Identification of polypharmacy patterns in new-users of metformin using the Apriori algorithm: A novel framework for investigating concomitant drug utilization through association rule mining.

PURPOSE: With increased concomitant chronic diseases in type 2 diabetes mellitus (T2DM), the use of multiple drugs increases as well as the risk of drug-drug interactions (DDI) and adverse drug reactions (ADR). Nevertheless, how medication patterns vary in T2DM patients across different sex and age groups is unclear. This study aims to identify and quantify common drug combinations in first-time metformin users with polypharmacy (≥5 co-medications). METHODS: New users of metformin were identified from the IQVIA Medical Research Data incorporating data from THIN, A Cegedim Database (2016-2019). A descriptive cohort study explored prescription patterns in patients with polypharmacy. The Apriori algorithm, used to find frequent item-sets in databases, was first-time applied to identify and quantify drug combinations of up to seven drugs to investigate potential harmful polypharmacy patterns. RESULTS: The cohort included 34 169 new-users of metformin, of which 20 854 (61.0%) received polypharmacy. Atorvastatin was the most frequently co-prescribed drug with metformin overall (38.7%), in women (34.3%) and men (42.6%). In the stratified analysis, a higher proportion of women received polypharmacy (65.6%) compared to men (57.4%). Moreover, the proportion of patients receiving polypharmacy increased with age (18-39 years = 30.4%, 40-59 years = 50.5%, 60-74 years = 70.9%, and ≥75 years = 84.3%). CONCLUSION: This study is the first to identify and quantify commonly prescribed combinations of drugs compounds in patients with polypharmacy using the Apriori algorithm. The high polypharmacy prevalence at all strata indicates the need to optimize polypharmacy to minimize DDI and ADR.

Return to Play Prediction Accuracy of the MLG-R Classification System for Hamstring Injuries in Football Players: A Machine Learning Approach.

BACKGROUND AND OBJECTIVE: Muscle injuries are one of the main daily problems in sports medicine, football in particular. However, we do not have a reliable means to predict the outcome, i.e. return to play from severe injury. The aim of the present study was to evaluate the capability of the MLG-R classification system to grade hamstring muscle injuries by severity, offer a prognosis for the return to play, and identify injuries with a higher risk of re-injury. Furthermore, we aimed to assess the consistency of our proposed system by investigating its intra-observer and inter-observer reliability. METHODS: All male professional football players from FC Barcelona, senior A and B and the two U-19 teams, with injuries that occurred between February 2010 and February 2020 were reviewed. Only players with a clinical presentation of a hamstring muscle injury, with complete clinic information and magnetic resonance images, were included. Three different statistical and machine learning approaches (linear regression, random forest, and eXtreme Gradient Boosting) were used to assess the importance of each factor of the MLG-R classification system in determining the return to play, as well as to offer a prediction of the expected return to play. We used the Cohen's kappa and the intra-class correlation coefficient to assess the intra-observer and inter-observer reliability. RESULTS: Between 2010 and 2020, 76 hamstring injuries corresponding to 42 different players were identified, of which 50 (65.8%) were grade 3r, 54 (71.1%) affected the biceps femoris long head, and 33 of the 76 (43.4%) were located at the proximal myotendinous junction. The mean return to play for grades 2, 3, and 3r injuries were 14.3, 12.4, and 37 days, respectively. Injuries affecting the proximal myotendinous junction had a mean return to play of 31.7 days while those affecting the distal part of the myotendinous junction had a mean return to play of 23.9 days. The analysis of the grade 3r biceps femoris long head injuries located at the free tendon showed a median return to play time of 56 days while the injuries located at the central tendon had a shorter return to play of 24 days (p = 0.038). The statistical analysis showed an excellent predictive power of the MLG-R classification system with a mean absolute error of 9.8 days and an R-squared of 0.48. The most important factors to determine the return to play were if the injury was at the free tendon of the biceps femoris long head or if it was a grade 3r injury. For all the items of the MLG-R classification, the intra-observer and inter-observer reliability was excellent (k > 0.93) except for fibres blurring (κ = 0.68). CONCLUSIONS: The main determinant for a long return to play after a hamstring injury is the injury affecting the connective tissue structures of the hamstring. We developed a reliable hamstring muscle injury classification system based on magnetic resonance imaging that showed excellent results in terms of reliability, prognosis capability and objectivity. It is easy to use in clinical daily practice, and can be further adapted to future knowledge. The adoption of this system by the medical community would allow a uniform diagnosis leading to better injury management.

Community pharmacist-administered seasonal influenza vaccination: a national customer survey.

BACKGROUND: In Switzerland, the influenza vaccination is recommended for high-risk groups and people who have contact with high-risk groups. Since 2015, Swiss pharmacists are allowed to vaccinate healthy adults after acquiring a certificate of competence for vaccination and blood sampling techniques. We aimed to assess customers of the seasonal influenza vaccination in pharmacies in regard to their satisfaction, motivation, and reasons. METHODS: Swiss pharmacies collected survey data during a period of 12 weeks from mid-October 2019 to mid-January 2020. Each participating pharmacy was sent 20 questionnaires to be handed out to vaccinated customers. The questionnaire was available in German and French and subdivided into four sections: demographic information, satisfaction, reasons for getting the vaccination, and reasons for choosing a pharmacy as a place of vaccination. We tested for statistical differences in answer tendencies across strata on questionnaire language, age groups, and levels of education. RESULTS: Of the 1600 surveys sent, 80 pharmacies sent back 656 completed questionnaires (return rate, 41%). Main age bracket was 65-74 years (26.2%), followed by 55-64 years (24.7%), with an equal distribution of reported sex (female, 49.5%). Of the respondents, 99% would have recommended the service and 88.5% felt very comfortable being vaccinated by a pharmacist. Satisfaction included injection technique, used facilities, preparatory discussions, and pricing of the service. Easy scheduling was a main motivation for choosing a pharmacy as the vaccination provider. We identified minor differences in answer tendencies across questionnaire language and age groups, but not across levels of education. CONCLUSION: Customer satisfaction with community pharmacist-administered seasonal influenza vaccinations is high in Switzerland.