Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts.
Por:
Sang H, Lee H, Lee M, Park J, Kim S, Woo HG, Rahmati M, Koyanagi A, Smith L, Lee S, Hwang YC, Park TS, Lim H, Yon DK and Rhee SY
Publicada:
28 jun 2024
Ahead of Print:
28 jun 2024
Resumen:
This study aimed to develop and validate a machine learning (ML) model tailored to the Korean population with type 2 diabetes mellitus (T2DM) to provide a superior method for predicting the development of cardiovascular disease (CVD), a major chronic complication in these patients. We used data from two cohorts, namely the discovery (one hospital; n = 12,809) and validation (two hospitals; n = 2019) cohorts, recruited between 2008 and 2022. The outcome of interest was the presence or absence of CVD at 3 years. We selected various ML-based models with hyperparameter tuning in the discovery cohort and performed area under the receiver operating characteristic curve (AUROC) analysis in the validation cohort. CVD was observed in 1238 (10.2%) patients in the discovery cohort. The random forest (RF) model exhibited the best overall performance among the models, with an AUROC of 0.830 (95% confidence interval [CI] 0.818-0.842) in the discovery dataset and 0.722 (95% CI 0.660-0.783) in the validation dataset. Creatinine and glycated hemoglobin levels were the most influential factors in the RF model. This study introduces a pioneering ML-based model for predicting CVD in Korean patients with T2DM, outperforming existing prediction tools and providing a groundbreaking approach for early personalized preventive medicine.
Filiaciones:
Sang H:
Department of Endocrinology and Metabolism, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, South Korea
Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
Lee H:
Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
Department of Regulatory Science, Kyung Hee University, Seoul, South Korea
Lee M:
Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
Department of Regulatory Science, Kyung Hee University, Seoul, South Korea
Park J:
Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
Department of Regulatory Science, Kyung Hee University, Seoul, South Korea
Kim S:
Department of Family Medicine, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
Woo HG:
Department of Neurology, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
Rahmati M:
Research Centre on Health Services and Quality of Life, Aix Marseille University, Marseille, France
Department of Physical Education and Sport Sciences, Faculty of Literature and Human Sciences, Lorestan University, Khoramabad, Iran
Department of Physical Education and Sport Sciences, Faculty of Literature and Humanities, Vali-E-Asr University of Rafsanjan, Rafsanjan, Iran
Koyanagi A:
Research and Development Unit, Parc Sanitari Sant Joan de Déu, Barcelona, Spain
Smith L:
Centre for Health, Performance and Wellbeing, Anglia Ruskin University, Cambridge, UK
Lee S:
Department of Internal Medicine, Gachon University College of Medicine, Incheon, South Korea
Hwang YC:
Division of Endocrinology and Metabolism, Department of Internal Medicine, Kyung Hee University Hospital at Gangdong and Kyung Hee University School of Medicine, Seoul, South Korea
Park TS:
Division of Endocrinology and Metabolism, Department of Internal Medicine, Research Institute of Clinical Medicine of Jeonbuk National University and Jeonbuk National University Hospital, Jeonju, South Korea
Lim H:
Department of Medical Nutrition, Graduate School of East-West Medical Science, Kyung Hee University, Yongin, South Korea
Yon DK:
Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
Department of Pediatrics, Kyung Hee University College of Medicine, 23 Kyungheedae-Ro, Dongdaemun-gu, Seoul, 02447, South Korea
Department of Regulatory Science, Kyung Hee University, Seoul, South Korea
Rhee SY:
Department of Endocrinology and Metabolism, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, South Korea
Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
Department of Regulatory Science, Kyung Hee University, Seoul, South Korea
Open Access
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