CHEST

We present our Stage-1 Registered Report as a full clinical trial article with all methods in past tense and including mock results, table and figures for the primary analysis. To remind the reader that this Stage-1 article is written before data collection, we highlight in color that these mock results are only for illustrative purposes and will be replaced by the actual results in the Stage-2 Registered Report. Background In patients experiencing out-of-hospital cardiac arrest, optimization of oxygen delivery during cardiopulmonary resuscitation is a critical. Although both positive end-expiratory pressure (PEEP) and zero end-expiratory pressure (ZEEP) are employed during CPR, their respective impacts on clinically relevant outcomes is yet to be clearly established. Methods This investigator-initiated, pragmatic, registry-based, multicenter, triple-blind randomized controlled superiority trial evaluates whether applying 8 cm H2O PEEP during cardiopulmonary resuscitation improves outcomes compared with ZEEP in adults with non-traumatic, non-drowning out-of-hospital cardiac arrest. Pre-randomized CPR kits (1:1 PEEP vs. sham) were used by ambulance sites during manual ventilation throughout the resuscitation process. The primary analysis was conducted in the principal stratum of patients who received either a supraglottic airway or endotracheal tube. The primary outcome was neurological status at hospital discharge measured by a utility-weighted score on the modified Rankin Scale. Secondary outcomes included prehospital return of spontaneous circulation, 30-day survival, and 6-month quality of life. The primary safety outcome was clinically significant pneumothorax.

Background: During emergency tracheal intubation, clinicians use waveform capnography or colorimetric carbon dioxide (CO2) detection to confirm exhaled CO2 from the endotracheal tube to determine its location. Both devices reliably yield a negative result when the tube is in the esophagus. However, in most emergency intubations the tube is correctly located in the trachea, and in this circumstance the relative diagnostic accuracy of the two devices is uncertain. If one device more often fails to detect exhaled CO2 despite correct tube placement (a false negative result), this could lead to unnecessary tube removal, repeated intubation attempts, and an increased risk of serious complications. Research Question: Among critically ill adults undergoing emergency tracheal intubation, is waveform capnography or colorimetric CO2 detection more sensitive for identifying an endotracheal tube correctly placed in the trachea? Study Design and Methods: The WAVE study is a pragmatic, prospective, paired diagnostic accuracy study conducted in emergency departments and intensive care units in the United States. The study compares waveform capnography and colorimetric CO2 detection (index tests) against a non-CO2-based reference standard for endotracheal tube location among 2,092 critically ill adults undergoing tracheal intubation. The primary outcome is the sensitivity of each index test for identifying a tube located in the trachea. The secondary outcome is specificity for identifying a tube located outside the trachea. Results: Enrollment began on April 21, 2025 and is ongoing, with completion anticipated in 2026. Interpretation: The WAVE study will provide prospective evidence comparing waveform capnography with colorimetric CO2 detection for determination of endotracheal tube location in critically ill adults. These findings may inform clinical practice and guideline recommendations regarding the optimal test of exhaled CO2 for use in the ED and ICU. Prespecifying the protocol and statistical analysis plan before completion of enrollment enhances the rigor, reproducibility, and transparency of the study.

Study Question. COPD exacerbations with respiratory failure have an outsized impact on morbidity, mortality, and healthcare costs. Physicians treat these severe events with high doses of corticosteroids out of concern for intrinsic corticosteroid resistance. This study was designed to determine whether people who suffer from exacerbations with respiratory failure have intrinsic steroid resistance that could affect that decision. Patients and Methods. A matched, case-control study was performed at University of Colorado. Subjects were enrolled into the Exacerbation group if they were hospitalized for an exacerbation requiring ventilation, or into the Stable COPD group if they had no inpatient exacerbation within 12 months. Stable COPD subjects were frequency-matched to Exacerbation subjects for age, smoking status and FEV1%. Corticosteroid sensitivity was assessed ex vivo and in vivo >45 days following the last exacerbation during a period of relative quiescence. The primary outcome was the group difference in methylprednisolone IC50 for LPS-stimulated IL-8 release by mononuclear cells. Measurements and Main Results. The Exacerbation group (N=8) had more exacerbations in the prior year (6.0 vs. 0.5; p<0.01) and had higher baseline blood lymphocytes (p<0.01) versus Stable controls (N=8). Ex vivo, methylprednisolone equally suppressed IL-8, IL-6, TNFalpha and IL-1bets release by mononuclear cells and whole blood across groups. In vivo, methylprednisolone decreased plasma mediators and mononuclear cells in both groups but had a small differential effect on mononuclear cell gene expression. Answer to the Question. Exacerbations with respiratory failure have persistent lymphocytosis, an altered gene response to corticosteroids, but no corticosteroid resistance to suppress inflammation.

