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Ann Intern Med. 1993 Sep 15;119(6):474-81.

A predictive model for delirium in hospitalized elderly medical patients based on admission characteristics.

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Yale University School of Medicine, New Haven Connecticut.



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.


Two prospective cohort studies done in tandem.


University teaching hospital.


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.


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.


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% Cl, 1.2 to 10.7); severe illness (relative risk, 3.5; Cl, 1.5 to 8.2); cognitive impairment (relative risk, 2.8; Cl, 1.2 to 6.7); and a high blood urea nitrogen/creatinine ratio (relative risk, 2.0; Cl, 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.


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.

[Indexed for MEDLINE]

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