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Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong

Lee, Sharen, Zhou, Jiandong, Leung, Keith Sai Kit, Wu, William Ka Kei, Wong, Wing Tak, Liu, Tong, Wong, Ian Chi Kei, Jeevaratnam, Kamalan, Zhang, Qingpeng, Tse, Gary and others. (2021) Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong. BMJ Open Diabetes Research & Care, 9 (1). Article Number e001950. ISSN 2052-4897. (doi:10.1136/bmjdrc-2020-001950) (KAR id:98739)


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.

Item Type: Article
DOI/Identification number: 10.1136/bmjdrc-2020-001950
Subjects: R Medicine
Divisions: Divisions > Division of Natural Sciences > Kent and Medway Medical School
Depositing User: Manfred Gschwandtner
Date Deposited: 06 Dec 2022 11:10 UTC
Last Modified: 07 Dec 2022 15:54 UTC
Resource URI: (The current URI for this page, for reference purposes)

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