Objective: To combine performance-weighted ensemble SHAP aggregation with resampling-based assessment of feature-importance variability and patient-level attribution alignment for interpreting COVID-19 mortality predictions. Methods: We analyzed a prospective cohort of 1,857 patients hospitalized with COVID-19 at two hospitals in Peru. Ten predictive algorithms were evaluated using five-fold cross-validation, and their mean AUROC values determined their attribution-aggregation weights. Within each model and training trial, signed SHAP values were normalized by the mean total absolute attribution across evaluation patients before performance-weighted aggregation. Feature importance was summarized across 30 resampled training trials using means and normal-approximation 95% confidence intervals. Patient-level attribution alignment was assessed using cosine similarity between signed feature-attribution vectors for corresponding patients, with whole-trial patient-correspondence randomization and Benjamini--Hochberg correction. Subgroup observations were reweighted toward the evaluated population's feature distributions for secondary comparisons of existing absolute SHAP values. Results: Among the 1,857 included patients, 982 (52.9%) died during hospitalization. Random forest achieved the highest mean AUROC (0.925 +/- 0.010), followed by AdaBoost (0.919 +/- 0.017) and logistic regression (0.918 +/- 0.007), with variability reported as the SEM. Attribution profiles were more similar across repeated training trials of the same algorithm (mean Pearson r=0.783; SEM, 0.004) than across algorithms within the same trial (mean Pearson r=0.369; SEM, 0.006). The largest normalized attribution magnitudes were observed for dexamethasone use at home without oxygen support (6.186%; 95% CI, 5.795--6.577), PaO2/FiO2 ratio (3.014%; 95% CI, 2.787--3.242), shortness of breath (2.774%; 95% CI, 2.592--2.956), and FiO2 (2.560%; 95% CI, 2.284--2.836). Features differed in patient-level attribution alignment, indicating that importance magnitude and robustness provided complementary information. Among 1,763 eligible subgroup--feature comparisons, 64 had nominal one-sided p < 0.05, although none remained below 0.05 after Benjamini--Hochberg adjustment. Conclusions: Algorithms with similar predictive performance produced substantially different feature-attribution profiles. Normalized performance-weighted SHAP aggregation provided a representative relative-importance summary across algorithms, while resampling-based alignment assessment qualified the consistency of individual feature contributions. The resulting attribution patterns describe fitted-model behavior and should not be interpreted as causal clinical effects.

Background: Sex differences in asthma are well recognized, but sex-associated airway DNA methylation (DNAm) patterns and their relationships with gene expression remain incompletely characterized. We therefore conducted cis- and trans-expression quantitative trait methylation (eQTM) analyses using sex-stratified and CpG-by-sex interaction models. Methods: We analyzed nasal airway epithelial DNAm and transcriptomic data from 298 participants in the CAAPA discovery cohort. Asthma-associated differentially methylated CpGs (DMCs) were identified using sex-combined and sex-stratified models. Cis- and trans-eQTM analyses were then performed in participants with asthma using sex-combined, sex-stratified, and CpG-by-sex interaction models. To prioritize asthma-associated regulatory relationships, eQTMs that were also significant in non-asthmatic controls were excluded from downstream analyses. Replication in an independent bronchial epithelial cohort was defined by concordant effect direction and P < 0.05. Replicated targets underwent pathway and network analyses. Findings: Asthma-associated DMCs were predominantly hypomethylated in females and hypermethylated in males. Among trans-eQTMs, 379 associations were shared across all four models, whereas the majority were model-specific, indicating extensive sex-dependent regulatory architecture. We replicated 3, 1, 9, and 2 cis-eQTMs and 96, 84, 216, and 41 trans-eQTMs in the sex-combined, female-stratified, male-stratified, and interaction models, respectively. Sex-combined targets implicated innate immune and inflammatory signaling (TNF, IRF8); female-stratified targets implicated cytoskeletal remodeling, focal adhesion, and PI3K-Akt signaling (ITGA10); male-stratified targets implicated transcriptional regulation and post-translational modification (MOV10, HDAC7, CSF1, KAT2A); and interaction targets implicated lipid-mediator metabolism and inflammatory signaling and resolution (CYP2C9, LTB4R, CCL19). Our sex-dependent analyses showed that the majority of eQTMs resided within the trans-regulatory architecture, suggesting that sex-dependent gene expression differences are associated predominantly with trans rather than cis regulatory mechanisms. Pathway analyses revealed enrichment of innate immunity in the sex-combined model, structural remodeling in females, transcriptional control in males, and inflammatory resolution in the sex-interaction model. Interpretation: Sex-specific and sex-dependent airway methylation-expression associations were largely undetected by sex-combined analysis. Sex-aware analytical models may help resolve epigenetic mechanisms underlying sex differences in asthma biology.

