Purpose The aim of this study was to analyze the persistence of women on tamoxifen (TAM) and aromatase inhibitors (AIs) in Germany, and to investigate possible determinants of non-persistence. Methods The present retrospective cohort study was based on the IQVIA longitudinal prescription database (LRx). The study included women with an initial prescription of TAM or AIs (anastrozole, letrozole, and exemestane) between January 2016 and December 2020 (index date). Kaplan–Meier analyses were performed to show the persistence for TAM and AI, using a therapy gap of 90 or 180 days, respectively. A multivariable Cox proportional hazards regression model was further used to estimate the relationship between non-persistence and drug prescription (AI versus TAM), age, and the specialty of the physician initiating therapy (gynecologist, oncologist, or general practitioner). Results Up to 5 years after the index date, only 35.1% of AI and 32.5% of TAM patients were continuing therapy when therapy discontinuation was defined as at least 90 days without therapy. Using a 180-day therapy gap, 51.9% of AI and 50.4% of TAM patients remained on therapy after 5 years. Cox regression models reveal that initial therapy with TAM (HR 1.06, 95% CI 1.04–1.07), therapy initiation by oncologists (HR 1.09, 95% CI 1.07–1.11), or general practitioners (HR 1.24, 95% CI 1.21–1.27) and age ≤ 50 (HR 1.08, 95% CI 1.06–1.10) were significantly associated with an increased risk of therapy discontinuation. Conclusion Overall, the present study indicates that persistence rates are low in all age groups for both TAM and AI treatment. We found several factors (e.g., physician specialty, younger age, and type of endocrine therapy) to be associated with an increased risk for non-persistence.

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Background: Liver transplantation (LT) is a routine therapeutic approach for patients with acute liver failure, end-stage liver disease and/or early-stage liver cancer. While 5-year survival rates have increased to over 80%, long-term outcomes are critically influenced by extrahepatic sequelae of LT and immunosuppressive therapy, including diabetes mellitus (DM). In this study, we used machine learning (ML) to predict the probability of new-onset DM following LT. Methods: A cohort of 216 LT patients was identified from the Disease Analyzer (DA) database (IQVIA) between 2005 and 2020. Three ML models comprising random forest (RF), logistic regression (LR), and eXtreme Gradient Boosting (XGBoost) were tested as predictors of new-onset DM within 12 months after LT. Results: 18 out of 216 LT patients (8.3%) were diagnosed with DM within 12 months after the index date. The performance of the RF model in predicting the development of DM was the highest (accuracy = 79.5%, AUC 77.5%). It correctly identified 75.0% of the DM patients and 80.0% of the non-DM patients in the testing dataset. In terms of predictive variables, patients’ age, frequency and time of proton pump inhibitor prescription as well as prescriptions of analgesics, immunosuppressants, vitamin D, and two antibiotic drugs (broad spectrum penicillins, fluocinolone) were identified. Conclusions: Pending external validation, our data suggest that ML models can be used to predict the occurrence of new-onset DM following LT. Such tools could help to identify LT patients at risk of unfavorable outcomes and to implement respective clinical strategies of prevention.