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To develop and validate a simple clinical prediction rule, based on variables easily measurable at admission, to identify patients at high risk of developing delirium during their hospital stay on an internal medicine ward.
Prospective study of two cohorts of patients admitted between 1 May and 30 June 2008 (derivation cohort), and between 1 May and 30 June 2009 (validation cohort).
A tertiary hospital in Donostia-Gipuzkoa (Spain).
In total 397 patients participated in the study. The mean age and incidence of delirium were 75.9 years and 13%, respectively, in the derivation cohort, and 75.8 years and 25% in the validation cohort.
The predictive variables analysed and finally included in the rule were: being aged 85 years old or older, being dependent in five or more activities of daily living, and taking two or more psychotropic drugs (antipsychotics, benzodiazepines, antidepressants, anticonvulsant and/or antidementia drugs). The variable of interest was delirium as defined by the short Confusion Assessment Method, which assesses four characteristics: acute onset and fluctuating course, inattention, disorganised thinking and altered level of consciousness.
We developed a rule in which the individual risk of delirium is obtained by adding one point for each criterion met (age≥85, high level of dependence, and being on psychotropic medication). The result is considered positive if the score is ≥1. The rule accuracy was: sensitivity=93.4% (95% CI 85.5% to 97.2%), specificity=60.6% (95% CI 54.1% to 66.8%), positive predictive value=44.4% (95% CI 36.9% to 52.1%) and negative predictive value=96.5% (95% CI: 92% to 98.5%). The area under the receiver operator characteristic (ROC) curve was 0.85 for the validation cohort.
The presence or absence of any of the three predictive factors (age≥85, high level of dependence and psychotropic medication) allowed us to classify patients on internal medicine wards according to the risk of developing delirium. The simplicity of the variables in our clinical prediction rule means that the data collection required is feasible in busy medicine units.