CHEST

Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of global morbidity and mortality, and its heterogeneity demands better biomarkers of severity and progression risk. Extracellular vesicles (EVs) are promising blood-based biomarkers, but have not been examined for COPD severity and outcomes in a large multicentre cohort. Methods: We analysed 600 COSYCONET participants (up to 54 months of follow-up). EV surface markers were profiled with the MACSPlex EV Kit IO. Cross-sectional associations with severity (GOLD, FEV1) were primary (ordinal and linear regression); longitudinal trajectories and all-cause mortality were prespecified exploratory endpoints. Results: Six EV markers showed robust associations with cross-sectional severity: CD29, CD49e and CD31 increased with severity (a cell-adhesion/matrix-remodelling signal), whereas CD81 and CD8 decreased; HLA-ABC (increasing) was less specific. No marker was linked to FEV1 decline. After FDR correction, lower levels of three markers with higher 54-month mortality (all HR<1): CD25 (HR 0.77, 95% CI 0.65-0.90, q=0.018), CD56 (HR 0.75, 95% CI 0.63-0.89, q=0.018) and CD142 (HR 0.74, 95% CI 0.60-0.90, q=0.024). CD25 and CD142 also improved reclassification, CD56 did not; a CD25 + CD69 combination showed the largest incremental signal ({Delta}C 0.017, 95% CI 0.002-0.032, p=0.027). Conclusion: Circulating EV surface markers are associated with cross-sectional COPD severity. Exploratory analyses nominate CD25, CD142 and CD25 + CD69 as candidate prognostic markers requiring external validation, suggesting minimally invasive EV profiling could complement clinical assessment in COPD.

Machine-learning survival models are increasingly proposed for intensive-care mortality prediction and are almost always selected and reported using the concordance index, a ranking metric averaged over follow-up. Yet most bedside decisions hinge on a probability at a specific time, such as 60- or 180-day mortality. We asked whether ranking-optimized models remain competitive at fixed clinical horizons against two reference points clinicians actually rely on: unaided attending-physician judgment and the original 1995 SUPPORT logistic model. Reanalyzing the SUPPORT2 cohort (9,105 critically ill adults from five United States centers, 1989-1994) under a stratified 70/15/15 split, we compared a gradient-boosted survival model, the physician's recorded prognosis, and the 1995 model at 60 and 180 days, alongside several alternative learners. The survival model achieved competitive ranking concordance (0.705) yet underperformed both comparators at fixed horizons: at 60 days its area under the ROC curve was 0.750, against 0.808 for physicians on the matched sample and 0.827 for the 1995 model, a gap that held across eight independent data splits and remained statistically reliable after multiplicity correction. The shortfall was not miscalibration, since post-hoc recalibration left discrimination unchanged, nor limited capacity, since neural networks, a deep ranking model, and two timepoint-aware discrete-time models also failed to close it; replacing the ranking objective with timepoint-matched binary training recovered roughly half the gap, pointing to an objective-horizon mismatch. Discrimination was equitable across sex, race, and age, but leave-one-disease-out validation exposed severe failure for disease groups absent from training, and the physician advantage was conditional on a physician electing to provide an estimate. We recommend reporting timepoint-specific discrimination alongside concordance, timepoint-matched training when fixed-horizon predictions drive care, leave-one-subgroup validation, and distribution-free prediction intervals to support selective deployment.

