Development and validation of an automated algorithm for identifying patients at high risk for drug-induced hypoglycemia. (1st November 2018) - Winterstein, Almut G.; Jeon, Nakyung; Staley, Ben; Xu, Dandan; Henriksen, Carl; Lipori, Gloria Pflugfelder
Abstract: Purpose: Hypoglycemia is one of the most concerning adverse drug events in hospitalized patients. Using information from institutional electronic health records, we aimed to develop dynamic predictive models to identify patients at high risk for hypoglycemia during antihyperglycemic therapy. Methods: The study population consisted of 21, 840 patients who received antihyperglycemic medication on any of the first 5 hospital days (the "risk model days") at 2 large hospitals. Data on candidate predictors were extracted from discrete electronic health record fields to construct models for predicting hypoglycemia within 24 hours after each risk model day. Final models were internally validated by replication in 100 bootstrap samples and reapplying model parameters to the original risk population. Results: The development and validation sample included 60, 762 risk model days followed by 1, 256 days with hypoglycemic events (2.07 events per 100 risk model days). The days 3, 4, and 5 models presented similar associations between predictors and the risk of hypoglycemia and were therefore collapsed into a single model. The strongest hypoglycemia risk factors across all 3 risk periods (day 1, day 2, and days 3–5) were blood glucose (BG) fluctuations, BG trend, history of hypoglycemia, lower body weight, lower creatinine clearance, use of long-acting or high-dose insulin, and sulfonylurea use. C statistics for the 3 models ranged from 0.844 to 0.887. Depending on the model used, risk scores in the upper 90th percentile predicted 48.5–63.1% of actual hypoglycemic events. It was estimated that by targeting only patients in the upper 90th percentile, providers would need to intervene during fewer than 9 admissions to prevent 1 hypoglycemic event. Conclusion: The developed prediction models were found to have excellent discriminative validity and good calibration, allowing clinicians to focus interventions on a select high-risk population in which the majority of hypoglycemic events occur.