Background. Sex-based disparities in chronic obstructive pulmonary disease (COPD) diagnosis remain underexplored, particularly in primary care settings. This study assessed sex differences in clinical burden, diagnostic delay, and missed diagnostic opportunities using conventional and composite metrics. Methods. A cross-sectional analysis was conducted in 166 newly diagnosed COPD patients (76 women, 90 men) from Spanish primary care. Clinical severity, healthcare use, and diagnostic timing were compared using Mann–Whitney and chi-squared tests. Composite indices included the Symptom Intensity Score, Diagnostic Inertia Indices, DOSE Index, and Diagnosis Complexity Score. Multivariable regressions evaluated independent associations. Results. At diagnosis, women showed a greater clinical and functional burden (FEV₁ % predicted: 50.4% vs. 61.4%, p < 0.001; symptom intensity z-score: 0.13 vs. −0.67, p < 0.001), higher diagnostic complexity (Diagnosis Complexity Score: 403.5 vs. 272.0, p < 0.001), and longer diagnostic delay (median: 133.0 vs. 66.5 days, p < 0.001). Stratified and composite analyses confirmed consistent sex-based asymmetries. In adjusted models, being female independently predicted a longer diagnostic delay (β = 0.888, p = 0.005), but was not significantly associated with the burden of missed diagnostic opportunities (MDOs) (β = 0.112, p = 0.395). Conclusions. Women with newly diagnosed COPD experience greater symptom burden and longer diagnostic delays. Composite metrics may improve the identification of diagnostic disparities in routine clinical settings.

Background: Delayed diagnosis of Chronic Obstructive Pulmonary Disease (COPD) in primary care is common and contributes to preventable morbidity. A deeper understanding of pre-diagnostic patterns is needed to develop targeted detection strategies. We aimed to characterize diagnostic delay and missed diagnostic opportunities (MDOs) and identify high-risk clinical profiles. Methods: We conducted a retrospective cohort study of 167 patients newly diagnosed with COPD in primary care centers in Madrid, Spain. Healthcare utilization in the 12 months preceding diagnosis was analyzed. Multivariable logistic regression was used to identify predictors of MDOs, and K-means clustering was used to identify patient phenotypes. Results: Diagnostic delay (>30 days) was present in 45.5% of patients, and MDOs in 47.3%. MDO-positive patients had significantly worse lung function (mean FEV₁: 1577 vs. 1898 mL, p = 0.008), greater symptom burden (CAT score ≥ 10: 79.7% vs. 59.1%, p = 0.003), and more frequent pre-diagnostic exacerbations (mean: 1.24 vs. 0.71, p = 0.032). After multivariable adjustment, diagnostic delay remained a powerful independent predictor of MDOs (OR 10.25, 95% CI 4.39–24.88; p < 0.001). Cluster analysis identified three distinct clinical phenotypes: ‘Paucisymptomatic–Preserved’, ‘Frequent Attenders/High-Risk’, and ‘Silent Decliners’. Conclusions: The pre-diagnostic period in COPD is a dynamic window of detectable, and potentially preventable, clinical deterioration driven by diagnostic inertia. The identification of distinct patient phenotypes suggests that a proactive, stratified, and personalized approach, rather than a one-size-fits-all strategy, is required to improve early diagnosis in primary care.

