Open Respiratory Archives最新文献

Introduction

Patients with severe chronic obstructive pulmonary disease (COPD) are often underrepresented in cohorts, creating uncertainty about the natural history and prognostic factors of this subgroup. Our goal was to describe the SPOCCAT (Severe COPD: Prospective Observational study of COPD in Catalonia) study protocol.

Material and methods

SPOCCAT is a non-interventional, multicenter, prospective cohort study of patients with severe COPD (FEV₁% predicted < 50%). The study aims to: (1) establish a five-year prospective cohort; (2) identify demographic and clinical characteristics; (3) describe treatment patterns; (4) better understand the natural history of severe COPD, including lung function decline, exacerbation rates, and mortality; and (5) identify prognostic factors for poor outcomes.

Recruitment began in January 2024, and the cohort will be followed for a minimum of five years (or until death or lung transplant) with follow-up visits every 12 months. Baseline data include demographics, laboratory analyses, comorbidities, lung function, respiratory symptoms, respiratory disease exacerbations and etiology, quality of life, physical activity, chest computed tomography, and treatment. Annual follow-up visits will assess changes in treatment, exacerbation frequency and severity, microbiological outcomes, complementary tests, and mortality. Participation requires written informed consent from all patients, with data collected in an anonymized electronic Case Report Form.

Results

The results of the SPOCCAT study will provide relevant information about the characteristics, treatment, and prognostic factors of severe COPD.

Conclusions

SPOCCAT has the potential to enhance understanding of severe COPD, exploring innovative aspects and establishing a robust research framework for future COPD-related projects.

Introduction

This study aims to create an artificial intelligence (AI) based machine learning (ML) model capable of predicting a spirometric obstructive pattern using variables with the highest predictive power derived from an active case-finding program for COPD in primary care.

Material and methods

A total of 1190 smokers, aged 30–80 years old with no prior history of respiratory disease, underwent spirometry with bronchodilation. The sample was analyzed using AI tools. Based on an exploratory data analysis (EDA), independent variables (according to mutual information analysis) were trained using a gradient boosting algorithm (GBT) and validated through cross-validation.

Results

With an area under the curve close to unity, the model predicted a spirometric obstructive pattern using variables with the highest predictive power: FEV1_theoretical_pre values. Sensitivity: 93%. Positive predictive value: 94%. Specificity: 97%. Negative predictive value: 96%. Accuracy: 95%. Precision: 94%.

Conclusion

An ML model can predict the presence of an obstructive pattern in spirometry in a primary care smoking population with no prior diagnosis of respiratory disease using the FEV1_theoretical_pre values with an accuracy and precision exceeding 90%. Further studies including clinical data and strategies for integrating AI into clinical workflow are needed.

Introduction

The patient experience is defined as all the interactions that occur between patients and the healthcare system. The experience of patients with respiratory disease with home respiratory treatments (HRT) is not captured in currently available Patient-Reported Outcome Measures (PROM). We present the psychometric validation of the Patient-Reported Experience Measure (PREM) ‘HowRwe’ in Spanish and for respiratory patients with HRT.

Methods

After translation following ISPOR guidelines (International Society for Pharmacoeconomics and Outcomes Research), the questionnaire was administered to adult respiratory patients who were receiving treatment at Hospital Universitario de La Princesa. The administration was done in two stages with 6 months of difference between the pre- and post-test.

Results

We studied 228 respiratory patients, with a mean (SD) age of 64.1 (13.2) years, 52.2% were men, 68.0% were married or coupled, and 56.6% were retired. Reliability coefficients of the scale were adequate, with α = .921 and Ω = .929 for pre-test, and α = .940 and Ω = .958 for post. The confirmatory factor analysis tested for pre- and post-intervention, showed an excellent overall fit: χ²(2) = 49.380 (p < .001), CFI = .941 and SRMR = .072; and χ²(2) = 37.579 (p < .001), CFI = .982 and SRMR = .046, respectively. No statistically significant associations were observed for neither age, adherence nor quality of life, except between HowRwe post-test and quality of life pre-test (r = .14 [.01,.26]; p = .035). No significant differences were found in sociodemographic variables. No differences in pre-test or post-test were found in effect of HRT. 85.6% of patients found the content of HowRwe “Useful”, and the preferred channel to respond it were paper, app and email.

Conclusions

The Spanish version of the ‘HowRwe’ questionnaire to measure the experience in respiratory patients with home respiratory treatments (HRT), has adequate psychometric properties and conceptual and semantic equivalence with the original English version.