The goal of this study was to estimate the prevalence of spirometry testing among patients with asthma and chronic obstructive pulmonary disease (COPD) in general practices (GPs) in Germany. This retrospective cross-sectional study was based on data from the Disease Analyzer database (IQVIA), This retrospective cross-sectional study included all patients with at least one confirmed asthma or COPD diagnosis in one of those 50 general practices in Germany between January 2020, and January 2021, as well as at least one visit to these general practices between January 2021, and January 2022. The main outcomes of the study aimed to ascertain the proportion of spirometry testing among asthma and COPD patients between January 2021, and January 2022, overall, and separately, in men, women, six age groups (≤30, 31–40, 41–50, 51–60, 61, 70, >70), and patients who received at least one prescription of anti-asthma or anti-COPD drugs. This study included 8835 patients with asthma only, 5597 with COPD only, and 1897 with both asthma and COPD diagnoses. Of these, 27.2% of COPD patients, 7% of asthma patients, and 54.7% of asthma + COPD patients, received spirometry testing during the study period. Among COPD and asthma + COPD patients, the prevalence of spirometry testing was much higher in women than in men (COPD: 31.6% vs. 23.2%; asthma + COPD: 59.6% vs. 46.3%) and much higher in treated than in non-treated patients (COPD: 31.7% vs. 15.0%; asthma + COPD: 57.5% vs. 27.8%). The prevalence of spirometry testing was relatively low among COPD and asthma patients followed in GP practices, but usually higher in female patients, treated patients, and patients suffering from both asthma and COPD.

Background: Inflammatory bowel disease (IBD) is of high medical and socioeconomic relevance. Moderate and severe disease courses often require treatment with biologics. The aim of this study was to evaluate machine learning (ML)-based methods for the prediction of biologic therapy in IBD patients using a large prescription database. Methods: The present retrospective cohort study utilized a longitudinal prescription database (LRx). Patients with at least one prescription for an intestinal anti-inflammatory agent from a gastroenterologist between January 2015 and July 2021 were included. Patients who had received an initial biologic therapy prescription (infliximab, adalimumab, golimumab, vedolizumab, or ustekinumab) were categorized as the “biologic group.” The potential predictors included in the machine learning-based models were age, sex, and the 100 most frequently prescribed drugs within 12 months prior to the index date. Six machine learning-based methods were used for the prediction of biologic therapy. Results: A total of 122,089 patients were included in this study. Of these, 15,824 (13.0%) received at least one prescription for a biologic drug. The Light Gradient Boosting Machine had the best performance (accuracy = 74%) and was able to correctly identify 78.5% of the biologics patients and 72.6% of the non-biologics patients in the testing dataset. The most important variable was prednisolone, followed by lower age, mesalazine, budesonide, and ferric iron. Conclusions: In summary, this study reveals the advantages of ML-based models in predicting biologic therapy in IBD patients based on pre-treatment and demographic variables. There is a need for further studies in this regard that take into account individual patient characteristics, i.e., genetics and gut microbiota, to adequately address the challenges of finding optimal treatment strategies for patients with IBD.