Validation of a Delirium Risk Assessment Using Electronic Medical Record Information

Abstract Objective Identifying patients at risk for delirium allows prompt application of prevention, diagnostic, and treatment strategies; but is rarely done. Once delirium develops, patients are more likely to need posthospitalization skilled care. This study developed an a priori electronic prediction rule using independent risk factors identified in a National Center of Clinical Excellence meta-analysis and validated the ability to predict delirium in 2 cohorts. Design Retrospective analysis followed by prospective validation. Setting Tertiary VA Hospital in New England. Participants A total of 27,625 medical records of hospitalized patients and 246 prospectively enrolled patients admitted to the hospital. Measurements The electronic delirium risk prediction rule was created using data obtained from the patient electronic medical record (EMR). The primary outcome, delirium, was identified 2 ways: (1) from the EMR (retrospective cohort) and (2) clinical assessment on enrollment and daily thereafter (prospective participants). We assessed discrimination of the delirium prediction rule with the C-statistic. Secondary outcomes were length of stay and discharge to rehabilitation. Results Retrospectively, delirium was identified in 8% of medical records (n = 2343); prospectively, delirium during hospitalization was present in 26% of participants (n = 64). In the retrospective cohort, medical record delirium was identified in 2%, 3%, 11%, and 38% of the low, intermediate, high, and very high-risk groups, respectively (C-statistic = 0.81; 95% confidence interval 0.80–0.82). Prospectively, the electronic prediction rule identified delirium in 15%, 18%, 31%, and 55% of these groups (C-statistic = 0.69; 95% confidence interval 0.61–0.77). Compared with low-risk patients, those at high- or very high delirium risk had increased length of stay (5.7 ± 5.6 vs 3.7 ± 2.7 days; P  = .001) and higher rates of discharge to rehabilitation (8.9% vs 20.8%; P  = .02). Conclusions Automatic calculation of delirium risk using an EMR algorithm identifies patients at risk for delirium, which creates a critical opportunity for gaining clinical efficiencies and improving delirium identification, including those needing skilled care.