Annals of the American Thoracic Society

Background: Racial disparities in incident heart failure (HF) are well documented, yet the extent to which systemic inflammatory burden-shaped by structural determinants of environmental exposure-accounts for these disparities remains unquantified in multi-ethnic prospective cohorts. Methods: Among 6785 participants in the Multi-Ethnic Study of Atherosclerosis (enrolled 2000-2002; 4 racial/ethnic groups) followed for approximately 16 years, we estimated associations of 4 prespecified baseline inflammatory and hemostatic biomarkers (interleukin-6 [IL-6], C-reactive protein [CRP], fibrinogen, and D-dimer; per 1-SD increment) with incident HF (485 events) using Cox proportional hazards models. A composite inflammatory burden index was derived by principal components analysis. Race x biomarker multiplicative interactions were tested with Holm correction. Sequential biomarker adjustment quantified the percentage of the Black-White log-hazard difference explained relative to a clinical-only model. Robustness to ambient air pollution (PM2.5, NO2) was assessed in a geocoded subcohort. Results: All 4 biomarkers were independently associated with incident HF (IL-6: hazard ratio [HR], 1.20 [95% CI, 1.08-1.34]; D-dimer: 1.18 [1.06-1.30]; CRP: 1.14 [1.02-1.27]; fibrinogen: 1.13 [1.02-1.24]). The composite index (first principal component, 53.7% of variance explained) yielded an HR of 1.25 (1.12-1.39) per 1-SD increase. No multiplicative race x biomarker interaction was detected (all Holm-corrected P=1.0), indicating that the per-SD inflammatory hazard was directionally consistent across groups. Sequential biomarker adjustment reduced the Black-White log-hazard difference by 57.5%, a pattern consistent with-but not sufficient to prove-attenuation by inflammatory pathways. Associations were robust to PM2.5/NO2 co-adjustment. Conclusions: Inflammatory and hemostatic burden partially explains the Black-White disparity in incident HF. The null multiplicative interaction pattern indicates that per-unit biological risk is equitable across groups, consistent with differential structural exposure-rather than differential susceptibility-as a primary driver of disparity. Key Words: heart failure; inflammation; health equity; structural determinants; MESA; biomarkers

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.

BackgroundEarly-life air pollution exposure has been associated with childhood asthma, but its relative contribution is poorly characterised in cohorts with repeated indoor measurements. Short sampling windows and seasonal bias limit how well any single measurement can represent cumulative exposure. ObjectiveTo evaluate the association between early-life indoor and outdoor pollutant exposure from birth to 18 months and asthma development at ages 7, 12, and 18 years, while testing potential exposure-exposure and gene-environment interactions. MethodsIn the COPSAC2000 birth cohort (n = 411), indoor PM2.5, black carbon, NO2, NOx and VOCs, and modelled outdoor PM2.5, NO2 and NOx, were assessed at 6, 12 and 18 months of age. Indoor metrics were corrected for pump-flow drift and seasonal bias via a Gaussian process with a periodic kernel. Hierarchical Bayesian logistic regression(HBLR) and calibrated random forests (CRF) were evaluated using stratified nested cross-validation for discrimination using AUC-ROC, Calibration using integrated calibration index (ICI), and Brier skill scores. Exposure-exposure and gene-environment interactions were quantified via Shapley interaction indices. ResultsDiscrimination was weak at all endpoints with test AUC-ROC: 0.58-0.67, BSS near zero (0.012 to 0.05) and ICI: 0.043-0.061 for random forest and AUC-ROC 0.53-0.61, BSS equal to zero and ICI: 0.054-0.067 for hierarchical Bayesian logistic regression (HBLR). No pollutant reached a highest density interval excluding OR = 1. The polygenic risk score(PRS) was the only term non-null at all three ages in the adjusted models. While gene-environment interactions were detected, effect sizes were negligible and statistically indistinguishable from zero. SignificanceWhere ambient pollution is low, cumulative early-life exposure adds little to long-term asthma prediction beyond genetic and contextual factors, and an improved exposure correction does not uncover an effect. ImpactRepeated bedroom measurements across infancy are rare, and short sampling windows make them hard to read as cumulative exposure. We corrected measured concentrations for pump-flow drift and for seasonal sampling bias on the pollutants and used two complementary machine learning models to predict interaction and risk. In this low-exposure Danish cohort, genetic risk and residential context dominated, and the measured pollutants added almost nothing to the asthma risk at 7,12 and 18 years. These null results indicate that early cumulative exposures of indoor and outdoor air pollution contribute little to nothing to asthma up to 18 years.