Background: Chronic obstructive pulmonary disease (COPD) is characterized by airway remodeling, which may extend to the central airways and alter tracheal morphology. Whether tracheal shape relates to disease severity, quality of life, functional capacity, or survival is not established. Objective: To determine whether CT-derived tracheal lumen eccentricity is associated with cross-sectional and longitudinal clinical outcomes in COPD. Methods: We analyzed 8,383 participants from the multicenter COPDGene study. At each axial cross-section of the trachea, lumen eccentricity ({epsilon}) was computed from the major and minor axis lengths, and the participant-level value was taken as the arithmetic mean across all tracheal slices; {epsilon} = 0 denotes a circular lumen and values approaching 1 denote progressively elongated ellipses. Baseline outcomes were spirometry, 6-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ) total score, and modified Medical Research Council (mMRC) dyspnea score. Longitudinal outcomes were annualized change in FEV1 and all-cause mortality. Multivariable linear and Cox proportional hazards models were adjusted for [age, sex, race, BMI, smoking status, pack-years, scanner model]. Results: Tracheal eccentricity rose monotonically with severity of airflow obstruction (0.60{+/-}0.07, 0.61{+/-}0.07, 0.62{+/-}0.07, 0.64{+/-}0.08, and 0.64{+/-}0.08 in GOLD stages 0 through 4, respectively). Higher eccentricity was independently associated with lower FEV1 ({beta} = -0.30 L, 95% CI -0.47 to -0.14; p < 0.001), lower FEV1/FVC ({beta} = -0.06, 95% CI -0.09 to -0.04; p < 0.001), shorter 6MWD ({beta} = -15.7 m, 95% CI -25.2 to -6.2; p = 0.001), higher SGRQ score ({beta} = 16.9, 95% CI 11.2 to 22.5; p < 0.001), higher mMRC dyspnea score ({beta} = 0.4, 95% CI 0.1 to 0.6; p = 0.007), and accelerated lung function decline ({beta} = -23.8 mL/year, 95% CI -45.13 to -2.48; p = 0.029). Eccentricity was also associated with all-cause mortality (adjusted HR 1.81, 95% CI 1.01 to 3.26; p = 0.047). Conclusions: Tracheal lumen eccentricity is associated with worse quality of life, reduced functional capacity, accelerated lung function decline, and mortality in COPD, supporting central airway morphology as a candidate imaging biomarker.

Introduction Maintaining long-term positive airway pressure (CPAP) adherence is a major challenge when treating obstructive sleep apnea (OSA). We evaluated factors associated with one-year CPAP adherence and developed models predicting Month-12 adherence and CPAP-use trajectories. Methods CPAP-naive adults with OSA (apnea-hypopnea index[&ge;]5) at Kaiser Permanente Southern California with 364-day observation period were analyzed (development/internal-validation cohort, n=14,906; temporal holdout, n=1,676). Logistic regression evaluated baseline associations with Month-12 adherence (mean[&ge;]4 hours/night during days 331-360). XGBoost models utilized baseline features (clinical, sleep-study, questionnaire data), early CPAP data (days 1-7), or combined to predict long-term CPAP use. A latent-class model identified trajectories over days 8-364. Predictive models were evaluated in both random and temporal holdouts. Results About one-third (34.6%) of the primary cohort (49.7{+/-}13.8 years, 63.2% male) were adherent during Month-12. Higher AHI and longer self-reported sleep duration favored adherence while greater comorbidity burden, depression, and substance-use disorder were associated with lower adherence. In held-out testing, adherence prediction using baseline features yielded an AUROC of 0.678. Using CPAP-only data, AUROCs were higher from 0.787 with seven days of usage data to 0.816 and 0.854 with 14 and 30 days. Models combining CPAP and baseline features increased Day-7 AUROC to 0.810. We identified four one-year trajectories: Early-Decliners (42.9%), Gradual-Decliners (17.4%), Stable-Moderate (19.0%), Stable-High (20.7%). Models predicting patient trajectory revealed macro-average AUROCs of 0.619, 0.756, and 0.770 for baseline, CPAP-only, and combined models. Conclusion Baseline predictors may identify strategies for adherence support, while early CPAP data substantially improved predictive performance. These models may guide personalized follow-up but should not restrict CPAP access. External and implementation validation are needed.

Background Agenda-setting is a fundamental patient-centered communication practice in which a clinician works with a patient to elicit, propose, and organize topics for discussion during a clinical encounter. Various agenda-setting interventions have been developed, including patient-facing tools and clinician training, but their effects have not been systematically evaluated. We aimed to determine the effects of these interventions on encounter, patient, care partner, and clinician outcomes. Methods We searched grey literature and seven databases, including PubMed, from inception through July 2025 for randomized and non-randomized comparative studies of interventions designed to promote or improve clinical visit agenda-setting. Two reviewers independently screened articles and extracted data, with a third reviewer resolving conflicts. We assessed risk of bias using RoB 2 for randomized studies and ROBINS-I for non-randomized studies. We conducted random effects meta-analyses when outcomes were sufficiently comparable, assessed heterogeneity using I2, and rated certainty of evidence using GRADE. Post hoc exploratory subgroup analyses examined study design, adjustment status, and intervention structure. Results Twenty-nine articles describing 22 unique studies met the inclusion criteria, including 13 randomized and nine non-randomized studies. Agenda-setting interventions increased the occurrence of agenda-setting (risk ratio 5.43, 95% confidence interval (CI) 2.06 to 14.28, I2=34.6%) and favored the intervention for concerns addressed when measured as a continuous outcome (standardized mean difference (SMD) 0.37, 95% CI 0.16 to 0.57, I2=65.3%) and overall clinician satisfaction (SMD 0.50, 95% CI 0.23 to 0.78, I2=0.0%). There were no clear differences in the number of concerns raised (mean difference (MD) 0.21, 95% CI -0.19 to 0.61, I2=59.6%), visit duration (MD 0.64 minutes, 95% CI -0.83 to 2.12, I2=51.4%), or overall patient satisfaction (SMD 0.05, 95% CI -0.05 to 0.15, I2=47.0%). Potentially important heterogeneity was present for four of these six outcomes. Post hoc exploratory subgroup analyses did not provide clear evidence that effects varied by study design, adjustment status, or intervention structure. Risk of bias was often high, serious, or critical, and certainty of evidence was low or very low for all pooled outcomes. Conclusions To our knowledge, this is the first comprehensive synthesis of clinical visit agenda-setting interventions. Such interventions may increase the occurrence of agenda-setting and the extent to which patient concerns are addressed without increasing visit length. However, the certainty of evidence was low or very low, and the available evidence does not establish a superior intervention structure.

