Real-world characteristics and use patterns of patients treated with vericiguat: A nationwide longitudinal cohort study in Germany.

PURPOSE: Vericiguat reduced clinical endpoints in patients experiencing worsening heart failure in clinical trials, but its implementation outside trials is unclear. METHODS: This retrospective analysis of longitudinally collected data was based on the IQVIA™ LRx database, which includes ~ 80% of the prescriptions of the 73 million people covered by the German statutory health insurance. RESULTS: Between September 2021 and December 2022, vericiguat was initiated in 2916 adult patients. Their mean age was 73 ± 13 years and 28% were women. While approximately 70% were uptitrated beyond 2.5 mg, only 36% reached 10 mg. Median time to up-titration from 2.5 mg to 5 mg was 17 (quartiles: 11-33) days, and from 2.5 to 10 mg 37 (25-64) days, respectively. In 87% of the patients, adherence to vericiguat was high as indicated by a medication possession ratio of  ≥ 80%, and 67% of the patients persistently used vericiguat during the first year. Women and older patients reached the maximal dose of 10 mg vericiguat less often and received other substance classes of guideline-recommended therapy (GDMT) less frequently. The proportion of patients receiving four pillars of GDMT increased from 29% before vericiguat initiation to 44% afterwards. CONCLUSION: In a real-world setting, despite higher age than in clinical trials, adherence and persistence of vericiguat appeared satisfactory across age categories. Initiation of vericiguat was associated with intensification of concomitant GDMT. Nevertheless, barriers to vericiguat up-titration and implementation of other GDMT, applying in particular to women and elderly patients, need to be investigated further.

Predictive Attributes for Developing Long COVID-A Study Using Machine Learning and Real-World Data from Primary Care Physicians in Germany.

(1) In the present study, we used data comprising patient medical histories from a panel of primary care practices in Germany to predict post-COVID-19 conditions in patients after COVID-19 diagnosis and to evaluate the relevant factors associated with these conditions using machine learning methods. (2) Methods: Data retrieved from the IQVIATM Disease Analyzer database were used. Patients with at least one COVID-19 diagnosis between January 2020 and July 2022 were selected for inclusion in the study. Age, sex, and the complete history of diagnoses and prescription data before COVID-19 infection at the respective primary care practice were extracted for each patient. A gradient boosting classifier (LGBM) was deployed. The prepared design matrix was randomly divided into train (80%) and test data (20%). After optimizing the hyperparameters of the LGBM classifier by maximizing the F2 score, model performance was evaluated using several test metrics. We calculated SHAP values to evaluate the importance of the individual features, but more importantly, to evaluate the direction of influence of each feature in our dataset, i.e., whether it is positively or negatively associated with a diagnosis of long COVID. (3) Results: In both the train and test data sets, the model showed a high recall (sensitivity) of 81% and 72% and a high specificity of 80% and 80%; this was offset, however, by a moderate precision of 8% and 7% and an F2-score of 0.28 and 0.25. The most common predictive features identified using SHAP included COVID-19 variant, physician practice, age, distinct number of diagnoses and therapies, sick days ratio, sex, vaccination rate, somatoform disorders, migraine, back pain, asthma, malaise and fatigue, as well as cough preparations. (4) Conclusions: The present exploratory study describes an initial investigation of the prediction of potential features increasing the risk of developing long COVID after COVID-19 infection by using the patient history from electronic medical records before COVID-19 infection in primary care practices in Germany using machine learning. Notably, we identified several predictive features for the development of long COVID in patient demographics and their medical histories.