Rationale: Obstructive sleep apnea (OSA) is highly prevalent yet largely under-diagnosed. Current screening strategies rely on effortful questionnaires with modest accuracy, and prior machine learning models often require resource-intensive inputs and lack external validation. Objectives: To develop and validate machine learning models to predict OSA (apnea-hypopnea index [AHI4%][&ge;]5) and moderate-severe OSA (AHI4%[&ge;]15) using only routinely collected electronic health record (EHR) data, test cross-system transferability and compare performance with clinical screeners and a Bayesian risk-updating framework. Methods: Three tree-ensemble families (XGBoost, random forests, and histogram-based gradient boosting) were trained on an adult cohort with sleep-study results at Kaiser Permanente (KPSC), using full (106-feature) and minimal (age, sex, body-mass index, race/ethnicity) predictor sets and tuned by nested cross-validation. XGBoost models were selected, internally and temporally validated, and externally validated at University of Kansas Medical Center (KUMC). Measurements and Main Results: On our internal hold-out set (n=53,048), the full XGBoost model reached a ROC-AUC of 0.791 for any OSA and 0.768 for moderate-severe OSA. The minimal model retained most performance (0.764 and 0.736). Temporal performance was stable. Externally, frozen models showed lower AUCs (0.718 and 0.690 for full models; 0.725 and 0.706 for minimal models) but similar to KUMC-native models and improved further with transfer learning. The models significantly outperformed STOP-BANG (AUC difference 0.06-0.09 for predicting any OSA; p<0.001). Bayesian updating with select patient-reported items further improved discrimination. Conclusions: Machine learning models based on EHR data can predict OSA probability that outperform existing screeners, transfers across systems, and offers potential for EHR implementation.

Background: Observational studies have linked air pollution to otitis media, but confounding and reverse causation preclude causal inference. We used two-sample Mendelian randomization (MR) to assess genetic causal effects of six air pollutants on non-suppurative otitis media, represented by otitis media with effusion (OME). Methods: Exposure GWAS for PM2.5, PM2.5 absorbance, PM2.5-10, PM10, NO2 and NOx were obtained from UK Biobank (MRC-IEU), and the outcome was FinnGen R13 H8_NONSUPPNAS (12,397 cases, 464,237 controls). Inverse-variance weighting was primary, with MR-Egger, weighted median and mode, MR-PRESSO, leave-one-out and Steiger analyses, reverse MR and exploratory multivariable MR (MVMR). Asthma, allergic rhinitis and chronic rhinosinusitis served as positive controls, and three suppurative otitis media phenotypes served as negative controls. Results: After false-discovery-rate correction, none of the six pollutants was significantly associated with OME; the sole nominal signal was PM10 (OR=1.555, 95% CI 0.947-2.553, P=0.081), which reversed with the 88-SNP pooled set (OR=0.64, 0.24-1.69, P=0.365). At 80% power, minimum detectable ORs ranged widely from 1.69 to 32.09; only PM2.5-10 and PM10 had values near 2, so moderate effects (OR 1.2-1.5) cannot be excluded. Confirmatory positive controls using phenotype-specific instruments were strongly positive, whereas negative-control outcomes showed no associations (all FDR>0.85). Every nominal reverse-MR signal had a significant MR-Egger intercept (the PM2.5 absorbance signal also showed significant heterogeneity and MR-PRESSO tests), indicating horizontal pleiotropy rather than reverse causation. MVMR was exploratory only, as conditional F statistics were below 10. Conclusions: We found no evidence for large-effect genetic causal associations between the six air pollutants and OME. These null findings argue against large, lifetime-average causal effects, and the associations reported in observational studies may instead reflect residual confounding, shared comorbidity or differences in exposure windows.

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: 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.

