Effect of nationwide concurrent drug utilization review program on drug-drug interactions and related health outcome

Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population

BACKGROUND

Falls impact over 25% of older adults annually, making fall prevention a critical public health focus. We aimed to develop and validate a machine learning-based prediction model for serious fall-related injuries (FRIs) among community-dwelling older adults, incorporating various medication factors.

METHODS

Utilizing annual national patient sample data, we segmented outpatient older adults without FRIs in the preceding three months into development and validation cohorts based on data from 2018 and 2019, respectively. The outcome of interest was serious FRIs, which we defined operationally as incidents necessitating an emergency department visit or hospital admission, identified by the diagnostic codes of injuries that are likely associated with falls. We developed four machine-learning models (light gradient boosting machine, Catboost, eXtreme Gradient Boosting, and Random forest), along with a logistic regression model as a reference.

RESULTS

In both cohorts, FRIs leading to hospitalization/emergency department visits occurred in approximately 2% of patients. After selecting features from initial set of 187, we retained 26, with 15 of them being medication-related. Catboost emerged as the top model, with area under the receiver operating characteristic of 0.700, along with sensitivity and specificity rates around 65%. The high-risk group showed more than threefold greater risk of FRIs than the low-risk group, and model interpretations aligned with clinical intuition.

CONCLUSION

We developed and validated an explainable machine-learning model for predicting serious FRIs in community-dwelling older adults. With prospective validation, this model could facilitate targeted fall prevention strategies in primary care or community-pharmacy settings.

Community pharmacists' response to complaints of gastroesophageal reflux: A simulated patient study in the Northern United Arab Emirates

INTRODUCTION

Patients frequently use gastric acid-reducing agents (ARAs) to treat symptoms affecting the gastrointestinal tract. Thus, the risk for drug-drug interactions (DDI) is a serious concern. This potentially makes the community pharmacist (CP) act as a primary intervention by providing the appropriate counseling and dispensing practice.

OBJECTIVE

To evaluate CPs' counseling and dispensing practices regarding complaints of Gastroesophageal Reflux Disease (GERD), including recommending an appropriate course of action to prevent possible DDIs.

MATERIALS AND METHODS

A simulated patient (SP) methodology was used in this study. The community pharmacies in Ajman and Sharjah were visited by SP who's responsible for acting as a patient, and by an observer who's responsible for focusing on the interaction between the SP and the CPs without engagement. Data were recorded using a preprepared data collection form. Performance feedback was sent to the CPs after concluding all visits. Counseling and dispensing scores were classified based on the total scores to poor, inadequate, and complete. Appropriateness of the pharmacist's decision was defined as dispensing antacid and advising of separating doses apart in time.

RESULTS

A total of 150 community pharmacies was included in the data analysis. The findings of the current study demonstrated poor counseling and dispensing for the vast majority of the participants (81.3% and 67.3% of respondents, respectively). Only 4% of the CPs advised the SP to have a time interval between antacid and cefuroxime axetil. A significant difference in counseling scores was found between pharmacies located in Ajman and Sharjah (p = 0.01). Also, there was a significant difference in dispensing scores between independent and chain pharmacies (p = 0.003).

CONCLUSIONS

The findings revealed inadequate counseling and dispensing practice by CPs. This study highlighted the need for continuous professional training programs to endow the CPs with the knowledge necessary for improving the CPs' counseling and dispensing practices.

Co-prescription of metoprolol and CYP2D6-inhibiting antidepressants before and after implementation of an optimized drug interaction database in Norway

Purpose

To compare the co-prescription of metoprolol and potent CYP2D6-inhibiting antidepressants before and during a 10-year period after implementation of an optimized drug interaction database into clinical decision support systems in Norway.

Methods

The study was a retrospective, cross-sequential nationwide analysis of drug-dispensing data retrieved from the Norwegian Prescription Database over a 1-year period before (2007) and two 1-year periods after (2012 and 2017) implementation of a drug interaction database providing recommendations on non-interacting alternative medications. Primary outcome was changes in co-prescription rates of metoprolol and the potent CYP2D6-inhibiting antidepressants fluoxetine, paroxetine, or bupropion relative to alternative antidepressants with no or limited CYP2D6 inhibitory potential. To control for potential secular trend bias, a comparison group consisting of atenolol/bisoprolol users was included.

Results

The co-prescription rate of metoprolol with potent CYP2D6 inhibitors declined following implementation of the optimized database, by 21% (P < 0.001) after 5 years and by 40% (P < 0.001) after 10 years. Compared with atenolol/bisoprolol users, patients treated with metoprolol had significantly reduced likelihood of being prescribed a CYP2D6-inhibiting antidepressant in the two post-implementation periods (OR 0.61 (95% CI 0.54–0.69) and OR 0.45 (95% CI 0.40–0.51), respectively, versus OR 0.84 (95% CI 0.74–0.94) prior to implementation). Small and mostly insignificant differences in average daily metoprolol dosage were found between patients treated with the various antidepressants.

Conclusion

The present study suggests that implementation of a drug interaction database providing recommendations on non-interacting drug alternatives contributes to reduced co-prescribing of drug combinations associated with potentially serious adverse effects.