A polypharmacy risk prediction model for elderly patients based on sociodemographic and clinical factors.

OBJECTIVE

Elderly people take increasing amounts of medication. The aim of our study was to determine the effects of different sociodemographic and clinical factors on polypharmacy and to develop a risk prediction model in outpatients aged 65 years and older.

MATERIALS AND METHODS

Cross-sectional, observational, descriptive study of outpatients aged 65 years and older scheduled for a specialist visit. Data on sociodemographic (age, sex, place of residence, and institutionalization) as well as on clinical variables (number of prescribing physicians and number of diagnoses) were collected. Polypharmacy was defined as the uninterrupted use of more than 5 medications within the last 3 months. To determine the risk factors for polypharmacy among these patients, a multivariate logistic regression model was developed and subsequently validated using bootstrap resampling techniques. The model was assessed for its discrimination accuracy using the area under the curve (ROC AUC).

RESULTS

A total of 225 outpatients were included for development of the model. Polypharmacy was found in 46.7% of patients. The determinants that best predicted polypharmacy included: age, institutionalization, number of prescribing physicians, and number of diagnoses. The ROC AUC was 0.85.

CONCLUSION: The predictive model developed in this study, which consists of 4 readily obtainable variables, may be a useful tool for identifying and monitoring elderly patients at risk for polypharmacy.
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