Background Risk stratification of hospital patients for adverse drug events would enable targeting patients who may benefit from interventions aimed at reducing drug-related morbidity. It would support clinicians and hospital pharmacists in selecting patients to deliver a more efficient health care service. This study aimed to develop a prediction model that helps to identify patients on the day of hospital admission who are at increased risk of developing a clinically relevant, preventable adverse drug event during their stay on a surgical ward. Methods Data of the pre-intervention measurement period of the P-REVIEW study were used. This study was designed to assess the impact of a multifaceted educational intervention on clinically relevant, preventable adverse drug events in surgical patients. Thirty-nine variables were evaluated in a univariate and multivariate logistic regression analysis, respectively. Model performance was expressed in the Area Under the Receiver Operating Characteristics. Bootstrapping was used for model validation. Results 6780 admissions of patients at surgical wards were included during the pre-intervention period of the PREVIEW trial. 102 patients experienced a clinically relevant, adverse drug event during their hospital stay. The prediction model comprised five variables: age, number of biochemical tests ordered, heparin/LMWH in therapeutic dose, use of opioids, and use of cardiovascular drugs. The AUROC was 0.86 (95% CI 0.83–0.88). The model had a sensitivity of 80.4% and a specificity of 73.4%. The positive and negative predictive values were 4.5% and 99.6%, respectively. Bootstrapping generated parameters in the same boundaries. Conclusions The combined use of a limited set of easily ascertainable patient characteristics can help physicians and pharmacists to identify, at the time of admission, surgical patients who are at increased risk of developing ADEs during their hospital stay. This may serve as a basis for taking extra precautions to ensure medication safety in those patients.

Additional Metadata
Persistent URL dx.doi.org/10.1371/journal.pone.0201645, hdl.handle.net/1765/109957
Journal PLoS ONE
Citation
Bos, J.M, Kalkman, G.A. (Gerard A.), Groenewoud, H. (Hans), van den Bemt, P.M.L.A, de Smet, P.A, Elsbeth Nagtegaal, J. (J.), … Kramers, C. (2018). Prediction of clinically relevant adverse drug events in surgical patients. PLoS ONE, 13(8). doi:10.1371/journal.pone.0201645