Background: Computer vision-enabled airway workflows can turn airway video into timestamped model-observation fields, but later blinded review and training studies require source-video linkage, run identifiers, quality-control status, and app/model provenance. Objective: To audit whether a public post-reconciliation 30-run manikin evidence package from a computer vision-enabled video laryngoscopy workflow preserved prespecified, video-linked procedural telemetry in structured JSON, while keeping detection accuracy, report quality, and reviewer agreement outside the current claim. Methods: Thirty manikin runs were captured on a HEALTHIBLE Intubation Simulator using an IntuBlade device connected to an iPhone 15 Pro Max. Six predefined conditions were tested with five runs each in planned round-robin order by a board-certified emergency physician operator. The author-affiliated team analyzed corrected Study Metrics JSON exports, the video manifest, app/model metadata, QC fields, and the frozen package checker after reconciliation against the assigned run guide. Blinded video review, independent analysis, and report-quality adjudication were not performed. Results: After reconciliation, all 30 rows contained parseable Study Metrics JSON, a companion videoFilename, run-named Drive video status, QC pass status, and corrected identifiers matching assigned row labels (30/30 for each completeness field; descriptive exact binomial 95% CI, 88.4% to 100.0%). App/model metadata were complete: appVersion 3.3.0 (75), source revision b94cd63, Navigation model, model version 31, and detection threshold 0.1. The exported JSON target-state flag was true in 25 of 25 target-condition rows (95% CI, 86.3% to 100.0%) and false in 5 of 5 no-target controls (95% CI, 47.8% to 100.0%), with zero glottic-detected frames and zero acceptable-view time in no-target controls. Among target-condition rows, median time to first model-detected glottic target was 2 seconds (IQR 1 to 3), median acceptable-view duration was 2.2 seconds (IQR 1.0 to 3.8), and median glottic visibility was 35.8% (IQR 25.8 to 45.6). Interpretation: The corrected package supports a bounded formative claim: simulated airway video can be represented as specified, video-linked computer-vision procedural telemetry after documented reconciliation. It supports package completeness, traceability, and assigned-condition consistency only; it does not establish native uncorrected export reliability, computer-vision detection accuracy, report quality, reviewer agreement, training effectiveness, autonomous guidance, tube-placement confirmation, clinical efficacy, or patient outcomes.

Rationale: Pulmonary artery (PA) enlargement is a non-invasive imaging biomarker associated with pulmonary hypertension and mortality in COPD; however, its genetic determinants remain incompletely understood. Objectives: To characterize the genetic architecture of PA size across COPD-enriched and population-based cohorts. Methods: We performed genome-wide association analyses of PA diameter using whole-genome sequencing in COPDGene (n=9,418) and ECLIPSE (n=1,859), and imputed-genotype data from the UK Biobank (n=37,073). We replicated lead variants in the Framingham Heart Study (FHS; n=3,289), incorporated all four studies into a joint meta-analysis, and identified independent signals through conditional analyses. Candidate effector genes were prioritized using coding variant annotation, colocalization, and integrative regulatory evidence. Measurements and Main Results: We identified 44 independent genome-wide significant PA diameter signals within 39 loci, including 8 variants replicated in FHS, novel associations near FRMD4B, SLC20A2, BORCS7-ASMT, and KCNRG, and 5 signals in conditional analysis including multiple signals at ANO1. Genetic effects were concordant across imaging modalities and cohorts of differing COPD burden. Effector-gene prioritization nominated ABCC8, PDGFD, HMCN1, CCNE1, and TBX20, implicating pathways in vascular remodeling, developmental regulation, smooth muscle and endothelial function, ion-channel signaling, and extracellular matrix organization. Colocalization with pulse pressure GWAS demonstrated substantial shared causal variation between pulmonary and systemic vascular biology. Conclusions: In this largest genetic study of pulmonary vascular imaging to date, PA diameter exhibits a polygenic architecture consistent across imaging modalities and cohorts of differing COPD burden. The prioritized effector genes bridge rare-variant pulmonary hypertension biology with common-variant systemic vascular biology.

In idiopathic pulmonary fibrosis (IPF), the lung is remodeled through coordinated epithelial, stromal, and immune-associated programs, but individual transcriptomic cohorts are often too small to separate shared disease signals from demographic and study-level variation. To increase statistical power while preserving study-aware interpretation, we integrated raw bulk lung RNA sequencing (RNA-seq) data from five well-annotated studies and analyzed 223 samples in a common framework that modeled sex, age, library layout and repeated sampling. IPF showed a broad and reproducible expression shift, with 2,443 genes meeting the differential-expression threshold of false discovery rate (FDR) <0.05 and absolute log_2 fold change at least 1. The dominant program combined extracellular matrix remodeling, stromal and epithelial activation, complement and B-cell-related pathways, cilium-associated processes, and relative depletion of oxidative phosphorylation and proteasome pathways. Sex-stratified analyses recovered a shared fibrotic core with smaller sex-skewed components, whereas age-related disease effects were weaker and centered on immune activation. A leave-one-study-out elastic-net analysis using fixed disease-gene panels classified IPF across held-out studies, supporting cross-study portability of the core signature. This integrated reanalysis strengthens evidence for a stable matrix-immune IPF program and reinforces the view that core disease-associated transcriptional programs are reproducible across heterogeneous cohorts.

