospital MVA after PCI in patients with STEMI in the training cohort. The construction of a nomogram model used independent predictors to predict the risk of in-hospital MVA, and C-index, Hosmer-Lemeshow (HL) test, calibration curves, decision curve analysis (DCA), and receiver operating characteristic (ROC) were used to validate the nomogram. Results Killip class [OR=5.034 (95% CI: 1.596-15.809), P=0.005], CK-MB [OR=1.002 (95% CI: 1.001-1.004), P=0.022], serum potassium [OR=0.618 (95% CI: 0.406-0.918), P=0.020], NLR [OR=1.073 (95% CI: 1.034-1.115), P<0.001], and monocyte [OR=1.974 (95% CI: 1.376-2.925), P<0.001] were the independent predictors of in-hospital MVA after PCI in patients with STEMI. A nomogram including the 5 independent predictors was developed to predict the risk of in-hospital MVA. The C-index, equivalent to the area under the ROC curve (AUC), was 0.803 (95% confidence interval [CI]: 0.738-0.868) in the training cohort, and 0.801 (95% CI:0.692-0.911) in the validation cohort, showing that the nomogram had a good discrimination. The HL test (χ2=8.439, P=0.392 in the training cohort; χ2=9.730, P=0.285 in the validation cohort) revealed a good calibration. The DCA suggested an obvious clinical net benefit. Conclusion Killip class, CK-MB, serum potassium, NLR, and monocyte were independent factors for in-hospital MVA after PCI in patients with STEMI. The nomogram model constructed based on the above factors to predict the risk of in-hospital MVA had satisfactory discrimination, calibration, and clinical effectiveness, and was an excellent tool for early prediction of the risk of in-hospital MVA after PCI in patients with STEMI.
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Affiliation(s)
- Liang Wang
- Department of Cardiology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, Jiangsu, People’s Republic of China
| | - Liting Yang
- Department of Cardiology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, Jiangsu, People’s Republic of China
| | - Tao Li
- Department of Cardiology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, Jiangsu, People’s Republic of China
| | - Shanshan Geng
- Department of Cardiology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, Jiangsu, People’s Republic of China
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Lv J, Wang C, Gao X, Yang J, Zhang X, Ye Y, Dong Q, Fu R, Sun H, Yan X, Zhao Y, Wang Y, Xu H, Yang Y. Development and validation of dynamic models to predict postdischarge mortality risk in patients with acute myocardial infarction: results from China Acute Myocardial Infarction Registry. BMJ Open 2023; 13:e069505. [PMID: 36990493 PMCID: PMC10069604 DOI: 10.1136/bmjopen-2022-069505] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/31/2023] Open
Abstract
OBJECTIVES The risk of adverse events and prognostic factors are changing in different time phases after acute myocardial infarction (AMI). The incidence of adverse events is considerable in the early period after AMI hospitalisation. Therefore, dynamic risk prediction is needed to guide postdischarge management of AMI. This study aimed to develop a dynamic risk prediction instrument for patients following AMI. DESIGN A retrospective analysis of a prospective cohort. SETTING 108 hospitals in China. PARTICIPANTS A total of 23 887 patients after AMI in the China Acute Myocardial Infarction Registry were included in this analysis. PRIMARY OUTCOME MEASURES All-cause mortality. RESULTS In multivariable analyses, age, prior stroke, heart rate, Killip class, left ventricular ejection fraction (LVEF), in-hospital percutaneous coronary intervention (PCI), recurrent myocardial ischaemia, recurrent myocardial infarction, heart failure (HF) during hospitalisation, antiplatelet therapy and statins at discharge were independently associated with 30-day mortality. Variables related to mortality between 30 days and 2 years included age, prior renal dysfunction, history of HF, AMI classification, heart rate, Killip class, haemoglobin, LVEF, in-hospital PCI, HF during hospitalisation, HF worsening within 30 days after discharge, antiplatelet therapy, β blocker and statin use within 30 days after discharge. The inclusion of adverse events and medications significantly improved the predictive performance of models without these indexes (likelihood ratio test p<0.0001). These two sets of predictors were used to establish dynamic prognostic nomograms for predicting mortality in patients with AMI. The C indexes of 30-day and 2-year prognostic nomograms were 0.85 (95% CI 0.83-0.88) and 0.83 (95% CI 0.81-0.84) in derivation cohort, and 0.79 (95% CI 0.71-0.86) and 0.81 (95% CI 0.79-0.84) in validation cohort, with satisfactory calibration. CONCLUSIONS We established dynamic risk prediction models incorporating adverse event and medications. The nomograms may be useful instruments to help prospective risk assessment and management of AMI. TRIAL REGISTRATION NUMBER NCT01874691.
