BACKGROUND: The adverse effects of the phosphodiesterase-4 inhibitor roflumilast, appear to be more frequent in clinical practice than what was observed in chronic obstructive pulmonary disease (COPD) clinical trials. Thus, we designed this study to determine whether adverse effects could be reduced by starting roflumilast at half the dose, and then increasing a few weeks later to 500 microg daily. METHODS: We retrospectively investigated 85 patients with COPD who had taken either 500 microg roflumilast, or a starting dose of 250 microg and then increased to 500 microg. We analyzed all adverse events and assessed differences between patients who continued taking the drug after dose escalation and those who had stopped. RESULTS: Adverse events were reported by 22 of the 85 patients (25.9%). The most common adverse event was diarrhea (10.6%). Of the 52 patients who had increased from a starting dose of 250 microg roflumilast to 500 microg, 43 (82.7%) successfully maintained the 500 microg roflumilast dose. No difference in factors likely to affect the risk of adverse effects, was detected between the dose-escalated and the discontinued groups. Of the 26 patients who started with the 500 microg roflumilast regimen, seven (26.9%) discontinued because of adverse effects. There was no statistically significant difference in discontinuation rate between the dose-escalated and the control groups (p=0.22). CONCLUSION: Escalating the roflumilast dose may reduce treatment-related adverse effects and improve tolerance to the full dose. This study suggests that the dose-escalated regimen reduced the rate of discontinuation. However, longer-term and larger-scale studies are needed to support the full benefit of a dose escalation strategy.
Clinical Protocols ; Cyclic Nucleotide Phosphodiesterases, Type 4 ; Diarrhea ; Humans ; Phosphodiesterase 4 Inhibitors ; Pulmonary Disease, Chronic Obstructive* ; Retrospective Studies

Purpose: Hypoxaemia is a significant adverse event during endoscopic retrograde cholangiopancreatography (ERCP) under monitored anaesthesia care (MAC); however, no model has been developed to predict hypoxaemia. We aimed to develop and compare logistic regression (LR) and machine learning (ML) models to predict hypoxaemia during ERCP under MAC. Materials and Methods: We collected patient data from our institutional ERCP database. The study population was randomly divided into training and test sets (7:3). Models were fit to training data and evaluated on unseen test data. The training set was further split into k-fold (k=5) for tuning hyperparameters, such as feature selection and early stopping. Models were trained over k loops; the i-th fold was set aside as a validation set in the i-th loop. Model performance was measured using area under the curve (AUC). Results: We identified 6114 cases of ERCP under MAC, with a total hypoxaemia rate of 5.9%. The LR model was established by combining eight variables and had a test AUC of 0.693. The ML and LR models were evaluated on 30 independent data splits. The average test AUC for LR was 0.7230, which improved to 0.7336 by adding eight more variables with an l 1 regularisation-based selection technique and ensembling the LRs and gradient boosting algorithm (GBM). The high-risk group was discriminated using the GBM ensemble model, with a sensitivity and specificity of 63.6% and 72.2%, respectively. Conclusion We established GBM ensemble model and LR model for risk prediction, which demonstrated good potential for preventing hypoxaemia during ERCP under MAC.