Tobacco smoke shapes mutations, selection and clonal expansion in lung epithelial cells. Smoking cessation leads to divergent epidemiology in the two most common lung cancers: squamous cell carcinoma risk declines sharply, while adenocarcinoma risk is preserved. To investigate this discrepancy, we analysed 806 genomes of alveolar type II (AT2) cells and found persistently elevated mutation burdens after cessation. In contrast, in the proximal airway, rare basal stem cells with near- normal mutation burden expand after cessation, protecting against squamous cell carcinoma. Targeted single-molecule DNA sequencing of AT2 cells revealed positive selection for TP53 and cell cycle and MAPK genes, supporting continued cancer risk. A multistage carcinogenesis model emphasised the importance of a small population of hypermutated cells in the alveoli and reproduced the divergent epidemiological trajectories following cessation due to distinct regenerative dynamics. Our findings suggest that differences in mutational burden and clonal regeneration explain post-cessation trends in lung cancer subtypes.

Background: Biologically distinct asthma subgroups may obscure genetic effects when analyzed as a single phenotype. We examined whether asthma susceptibility signals are shared, heterogeneous, or stratum-specific across ancestry, obesity status, and sex. Methods: We performed ancestry-specific GWAS meta-analyses in African ancestry participants (9,965 asthma cases; 37,391 controls) and European ancestry participants (6,074 cases; 116,255 controls), followed by obesity- and sex-stratified analyses. Analyses used imputed dosages and fixed-effect meta-analysis within ancestry. Results: Stratification detected asthma association signals that were less apparent in the combined phenotype. Shared cross-ancestry loci implicated epithelial antiviral susceptibility and immune regulation, represented by signals near CDHR3 and FOXO1. An ancestry-heterogeneous signal at the 17q21 locus, harboring ORMDL3/GSDMB, supported population-dependent effects at an epithelial inflammatory locus. Obesity stratification mapped the genome-wide significant burden to asthma without obesity. Sex stratification detected genome-wide significant signals in AFR females with asthma and obesity and in both sex strata with asthma without obesity, with the strongest signal burden in EU females without obesity. Conclusions: Asthma genetic architecture differed by ancestry, obesity status, and sex. Stratified analyses identified group-specific susceptibility related to epithelial and immune regulation, airway inflammation, remodeling, and neural signaling, supporting precision approaches to asthma.

RATIONALE: Airway mucus plugging is a clinically relevant manifestation of airway pathology in chronic obstructive pulmonary disease (COPD) and is associated with increased mortality even in early disease; however, visual computed tomography (CT) assessment is subjective and labor intensive. OBJECTIVES: To develop an AI-based quantitative CT method for automated detection of airway mucus plugging and evaluate associations with physiologic impairment and clinical outcomes. METHODS: Inspiratory CT scans from 8,971 COPDGene Phase 1 (GOLD 0-4 and PRISm) participants were analyzed. An AI-based framework combining 3D airway segmentation discontinuities and convolutional neural network classification identified mucus plug obstructions, yielding mucus plug burden (total plug count). Associations with outcomes were evaluated using covariate-adjusted models. MEASUREMENTS AND MAIN RESULTS : Higher mucus plug burden was associated with lower post-bronchodilator FEV % predicted ({rho} = -0.41; P < 0.001), greater air trapping (LAA < -856 HU; {rho} = 0.33; P < 0.001), worse health status (SGRQ; {rho} = 0.31; P < 0.001), and shorter 6-minute walk distance ({rho} = -0.26; P < 0.001). Among GOLD 1-4 participants, mucus plug presence was independently associated with increased all-cause mortality (adjusted hazard ratio, 1.28; P < 0.005) and exacerbation frequency (adjusted incidence rate ratio, 1.32; P < 0.005). Plug presence was also associated with increased respiratory mortality across GOLD categories and cardiovascular mortality in GOLD 1-2. CONCLUSIONS: AI-based quantitative CT assessment of airway mucus plugging provides a scalable, reproducible measure associated with physiologic impairment and adverse outcomes in COPD, supporting its role in risk stratification and future therapeutic studies.