Background and Objective: Lung function reference equations are essential for interpreting spirometry and related modalities, yet existing tools are fragmented, modality- and language-specific (predominantly R), and lack integrated severity classifiers. We present PySpiro, an open-source Python library unifying 23 spirometry, 1 multiple breath washout, 2 diffusing capacity, 1 static lung volume, and 2 oscillometry reference equations under a single API, with 10 clinical severity classifiers. Methods: PySpiro was implemented in Python 3 using NumPy and pandas, with all equations coded directly from literature. The unified API exposes predicted medians, z-scores, limits of normal (LLN/ULN), and raw Lambda-Mu-Sigma (LMS) triplets per parameter, plus a vectorised compute() method for batch processing at the DataFrame level. Unit-tests covering every equation and classifier verify agreement with published values and confirm correct boundary, out-of-range, and missing-input handling. Results: PySpiro covers spirometry equations from 1971 to 2022 across Caucasian, Asian, Indian, and race-neutral global populations, plus non-spirometry modalities (multiple breath washout, diffusing capacity, static lung volumes, oscillometry). Classifiers span COPD, interstitial lung disease, pattern and severity grading, bronchodilator reversibility, and primary ciliary dyskinesia. Compared with rspiro (5 equations, 4 spirometry) and the only prior Python package (spiref, 2 equations, no z-scores or % predicted), PySpiro offers substantially broader coverage, multi-modality support, a richer output API, and 10 classifiers absent from both. Conclusions: PySpiro is freely available (PyPI: pyspiro; doi:10.5281/zenodo.15519193) and provides a comprehensive, validated, and extensible platform for lung function data analysis in research pipelines.

Background As medical education transitions toward competency-based models, longitudinal programs emphasizing deliberate practice and repeated assessment are essential for mastery of complex concepts. Frequent formative assessment requirements, limited expert pools, and growing cohorts that outpace traditional infrastructure create substantial scalability constraints. Preceptorial programs, in which learners demonstrate mastery to expert educators, are particularly vulnerable. As enrollment expands, administrative demands for portfolio tracking, examination logistics, and case development fall disproportionately on the volunteer expert educators who sustain them. Objective We sought to implement a unified, zero-cost digital infrastructure to support scalable formative and summative assessment of a multi-center year-long mechanical ventilation preceptorial program while minimizing administrative burden for expert educators. Methods We implemented two complementary digital systems built on the Google ecosystem, requiring no institutional licensing or IT support. For formative tracking, we first deployed a standardized WP with defined milestone deadlines and a zero-cost automated tracking and reminder system. Completion of the fall milestone was compared between Fall 2024 (manual educator-dependent tracking and reminders) and Fall 2025 (standardized requirements plus automated tracking). For summative assessment, we also deployed an automatically graded multiple-choice case-based waveform examination (CBWE), operationalizing an expert-defined partial-credit scoring rubric. Results There was a 7.7-fold increase in the total number of waveforms submitted (194 images in Fall 2024 to 1498 images in Fall 2025). Portfolio submission rate rose from 45.6% (31/68 learners) in Fall 2024 to 68.5% (63/92 learners) in Fall 2025. Among learners who submitted their portfolios, the proportion submitting all required waveforms increased from 29.0% to 55.6%. Proof-of-concept automated CBWE grading confirmed scoring accuracy, eliminated 8.5 hours of manual grading per grader per cycle, generated item-level analytics, and demonstrated capacity for immediate individualized learner feedback. Conclusion A unified, zero-cost digital infrastructure addressed parallel administrative and content scarcity constraints limiting scalability of a national MV preceptorial program. By reallocating administrative burden away from expert faculty, these tools preserve the high-fidelity teaching relationship at the core of the preceptorial model while enabling it to operate at scale, ultimately providing a replicable model for competency-based assessment in diverse training environments.

