Fernando María Navarro Ros

Background/Objectives: Comparative effects of oral second-generation H1-antihistamines vary by rhinitis category, outcome, and treatment period. We assessed evidence structure, estimability, and differentiation among regimens. Methods: PubMed, Embase, Web of Science Core Collection, CENTRAL, WHO ICTRP, ClinicalTrials.gov, and the EU Clinical Trials Register were searched on 1–3 July 2026 for records through 30 June 2026. Eligible studies were natural-exposure randomized trials. Continuous outcomes were analyzed using covariance-aware generalized least squares, and binary harms were analyzed using Bayesian arm-level models. RoB 2, CINeMA, and ROB-MEN were applied in duplicate. Registration was retrospective. Results: Sixty-one reports generated 66 report–randomization entries from 64 cohorts (34,831 participants); 35 model-ready randomizations informed nine structures. In seasonal allergic rhinitis, six of seven TNSS estimates, two of three ocular estimates, and four RQLQ estimates favored active treatment. Any-adverse-event estimates were imprecise. Under the primary prior, cetirizine yielded a WDAE OR of 0.25 (95% CrI 0.08–0.66), but predictive classification was prior-sensitive. Of 34 continuous active–active comparisons, seven were directly informed and 27 indirect-only. All 147 estimable binary active–active contrasts were reported as secondary derivations: 141 CrIs included one and six excluded one, all indirect-only and not formally appraised with CINeMA. Six of 14 regimen-level nodes contributed only to tolerability structures, precluding balanced regimen-specific benefit–harm assessment. All 89 estimable comparisons within the defined CINeMA appraisal scope were rated very low; two non-estimable effects were not evaluable. Conclusions: Predominantly placebo-anchored and sparse evidence established neither a reliable regimen hierarchy nor therapeutic equivalence. Findings were hypothesis-generating, and somnolence remained an evidence gap. No external funding supported conduct; NEXUS PEOPLE, S.L. funded the APC. OSF registration was retrospective: doi:10.17605/OSF.IO/DPT5N.

Background/Objectives: COPD instability is heterogeneous; GOLD 2026 lowers the prior-year threshold to ≥1 moderate or severe exacerbation. We assessed whether this low-threshold criterion behaves as a high-sensitivity operational signal in primary-care EHRs. Methods: A retrospective multicenter same-window EHR pilot study in two Spanish primary-care centers (n = 106). Predictors and six binary endpoints were aggregated over the same 12-month window: any exacerbation, high-risk history, severe hospitalization, SABA dispensing, SAMA dispensing, and any rescue dispensing. We fitted Bayesian multi-outcome hierarchical logistic models with patient-level random intercepts, cross-endpoint partial pooling, regularizing priors, and missingness indicators. Robustness used prespecified scenarios, 10-fold ELPD cross-validation, and high-missingness exclusion. Results: Any exacerbation occurred in 53/106 patients; high-risk history in 25/106; hospitalization in 16/106; and any rescue dispensing in 65/106. Diagnostics were stable, and posterior predictive checks supported marginal adequacy. Heart failure showed the clearest positive pattern across exacerbation-defined endpoints; reliever-dispensing endpoints showed a distinct care-pathway-sensitive pattern. No scenario improved out-of-sample adequacy. High-missingness exclusion preserved directionality in 120/120 overlapping pairs; the median |ΔlogOR| was 0.061; and 119/120 remained within ±log(1.25). Conclusions: GOLD 2026 “any exacerbation” behaved as a high-sensitivity operational signal in an endpoint-operating-point sense, not as a homogeneous phenotype. Findings are within-window associations, not causal, medication-effect, or prospective prediction estimates; external validation is required.

