Advances in Respiratory Medicine

Pulmonary hypertension associated with interstitial lung disease (PH-ILD) is a progressive condition with limited treatment options and associated with high mortality rates. Inhaled treprostinil (iTre) is the only approved therapy for PH-ILD and has been shown to improve exercise capacity and delay disease progression. However, the conventional outpatient titration schedule requires 8–16 weeks to achieve therapeutic dosing, which may delay clinical benefit in those with advanced disease. We conducted a retrospective study of six patients with severe PH-ILD admitted to a tertiary academic center for initiation of iTre using a rapid inpatient uptitration protocol. iTre was started at 3 breaths four times daily (QID) and increased by 2 additional breaths every 12–24 h as tolerated, aiming for ≥9–12 breaths QID within one week under close monitoring. All six patients achieved target dosing without dose reduction or interruption. At three-month follow-up, mean pulmonary artery pressure decreased from 42 ± 5.5 to 35.2 ± 4.5 mmHg, pulmonary vascular resistance from 8.0 ± 1.2 to 6.0 ± 0.9 WU, and cardiac index increased from 2.05 ± 0.13 to 2.15 ± 0.12 L/min/m². No readmissions occurred within 90 days. This study demonstrates that rapid inpatient uptitration of iTre in severe PH-ILD is feasible and well-tolerated, with preliminary evidence of short-term hemodynamic improvement.

Background: Nintedanib and pirfenidone are two anti-fibrotic agents for diseases within the interstitial lung diseases (ILDs) spectrum. Here, we provide a comprehensive analysis regarding treatment persistence and adherence rates for the Greek territory. Methods: This was a retrospective cohort study of patients initiating anti-fibrotic treatment during the period 2019–2023, utilizing data extracted from the National Electronic Prescription Database. Treatment persistence was defined as the duration from the date of the first prescription to the end of follow-up, death, or switching to another agent. Adherence was estimated based on the Medication Possession Ratio (MPR) metric. Results: Overall, 2112 patients were analyzed. The majority were naive, male patients with a diagnosis of idiopathic pulmonary fibrosis (IPF). The overall median treatment persistence was 40.2 months (95% CI: 35.5–44.6). Women and treatment-naive patients demonstrated longer median treatment persistence compared to their counterparts, while older patients demonstrated the lowest median persistence rates. Adherence levels remained high across the follow-up period (90%). Diagnosis of IPF and gastrointestinal comorbidities were associated with a higher risk of discontinuation. Conclusions: We have generated novel data concerning the factors that affect patients’ outcomes under anti-fibrotic therapy. These findings may provide helpful insights for the therapeutic management of ILDs.

Introduction: Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality. Early identification and timely intervention for COPD exacerbations can reduce hospitalizations and complications, as well as improve patient outcomes. Methods: To develop and evaluate predictive models for COPD exacerbations using machine learning (ML), we performed a retrospective study using intensive care unit patient records. Records including 31,667 clinical notes and 10,489 vital signs were used to train and validate two machine learning models to predict COPD exacerbations in patients with known or suspected COPD. Predictive performance was evaluated for support vector machine, quadratic discriminant analysis, and adaptive boosting algorithms using area under the receiver operating characteristic curve (AUC). Results: The clinical note-based support vector machine model achieved an AUC of 0.81 and accuracy of 84.0% in predicting COPD exacerbations. Data from patient monitors and hospital information systems provided sufficient information for accurate prediction, demonstrating the utility of combining physiological signals with clinical text data. Discussion: Clinically available patient data and vital signs can effectively predict COPD exacerbations, potentially enabling earlier interventions, improved outcomes, and reduced healthcare burden. These findings suggest that integrating unstructured clinical notes with structured vital signs using ML frameworks may improve early detection of exacerbation risk, thus enabling appropriate patient counseling, triage, and treatment based on COPD severity.