Background: Poor clinical control in Chronic Obstructive Pulmonary Disease (COPD) is prevalent, yet the interplay of disease severity, modifiable factors, and clinician perception remains poorly understood. This study aimed to determine the frequency of poor control, identify its independent determinants, and characterize the heterogeneity of the poorly controlled population receiving maintenance inhaled therapy with various devices in primary care. Methods: In a multicenter, cross-sectional analysis of 988 patients from the Study SIMPLIFY, clinical control of COPD was classified using the objective RADAR score. We used multivariable logistic regression and Machine Learning (Random Forest with SHAP analysis) to identify determinants of poor control (RADAR ≥ 4) and k-medoids cluster analysis to characterize the poorly controlled subgroup (n = 452). Results: Nearly half the cohort (45.7%, n = 452) had poor clinical control. Agreement between physician-assessed control (five categories) and RADAR classification was 49.3%, with overestimation in 34.0% and underestimation in 16.7% of cases (Cohen’s κ = −0.081; weighted κ = −0.037). The strongest independent determinants were the exacerbator phenotypes (eosinophilic aOR 6.85; non-eosinophilic aOR 4.91). Key modifiable factors included active smoking (aOR 1.92), lower TAI-12 adherence score (per point; aOR 0.96), high dosing frequency (≥4 inhalations/day; aOR 1.54) and high inhaler burden (≥3 devices; aOR 1.84). Machine learning analysis identified clinical phenotype and adherence behavior as the top two scale-independent predictors of poor control. Cluster analysis of the poorly controlled group revealed five reproducible and clinically meaningful phenotypes (C0–C4), primarily separated by treatment complexity, comorbidities, and adherence. Conclusions: Poor clinical control is common and critically under-recognized in primary care patients with COPD on maintenance inhaled therapy. This is driven by a profound clinician perception gap and a failure to address key modifiable determinants, such as high dosing frequency, regimen complexity, and poor adherence, which likely drives therapeutic inertia. Our findings underscore the need to integrate objective tools to unmask poor control and highlight the importance of treatment simplification. The identification of distinct clinical phenotypes provides a roadmap toward a more personalized, evidence-based standard of care.

Smoking is a key determinant of chronic obstructive pulmonary disease (COPD) development in patients with the SZ genotype. Few studies have evaluated the impact of other factors associated with emphysema progression. Objectives: To evaluate the progression of lung function and densitometric parameters in PiSZ alpha-1 antitrypsin deficiency (AATD) patients, and to assess the impact of smoking, exacerbation frequency, severity and time since diagnosis. The study also explores correlations between functional and densitometric measures, as well as regional emphysema patterns. Methods: This two-year observational study included 31 PiSZ AATD patients stratified by time since diagnosis (<5 vs. ≥5 years), smoking status (current, former, and never smokers), and exacerbation frequency (<2 vs. ≥2 exacerbations/year). Functional [forced expiratory volume in 1 s (FEV1), carbon monoxide diffusion (DLCO), and carbon monoxide transfer coefficient (KCO)] and densitometric [15th percentile lung density (PD-15) and lung volume with density less than -950 Hounsfield Units (HU-950)] parameters were assessed at baseline and follow-up. Mixed-effects models evaluated disease progression, while correlation and regional analyses highlighted structural–functional relationships and spatial emphysema patterns. Results: Patients diagnosed <5 years previously exhibited faster PD-15 decline (−6.0 ± 1.4 HU/year) than those diagnosed ≥5 years previously (−5.1 ± 1.3 HU/year; p < 0.05). Current smokers showed the most pronounced deterioration in PD-15 (−7.1 ± 1.6 HU/year) and HU-950 (+0.8 ± 0.3% volume/year) versus never smokers (−4.6 ± 1.3 HU/year and +0.4 ± 0.2% volume/year; p < 0.05). Frequent and severe exacerbations, along with pulmonary-related hospitalizations, worsened structural decline, particularly in basal regions. Strong correlations between both PD-15 and HU-950 with FEV1, DLCO, and KCO were observed in advanced stages (≥5 years since diagnosis). Conclusions: This study underscores the pivotal role of densitometry in PiSZ AATD, highlighting its ability to detect early structural changes often missed by functional measures. These findings support integrating densitometry into clinical practice to guide personalized interventions and improve outcomes.