Background: Chronic obstructive pulmonary disease (COPD) remains a leading global health burden. In primary care, the inconsistent availability of spirometry and symptom scores limits the detection of patients with poor disease control. There is a pressing need for scalable, data-driven tools that leverage routinely collected clinical information to support timely, equitable, and guideline-concordant interventions. Objective: This study aims to validate the performance of Seleida—a fully automated, deterministic, and bijective model for COPD control assessment and phenotyping—using real-world primary care data and to evaluate its feasibility for integration into electronic health record (EHR)–based informatics systems. Methods: Seleida estimates the probability of poor control ( Pr ) using two objective EHR variables: (1) annual dispensations of short-acting bronchodilators—specifically short-acting β2-agonists (SABA), short-acting muscarinic antagonists (SAMA), or both, and (2) number of dispensed antibiotic courses for bronchitis or COPD exacerbations. Its bijective structure supports both forward risk estimation and reverse phenotype inference. In a retrospective cohort of 106 patients, agreement was assessed between 2 phenotyping systems (a 126-combination model and a streamlined 21-combination version) and with clinician-assigned classifications. Due to sample size limitations, a provisional risk threshold of Pr >.50 was adopted for internal stratification. Results: Seleida showed perfect agreement between phenotyping systems (Cohen κ=1.00; P <.001) and substantial concordance with clinician-assigned profiles (Cohen κ=0.70; P <.001). The model operates transparently, without machine learning, and can be embedded into EHR platforms or applied manually using a visual framework. It enables individualized risk estimation, phenotype-driven treatment planning, and population-level case identification—particularly in settings with limited access to traditional diagnostic tools. Conclusions: Seleida provides a reproducible and interpretable framework for COPD control monitoring using high-frequency prescribing data. Its transparent logic, low data burden, and interoperability enable integration across diverse digital infrastructures, including resource-limited settings. By supporting both individualized care and population-level risk stratification, Seleida bridges predictive analytics with real-world clinical decision-making. Ongoing multicenter validation will determine its generalizability, clinical impact, and cost-effectiveness at scale.

Background/Objectives: The overuse of short-acting β₂-agonists (SABAs) has been associated with increased asthma morbidity and mortality, prompting changes in treatment guidelines. However, the role of frequent short-acting muscarinic antagonists (SAMAs) use remains poorly defined and unaddressed in current recommendations. This study offers the first real-world analysis of SAMA overuse in asthma, quantifying its association with exacerbation risk and healthcare utilization and comparing its predictive value to that of SABAs. Methods: A retrospective multicenter cohort study analyzed electronic health records (EHRs) from 132 adults with asthma in the Spanish National Health System (SNS). Associations between annual SAMA use and clinical outcomes were assessed using negative binomial regression and 5000-sample bootstrap simulations. Interaction and threshold models were applied to explore how SAMA use affected outcomes and identify clinically actionable cutoffs. Results: SAMA use was independently associated with a 19.2% increase in exacerbation frequency per canister and a nearly sixfold increase in the odds of experiencing ≥1 exacerbation (OR = 5.97; 95% CI: 2.43–14.66). An inflection point at 2.5 canisters/year marked the threshold beyond which annual exacerbations exceeded one. Increased SAMA use was also associated with a higher number of respiratory consultations and with more frequent prescriptions of systemic corticosteroids and antibiotics. The risk increased more sharply with SAMAs than with SABAs, and the lack of correlation between them suggests distinct clinical patterns underlying their use. Conclusions: SAMA use emerges as a digitally traceable and clinically meaningful indicator of asthma instability. While the associations observed are robust and consistent across multiple outcomes, they should be considered provisional due to the study’s retrospective design and limited sample size. Replication in larger and more diverse cohorts is needed to confirm external validity. These findings support the integration of SAMA tracking into asthma management tools—alongside SABAs—to enable the earlier identification of uncontrolled disease and guide therapeutic adjustment.

Background and Objectives: Chronic obstructive pulmonary disease (COPD) remains a critical global health challenge, characterized by high morbidity, mortality, and healthcare costs. Current guidelines may overlook patients who present with only one moderate exacerbation or with frequent short-acting beta-agonist (SABA) use. Building on findings from the Seleida study, this research refines the criteria for poor COPD control to include these patients, aiming to improve early identification of high-risk cases in primary care. Methods: A retrospectiveand multicenter study is conducted using data from 110 COPD patients in Spain. Poor control is redefined as having at least one moderate exacerbation or as using three or more SABA inhalers annually. Key predictors, such as SABA/short-acting muscarinic antagonist (SAMA) inhalers and antibiotic prescriptions, are identified using logistic regression and LASSO regularization to enhance predictive accuracy. Results: The model achieves a good predictive performance, with an AUC-ROC of 0.978, sensitivity of 92.86%, and specificity of 87.50%. Key predictors reliably identify high-risk patients, enabling timely interventions. This study demonstrates a statistically significant association between once-daily inhaler therapies and better COPD control compared to multiple daily doses, supported by chi-square analysis (p = 0.008) and binary logistic regression (p = 0.018). Nevertheless, the variable ‘daily inhalation frequency’ (1 vs. >1 inhalation/day) was excluded from the final model to prevent overfitting. Conclusions: By refining the criteria for COPD control to include patients with at least one moderate exacerbation or frequent SABA use, this model provides a practical tool for early risk stratification in primary care, particularly in resource-limited settings. Early identification of high-risk patients can reduce hospitalizations and healthcare costs, supporting a proactive approach to COPD management. Further validation in larger cohorts is essential to confirm its broader applicability.