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Affiliation(s)
- Junxing Lv
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chuangshi Wang
- Medical Research and Biometrics Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaojin Gao
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jingang Yang
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuan Zhang
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yunqing Ye
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qiuting Dong
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Rui Fu
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hui Sun
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinxin Yan
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanyan Zhao
- Medical Research and Biometrics Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yang Wang
- Medical Research and Biometrics Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haiyan Xu
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuejin Yang
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2021:5745304. [PMID: 34976110 PMCID: PMC8720014 DOI: 10.1155/2021/5745304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 11/03/2021] [Accepted: 12/08/2021] [Indexed: 11/17/2022]
Abstract
Background A survival prediction model based on deep learning has higher accuracy than the CPH model in predicting the survival of CCU patients, and it also has a better discrimination ability. We collected information on patients with various diseases in coronary care units (CCUs) from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The purpose of this study was to use this information to construct a neural-network model based on deep learning to predict the survival probabilities of patients with conditions that are common in CCUs. Method We collected information on patients in the United States with five common diseases in CCUs from 2001 to 2012. We randomly divided the patients into a training cohort and a testing cohort at a ratio of 7 : 3 and applied a survival prediction method based on deep learning to predict their survival probability. We compared our model with the Cox proportional-hazards regression (CPH) model and used the concordance indexes (C-indexes), receiver operating characteristic (ROC) curve, and calibration plots to evaluate the predictive performance of the model. Results The 3,388 CCU patients included in the study were randomly divided into 2,371 in the training cohort and 1,017 in the testing cohort. The stepwise regression results showed that the important factors affecting patient survival were the type of disease, age, race, anion gap, glucose, neutrophils, white blood cells, potassium, creatine kinase, and blood urea nitrogen (P < 0.05). We used the training cohort to construct a deep-learning model, for which the C-index was 0.833, or about 5% higher than that for the CPH model (0.786). The C-index of the deep-learning model for the test cohort was 0.822, which was also higher than that for the CPH model (0.782). The areas under the ROC curve for the 28-day, 90-day, and 1-year survival probabilities were 0.875, 0.865, and 0.874, respectively, in the deep-learning model, respectively, and 0.830, 0.843, and 0.806 in the CPH model. These values indicate that the survival analysis model based on deep learning is better than the traditional CPH model in predicting the survival of CCU patients. Conclusion A survival prediction model based on deep learning has higher accuracy than the CPH model in predicting the survival of CCU patients, and it also has a better discrimination ability.
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Zheng X, Liu G, Huang R. Identification and Verification of Feature Immune-Related Genes in Patients With Hypertrophic Cardiomyopathy Based on Bioinformatics Analyses. Front Cardiovasc Med 2021; 8:752559. [PMID: 34765659 PMCID: PMC8577723 DOI: 10.3389/fcvm.2021.752559] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 09/28/2021] [Indexed: 12/27/2022] Open
Abstract
Objective: To identify feature immune-related genes (IRGs) in patients with hypertrophic cardiomyopathy (HCM) and verify their ability to diagnose HCM. Methods: The GSE160997 dataset on cardiac tissue from 18 HCM patients and 5 controls was downloaded from the Gene Expression Omnibus database. A false discovery rate <0.05 and |log2 fold change| >1 were the filters applied to identify the differentially expressed genes (DEGs). The differentially expressed IRGs were the intersection results between the DEGs and an IRG dataset from the IMMPORT database. The protein-protein interaction network of differentially expressed IRGs was constructed, and the top 20 hub genes with the most adjacent nodes in the network were selected. The least absolute shrinkage and selection operator regression algorithm and a random forest algorithm were used to identify the feature IRGs as biomarkers that were then verified against GSE36961. Results: A total of 1079 DEGs were identified in GSE160997. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses indicated that immune-related mechanisms play an important role in the pathogenesis of HCM. A total of 121 differentially expressed IRGs were identified, and 5 feature IRGs were selected, 4 of which were confirmed as potential biomarkers of HCM by external verification with excellent discrimination ability. A diagnosis model of HCM based on the four feature IRGs was developed and visualized as a nomogram with a C-index of 0.925 (95% confidence interval 0.869–0.981). Conclusion: Our study identified four feature IRGs as biomarkers for the diagnosis of HCM, offering an innovative perspective of the underlying immune-related pathological molecular mechanisms.
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Affiliation(s)
- Xifeng Zheng
- Department of Geriatrics, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Guangyan Liu
- Department of Geriatrics, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Ruina Huang
- Department of Cardiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
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Li H, Chen L, Zeng H, Liao Q, Ji J, Ma X. Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma. Front Oncol 2021; 11:636451. [PMID: 34646756 PMCID: PMC8504715 DOI: 10.3389/fonc.2021.636451] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 08/31/2021] [Indexed: 02/05/2023] Open
Abstract
Background Colon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD. Methods We downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF). Results There were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group. Conclusions These results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.