Background: Alpha-1 antitrypsin deficiency (AATD) is a genetic disorder associated with an increased risk of developing chronic obstructive pulmonary disease (COPD) with variable phenotypic expression among different genotypes. While the PiZZ genotype is well characterized, the clinical and structural progression of PiSZ individuals remains less defined. This study evaluates genotype-specific disease trajectories and the impact of augmentation therapy over a two-year follow-up. Methods: A prospective observational cohort study was conducted, including 74 AATD patients (41 PiSZ, 33 PiZZ), stratified by augmentation therapy status. Disease progression was assessed through lung function decline (forced expiratory volume in one second [FEV1], diffusing capacity for carbon monoxide [DLCO], carbon monoxide transfer coefficient [KCO]) and densitometric changes (15th percentile lung density [PD-15], percentage of lung voxels below −950 Hounsfield units [HU-950]). Mixed-effects models and multivariable regression analyses were performed to evaluate genotype-specific progression patterns and treatment effects. Results: Results: PiZZ individuals exhibited significantly greater annual decline in lung function and densitometric parameters compared to PiSZ individuals, with more pronounced loss in basal lung regions and with greater decline in advanced stages, in contrast to the PiSZ genotype, which showed greater progression in earlier stages. Augmentation therapy was associated with a significant reduction in PD-15 decline in both genotypes, with the greatest benefit observed in PiZZ patients and in those diagnosed within five years of disease onset. Smoking and frequent exacerbations were identified as independent risk factors for accelerated disease progression. Conclusions: PiZZ individuals experience a more aggressive disease trajectory than PiSZ individuals in the absence of treatment. Augmentation therapy effectively mitigates disease progression in both genotypes, with greater efficacy when initiated early. Smoking and frequent exacerbations were identified as independent risk factors for accelerated disease progression. These findings underscore the importance of genotype-specific monitoring and personalized therapeutic strategies in AATD to optimize clinical outcomes.

Background: Hospitalized patients on chronic inhaled therapy suffer high rates of therapeutic failure. Current approaches often overlook patient heterogeneity, treating failure as a uniform problem. We hypothesized that clinical inertia, a key driver of failure, is not a monolithic entity but is governed by specific, non-overlapping factors. Methods: In this unicentric, observational cohort study of 499 hospitalized adults on chronic inhaled therapy, we used unsupervised clustering to identify patient phenotypes. Multivariable logistic regression was used to model predictors of critical inhaler errors and three distinct forms of clinical inertia: Therapeutic Class (TCI), Device-Level (DLI), and Adherence-Related (ARI). Results: Inhaler misuse was driven by objective capability—deficient knowledge (aOR 6.03, 95% CI 2.88–12.64) and low inspiratory flow (aOR 3.11, 95% CI 1.06–9.12)—while patient-reported adherence was not a significant independent predictor. Crucially, the three forms of clinical inertia were governed by distinct, non-overlapping predictors: TCI was predicted by high therapeutic potency (aOR 7.80, 95% CI 3.65–16.64), DLI by a failure in the clinical process (lack of patient training, aOR 3.49, 95% CI 1.21–10.03), and ARI by regimen complexity (aOR 0.06, 95% CI 0.02–0.25). Post-discharge mortality (21.6% overall; 25.8% in Cluster 1 vs. 18.3% in Cluster 2) was independently predicted by objective risk factors, including older age (aOR 1.51, 95% CI 1.20–1.89) and an unassessed inspiratory flow (aOR 2.44, 95% CI 1.19–5.03). Two underlying patient phenotypes were identified—an “Unassessed/Older” (n = 225) and an “Assessed/Younger” (n = 274)—which represented distinct in-hospital care pathways but did not independently predict mortality after multivariate adjustment. Conclusions: Therapeutic failure in hospitalized patients is a predictable outcome driven by distinct, non-overlapping factors. This study deconstructs this failure by identifying the specific, actionable drivers of inhaler misuse (patient capability) and the three forms of clinical inertia (therapeutic potency, failures in the care process, and regimen complexity). These processes occur within two distinct patient phenotypes that represent different in-hospital care pathways. Our findings provide a new framework to move beyond generic interventions toward a more precise, evidence-based approach to inhaled therapy.