Background and Objective: Chronic Obstructive Pulmonary Disease (COPD) remains a critical global health challenge, char-acterized by high morbidity, mortality, and healthcare costs. Current guidelines may overlook pa-tients who present with only one moderate exacerbation or frequent short-acting beta-agonist (SABA) use. Building on findings from the Seleida study, this research refines the criteria for poor COPD control to include these patients, aiming to improve early identification of high-risk cases in primary care. Methods: A retrospective, multicenter study was conducted using data from 110 COPD patients in Spain. Poor control was redefined as having at least one moderate exacerbation or using three or more SABA inhalers annually. Key predictors, such as SABA/short-acting muscarinic antagonist (SAMA) inhalers and antibiotic prescriptions, were identified using logistic regression and LASSO regular-ization to enhance predictive accuracy. Results: The model demonstrated excellent predictive performance with an AUC-ROC of 0.978, sensitivity of 92.86%, and specificity of 87.50%. Key predictors effectively identified high-risk patients, facili-tating timely interventions. Although the variable ‘daily inhalation frequency’ (categorized as 1 vs. >1 inhalation/day) showed that patients using a single inhalation daily had significantly better control than those requiring multiple doses (p = 0.018), it was excluded to avoid model overfitting. Conclusions: By refining the criteria for COPD control to include patients with at least one moderate exacerbation or frequent SABA use, this model provides a practical tool for early risk stratification in primary care, particularly in resource-limited settings. Early identification of high-risk patients can reduce hospitalizations and healthcare costs, supporting a proactive approach to COPD management. Further validation in larger cohorts is essential to confirm its broader applicability.

Background/Objectives: Managing chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) within the Spanish Sistema Nacional de Salud (SNS) presents significant challenges, particularly due to their high prevalence and poor disease control rates—approximately 45.1% for asthma and 63.2% for COPD. This study aims to develop a novel predictive model using electronic health records (EHRs) to estimate the likelihood of poor disease control in these patients, thereby enabling more efficient management in primary care settings. Methods: The Seleida project employed a bioinformatics approach to identify significant clinical variables from EHR data in primary care centers in Seville and Valencia. Statistically significant variables were incorporated into a logistic regression model to predict poor disease control in patients with asthma and COPD patients. Key variables included the number of short-acting β-agonist (SABA) and short-acting muscarinic antagonist (SAMA) canisters, prednisone courses, and antibiotic courses over the past year. Results: The developed model demonstrated high accuracy, sensitivity, and specificity in predicting poorly controlled disease in both asthma and COPD patients. These findings suggest that the model could serve as a valuable tool for the early identification of at-risk patients, allowing healthcare providers to prioritize and optimize resource allocation in primary care settings. Conclusions: Integrating this predictive model into primary care practice could enhance the proactive management of asthma and COPD, potentially improving patient outcomes and reducing the burden on healthcare systems. Further validation in diverse clinical settings is warranted to confirm the model’s efficacy and generalizability.

Background/Objectives: Managing chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) within the Spanish Sistema Nacional de Salud (SNS) presents significant challenges, particularly due to their high prevalence and poor disease control rates—approximately 45.1% for asthma and 63.2% for COPD. This study aims to develop a novel predictive model using Electronic Health Records (EHR) to estimate the likelihood of poor disease control in these patients, thereby enabling more efficient management in primary care settings. Methods: The Seleida project employed a bioinformatics approach to identify significant clinical variables from EHR data in primary care centers in Seville and Valencia. Statistically significant variables were incorporated into a logistic regression model to predict poor disease control in patients with asthma and COPD patients. Key variables included the number of short-acting β-agonist (SABA) and short-acting muscarinic antagonist (SAMA) canisters, prednisone courses, and antibiotic courses over the past year. Results: The developed model demonstrated high accuracy, sensitivity, and specificity in predicting poorly controlled disease in both asthma and COPD patients. These findings suggest that the model could serve as a valuable tool for the early identification of at-risk patients, allowing healthcare providers to prioritize and optimize resource allocation in primary care settings. Conclusions: Integrating this predictive model into primary care practice could enhance the proactive management of asthma and COPD, potentially improving patient outcomes and reducing the burden on healthcare systems. Further validation in diverse clinical settings is warranted to confirm the model's efficacy and generalizability.