BACKGROUND: The aim of the present study was to develop and validate a method of identifying elderly patients who are at increased risk for an adverse drug reaction (ADR). METHODS: Data from the Gruppo Italiano di Farmacoepidemiologia nell'Anziano (Italian Group of Pharmacoepidemiology in the Elderly) were used to develop an ADR risk score. Variables associated with ADRs were identified by a stepwise logistic regression analysis and used to compute the ADR risk score. The ADR risk score was then validated in a sample of older adults who were admitted to 4 university hospitals in Europe (validation study). RESULTS: Of 5936 patients (mean [SD] age, 78.0 [7.2] years) in the Gruppo Italiano di Farmacoepidemiologia nell'Anziano sample, 383 (6.5%) experienced an ADR. The number of drugs and a history of an ADR were the strongest predictors of ADRs, followed by heart failure, liver disease, presence of 4 or more conditions, and renal failure. These variables were used to compute the ADR risk score. The area under the receiver operator characteristic curve, which assesses the ability of the risk score to predict ADRs, was 0.71 (95% confidence interval, 0.68-0.73). Overall, 483 patients entered the validation study (mean [SD] age, 80.3 [7.6] years), and 56 (11.6%) experienced an ADR. The area under the receiver operator characteristic curve in this sample was 0.70 (95% confidence interval, 0.63-0.78). CONCLUSIONS: This study proposes a practical and simple method of identifying patients who are at an increased risk of an ADR. This approach may be useful in clinical practice as a tool to identify patients at risk and in research to target a population that can benefit from interventions aimed to reduce drug-related illness.
BACKGROUND: The aim of the present study was to develop and validate a method of identifying elderly patients who are at increased risk for an adverse drug reaction (ADR). METHODS: Data from the Gruppo Italiano di Farmacoepidemiologia nell'Anziano (Italian Group of Pharmacoepidemiology in the Elderly) were used to develop an ADR risk score. Variables associated with ADRs were identified by a stepwise logistic regression analysis and used to compute the ADR risk score. The ADR risk score was then validated in a sample of older adults who were admitted to 4 university hospitals in Europe (validation study). RESULTS: Of 5936 patients (mean [SD] age, 78.0 [7.2] years) in the Gruppo Italiano di Farmacoepidemiologia nell'Anziano sample, 383 (6.5%) experienced an ADR. The number of drugs and a history of an ADR were the strongest predictors of ADRs, followed by heart failure, liver disease, presence of 4 or more conditions, and renal failure. These variables were used to compute the ADR risk score. The area under the receiver operator characteristic curve, which assesses the ability of the risk score to predict ADRs, was 0.71 (95% confidence interval, 0.68-0.73). Overall, 483 patients entered the validation study (mean [SD] age, 80.3 [7.6] years), and 56 (11.6%) experienced an ADR. The area under the receiver operator characteristic curve in this sample was 0.70 (95% confidence interval, 0.63-0.78). CONCLUSIONS: This study proposes a practical and simple method of identifying patients who are at an increased risk of an ADR. This approach may be useful in clinical practice as a tool to identify patients at risk and in research to target a population that can benefit from interventions aimed to reduce drug-related illness.
Authors: Aileen B Dequito; Peter G M Mol; Jasperien E van Doormaal; Rianne J Zaal; Patricia M L A van den Bemt; Flora M Haaijer-Ruskamp; Jos G W Kosterink Journal: Drug Saf Date: 2011-11-01 Impact factor: 5.606
Authors: F Sganga; D L Vetrano; S Volpato; A Cherubini; C Ruggiero; A Corsonello; P Fabbietti; F Lattanzio; R Bernabei; G Onder Journal: J Nutr Health Aging Date: 2014 Impact factor: 4.075