Background Hypercapnia may indicate a primary ventilatory syndrome, a complication of another illness, or an epiphenomenon of severe disease. The presenting context of hypercapnia is poorly quantified, limiting clinical interpretation and synthesis of epidemiologic studies. Methods We performed a retrospective cross-sectional study of Medical Information Mart for Intensive Care IV (MIMIC-IV) hospital admissions linked to an emergency department (ED) presentation from 2011 through 2019. Admissions were included if the triage chief complaint was not missing and at least one prespecified criterion for hypercapnia was met: an International Classification of Diseases (ICD) code for hypercapnic respiratory failure or obesity hypoventilation syndrome, arterial blood gas (ABG) PCO2 45 mmHg, venous blood gas (VBG) PCO2 50 mmHg, or indeterminate-source blood gas PCO2 50 mmHg. Triage chief-complaint text was classified by natural language processing (NLP) into 17 National Hospital Ambulatory Medical Care Survey reason-for-visit (RFV) categories using a multi-label framework. Primary analyses estimated admission-level RFV category prevalences; secondary analyses compared distributions by overlapping ascertainment indicator, age, and acidemia. Results The total cohort included 11,941 admissions: 1,542 (12.9%) met both blood-gas and ICD-code criteria, 9,958 (83.4%) met blood-gas criteria only, and 441 (3.7%) met ICD-code criteria only. Median age at admission was 68 years (IQR 56-78), and 6,423 admissions (53.8%) were for male patients. Respiratory RFV categories were most prevalent (30.2%), followed by administrative reasons (17.5%), digestive symptoms (14.0%), injuries and adverse effects (14.0%), and nervous-system symptoms (13.8%); categories were not mutually exclusive. Respiratory categories were more common in ICD-positive admissions (50.2%) than in VBG-defined (36.3%) or ABG-defined admissions (27.3%). Injuries and adverse effects were most prevalent among admissions for patients aged 18-39 years (34.4%), whereas respiratory categories increased from 13.7% among admissions for patients aged 18-39 years to 36.5% among admissions for patients aged 80 years. NLP-derived classifications showed mean set-F1 of 0.84 against adjudicated clinician labels in the full annotated benchmark sample. Conclusions Among ED-linked admissions with hypercapnia by diagnosis code, blood gas, or both, respiratory complaints were the most common chief-complaint category but represented fewer than one-third of admissions. Presentation context should be incorporated when defining, comparing, and interpreting hypercapnia cohorts, particularly those ascertained by blood-gas criteria.

Background and Aims: Residual pulmonary vascular obstruction (RPVO) defined as the persistence of thrombotic material within the pulmonary arteries several months after an acute pulmonary embolism (PE) is associated with an increased risk of severe complications, including recurrent events and chronic pulmonary hypertension. However, the genomic architecture underlying RPVO in unprovoked PE remains poorly understood, and this study aims to address this gap. Method: By leveraging genetic and imaging RPVO data from three independent cohorts totaling 586 unprovoked PE patients, we conducted a meta-analysis of genome wide association study (GWAS) of RPVO using a dedicated statistical method to handle the semi-continuous distribution of RPVO. The meta-GWAS was complemented by haplotype association analyses and transcriptome wide association studies as well as Mendelian Randomization (MR) approaches based on plasma metabolites and proteins. Results: Through meta-GWAS, we identified one locus, OSTN, associated with RPVO (lead variant rs59109356 associated with a ~2-fold increase of RPVO, p=3.92x10-8). A second locus, CCN4, previously reported to associate with pulmonary fibrosis, was also identified, with evidence of association approaching genome-wide significance (p=6.7x10-8). We also identified a common haplotype spanning over AHSG/HRG/KNG1 associated with a ~3-fold increase of RPVO (p=2.96x10-8). Using plasma protein-based MR, we demonstrated that one unit increase in genetically determined plasma levels of IL-1 R AcP encoding IL1RAP was associated with a 28% (p=1.32x10-6) reduction in RPVO. We also observed statistical evidence that the CCN4 (p=0.06) and IL1RAP (p=0.02) loci associate with the risk of PE recurrence in a sample of 1,617 unprovoked PE patients. Conclusions: By identifying novel molecular determinants of RPVO that map to loci involved in inflammatory pathways and vascular remodeling, our study provides evidence that inflammation is the predominant, and likely the key mechanism underlying RPVO, whereas impaired fibrinolysis appears to play a more limited role.

