No abstract

Aims: Few studies have quantified the impact of risk factors on GI complications in elderly nonsteroidal anti-inflammatory drug (NSAID) users. This study aimed to develop and validate a risk prediction score for severe GI complications to identify high-risk elderly patients using NSAID.Methods: We used the following two Korean claims datasets: customized data with an enrolment period 2016-2017 for model development, and the sample data in 2019 for external validation. We conducted a nested case-control study for model development and validation. NSAID users were identified as the elderly (≥65 years) who received NSAIDs for more than 30 days. Serious GI complications were defined as hospitalizations or emergency department visits, with a main diagnosis of GI bleeding or perforation. We applied the logistic least absolute shrinkage and selection operator (LASSO) regression model for variable selection and model fitting.Results: We identified 8176 cases and 81 760 controls with a 1:10 matched followup period in the derivation cohort. In the external validation cohort, we identified 372 cases from 254 551 patients. The risk predictors were high-dose NSAIDs, nonselective NSAID, complicated GI ulcer history, male sex, concomitant gastroprotective agents, relevant co-medications, severe renal disease and cirrhosis. Area under the receiver operating characteristic curve was 0.79 (95% confidence interval, 0.77-0.81) in the external validation dataset.Conclusions: The prediction model may be a useful tool for reducing the risk of serious GI complications by identifying high-risk elderly patients.

Despite the fact that a considerable number of preventable adverse events are managed in primary care settings, medication-related risks have been evaluated mainly through hospital admissions. While interest in high-alert medication management in the community and primary care settings is increasing internationally, Korea does not have accreditation for the same and lacks established standards for high-alert medications. Therefore, this study aimed to develop a nationallevel list of high-alert medications for community and primary care. The candidates for the high-alert medication list included medications recommended by Institute for Safe Medication Practices and those suggested by each guideline of the World Health Organization, Japan and Korea. We analyzed the adverse events related to medication errors reported by the patients' safety reporting system. Seven experts working in the community and primary care settings evaluated the adequacy and priority of the candidate medications. The final list included 20 high-alert medications, including nine essential and eleven optional medications. The national-level list of high-alert medications which we developed may be useful in formulating accreditation guidelines or treatment standards for community and primary care patients.