BACKGROUND: Older patients are at an increased risk of developing adverse drug reactions (ADR). Of particular concern are the oldest old, which constitute an increasingly growing population. Having a validated clinical tool to identify those older patients at risk of developing an ADR during hospital stay would enable healthcare staff to put measures in place to reduce the risk of such an event developing. The current study aimed to (1) develop and (2) validate an ADR risk prediction model. METHODS: We used a combination of univariate analysis and multivariate binary logistic regression to identify clinical risk factors for developing an ADR in a population of older people from a UK teaching hospital. The final ADR risk model was then validated in a European population (European dataset). RESULTS: Six-hundred-ninety patients (median age 85 years) were enrolled in the development stage of the study. Ninety-five reports of ADR were confirmed by independent review in these patients. Five clinical variables were identified through multivariate analysis and included in our final model; each variable was attributed a score of 1. Internal validation produced an AUROC of 0.74, a sensitivity of 80%, and specificity of 55%. During the external validation stage the AUROC was 0.73, with sensitivity and specificity values of 84% and 43% respectively. CONCLUSIONS: We have developed and successfully validated a simple model to use ADR risk score in a population of patients with a median age of 85, i.e. the oldest old. The model is based on 5 clinical variables (≥8 drugs, hyperlipidaemia, raised white cell count, use of anti-diabetic agents, length of stay ≥12 days), some of which have not been previously reported.
|Number of pages||9|
|Publication status||Published - 30 Oct 2014|
Bibliographical note© 2014 Tangiisuran et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- School of Applied Sciences - Clinical Principal Lecturer
- Centre for Precision Health and Translational Medicine
- Medicines Optimisation Research and Enterprise Group