Rationale. Racial disparities in outcomes among patients with acute respiratory failure are well-described, but the contributions of clinicians to these disparities have not been evaluated. Objectives. Among mechanically ventilated patients, we evaluated racial disparities in severity of illness trajectories and adapted value-added modeling to quantify nurse and physician relationships with these disparities. Methods. In a retrospective cohort of mechanically ventilated patients across five hospitals between 2018 and 2022, we used generalized estimating equations to model the change in Laboratory-based Acute Physiology Score version 2 (LAPS) from the start to end of intensive care unit admission ({Delta}LAPS). Consistent with value-added modeling, we randomly allocated the cohort into development and testing partitions, and fit separate multiple linear regression models of {Delta}LAPS using concurrent nurse and physician assignments (determined at 4-hour intervals), patient race, and clinician-race interaction terms as fixed effects. Clinician-specific and clinician-race interaction coefficients were extracted to determine race-specific value-add for each clinician. We defined the race-contextual value-add difference (RCVAD) as a clinician-level measurement of the difference in that clinician's value-add between Black and White patients in their care; a positive RCVAD indicates a more favorable severity of illness trajectory for Black relative to White patients and vice versa. Measurement and Main Results. Among 6,555 distinct patients, 7,247 clinical encounters, 405 nurses, and 70 physicians, Black patients accounted for 2,926 (40%) encounters. Overall, Black patients had significantly less improvement in {Delta}LAPS than White patients (difference in LAPS decline = 2.26 [0.23, 4.29], p=0.029). In the development partition, median nurse RCVAD was -0.10 (interquartile range [IQR]: -1.17, 1.14) with 191 (47%) nurses having a positive RCVAD; median physician RCVAD was -0.18 (IQR: -1.34, 0.56) with 29 (41%) having a positive RCVAD. Conclusions. Black mechanically ventilated patients experience less improvement in severity of illness during intensive care unit admission than White patients. While the majority of physicians and nurses were associated with disparities-exacerbating illness trajectories, many other clinicians were associated with disparities-mitigating trajectories. Future work to understand practices associated with disparities-exacerbating and disparities-mitigating care profiles could inform interventions to reduce disparities overall.

Introduction: Standardizing fluid and vasopressor resuscitation in sep- tic shock is challenging due to patient heterogeneity. We trained a causal model to identify optimal dosing during the first six hours of intensive care unit (ICU) admission. Methods: Graphical causal inference models were applied to estimate het- erogeneous treatment effects. Grounding models in expert clinical knowl- edge minimizes bias from spurious correlations to generate robust, contextu- ally meaningful recommendations. Our model was trained on 1,702 MIMIC database admissions and externally validated on 1,434 eICU admissions. Pri- mary outcomes were in-hospital survival and 24-hour clinical improvement (SOFA score reduction of two points or more). Findings: The cohort comprised 3,136 participants (median age 65 years [IQR 53-75]; 42.7% female). Deviation from vasopressor recommendations was associated with increased in-hospital mortality (median OR 5.61, 95% CI 5.44-5.78) and failed clinical improvement (median OR 6.33, 95% CI 6.17-6.50). Fluid deviations yielded corresponding median ORs of 1.02 (95% CI 1.02-1.02) and 1.14 (95% CI 1.14-1.14). In external validation, the model achieved a median survival AUROC of 0.73 (95% CI 0.69-0.77) and clini- cal improvement AUROC of 0.69 (95% CI 0.66-0.72), matching predictive baselines. Treatment effects were heterogeneous: optimal fluids increased survival by up to 4% in low-severity subgroups, while vasopressor responses varied from 0.5% to 17% across acute severity levels. Sensitivity analyses across 36 scenarios confirmed primary associations in 33 cases (91.7%). Interpretation: Recommendations from expert-grounded causal models correlate with improved septic shock outcomes in external validation, cap- turing significant heterogeneity in patient response.

Despite contributing substantially to clinician burnout, nursing documentation lacks empirical evidence distinguishing clinically essential from administratively driven documentation. Exploiting a COVID-19 documentation relaxation policy as a natural experiment, we analyzed 520,357 patient shifts from 36,321 patients in 54 inpatient units (2019 - 2022) using large language model-assisted flowsheet classification and structural equation modeling. When permitted, front-line nurses reliably distinguished two types of documentation: in acute care units, primary nurses reduced compliance-driven Cares & Safety documentation by 19% (106.4 to 86.2 entries, r = -0.19), while maintaining or increasing documentation directly relevant to respiratory management, with no impact on patient respiratory outcomes. Documentation intensity also co-varied with real-time patient deterioration, consistently across unit types (|{beta}| = 0.13 - 0.14). Together, these findings provide the first large-scale quantitative evidence distinguishing clinically essential documentation from compliance-driven documentation and demonstrate that targeted reduction of the latter is a viable strategy for alleviating documentation burden without compromising care quality for respiratory care management.