Background: Sleep-disordered breathing (SDB) is an established adverse prognostic factor in heart failure (HF). Sleep apnea-specific hypoxic burden (SASHB), which reflects the duration and depth of respiratory event-related oxygen desaturation, has shown stronger prognostic value than the apnea-hypopnea index (AHI) in general populations and patients with coronary artery disease. However, its clinical utility in patients hospitalized due to acute HF remains unclear. Objectives: This study evaluated the prognostic value of SASHB in patients hospitalized for acute HF and compared its performance with AHI for risk stratification and identification of patients who may benefit from positive airway pressure (PAP) therapy. Methods: This post hoc analysis included 230 patients from a prospective single-center observational cohort of adults hospitalized for acute HF. The primary analysis included 178 patients who did not receive PAP treatment. Associations of SASHB and AHI with the composite endpoint of cardiovascular death or rehospitalization for worsening HF were evaluated using Cox proportional hazards models. Model improvement after adding SASHB or AHI to a model including established clinical risk factors was assessed using changes in the C index and Brier score, reflecting changes in model discrimination and prediction error, respectively. In the full cohort, the interaction between PAP treatment and SDB metrics was evaluated using the g-formula. Results: During a median follow-up of 3.7 years, 58 of 178 patients (32.6%) reached the composite endpoint. Both SASHB and AHI were independently associated with the composite outcome after multivariable adjustment. However, only SASHB consistently predicted outcomes across analyses and improved model performance beyond established prognostic factors (bootstrap {Delta} C index, 0.020 [95% CI: 0.008 - 0.031]; bootstrap {Delta} Brier score, - 0.007 [95% CI: - 0.014 to - 0.001]), whereas AHI did not. In the analysis including PAP-treated patients, both SASHB and AHI showed interaction with PAP treatment. Conclusions: In patients hospitalized for acute HF, SASHB showed a consistent independent association with adverse clinical outcomes and provided additional prognostic information beyond AHI. SASHB may improve risk stratification and may help identify patients who are more likely to benefit from PAP therapy.

Background: The Lung Transplant Frailty Scale (LT-FS) is a validated binary measure of frailty in lung transplant candidates. We aimed to refine the LT-FS Base and Body Composition measures by developing a three-category classification to improve clinical risk stratification with serum albumin as a more clinically practical frailty biomarker. Methods: To develop the refined LT-FS, we applied a 4-step measure development method to a multicenter prospective cohort study of lung transplant candidates. Low albumin (<3.5 g/dL) replaced high serum C-reactive protein ([&ge;]10 mg/L) as the LT-FS biomarker. Cutpoints that balanced discrimination, risk prediction, and calibration were identified to define ''frail'', ''pre-frail'', and ''not frail'' categories. Next, we evaluated associations between the refined LT-FS and exercise capacity, patient-reported outcomes, and mortality using multivariate linear and Cox regression. Results: Among 419 participants, the refined LT-FS identified a graded increase in mortality risk across frailty categories. For example, compared with candidates who were not frail, pre-frailty and frailty by the LT-FS Body Composition were associated with a 2.9-fold (HR 2.9, 95% CI 1.2-6.9) and 7-fold (HR 6.9, 95% CI 2.6-18.2) higher risk of waitlist delisting or death, respectively. Pre-frailty and frailty by LT-FS Body Composition were associated with 2-fold (HR 2.1, 95% CI 1.2-3.8) and 3.6-fold (HR 3.6, 95% CI 1.8-7.3) higher risk of mortality after transplant. Relationships with exercise capacity and patient-reported disability were less consistent. Conclusions: A refined Lung Transplant Frailty Scale incorporating serum albumin demonstrates strong associations with waitlist and post-transplant mortality. The addition of a pre-frail category better captures the continuum of frailty and improves clinical risk stratification of lung transplant candidates.