Problem Authentic patient encounters are the raw material of clinical learning, yet the educational resources learners receive are rarely keyed to the diagnoses in front of them, creating temporal and cognitive gaps. Precision medical education (PME) proposes delivering the right resource to the right learner at the right moment, but practical implementation in the clinical learning environment remains limited. Approach We developed DxMentor, an electronic health record (EHR)-integrated platform that captures each learner's daily inpatient diagnostic exposures from documented International Classification of Diseases, Tenth Revision (ICD-10) codes. Artificial intelligence (AI) is used to match each diagnosis to an educator-curated formulary of micro-learning resources and board-style questions, and to PubMed-derived primary and synthesis literature converted into plain-language evidence summaries. A personalized email "nudge" is delivered before morning rounds, copying supervising attendings for residents, with engagement tracked longitudinally. We report implementation outcomes from July 2024-April 2026. Outcomes DxMentor evaluated 32,846 encounters from 335 medical students and 346 internal medicine residents, delivering 17,340 nudges containing 63,754 didactic resources, 17,038 question sets, and 23,594 summarized articles for approximately $390 in AI token costs. In a benchmarking sample, 91.5% (366/400) of diagnosis-resource pairs were rated relevant by physician-educators. Overall, 78.5% (12,393/15,793) of nudges were opened and 11.3% (1,955/17,340) had at least one click. Engagement was higher among residents than students (open: 80.7% vs. 65.8%; click-through: 12.7% vs. 3.1%; both P < .001), with substantial between-learner variability. Next Steps Email opens and clicks are engagement proxies rather than measures of learning. We are therefore linking nudges to educational outcomes, testing alternative recommendation strategies and timing, and expanding to additional specialties and ambulatory and surgical settings.

Background: The 2026 American Heart Association/American College of Cardiology (AHA/ACC) guidelines replaced the 2019 European Society of Cardiology (ESC) four-tier pulmonary embolism (PE) risk scheme with five clinical categories (A-E) and subcategories. These categories were set by expert consensus and have not been validated against outcomes. How patients are reclassified relative to ESC, or how the two systems compare prognostically, is unknown. Methods: We utilized three cohorts of patients with confirmed PE using structured electronic health record data, laboratory biomarkers, and large-language-model abstraction of radiology reports: Duke University Health System (n=12,992, drawn from 95,760 consecutive inpatient CT pulmonary angiography studies, 2014-2025, with no referral or registry enrollment step between imaging and cohort entry), INSPECT (Stanford; n=3,870), and MIMIC-IV (Beth Israel Deaconess; n=361). Patients were assigned AHA/ACC categories B through E, subcategorized where data allowed, and mapped to 2019 ESC risk strata. The primary outcome was 30-day mortality; discrimination was assessed with Harrell C-index. Results: Among 17,223 patients with confirmed PE, pooled 30-day mortality rose monotonically across categories: 1.5% (B), 8.9% (C), 15.5% (D), and 31.9% (E), with the ordering preserved in all three cohorts despite differing baseline mortality. Subcategory-level discrimination was reliable only at the high-acuity extreme (D2-E2); across subcategories C1 through D1, mortality did not order monotonically (9.2%, 10.8%, 8.1%, 10.9%), and adding subcategories to category C did not improve discrimination at Duke (C-index 0.699 vs 0.699). Category C patients lacking both echocardiography and biomarker testing (12.7% of category C) had mortality (10.4%) equal to or exceeding classified peers. Relative to ESC, the frameworks were concordant at the extremes, but 5.7%of ESC intermediate-risk patients were reclassified to category D, with modestly higher but non-significant 30-day mortality than those remaining in category C (10.8% versus 8.9%). Conclusions: Across a three-health-system cohort, the 2026 AHA/ACC framework produced a reproducible mortality gradient at the category level, with added subcategory granularity refining risk chiefly at the highest-acuity tiers. Discrimination across the broad intermediate band was limited, and reclassification from ESC fell almost entirely within this range.

Background Elexacaftor-tezacaftor-ivacaftor (ETI) has been demonstrated to improve lung function in clinical trials; however, evidence describing effects on trajectories and whether long-term improvements are sustained (>1-year) is lacking. We estimated within-person lung clearance index (LCI) trajectories before and after ETI initiation, assessing changes in level and rate of change, alongside acute LCI change, up to three years after ETI initiation. Methods Prospective observational study of children at a tertiary hospital. Children aged 3-17 years with [&ge;]2 LCI testing occasions (i) before and (ii) after starting ETI were used to describe lung function trajectories. Children with [&ge;]1 pre-ETI and [&ge;]1 post-ETI LCI occasion(s) were used to describe acute LCI change after ETI initiation. Age-adjusted LCI trajectories for time periods (i) before and (ii) after ETI initiation were estimated using linear mixed-effects models, and pre- and post-ETI LCIs were compared using paired Wilcoxon tests. Results Mean pre-ETI and post-ETI longitudinal changes in LCI were -0.007 (95% CI: -0.28, 0.27; n=35) and 0.12 (95% CI: -0.17, 0.41; n=20) turnovers per year, respectively. Before ETI initiation, 57% (30/53) of patients had an LCI[&ge;]7.1 turnovers (indicating impaired lung function), compared to 26% (14/53) post-ETI, with a median LCI difference of -0.70 (95% CI -0.84, -0.46; p<0.001) turnovers. Within-individual variability in LCI decreased post-ETI. Conclusions Our real-world data within a unique longitudinal study provide a comprehensive picture of ETI benefit by outlining not only acute improvement in LCI but maintained stability in LCI trajectories and improved LCI stability sustained up to three years post-initiation.