Background Ventilator-associated pneumonia (VAP) is the most frequent nosocomial infection in critical care, affecting 20-36% of mechanically ventilated patients. Early prediction is hampered by the absence of a reliable, objective diagnostic standard. We developed ADVISE (Automated Dudley Ventilation Infection Series Evaluation), a machine learning model to predict physiological deterioration consistent with developing VAP using routinely collected electronic health record data from a UK NHS intensive care unit. Methods Retrospective observational study of admissions at Russell's Hall Hospital ICU (2008-2026). Following National Data Opt-Out exclusion (158 admissions, 4.2%), 3,566 admissions generated 33,208 candidate 48-hour observation blocks. Six temporal variables - FiO2, ventilator mode, P:F ratio, procalcitonin (PCT), secretion amount, and secretion description - were extracted across the baseline window (hours 1-24). A composite VAP-surrogate outcome required concurrent P:F ratio decline (>=5%) and PCT rise (>=0.5 ng/mL) across the outcome window (hours 25-48). After sequential quality filters, 2,134 blocks (18 positive, 0.84% prevalence) were retained. An XGBoost classifier was trained using nested 5-fold cross-validation with scale_pos_weight=114.0 and ROC-based hyperparameter optimisation on 1,495 training blocks, evaluated on 639 held-out test blocks. Performance was assessed via AUROC, AUPRC, and calibration (Brier score). Bootstrap resampling (1,000 iterations) generated 95% confidence intervals. Results On the held-out test set (n=639, 5 positive outcomes), ADVISE achieved AUROC 0.874 [95% CI: 0.771-0.939] and AUPRC 0.031 [0.008-0.069], representing a 4.0-fold improvement over the no-skill baseline. Nested cross-validation mean AUROC was 0.844 +/- 0.078 (range 0.716-0.915). At the Youden-optimal threshold, sensitivity was 0% with specificity 97.8%, reflecting extreme class imbalance (0.78% test prevalence). A threshold targeting 80% sensitivity achieved sensitivity 80.0% [33.3-100.0%], specificity 87.4% [84.8-89.9%], positive predictive value 4.8% [1.1-9.9%], and negative predictive value 99.8% [99.4-100.0%], detecting 4 of 5 VAP cases with approximately 80 false alarms (12.6% false positive rate). Brier score was 0.0078. Feature importance identified baseline P:F ratio as the dominant predictor (41.3% total gain), followed by ventilator mode (26.1%), secretion amount (13.2%), secretion description (9.1%), procalcitonin (5.9%), and FiO2; (4.5%). Conclusions ADVISE demonstrates that baseline oxygenation trajectory and ventilatory support patterns - derived exclusively from routinely charted ICCA variables - can identify admissions at risk of VAP-related physiological deterioration with meaningful discrimination (AUROC 0.874) despite severe class imbalance. The 80% sensitivity operating point offers a clinically actionable alert rate (12.6% FPR), supporting integration into existing ICU workflows. This proof-of-concept study establishes feasibility; multi-site prospective validation is required before clinical deployment.

Background: Automatic tube compensation (ATC) was designed to compensate for the additional resistive load imposed by the endotracheal tube during spontaneous breathing. In ATC mode, the ventilator adds or subtracts the flow-dependent pressure drop across the tube during both inspiration and expiration so that tracheal pressure remains close to PEEP. Early prototype ventilators achieved true tracheal-pressure control and showed physiological and clinical benefits, but clinical studies with commercial systems have failed to confirm these earlier findings. A 2003 bench study found that commercial ventilators provided, at best, only partial tube compensation, unlikely to result in meaningful clinical benefit. We therefore tested whether this limitation has been remedied in contemporary ICU ventilators. Methods: We performed a bench comparison of five commercial ICU ventilators and an ATC prototype ventilator designed to accurately compensate for the flow-dependent resistance over a wide range of flow rates. An active lung simulator generated spontaneous breathing patterns with weak, moderate, and strong inspiratory efforts at different PEEP levels. We tested each breathing pattern through endotracheal tubes with inner diameters of 7 and 8 mm, and measured airway pressure, tracheal pressure, and flow during CPAP with and without ATC. Breathing through the tube against open atmosphere served as a zero-PEEP/T-piece reference. Results: In CPAP mode, the commercial ventilators showed flow-dependent airway-pressure deviations, amounting to a substantial added resistance of 1.5 - 6.5 mbar/(L/s), whereas the ATC prototype ventilator imposed an added resistance of only 0.6 mbar/(L/s). In ATC mode, the commercial ventilators reduced the resistive load by no more than by 25%, and large tracheal-pressure deviations remained, especially at higher inspiratory effort and during expiration. In some cases, the residual load during ATC was even greater than the load during unsupported breathing through the tube. By contrast, the ATC prototype ventilator maintained tracheal pressure close to PEEP throughout the breathing cycle and eliminated on average 79% of the tube-related resistive load. Conclusions: In the commercial ventilators evaluated in this study, the defining physiological objective of ATC was only partially achieved. Therefore, clinical benefits previously reported for tracheal-pressure control support should be interpreted with caution when applied to commercial ATC implementations, unless effective tube compensation has been demonstrated under relevant conditions. These findings suggest that more advanced control approaches, such as those implemented in the ATC prototype ventilator, may be required to achieve consistent and physiologically accurate tube compensation.

