Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong
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 a...
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BMJ Publishing Group
2021-03-01
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| Series: | BMJ Open Diabetes Research & Care |
| Online Access: | https://drc.bmj.com/content/9/1/e001950.full |
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| author | William Ka Kei Wu Tong Liu Ian Chi Kei Wong Qingpeng Zhang Jiandong Zhou Sharen Lee Keith Sai Kit Leung Kamalan Jeevaratnam Wing Tak Wong |
| author_facet | William Ka Kei Wu Tong Liu Ian Chi Kei Wong Qingpeng Zhang Jiandong Zhou Sharen Lee Keith Sai Kit Leung Kamalan Jeevaratnam Wing Tak Wong |
| author_sort | William Ka Kei Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d6512d43e4874e27a8421814a2b7f438 |
| institution | OA Journals |
| issn | 2052-4897 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open Diabetes Research & Care |
| spelling | doaj-art-d6512d43e4874e27a8421814a2b7f4382025-08-20T01:59:08ZengBMJ Publishing GroupBMJ Open Diabetes Research & Care2052-48972021-03-019110.1136/bmjdrc-2020-001950Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong KongWilliam Ka Kei Wu0Tong Liu1Ian Chi Kei Wong2Qingpeng Zhang3Jiandong Zhou4Sharen Lee5Keith Sai Kit Leung6Kamalan Jeevaratnam7Wing Tak Wong8Department of Anaesthesia and Intensive Care and Peter Hung Pain Research Institute, The Chinese University of Hong Kong, Hong Kong, People`s Republic of ChinaTianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, People`s Republic of China10 Research Department of Practice and Policy, UCL School of Pharmacy, London, UKSchool of Data Science, City University of Hong Kong, Kowloon, Hong Kong8University of Oxford6Chinese University of Hong KongAston Medical School, Aston University, Birmingham, UKFaculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UKSchool of Life Sciences, Chinese University of Hong Kong, Hong Kong, People`s Republic of ChinaIntroduction 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.https://drc.bmj.com/content/9/1/e001950.full |
| spellingShingle | William Ka Kei Wu Tong Liu Ian Chi Kei Wong Qingpeng Zhang Jiandong Zhou Sharen Lee Keith Sai Kit Leung Kamalan Jeevaratnam Wing Tak Wong Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong BMJ Open Diabetes Research & Care |
| title | Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong |
| title_full | Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong |
| title_fullStr | Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong |
| title_full_unstemmed | Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong |
| title_short | Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong |
| title_sort | development of a predictive risk model for all cause mortality in patients with diabetes in hong kong |
| url | https://drc.bmj.com/content/9/1/e001950.full |
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