#2868 prediction of major gastrointestinal bleeding events in hemodialysis

Abstract Background and Aims INitiativeS on advancing Patients’ outcomes In REnal disease (INSPIRE) is an academia and industry collaboration set forth to identify critical investigations needed to advance the practice of medicine in nephrology. At the inaugural INSPIRE meeting, the group came to a consensus that major bleeding events represent potentially preventable complications that occur more frequently in people with kidney disease versus the general population [1]. Given the most common class of bleeding event is gastrointestinal bleeding (GIB) [2], the INSPIRE group assessed if a machine learning model could be developed to assist with determining a hemodialysis (HD) patient's 180-day GIB hospitalization risk. Method 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. Results The 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). The 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 observed via 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 (Fig. 1). Conclusion The machine learning 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. A consistent signal has been observed in warfarin users without kidney disease among which 25OH vitamin D levels of 30–100 ng/mL were shown to associate with the highest GIB risk [3].