QJM : monthly journal of the Association of Physicians · 2025
Explainable machine learning prediction of 1-year kidney function progression among patients with type 2 diabetes mellitus and chronic kidney disease: a retrospective study.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wu Jinyi, Gao Qi, Tian Ming, Tan Shuangping, Dong Junwu, Wei Honglan
Аннотация
BACKGROUND: Diabetes mellitus (DM) is one of the most prevalent non-communicable chronic diseases globally, affecting an estimated 530 million adults in 2021, a number projected to rise to 780 million by 2045.
AIM: This study aimed to develop and validate a risk prediction model for 1-year chronic kidney disease (CKD) progression in patients with type 2 diabetes mellitus (T2DM) and CKD by employing various machine learning (ML) algorithms.
DESIGN AND METHODS: This study included a total of 12 151 patients with T2DM and CKD with eGFR between 30 and 59.9 ml/min/1.73 m2 from a tertiary hospital in Wuhan, enrolled between 2012 and 2024. The cohort was divided into a training set of 5954 patients, an internal validation set of 2552 patients, and an external validation set of 3645 patients. We developed 1-year CKD progression risk prediction models using 10 different ML algorithms. CKD progression was defined as a decline in eGFR by more than 30% from baseline and/or a reduction in eGFR to below 15 ml/min/1.73 m2. The SHAP (SHapley Additive exPlanations) method was utilized to explain the predictions of a model.
RESULTS: Among the 10 ML models, the XGBoost model achieved the best predictive performance for 1-year progression of kidney function with an area under the ROC curve of 0.906 in the internal validation set and 0.768 in the external validation set. The final predictive model incorporating only nine variables has been implemented into a web application to enhance its usability in clinical settings.
CONCLUSION: Our findings suggest that the XGBoost model may serve as a valuable decision-support tool for predicting kidney function decline in patients with T2DM and CKD.
AIM: This study aimed to develop and validate a risk prediction model for 1-year chronic kidney disease (CKD) progression in patients with type 2 diabetes mellitus (T2DM) and CKD by employing various machine learning (ML) algorithms.
DESIGN AND METHODS: This study included a total of 12 151 patients with T2DM and CKD with eGFR between 30 and 59.9 ml/min/1.73 m2 from a tertiary hospital in Wuhan, enrolled between 2012 and 2024. The cohort was divided into a training set of 5954 patients, an internal validation set of 2552 patients, and an external validation set of 3645 patients. We developed 1-year CKD progression risk prediction models using 10 different ML algorithms. CKD progression was defined as a decline in eGFR by more than 30% from baseline and/or a reduction in eGFR to below 15 ml/min/1.73 m2. The SHAP (SHapley Additive exPlanations) method was utilized to explain the predictions of a model.
RESULTS: Among the 10 ML models, the XGBoost model achieved the best predictive performance for 1-year progression of kidney function with an area under the ROC curve of 0.906 in the internal validation set and 0.768 in the external validation set. The final predictive model incorporating only nine variables has been implemented into a web application to enhance its usability in clinical settings.
CONCLUSION: Our findings suggest that the XGBoost model may serve as a valuable decision-support tool for predicting kidney function decline in patients with T2DM and CKD.