Rationale: Conventional CT-derived pulmonary vascular metrics quantify vascular pruning but do not assess the spatial heterogeneity of vascular architecture. In COPD, peripheral microvascular pruning coexists with compensatory central vessel dilation, paradoxically preserving total vessel volume while reducing effective vascular penetration into lung parenchyma. Objectives: To develop and evaluate Vascular Proximity Coverage (VPC), a distance-based CT biomarker quantifying the spatial efficiency of pulmonary vascular distribution. Methods: This retrospective analysis included 7,881 COPDGene participants [PHASE] with complete vascular segmentations. VPC was estimated by integrating the area under the cumulative lung tissue coverage curve at distances of 0-10 mm from segmented vessel voxels. Multivariable models assessed associations with lung function, six-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ) score, mMRC dyspnea score, and all-cause mortality over a median follow-up of [10.8] years, adjusting for demographics, smoking, emphysema, scanner type, and BV5. The cohort was stratified at median BV5 and VPC into concordant (high/high, low/low) and discordant (high-BV5/low-VPC, low-BV5/high-VPC) vascular phenotypes. Results: VPC declined across disease severity (5.82 {+/-} 0.50 [UNITS] in non-smokers to 5.21 {+/-} 0.64 in GOLD 4; p < 0.001). Among spirometrically normal smokers, current smokers had lower VPC than former smokers (5.31 vs. 5.68; p < 0.001). Each standard deviation decrease in VPC was independently associated with reduced 6MWD ({beta} = -16.8 m, 95% CI: -19.5 to -14.0), worse SGRQ ({beta} = 1.9, 95% CI: 1.4 to 2.4), and greater dyspnea (RR = 1.05, 95% CI: 1.03 to 1.08; all p < 0.001). The lowest VPC quartile was associated with 38% higher mortality than the reference quartile (HR = 1.38, 95% CI: 1.18 to 1.62; p < 0.001). The high-BV5/low-VPC phenotype showed 24% higher mortality than high-BV5/high-VPC (HR = 1.24, 95% CI: 1.08 to 1.42; p = 0.002) despite preserved vessel volume. Conclusions: VPC captures spatial heterogeneity of vascular architecture independent of vessel volume, is reduced in smokers without airflow obstruction, and identifies excess mortality risk among individuals whose total vessel volume appears preserved.

Introduction Aspiration events in hospitalized adults are common and linked to substantial morbidity, mortality, and healthcare burden. While antiemetics have been studied for aspiration prevention in specific settings, evidence for their broader preventive use in hospitalized adults is limited. We conducted a systematic review and meta-analysis to evaluate the effect of prophylactic scheduled antiemetics on aspiration events in adult inpatients. Methods: Ovid MEDLINE, Ovid Embase, CINAHL, and Cochrane Library (via Wiley) were searched in May 2025. We included randomized controlled trials (RCTs) and observational studies assessing prophylactic scheduled antiemetic use in hospitalized adults, with aspiration events as the primary outcome. Secondary outcomes were aspiration pneumonia, hospital length of stay, increased oxygen requirement, ICU admission, and mortality. Two investigators screened and independently extracted data in duplicate using standardized data collection forms. Extracted information included study characteristics, patient demographics and clinical characteristics, interventions, and outcomes. Results: Three RCTs met inclusion criteria (n = 515 patients; 237 antiemetic, 278 placebo). Pooled analysis of two RCTs demonstrated an 88% relative risk [RR] reduction of aspiration events; (2.8% [3/106] prophylactic antiemetic vs 27.9% [29/104] placebo; RR = 0.12, 95% confidence intervals [CI]: 0.02-0.73, p <0.0001, low certainty) in the antiemetic group. Pneumonia rates were also lower in the antiemetic group although they did not reach statistical significance (16.5% vs 32.4%; RR 0.44, 95% CI 0.15-1.28, P = 0.13, low certainty). ICU admission rates were lower in the antiemetic group (2.6% [2/76]) vs. placebo (23% [17/74]); RR = 0.11, 95% CI: 0.03-0.48, p=0.003, very low certainty). Mortality was similar between groups (35.0% vs 38.1%; RR 1.02, 95% CI: 0.84-1.24, P = 0.85, low certainty). No adverse events were reported among all studies. Conclusion: Prophylactic scheduled antiemetics were associated with a significant reduction in aspiration and ICU admission. There was a non-significant but favorable trend towards reduction of pneumonia among hospitalized adults. Larger, high-quality trials are needed to clarify their role in aspiration prevention.