Background: Obesity is associated with airway disease, but the contribution of specific thoracic adipose depots remains unclear. We examined whether intrathoracic adipose tissue (ITAT), defined as adipose tissue within the thoracic cavity excluding pericardial adipose tissue (PAT), was associated with airway phenotypes. Methods: We included Framingham Heart Study participants with CT-derived thoracic adipose tissue measures. Cross-sectional associations of ITAT with CT-based airway wall and lumen measurements, spirometry, and respiratory symptoms were evaluated using multivariable regression. Primary analyses used sex-specific ITAT z-scores and adjusted for waist circumference to account for central obesity. Secondary analyses examined sex-specific ITAT quintiles and compared ITAT and PAT in individual-depot and mutually adjusted models. Results: Among 2,825 adult participants (52.0% female), after adjustment for waist circumference, each 1-SD higher ITAT, relative to the sex-specific distribution, was associated with greater segmental ({beta} [95% CI]: 0.010 mm [0.006, 0.014]) and subsegmental airway wall thickness (0.006 mm [0.002, 0.009]), narrower segmental airway lumen diameter (-0.049 [-0.088, -0.011]), lower FEV1 percent predicted (-1.92 percentage points [-2.72, -1.12]), and higher odds of wheezing (OR [95% CI]: 1.16 [1.02, 1.31]) and dyspnea (1.22 [1.05, 1.42]). Associations were similar after further adjustment for PAT and across ITAT quintiles. In contrast, PAT was not independently associated with airway structural measures after adjustment for ITAT. Conclusion: Higher ITAT volume was associated with greater airway wall thickness and narrower airway lumen, as well as lower FEV1 and greater respiratory symptoms, independent of central obesity and pericardial adiposity. These findings support a depot-specific association of ITAT with airway disease.

Abstract Background: Respiratory failure (RF) and atrial fibrillation/flutter (AF/AFL) often occur together in older adults, but long-term national mortality trends for the two conditions combined have not been described. We examined trends and disparities in deaths with co-listed RF and AF/AFL among U.S. adults aged 65 years and older from 1999 to 2024, and projected rates through 2035. Methodology: We used the CDC WONDER Multiple Cause of Death database to identify deaths with ICD 10 codes J96 (RF) and I48 (AF/AFL) listed together. Age-adjusted mortality rates (AAMRs) per 100,000 were standardized to the 2000 U.S. population and stratified by sex, race/ethnicity, census region, urbanization, state, and place of death. Joinpoint regression was used to estimate annual percent changes (APCs) and average annual percent changes (AAPCs). Forecasts for 2025 to 2035 were generated with an exponential smoothing (ETS) model. Results: A total of 433,598 deaths were recorded. The overall AAMR rose from 12.8 in 1999 to 66.7 in 2024 (AAPC 8.11%; 95% CI, 7.35 to 8.87; P < 0.0001). Three segments were identified: an increase from 1999 to 2018 (APC 7.73%), a sharp rise from 2018 to 2021 (APC 18.10%), and a decline from 2021 to 2024 (APC -5.25%). Males had a higher AAMR and a steeper increase than females (AAPC 8.44% vs 7.81%). Non-Hispanic White adults had the highest mean AAMR, Hispanic adults had the largest relative increase (AAPC 8.72%), and Non-Hispanic Asian/Pacific Islander adults had the smallest (AAPC 6.23%). The Midwest had the fastest regional growth (AAPC 9.35%). From 1999 to 2020, non-metropolitan areas increased faster than metropolitan areas (AAPC 10.09% vs 8.21%). State AAMRs ranged from 24.7 in Ohio to 85.1 in Louisiana. Most deaths occurred in hospital inpatient settings (64.1%), followed by nursing homes (14.1%) and the home (11.9%). The model projects an AAMR of 122.96 per 100,000 by 2035 (95% prediction interval, 85.42 to 160.50). Conclusions: Mortality involving RF with AF/AFL among older U.S. adults increased more than fivefold over 25 years, with a marked surge during the COVID 19 period and only a partial decline afterward. Differences by sex, race/ethnicity, region, and urbanization point to groups that may benefit from earlier detection and coordinated cardiopulmonary care.

