Yoonyoung Choi1, Benjamin Staley2, Carl Henriksen3, Dandan Xu4, Gloria Lipori5, Babette Brumback6, Almut G Winterstein7. 1. Center for Observational and Real-World Evidence (CORE), Merck & Co, Inc., North Wales, PA. 2. UF Health Shands Hospital, Gainesville, FL. 3. Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL. 4. Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD. 5. UF Health and UF Health Sciences Center, Gainesville, FL. 6. College of Public Health and Health Professionals and College of Medicine, University of Florida, Gainesville, FL. 7. Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, and Department of Epidemiology, College of Public Health and Health Professionals and College of Medicine, University of Florida, Gainesville, FL almut@ufl.edu.
Abstract
PURPOSE: Construction and validation of a fall risk prediction model specific to inpatients receiving fall risk-increasing drugs (FRIDs) are described. METHODS: In a retrospective cohort study of 75,036 admissions to 2 hospitals over a designated 22-month period that involved FRID exposure during the first 5 hospital days, factors influencing fall risk were investigated via logistic regression. The resultant risk prediction model was internally validated and its performance compared with that of a model based on Morse Fall Scale (MFS) scores. RESULTS: A total of 220,904 patient-days of FRID exposure were evaluated. The three most commonly administered FRIDs were oxycodone (given on 79,697 patient-days, 36.08%), morphine (52,427, 23.73%) and hydromorphone (42,063, 19.04%). Within the 90th percentile of modeled risk scores, 144 of the 466 documented falls (30.9%) were captured by the developed risk prediction model (unbiased C statistic, 0.69), as compared with 94 falls (20.2%) captured using the MFS model (unbiased C statistic, 0.62). Strong predictors of inpatient falls included a history of falling (odds ratio [OR], 1.99; 95% confidence interval (CI), 1.42-2.80); overestimation of ability to ambulate (OR, 1.53; 95% CI, 1.12-2.09); and "comorbidity predisposition," a composite measure encompassing a history of falling and 11 past diagnoses (OR, 1.60; 95% CI, 1.30-1.97). CONCLUSION: The proposed risk model for inpatient falls achieved superior predictive performance when compared with the MFS model. All risk factors were operationalized from discrete electronic health record fields, allowing full automation of real-time identification of high-risk patients.
PURPOSE: Construction and validation of a fall risk prediction model specific to inpatients receiving fall risk-increasing drugs (FRIDs) are described. METHODS: In a retrospective cohort study of 75,036 admissions to 2 hospitals over a designated 22-month period that involved FRID exposure during the first 5 hospital days, factors influencing fall risk were investigated via logistic regression. The resultant risk prediction model was internally validated and its performance compared with that of a model based on Morse Fall Scale (MFS) scores. RESULTS: A total of 220,904 patient-days of FRID exposure were evaluated. The three most commonly administered FRIDs were oxycodone (given on 79,697 patient-days, 36.08%), morphine (52,427, 23.73%) and hydromorphone (42,063, 19.04%). Within the 90th percentile of modeled risk scores, 144 of the 466 documented falls (30.9%) were captured by the developed risk prediction model (unbiased C statistic, 0.69), as compared with 94 falls (20.2%) captured using the MFS model (unbiased C statistic, 0.62). Strong predictors of inpatient falls included a history of falling (odds ratio [OR], 1.99; 95% confidence interval (CI), 1.42-2.80); overestimation of ability to ambulate (OR, 1.53; 95% CI, 1.12-2.09); and "comorbidity predisposition," a composite measure encompassing a history of falling and 11 past diagnoses (OR, 1.60; 95% CI, 1.30-1.97). CONCLUSION: The proposed risk model for inpatient falls achieved superior predictive performance when compared with the MFS model. All risk factors were operationalized from discrete electronic health record fields, allowing full automation of real-time identification of high-risk patients.