ArticleClinical interventions in aging2023
Development and Validation of Prediction Models for All-Cause Mortality and Cardiovascular Mortality in Patients on Hemodialysis: A Retrospective Cohort Study in China.
Article in Clinical interventions in aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review and meta-analysis of cardiovascular event risk prediction models in maintenance hemodialysis patients.Scientific reports · 2025Pooled it
- Mortality Risk Prediction Models for People With Kidney Failure: A Systematic Review.JAMA network open · 2025Pooled it
- Two-Step Clinical Pathways to Cardiovascular Mortality in Chronic Kidney Disease and Dialysis -- A Narrative Review.Journal of atherosclerosis and thrombosis · 2026Review
- Developing an explainable machine learning model using body composition to predict cardiovascular mortality in initial dialysis patients: a multicenter study.Frontiers in physiology · 2026Article
- Malnutrition-inflammation-fluid overload complex syndrome and all-cause mortality in patients undergoing hemodialysisRenal failure · 2025Article
- An artificial intelligence model to predict mortality among hemodialysis patients: A retrospective validated cohort study.Scientific reports · 2025Article
- Delving into biomarkers and predictive modeling for CVD mortality: a 20-year cohort study.Scientific reports · 2025Article
- Article
- Construction and Evaluation of a Predictive Nomogram for Identifying Premature Failure of Arteriovenous Fistulas in Elderly Diabetic Patients.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study aimed to develop two predictive nomograms for the assessment of long-term survival status in hemodialysis (HD) patients by examining the prognostic factors for all-cause mortality and cardiovascular (CVD) event mortality. Patients and methods: A total of 551 HD patients with an average age of over 60 were included in this study. The patients' medical records were collected from our hospital and randomly allocated to two cohorts: the training cohort (n=385) and the validation cohort (n=166). We employed multivariate Cox assessments and fine-gray proportional hazards models to explore the predictive factors for both all-cause mortality and cardiovascular event mortality risk in HD patients. Two nomograms were established based on predictive factors to forecast patients' likelihood of survival for 3, 5, and 8 years. The performance of both models was evaluated using the area under the curve (AUC), calibration plots, and decision curve analysis. Results: The nomogram for all-cause mortality prediction included seven factors: age ≥ 60, sex (male), history of diabetes and coronary artery disease, diastolic blood pressure, total triglycerides (TG), and total cholesterol (TC). The nomogram for cardiovascular event mortality prediction included three factors: history of diabetes and coronary artery disease, and total cholesterol (TC). Both models demonstrated good discrimination, with AUC values of 0.716, 0.722 and 0.725 for all-cause mortality at 3, 5, and 8 years, respectively, and 0.702, 0.695, and 0.677 for cardiovascular event mortality, respectively. The calibration plots indicated a good agreement between the predictions and the decision curve analysis demonstrated a favorable clinical utility of the nomograms. Conclusion: Our nomograms were well-calibrated and exhibited significant estimation efficiency, providing a valuable predictive tool to forecast prognosis in HD patients.
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