%0 Journal Article %A Sharen Lee %A Jiandong Zhou %A Keith Sai Kit Leung %A William Ka Kei Wu %A Wing Tak Wong %A Tong Liu %A Ian Chi Kei Wong %A Kamalan Jeevaratnam %A Qingpeng Zhang %A Gary Tse %T Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong %D 2021 %R 10.1136/bmjdrc-2020-001950 %J BMJ Open Diabetes Research & Care %P e001950 %V 9 %N 1 %X 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.Data are available in a public, open access repository. Data are available on reasonable request. An anonymized version of the dataset has been deposited on Zenodo (https://zenodo.org/record/4383385), in fully compliance with University Regulations and Policy on Dataset Deposit and Sharing. For additional information: https://libguides.lib.cuhk.edu.hk/RDM/dataset_deposit. %U https://drc.bmj.com/content/bmjdrc/9/1/e001950.full.pdf