ArticleBMJ open diabetes research & care2021
Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong.
Article in BMJ open diabetes research & care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models for all-cause and cardiovascular mortality in type 2 diabetes: Systematic review.Diabetic medicine : a journal of the British Diabetic Association · 2026Pooled it
- Developing a prediction model for all-cause mortality risk among patients with type 2 diabetes mellitus in Shanghai, China.Journal of diabetes · 2023Trial
- Advancing Type 1 Diabetes Treatment: 3 PM-Oriented Innovations in Islet Transplantation and Biomaterial Engineering.Diabetes/metabolism research and reviews · 2026Review
- Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform?BMC endocrine disorders · 2025Article
- Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus.Frontiers in endocrinology · 2025Article
- Development and validation of a nomogram of all-cause mortality in adult Americans with diabetes.Scientific reports · 2024Article
- PowerAI-Diabetes: Review of glycemic and lipid variability to predict cardiovascular events in Chinese diabetic population.npj metabolic health and disease · 2024Review
- Role of angiotensin receptor-neprilysin inhibitor in diabetic complications.World journal of diabetes · 2024Review
- Behind the Hospital Ward: In-Hospital Mortality of Type 2 Diabetes Mellitus Patients in Indonesia (Analysis of National Health Insurance Claim Sample Data).International journal of environmental research and public health · 2024Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- Association Between Atherosclerosis-Related Cardiovascular Disease and Uveitis: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2022Review
- The Impact of Cardiac Comorbidity Sequence at Baseline and Mortality Risk in Type 2 Diabetes Mellitus: A Retrospective Population-Based Cohort Study.Life (Basel, Switzerland) · 2022Article
- Risk Factors of Pancreatic Cancer in Patients With Type 2 Diabetes Mellitus: The Hong Kong Diabetes Study.Journal of the Endocrine Society · 2022Article
- Adverse Cardiovascular Complications following prescription of programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) inhibitors: a propensity-score matched Cohort Study with competing risk analysis.Cardio-oncology (London, England) · 2022Article
- The effect of total cholesterol/high-density lipoprotein cholesterol ratio on mortality risk in the general population.Frontiers in endocrinology · 2022Article
- Development of an Electronic Frailty Index for Predicting Mortality and Complications Analysis in Pulmonary Hypertension Using Random Survival Forest Model.Frontiers in cardiovascular medicine · 2022Article
- Risk stratification of cardiac arrhythmias and sudden cardiac death in type 2 diabetes mellitus patients receiving insulin therapy: A population-based cohort study.Clinical cardiology · 2021Observational
- Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning.BMC endocrine disorders · 2021Observational
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Authors and funding
10 authors.
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Abstract
introductionPatients with diabetes mellitus are risk of premature death. In this study, we developed a machine learning-driven predictive risk model for all-cause mortality among patients with type 2 diabetes mellitus using multiparametric approach with data from different domains. RESEARCH DESIGN AND
methodsThis study used territory-wide data of patients with type 2 diabetes attending public hospitals or their associated ambulatory/outpatient facilities in Hong Kong between January 1, 2009 and December 31, 2009. The primary outcome is all-cause mortality. The association of risk variables and all-cause mortality was assessed using Cox proportional hazards models. Machine and deep learning approaches were used to improve overall survival prediction and were evaluated with fivefold cross validation method.
resultsA total of 273 678 patients (mean age: 65.4±12.7 years, male: 48.2%, median follow-up: 142 (IQR=106-142) months) were included, with 91 155 deaths occurring on follow-up (33.3%; annualized mortality rate: 3.4%/year; 2.7 million patient-years). Multivariate Cox regression found the following significant predictors of all-cause mortality: age, male gender, baseline comorbidities, anemia, mean values of neutrophil-to-lymphocyte ratio, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, HbA1c and fasting blood glucose (FBG), measures of variability of both HbA1c and FBG. The above parameters were incorporated into a score-based predictive risk model that had a c-statistic of 0.73 (95% CI 0.66 to 0.77), which was improved to 0.86 (0.81 to 0.90) and 0.87 (0.84 to 0.91) using random survival forests and deep survival learning models, respectively.
conclusionsA multiparametric model incorporating variables from different domains predicted all-cause mortality accurately in type 2 diabetes mellitus. The predictive and modeling capabilities of machine/deep learning survival analysis achieved more accurate predictions.
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