Background: Pharmacy-led medication reconciliation in elective surgery patients is often performed at the preoperative screening (POS). Because of the time lag between POS and admission, changes in medication may lead to medication errors at admission (MEAs). In a previous study, a risk prediction model for MEA was developed. Objective: To validate this risk prediction model to identify patients at risk for MEAs in a university hospital setting. Methods: The risk prediction model was derived from a cohort of a Dutch general hospital and validated within a comparable cohort from a Dutch University Medical Centre. MEAs were assessed by comparing the POS medication list with the reconciled medication list at hospital admission. This was considered the gold standard. For every patient, a risk score using the risk prediction model was calculated and compared with the gold standard. The risk prediction model was assessed with receiver operating characteristic (ROC) analysis. Results: Of 368 included patients, 167 (45.4%) had at least 1 MEA. ROC analysis revealed significant differences in the area under the curve of 0.535 (P = 0.26; validation cohort) versus 0.752 (P < 0.0001; derivation cohort). The sensitivity in this validating cohort was 66%, with a specificity of 40%. Conclusion and Relevance: The risk prediction model developed in a general hospital population is not suitable to identify patients at risk for MEA in a university hospital population. However, number of medications is a common risk factor in both patient populations and should, thus, form the basis of an adapted risk prediction model.

Additional Metadata
Keywords medication error, medication reconciliation, prediction model, risk factor, validation
Persistent URL dx.doi.org/10.1177/1060028018784905, hdl.handle.net/1765/109101
Journal The Annals of Pharmacotherapy
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Citation
Ebbens, M.M. (Marieke M.), Laar, S.A.V. (Sylvia A. van), Wesselink, E.J. (Elsbeth J.), Gombert-Handoko, K.B. (Kim B.), & van den Bemt, P.M.L.A. (2018). Prospective Validation of a Risk Prediction Model to Identify High-Risk Patients for Medication Errors at Hospital Admission. The Annals of Pharmacotherapy. doi:10.1177/1060028018784905