A PREDICTIVE MODEL FOR DELIRIUM IN HOSPITALIZED ELDERLY MEDICAL PATIENTS BASED ON ADMISSION CHARACTERISTICS

Objective: To prospectively develop and validate a predictive model for the occurrence of new delirium in hospitalized elderly medical patients based on characteristics present at admission. Design: Two prospective cohort studies done in tandem. Setting: University teaching hospital. Patients: The development cohort included 107 hospitalized general medical patients 70 years or older who did not have dementia or delirium at admission. The validation cohort included 174 comparable patients. Measurements: Patients were assessed daily for delirium using a standardized, validated instrument. The predictive model developed in the initial cohort was then validated in a separate cohort of patients. Results: Delirium developed in 27 of 107 patients (25%) in the development cohort. Four independent baseline risk factors for delirium were identified using proportional hazards analysis: These included vision impairment (adjusted relative risk, 3.5; 95% CI, 1.2 to 10.7); severe illness (relative risk, 3.5; CI, 1.5 to 8.2); cognitive impairment (relative risk, 2.8; CI, 1.2 to 6.7); and a high blood urea nitrogen/creatinine ratio (relative risk, 2.0; CI, 0.9 to 4.6). A risk stratification system was developed by assigning 1 point for each risk factor present. Rates of delirium for low- (0 points), intermediate-(1 to 2 points), and high-risk (3 to 4 points) groups were 9%, 23%, and 83% (P < 0.0001), respectively. The corresponding rates in the validation cohort, in which 29 of 174 patients (17%) developed delirium, were 3%, 16%, and 32% (P < 0.002). The rates of death or nursing home placement, outcomes potentially related to delirium, were 9%, 16%, and 42% (P = 0.02) in the development cohort and 3%, 14%, and 26% (P = 0.007) in the validation cohort. Conclusions: Delirium among elderly hospitalized patients is common, and a simple predictive model based on four risk factors can be used at admission to identify elderly persons at the greatest risk.