Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning

作者：John W. Larkin, Suman Lama, Sheetal Chaudhuri, Joanna Willetts, Anke Winter, Yue Jiao, Manuela Stauss‐Grabo, Len A. Usvyat, Jeffrey L. Hymes, Franklin W. Maddux, David C. Wheeler, Peter Stenvinkel, Jürgen Floege, on behalf of the INSPIRE Core Group, Anke Winter, Justin Zimbelman · 发表于：BMC Nephrology · 年份：2024 · DOI：10.1186/s12882-024-03809-2 · 被引用次数：9 · 研究领域：Parathyroid Disorders and Treatments、Dialysis and Renal Disease Management、Potassium and Related Disorders

BACKGROUND: Gastrointestinal bleeding (GIB) is a clinical challenge in kidney failure. INSPIRE group assessed if machine learning could determine a hemodialysis (HD) patient's 180-day GIB hospitalization risk. METHODS: An eXtreme Gradient Boosting (XGBoost) and logistic regression model were developed using an HD dataset in United States (2017-2020). Patient data was randomly split (50% training, 30% validation, and 20% testing). HD treatments ≤ 180 days before GIB hospitalization were classified as positive observations; others were negative. Models considered 1,303 exposures/covariates. Performance was measured using unseen testing data. RESULTS: Incidence of 180-day GIB hospitalization was 1.18% in HD population (n = 451,579), and 1.12% in testing dataset (n = 38,853). XGBoost showed area under the receiver operating curve (AUROC) = 0.74 (95% confidence interval (CI) 0.72, 0.76) versus logistic regression showed AUROC = 0.68 (95% CI 0.66, 0.71). Sensitivity and specificity were 65.3% (60.9, 69.7) and 68.0% (67.6, 68.5) for XGBoost versus 68.9% (64.7, 73.0) and 57.0% (56.5, 57.5) for logistic regression, respectively. Associations in exposures were consistent for many factors. Both models showed GIB hospitalization risk was associated with older age, disturbances in anemia/iron indices, recent all-cause hospitalizations, and bone mineral metabolism markers. XGBoost showed high importance on outcome prediction for serum 25 hydroxy (25OH) vitamin D levels, while logistic regr...