| Popis: |
Gastrointestinal bleeding (GIB) is a clinical challenge in kidney failure. The INSPIRE group assessed if machine learning could assist with determining a hemodialysis (HD) patient’s 180-day GIB hospitalization risk. Model was developed using adult HD patient data from 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, and others were negative observations. Datasets were randomly sampled to build an XGBoost model that considered 386 exposures initially and was refined to the top 50 exposures. Unseen testing dataset was used to determine final model performance. Incidence of 180-day GIB hospitalization was 1.18% in the HD population (n=451,579), and 1.16% among patients in the testing dataset (n=27,991). Model showed an area under the curve=0.69, sensitivity=57.9%, specificity=68.9%, accuracy=68.8% and balanced accuracy=63.4%. Exposures with largest effect size per Shapley values were older age (group mean GIB event=68.2 years vs no GIB event=63.4 years), shorter days since last all-cause hospital admission (group mean GIB event=203.2 days vs no GIB event=253.2 days), and higher serum 25-hydroxy (OH) vitamin D levels from most recent lab (group mean GIB event=33.4 ng/mL vs no GIB event=30.5 ng/mL). Other important predictors included lower hemoglobin and iron indices, longer dialysis vintage, and proton pump inhibitor use. Model appears suitable for early detection of GIB event risk in HD, yet prospective testing is needed. The association between higher 25OH vitamin D and GIB events was unexpected and warrants investigation. |