Background: Aging has a critical role in lung changes and the outcome of lung disease. Several lung aging equations have been proposed to measure deviation from physiological aging of the respiratory system. In this study, we aimed to develop a single measure of accelerated lung aging and show its application as a measure of lung aging. Method: We used a pre-bronchodilator pulmonary function test (PFT) from NHANES adult participants recruited from 2007 to 2011. We applied Klemera-Dubal Method (KDM) to four PFT measurements, FEV1, FVC, FEF25-75, and PEF, to calculate a measure of lung biological aging. Physiological Aging of the Respiratory System (PARS) was calculated from the residual method vs. chronological age. We tested the construct validity of PARS by measuring its association with risk factors of lung health. The prognostic validity was measured using a survival analysis. Sampling weights were applied to all analyses. Results: In 14,123 adult participants, the mean (SD) of accelerated lung age (PARS) was 0 (8.2) years. Participants with a history of asthma and emphysema had 4- and 10-year higher PARS. Cigarette smoking, lower socioeconomic status, black race, higher serum cadmium, and lower serum selenium and magnesium were associated with higher PARS. During 116 months of follow-up, PARS was associated with a higher mortality (HR = 1.06, 95%CI: 1.05-1.07 per year). Females with higher PARS had a higher risk of death (P for interaction < 0.001). Results were consistent across different subgroups and sensitivity analyses. Conclusion: PARS is a noninvasive lung aging marker and can be applied as a single measure of lung accelerated aging in the adult population. Its strong construct and predictive validity support its future application among different populations with and without lung disease.

Background: To develop and validate multiple Machine Learning (ML) algorithms that predict Mechanical Ventilation (MV) requirement in Guillain-Barre Syndrome (GBS), and to determine whether they outperform the additive, score-based prognostic models in current use. Methods: This retrospective study analysed 233 GBS patients (training set, n = 186; validation set, n = 47). Five algorithms (Deep Neural Network (DNN), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB)) were trained and compared. Predictors were chosen by a three-method consensus pipeline executed inside each nested cross-validation fold, retaining 11 features. Whether BorderlineSMOTE was applied was determined per model by Optuna hyperparameter tuning. Hyperparameter tuning, probability calibration, and bootstrap resampling were applied; performance used accuracy, recall, F1, specificity, AUROC, and Brier score, with SHapley Additive exPlanations (SHAP) for model interpretability. Results: XGBoost achieved the strongest clinical performance (AUROC 0.807, accuracy 0.787, and recall 0.857), exceeding the validated EGRIS for MV (AUROC = 0.62). Calibration preserved recall (0.857) and shifted the operating point by one false positive while lowering the Brier score from 0.210 to 0.110 (naive Brier baseline 0.127, BSS = 0.134), so the deployed tool was developed using the probabilities from the calibrated XGBoost model. Consensus selection retained eleven predictors; blood prealbumin, blood FT3, and NLR ranked highest by both embedded importance and SHAP. The model was deployed as an interactive prognostic tool predicting MV risk at admission. Conclusions: ML algorithms substantially improve GBS prognosis by integrating eleven biomarker predictors, modelling nonlinear relationships, and providing SHAP-based interpretability. The single-centre sample is small, so external validation in larger, multi-centre cohorts is required before clinical deployment.