IMPORTANCE: CD4+ T helper (T) 1, T2 and T17 cells have helped identify asthma endotypes in predominantly non-Hispanic White adults. Identifying asthma endotypes in diverse subgroups should help develop personalized medicine in asthma. OBJECTIVE: To characterize asthma endotypes and their association with selected variables and asthma severity/control in adults. DESIGN, SETTING, AND PARTICIPANTS: Cross-sectional and longitudinal analyses of nasal samples from the SOL-Asthma study of Puerto Rican and Dominican adults with (cases, n=313) and without (controls, n=371) asthma in the Bronx (NY) and Chicago (IL), with replication of selected findings in 376 adults of African ancestry in CAAPA. MAIN OUTCOMES AND MEASURES: Our primary outcome was asthma endotypes identified through nasal expression of "signature genes" for 3 T2, 5 T1, and 5 T17 pathways. Multinomial logistic regression was used for the analysis of asthma endotypes (with controls as the reference outcome category) at a baseline visit, as well as for the analysis of severe asthma exacerbations (SAEs) at baseline and during a 1-year follow-up. We attempted to replicate the identified profiles in CAAPA. RESULTS: In SOL-Asthma, cases were more likely to be female, Puerto Rican, and current/former smokers, and to have physician-diagnosed COPD than controls. Five transcriptomic profiles were identified in cases: T2HIGH (n=16 [5.1%]), T1HIGH (n=71 [22.7%]), T17HIGH (n=96 [30.7%]), T1HIGH/T17HIGH (n=53 [16.9%]), and T2LOW/T1LOW/T17LOW (n=77 [24.6%]). In a multivariable analysis, Puerto Ricans were more likely to have a T17HIGH profile than Dominicans (odds ratio [OR]=2.48, 95% confidence interval [CI]=1.08-5.74) and female sex was associated with T1HIGH (OR=3.44, 95% CI=1.33-8.88), T1HIGH/T17HIGH and T2LOW/T1LOW/T17LOW profiles, while COPD was associated with profiles other than T2HIGH or T1HIGH. The T1HIGH profile was associated with [&ge;]1 SAE in the year prior to baseline and during the one-year follow-up (OR=4.31, 95% CI=1.26-14.77). Each profile had unique differentially expressed genes (DEGs), with substantial replication of the profile distribution and DEGs in CAAPA. CONCLUSIONS AND RELEVANCE: Nasal transcriptomics identified profiles corresponding to five asthma endotypes in a cohort of Puerto Rican and Dominican adults. Puerto Rican background, female sex, and physician-diagnosed COPD were associated with non-T2HIGH (T2LOW) asthma endotypes, and T1HIGH asthma was strongly associated with SAEs.

Background: Hospital readmission is a common and costly outcome in patients with chronic obstructive pulmonary disease (COPD). Multimodal deep learning models combining structured electronic health record (EHR) data with physiological, imaging, and textual data have been proposed to improve its prediction, but whether such fusion outperforms EHR data alone, once temporal-leakage safeguards are applied, remains unclear. Methods: Using a stratified MIMIC-IV cohort of COPD patients enriched to about 50% readmission (natural prevalence 18.1%; n=1,279; 895/192/192 training/validation/test), we predicted 30-day, all-cause readmission from four frozen, pre-trained encoders (electrocardiogram, chest radiograph, clinical notes, structured EHR). We audited and corrected temporal leakage in the admission-linkage pipeline of each modality, then compared three fusion architectures (naive concatenation, scalar gated fusion, EHR-anchored residual fusion) against unimodal baselines and an EHR-alone reference, using bootstrap significance testing, a missingness stress test, and calibration analysis with and without post-hoc recalibration. Results: The audit corrected three previously undetected temporal-alignment issues, affecting 5-9% of patients for clinical notes, 29-43% for chest radiographs, and 53-58% for ECG. After correction, every modality except EHR showed near-chance discrimination alone (ROC-AUC 0.46-0.53), confirmed with a non-linear adapter probe. No fusion architecture significantly outperformed the EHR-alone baseline (ROC-AUC 0.63); removing auxiliary modalities improved rather than degraded fusion performance under stress testing. The raw Brier score was lower for all three fusion architectures than for EHR-alone (about 0.24 versus 0.32), but this reflected overconfidence of the EHR-alone probe (calibration slope 0.12); after logistic recalibration fitted on the validation set, Brier scores were indistinguishable (EHR-alone 0.239; fusion 0.240-0.244; all paired bootstrap 95% CIs included zero). Conclusions: Structured EHR data alone matched the discrimination and, after recalibration, the calibration of more complex multimodal fusion for 30-day COPD readmission prediction. These findings highlight the importance of rigorous temporal-leakage auditing in multimodal clinical prediction research and suggest that reformulating this task around temporal proximity to the outcome, rather than refining the fusion mechanism further, may be more productive for future work.