Background: Patients with heart failure (HF) experience high mortality, yet survival estimates remain challenging. Available prognostic tools commonly predict survival beyond 1-year, limiting goals of care discussions and enrollment on palliative care programs. The objective of this project was to develop a novel prognostic score to better predict 6-month mortality. Methods: Patients with HF admitted to the St. Boniface Hospital (Winnipeg, Canada) between January 1, 2019 and January 1, 2024 were identified. Patient characteristics, imaging parameters, laboratory values, and functional assessments (Palliative Performance Scale; PPSv2) were collected. Patients were randomly divided into development (80%) and validation (20%) cohorts. Penalized LASSO regression was used to select variables most predictive of 6-month mortality in the development cohort. Retained variables were incorporated into a multivariable logistic regression model to derive the CARDIO-PAL score, the performance of which was assessed in the holdout validation cohort. Results: Characteristics were similar between the development (n = 2036) and validation (n = 508) cohorts, with 6-month mortality rates of 29% and 31%, respectively. 8 candidate variables were retained: age (adjusted odds ratio [aOR] 1.01), PPSv2 (aOR 2.08), systolic blood pressure (aOR 1.14), discontinued use of angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker (aOR 2.58), beta-blocker (aOR 1.59), and mineralocorticoid receptor antagonist (aOR 2.67), right ventricular systolic pressure (aOR 2.06), and urea (aOR 2.13). The CARDIO-PAL score demonstrated good discrimination, with an area under the curve of 0.80 (95% confidence interval 0.75 - 0.84) and good calibration (slope 0.84, intercept -0.04). 6-month mortality across low, intermediate, and high-risk groups was 2%, 15%, and 54%, respectively. Conclusion: The CARDIO-PAL score is a novel tool to predict 6-month mortality in HF inpatients. A collaborative approach integrating CARDIO-PAL with other prognostic scores and serial clinical assessments has the potential to promote patient-centered care and improve referral to palliative care programs.

Abstract Background: Heart failure (HF) is a major public health challenge in ageing populations and a leading cause of unplanned hospital readmissions. Biological, social, and economic factors contribute to recurrent hospitalisation, but gaps remain in understanding how these factors interact and patterns of healthcare use following hospitalisation. Methods: We conducted an explanatory sequential mixed-methods study of patients aged ?60 years with an HF hospital admission between 2016 and 2020 who completed the Sax Institute?s 45 and Up Study baseline questionnaire. Data were linked to Admitted Patient Data Collection, Medicare Benefits Schedule, and Pharmaceutical Benefits Scheme data. HF readmission within 30 days of the index admission was the outcome. Cox proportional hazards models estimated crude and adjusted hazard ratios (HRs and adjHRs) with 95% confidence intervals (CIs). Kaplan-Meier curves and log-rank tests compared survival between groups. Time-series plots mapped healthcare interactions over 1 year following the index admission. Results: Among 4,252 patients with HF, 409 were readmitted within 30 days. Significant predictors included age ?80 years (adjHR, 1.30; 95%CI, 1.01?1.67), 1 chronic condition (adjHR, 2.49; 95%CI, 1.41?4.40), ?2 chronic conditions (adjHR, 1.75; 95%CI, 1.04?2.95), and alcohol consumption above national guidelines (adjHR, 1.45; 95%CI, 1.06?1.98). Time-series analysis indicated that participants with readmission had more frequent out-of-hospital health service contact following their initial HF admission than those without readmission. Conclusions: Older age, chronic conditions, and high alcohol intake increased 30-day HF readmission risk. Differences in healthcare use after admission highlight opportunities for targeted and coordinated post-discharge care.

Background: Multiple factors can drive heat susceptibility, but are typically analyzed individually. This study investigates high-dimensional heterogeneity across multiple domains in the effect of hot days on emergency hospitalizations across German districts. Methods: We obtained emergency hospitalization rates per 1,000 in 3-day windows for the summers of 2005-2023 from the German Diagnosis-Related Groups-Statistic, linked with spatio-temporal temperature information. A hot day was defined as at least one day above the district-level 98th percentile heat index. Multiple characteristics of the demographic, health/social, and environmental domains were selected as potential effect modifiers. We implemented a generalized random forest, where outcome and propensity scores were modeled with fixed effects estimators to control for seasonality and district-level confounding. We divided the conditional average treatment effects (CATE) into quartiles to map their spatial distribution and identify key effect modifiers via the variable importance and observed indicator profiles across quartiles. Results: We found substantial variation across districts regarding heat-related emergency hospitalizations. The top three indicators for predicting this effect heterogeneity were the percentage of people seeking protection, population below age 6, and air pollution levels. The observed indicator profile for the most susceptible districts was characterized by a larger percentage of vulnerable populations, higher car density, and fewer hospital beds. Conclusion: There is substantial variation in heat susceptibility across and within districts in Germany. We propose an approach that can help understanding such complex interplay of indicators from different domains driving heterogeneity.