Background: Post-tuberculosis lung disease (PTLD) constitutes a substantial global public health burden, yet its overall research landscape has not been systematically quantified. This bibliometric study characterizes global research trends and the knowledge architecture of PTLD publications issued between 2015 and 2025. Methods: We retrieved PTLD-related literature from the Web of Science Core Collection using search terms including "post-tuberculosis lung disease" and "tuberculosis sequelae". Only English original articles were retained, yielding a final dataset of 353 publications. We adopted VOSviewer, CiteSpace and the Bibliometrix R package to visualize annual publication outputs, cross-country and institutional collaborations, author co-authorship networks, keyword co-occurrence patterns, and citation burst dynamics. Results: Annual publications rose from 13 in 2015 to 67 in 2025, with 71.4% of all papers published from 2021 to 2025. The five most productive nations were the United States (72 papers), India (61), China (51), the United Kingdom (49), and South Africa (38). The US and UK occupied core hub positions in international collaboration networks. Leading institutions included Stellenbosch University (20 articles), the University of Cape Town (14), and the University of Liverpool/Liverpool School of Tropical Medicine (10). Keyword co-occurrence clustering identified eight thematic groups, with dominant hotspots covering post-tuberculosis bronchiectasis, pulmonary function impairment, chronic pulmonary aspergillosis (CPA), and quality of life. The keywords with the strongest recent citation bursts were "post-tuberculosis lung disease" (strength = 3.79), "prevalence" (3.70), and "quality of life" (2.51). Research frontiers extending to 2025 center on standardized PTLD conceptual framing, pulmonary rehabilitation, and long-term clinical outcome assessment. Conclusions: PTLD research has shifted from merely describing structural lung damage toward standardized disease definitions, functional pulmonary testing, complication management, and patient quality-of-life improvement. Future research priorities should include prospective multicenter cohort studies, individualized pulmonary rehabilitation programs, and host-directed therapies to mitigate the global burden of chronic PTLD.

Background: Proteins directly impact disease development and act as drug targets. Therefore, we integrated genomic and lung tissue proteomics data to identify lung cancer susceptibility proteins, elucidating genetic mechanisms and candidate drug targets. Method: We profiled the proteome and genome in non-neoplastic lung tissue from 200 lung cancer patients. Using this data, we constructed genetic models to predict abundance across the proteome in lung tissue. We applied these models to genome-wide association study (GWAS) data from 55,174 lung cancer cases and 1,294,174 controls to evaluate their associations with the risk of lung cancer, overall and by major histological subtypes. Bayesian colocalization and Mendelian randomization (MR) analyses were used to prioritize putative causal proteins, which were cross-referenced with three main drug-protein databases to identify potential therapeutic targets. Results: We identified 29 proteins associated with lung cancer risk at a false discovery rate < 5%, including 25 for overall lung cancer, two (AQP3 and IL18) specifically for adenocarcinoma, and another two (HMGN2 and HLA-DMB) for squamous cell carcinoma. Of them, genes encoding 17 proteins reside at least 2Mb away from any known GWAS risk loci, including 14 for overall lung cancer (HYI, GPX1, GMPPB, DSP, HDDC2, MTCH2, SUOX, JMJD7, PDIA3, IL16, IQGAP1, SULT1A2, ARHGAP27, and TYMP) and three for subtypes (AQP3, IL18, and HMGN2). Among the 12 proteins located within the known risk loci, EPHX2, CLDN18, PSMD5, and CYP2S1 proteins showed an association independent of the proximal GWAS-identified lead variant. Colocalization and/or MR analysis suggested 11 potential causal proteins. Five of these candidate causal proteins (DSP, CLDN18, IQGAP1, IL18 and TYMP) are targeted by nine drugs already approved by the FDA or in phase III trials. Conclusion: Our study identified novel lung cancer susceptibility proteins and potential drug targets, offering valuable insights into lung cancer biology and future translational utilities.

Background: Sepsis-associated liver injury (SALI) is commonly assessed using static laboratory values, although liver dysfunction during sepsis is dynamic.Methods: This retrospective cohort study included 162 ICU patients with SALI. Early trajectories of alanine aminotransferase, total bilirubin, and albumin during the first 7 days after ICU admission were identified using group-based multi-trajectory modeling. Landmark analysis and Cox regression were used to evaluate 60-day mortality.Results: Twenty-five patients died within 60 days. Four trajectory classes were identified. Between-class separation was driven mainly by alanine aminotransferase and total bilirubin, whereas albumin showed limited short-term variation. After the landmark time point, Class 3 (HR, 4.374; 95% CI, 1.960-9.759; P <0 .001) and Class 4 (HR, 7.451; 95% CI, 3.649-15.212; P <0 .001) had higher mortality risk than Class 1.Conclusions: Early joint trajectories of liver-related laboratory biomarkers may identify clinically meaningful SALI subphenotypes and support risk stratification in critically ill patients.