Rationale Airway remodelling (AR) contributes to airflow limitation and poor symptom control in severe asthma. While anti-IL-5 therapy improves clinical outcomes in severe asthma, the cellular and molecular mechanisms underlying its effects on AR remain incompletely understood. Objectives. To determine whether IL-5 blockade directly modulates airway epithelial biology and contributes to attenuation of AR in severe asthma with eosinophilia (SAE). Methods. Patients with SAE underwent bronchoscopy before and after 24 weeks of anti-IL-5 therapy. Paired bronchial brushings (n=12) were analysed using single-cell RNA-sequencing. Histological features of AR were assessed in paired bronchial biopsies (n=16). Functional effects of IL-5 were investigated using wound healing assays in differentiated air-liquid interface (ALI) cultures. Measurements and Main Results. Anti-IL-5 treatment improved clinical outcomes without altering airway epithelial cellular composition. Differential gene expression was predominantly restricted to bronchial ciliated epithelial cells, which expressed IL5RA. ALI cultures showed IL-5R protein. Anti-IL-5 therapy induced a transcriptional signature in ciliated cells that opposed IL-5-responsive genes. Pseudotime analyses demonstrated preserved epithelial differentiation trajectories but altered programmes related to mucus regulation and ion transport. Cell-cell communication analyses revealed decreased T2-inflammatory processes alongside enrichment of epithelial repair and barrier integrity processes after treatment. Functionally, IL-5 directly impaired epithelial wound repair in ALI cultures. Histological assessment demonstrated increased epithelial E-cadherin expression and reduced sub-basement membrane thickness, extracellular matrix deposition and goblet cell hyperplasia in bronchial biopsies. Conclusions. IL-5 blockade modulates epithelial biology at transcriptional, functional and structural levels in SAE and is associated with improved epithelial integrity and reduced features of AR.

Background. A primary constraint on the capacity of EMS programs to meet industry demand is psychomotor instruction and verification, requiring direct observation of each student by a qualified evaluator. Whether AI video analysis can relieve it is untested; none has been applied to EMS skill examination or compared with human examiners. Objective. To quantify human EMS evaluator inter-rater reliability and evaluate an AI video-analysis platform against it. Methods. In a prospective, fully crossed study, five certified EMS evaluators and an AI platform independently scored identical video-recorded EMT performances of cervical collar application (n=15), bag-valve-mask (BVM) ventilation (n=14), and medical assessment (n=15) on dichotomous checklists with critical-failure criteria. Agreement was assessed at item, score, and decision levels using Fleiss' kappa, Krippendorff's alpha, Gwet's AC1, and ICC(2,1)/ICC(2,k). Results. Human item agreement was moderate (kappa 0.409 to 0.467), as was single-rater reliability (ICC(2,1) 0.539 to 0.694), against good panel reliability (ICC(2,k) 0.854 to 0.919). Recorded pass/fail agreement was fair (kappa 0.297 to 0.388) and critical-failure agreement near zero for two skills (kappa 0.028, 0.119). AI alignment tracked rubric observability rather than task complexity: r = 0.857 (collar, exceeding every human), -0.173 (BVM), 0.664 (medical), and it was most lenient on two skills. Conclusions. Human evaluators are an imperfect standard, especially on critical failures. The AI was a legitimate additional rater where checklist items were discrete and visually verifiable, but not where credit required judging continuous quantities such as ventilation rate, volume, or suction duration. Defensible uses are formative and archival, not summative. These results reflect an early, non-specialist configuration: a baseline, not a limit.

Background: Patients increasingly consult free artificial intelligence (AI) chatbots about their symptoms, but these models may favour agreement with the patient over accuracy. In obstructive sleep apnoea (OSA), where most cases in the general population remain undiagnosed and diagnosis depends on referral, this could validate symptom denial. We tested whether consumer chatbots maintain guideline-concordant referral when a simulated patient minimises symptoms. Methods: Seven guideline-concordant and standardised OSA vignettes (apnoea-hypopnoea-index, AHI 8-52 events/h; body mass index 28-42 kg/m2) were presented to five free chatbots (GPT-5, Gemini 2.5 Flash, Claude Sonnet 4.6, DeepSeek V4 Flash and Grok-4.3) under neutral and symptom-minimising conditions (10 simulated conversations per model-vignette-condition cell; N=700). Referral maintenance was scored by a structured rubric and every capitulation was human-confirmed. Results: All chatbots maintained referral recommendations in neutral conversations (350/350; 100%). When simulated patients minimised symptoms and resisted referral, maintenance fell to 64.3% (225/350), a reduction of 35.7 percentage points (95% CI 5.5-66.0; p=0.028). Rates ranged from 55.7% to 85.7% across chatbots, but evidence of between-chatbot differences was inconsistent across analyses. Rates differed across clinical vignettes (p=0.001). A variance analysis estimated greater variation between vignettes than between chatbots. In the three driving-risk vignettes, referral recommendations were abandoned in 46-68% of symptom-minimising conversations, pooling all five chatbots within each vignette. Conclusions: Consumer AI chatbots abandoned correct OSA referral recommendations in about one-third of symptom-minimising conversations, with higher observed abandonment in simulated cases involving driving risk. This may delay appropriate assessment and raises patient- and public-safety concerns.