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Affiliation(s)
- Hui Li
- Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Linyan Chen
- Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Hao Zeng
- Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Qimeng Liao
- Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Jianrui Ji
- Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
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Zheng X, Fang F, Nong W, Feng D, Yang Y. Development and validation of a model to estimate the risk of acute ischemic stroke in geriatric patients with primary hypertension. BMC Geriatr 2021; 21:458. [PMID: 34372766 PMCID: PMC8353783 DOI: 10.1186/s12877-021-02392-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 07/18/2021] [Indexed: 12/29/2022] Open
Abstract
Objectives This study aimed to construct and validate a prediction model of acute ischemic stroke in geriatric patients with primary hypertension. Methods This retrospective file review collected information on 1367 geriatric patients diagnosed with primary hypertension and with and without acute ischemic stroke between October 2018 and May 2020. The study cohort was randomly divided into a training set and a testing set at a ratio of 70 to 30%. A total of 15 clinical indicators were assessed using the chi-square test and then multivariable logistic regression analysis to develop the prediction model. We employed the area under the curve (AUC) and calibration curves to assess the performance of the model and a nomogram for visualization. Internal verification by bootstrap resampling (1000 times) and external verification with the independent testing set determined the accuracy of the model. Finally, this model was compared with four machine learning algorithms to identify the most effective method for predicting the risk of stroke. Results The prediction model identified six variables (smoking, alcohol abuse, blood pressure management, stroke history, diabetes, and carotid artery stenosis). The AUC was 0.736 in the training set and 0.730 and 0.725 after resampling and in the external verification, respectively. The calibration curve illustrated a close overlap between the predicted and actual diagnosis of stroke in both the training set and testing validation. The multivariable logistic regression analysis and support vector machine with radial basis function kernel were the best models with an AUC of 0.710. Conclusion The prediction model using multiple logistic regression analysis has considerable accuracy and can be visualized in a nomogram, which is convenient for its clinical application.
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Affiliation(s)
- Xifeng Zheng
- Department of Geriatrics, Affiliated Hospital of Guangdong Medical University, No.57, South of Renming Road, Zhanjiang, Guangdong, 524001, People's Republic of China.
| | - Fang Fang
- Department of General Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Weidong Nong
- Department of Neurology, Affiliated Minzu Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Dehui Feng
- Department of Geriatrics, Affiliated Hospital of Guangdong Medical University, No.57, South of Renming Road, Zhanjiang, Guangdong, 524001, People's Republic of China
| | - Yu Yang
- Department of Geriatrics, Affiliated Hospital of Guangdong Medical University, No.57, South of Renming Road, Zhanjiang, Guangdong, 524001, People's Republic of China
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Li Z, Wei J, Gan X, Song M, Zhang Y, Cao H, Jin Y, Yang J. Construction, validation and, visualization of a web-based nomogram for predicting the overall survival and cancer-specific survival of leiomyosarcoma patients with lung metastasis. J Thorac Dis 2021; 13:3076-3092. [PMID: 34164199 PMCID: PMC8182497 DOI: 10.21037/jtd-21-598] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Background This study sought to assess the prognostic factors for leiomyosarcoma (LMS) patients with lung metastasis and construct web-based nomograms to predict overall survival (OS) and cancer-specific survival (CSS). Method Patients diagnosed with LMS combined with lung metastasis between 2010 and 2016 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into a training set and a testing set. The X-tile analysis provides the best age and tumor size cut-off point, and changes continuous variables into categorical variables. The independent prognostic factors were determined by Cox regression analysis, and 2 nomograms were established. Receiver operating characteristic curves and calibration curves were used to evaluate the nomograms. Based on the nomograms, 2 web-based nomograms were established. Results Two hundred and twenty-eight cases were included in the OS nomogram construction, and were randomly divided into a training set (n=160) and a validation set (n=68). Age, T stage, bone metastasis, surgery, chemotherapy, marital status, tumor size, and tumor site were found to be correlated with OS. One hundred and eighty-three cases were enrolled in the CSS nomogram construction, and randomly divided into a training set (n=129) and a validation set (n=54). Age, bone metastasis, surgery, chemotherapy, tumor size, and tumor site were found to be correlated with CSS. Two nomograms were established to predict OS and CSS. In the training set, the areas under the curve of the nomogram for predicting 1-, 2-, and 3-year OS were 0.783, 0.830, and 0.832, respectively, and those for predicting 1-, 2-, and 3-year CSS were 0.889, 0.777, and 0.884, respectively. Two web-based nomograms were established to predict OS (https://wenn23.shinyapps.io/lmslmosapp/), and CSS (https://wenn23.shinyapps.io/lmslmcssapp/). Conclusion The developed web-based nomogram is a useful tool for accurately analyzing the prognosis of LMS patients with lung metastasis, and could help clinical doctors to make personalized clinical decisions.
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Affiliation(s)
- Zhehong Li
- Postgraduate Medical School, Chengde Medical College, Chengde, China
| | - Junqiang Wei
- Department of Bone and Soft Tissue Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Centre for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Department of Orthopedics, Affiliated Hospital of Chengde Medical College, Chengde, China
| | - Xintian Gan
- Postgraduate Medical School, Chengde Medical College, Chengde, China
| | - Mingze Song
- Postgraduate Medical School, Chengde Medical College, Chengde, China
| | - Yafang Zhang
- Postgraduate Medical School, Chengde Medical College, Chengde, China
| | - Haiying Cao
- Department of Orthopedics, Affiliated Hospital of Chengde Medical College, Chengde, China
| | - Yu Jin
- Department of Orthopedics, Affiliated Hospital of Chengde Medical College, Chengde, China
| | - Jilong Yang
- Department of Bone and Soft Tissue Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Centre for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
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