Almut G Winterstein1,2, Ben Staley3, Carl Henriksen4, Dandan Xu4, Gloria Lipori5, Nakyung Jeon4, YoonYoung Choi4, Yan Li4, Juan Hincapie-Castillo6, Rene Soria-Saucedo4, Babette Brumback7, Thomas Johns3. 1. Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL almut@ufl.edu. 2. Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL almut@ufl.edu. 3. Department of Pharmacy Services, UF Health Shands Hospital, Gainesville, FL. 4. Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL. 5. UF Health Shands Hospital, University of Florida, Gainesville, FL. 6. Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL. 7. Department of Biostatistics, College of Public Health and Health Professions, and College of Medicine, University of Florida, Gainesville, FL.
Abstract
PURPOSE: The development of risk models for 16 preventable adverse drug events (pADEs) and their aggregation into the final complexity score (C-score) are described. METHODS: Using data from 2 tertiary care facilities, logistic regression models were constructed for the first 5 hospital days that admissions were at risk for each of 16 pADEs. The best model for each pADE was validated in 100 bootstrap samples. The C-score was then aggregated and predicted individual pADE risk as the probability to develop at least 1 pADE. Using the 100 bootstrap samples for each pADE, 100 C-scores for validation were generated. RESULTS: We utilized electronic health records (EHR) data from 65,518 admissions to UF Health Shands and 18,269 admissions to UF Health Jacksonville to develop risk models for 16 pADEs. Most models had very strong discriminant validity (C-statistic > 0.8), with the highest predicted decile representing about half of manifest pADEs. Among admissions in the highest C-score decile, about two thirds experienced at least 1 pADE (C-statistic, 0.838; 95% confidence interval, 0.838-0.839). C-score precision, defined as the percentage of patients consistently (i.e., at least 95 of 100 samples) ranked in the 90th percentile, was 80-84%. CONCLUSION: The C-score was developed and validated for the identification of hospitalized patients at highest risk for pADEs. Aggregation of individual prediction models into a single score reduced its predictive power for most pADEs, compared with the individual risk models, but concentrated in the highest C-score decile a patient group more than two thirds of whom experienced at least 1 pADE.
PURPOSE: The development of risk models for 16 preventable adverse drug events (pADEs) and their aggregation into the final complexity score (C-score) are described. METHODS: Using data from 2 tertiary care facilities, logistic regression models were constructed for the first 5 hospital days that admissions were at risk for each of 16 pADEs. The best model for each pADE was validated in 100 bootstrap samples. The C-score was then aggregated and predicted individual pADE risk as the probability to develop at least 1 pADE. Using the 100 bootstrap samples for each pADE, 100 C-scores for validation were generated. RESULTS: We utilized electronic health records (EHR) data from 65,518 admissions to UF Health Shands and 18,269 admissions to UF Health Jacksonville to develop risk models for 16 pADEs. Most models had very strong discriminant validity (C-statistic > 0.8), with the highest predicted decile representing about half of manifest pADEs. Among admissions in the highest C-score decile, about two thirds experienced at least 1 pADE (C-statistic, 0.838; 95% confidence interval, 0.838-0.839). C-score precision, defined as the percentage of patients consistently (i.e., at least 95 of 100 samples) ranked in the 90th percentile, was 80-84%. CONCLUSION: The C-score was developed and validated for the identification of hospitalized patients at highest risk for pADEs. Aggregation of individual prediction models into a single score reduced its predictive power for most pADEs, compared with the individual risk models, but concentrated in the highest C-score decile a patient group more than two thirds of whom experienced at least 1 pADE.
Authors: Aseel S Abuzour; Gillian Hoad-Reddick; Memona Shahid; Douglas T Steinke; Mary P Tully; Steven David Williams; Penny J Lewis Journal: Eur J Hosp Pharm Date: 2020-12-01