Development of a Risk Model for Adverse Drug Events in the Elderly

The aim of this study was to develop a predictive model for adverse drug events (ADEs) in elderly patients. Socio-demographic and medical data were collected from chart reviews, computerised information and a patient interview, for a population of 929 elderly patients (aged > 65 years) whose admission to the Waveney/Braid Valley Hospital in Northern Ireland was not scheduled. A further 204 patients formed a validation group. An ADE score was assigned to each patient using a modified Naranjo algorithm scoring system. The ADE scores ranged from 0 to 8. For the purposes of developing a risk model, scores of 4 or more were considered to constitute a high risk of an ADE. Logistic regression analysis was used to produce a risk model for ADEs in the elderly. Seven variables significantly influenced the risk of an elderly person developing an ADE. Prescribed digoxin [odds ratio (OR) = 1.99], antidepressants (OR = 5.79), and a number of disease states, i.e. gastrointestinal disorders (nausea, vomiting, diarrhoea) [OR = 2.16], chronic obstructive airways disease (OR = 2.41) and angina (OR = 0.17), significantly influenced ADE score. An abnormal potassium level (OR = 2.57) and patient belief that their medication was in some way responsible for their hospital admission (OR = 4.21) also significantly influenced ADE score. Validation of the model revealed that it had a specificity of 69%, a sensitivity of 41%, with an overall accuracy of 63%. This model was therefore better at predicting elderly patients with ADE scores of 3 or less. Nonetheless, the variables highlighted are significant risk factors for ADEs in the elderly.