Lee, S;
Zhou, J;
Leung, KSK;
Wu, WKK;
Wong, WT;
Liu, T;
Wong, ICK;
... Tse, G; + view all
(2021)
Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong.
BMJ Open Diabetes Research and Care
, 9
(1)
, Article e001950. 10.1136/bmjdrc-2020-001950.
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Abstract
INTRODUCTION: Patients 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 METHODS: This 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. RESULTS: A 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. CONCLUSIONS: A 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.
| Type: | Article |
|---|---|
| Title: | Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong. |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1136/bmjdrc-2020-001950 |
| Publisher version: | http://dx.doi.org/10.1136/bmjdrc-2020-001950 |
| Language: | English |
| Additional information: | This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Epidemiology, risk factors, Aged, Cholesterol, HDL, Diabetes Mellitus, Type 2, Hong Kong, Humans, Male, Middle Aged, Proportional Hazards Models, Risk Factors |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy > Practice and Policy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10131374 |
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