Background Several disease-modifying agents for transthyretin amyloid cardiomyopathy (ATTR-CM) have been investigated, but differences in trial design and background stabilizer use complicate interpretation of their efficacy. This meta-analysis assessed their efficacy overall and by background stabilizer use. Methods PubMed, Embase, and the Cochrane Library were searched through September 2026 for phase III randomized, placebo-controlled trials of disease-modifying therapy in ATTR-CM. Primary endpoints were all-cause mortality, 6-minute walk distance (6MWD), Kansas City Cardiomyopathy Questionnaire-Overall Summary (KCCQ-OS) score, and N-terminal pro-B-type natriuretic peptide (NT-proBNP), pooled with a random-effects model. Treatment effects were compared between the monotherapy and baseline stabilizer strata using a mixed-effects model. Results Five trials (ATTR-ACT, APOLLO-B, ATTRibute-CM, HELIOS-B, CARDIO-TTRansform; N=3497) were included. Disease-modifying therapy reduced all-cause mortality (risk ratio, 0.76; 95% confidence interval [CI], 0.66-0.87; I2=0%) and improved 6MWD (pooled standardized mean difference [SMD], 0.43; 95% CI, 0.20-0.67), KCCQ-OS score (SMD, 0.37; 95% CI, 0.19-0.55), and NT-proBNP (SMD, -0.53; 95% CI, -0.89 to -0.18). The mortality benefit was similar whether therapy was given alone or added to a background stabilizer (pooled risk ratio, 0.77 vs. 0.71; P for interaction = 0.59), but treatment effects on 6MWD (pooled SMD, 0.49 vs. 0.14), KCCQ-OS score (0.44 vs. 0.07), and NT-proBNP (-0.65 vs. -0.13) were smaller among patients receiving a background stabilizer (P for interaction = 0.012, <0.001, and 0.029, respectively). Conclusion Disease-modifying treatments reduced all-cause mortality and favored functional capacity, health status, and NT-proBNP outcomes in patients with ATTR-CM. Exploratory subgroup analyses showed smaller treatment effects on 6MWD, KCCQ-OS score, and NT-proBNP among patients receiving a background stabilizer, with no statistically significant difference in the mortality effect according to baseline stabilizer use.

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.

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: Cardiovascular (CV) screening is critical for personnel deploying to Antarctica, where geographic isolation and limited medical care make the management of acute cardiovascular events challenging. The utility of contemporary risk prediction tools such as the AHA PREVENT equations in this unique population remains uncertain. Methods: We retrospectively analyzed 154 individuals referred for enhanced CV evaluation before deployment to the U.S. Antarctic Program (USAP). Participants were evaluated across three temporal cohorts (2018-2020, 2021-2023, and 2024-2026). Demographics, CV risk factors, cardiac testing, and deployment clearance outcomes were collected. Risk was estimated using the Framingham Risk Score (FRS), AHA/ACC Pooled Cohort Equations (PCE), and AHA PREVENT risk models. Results: The cohort was predominantly male (79.9%; mean age 44.6 {+/-} 14.6 years), and abnormal ECG was the most common referral trigger (70.8%). Median 10-year risk was low by PREVENT and FRS and borderline elevated by PCE (PREVENT 2.7% [IQR 1.1-6.6], FRS 2.3% [IQR 0.5-7.1], and PCE 6.1% [IQR 2.4-9.2]). Among 72 participants with all scores, 51% were classified discordantly; PREVENT ASCVD agreed closely with FRS (=0.77) but poorly with PCE (=0.24-0.35), which consistently estimated higher risk. PCE was calculable in 52.6% versus 70.8% for PREVENT. Overall, 132 participants (85.7%) were cleared for deployment, while 8 (5.2%) were not physically qualified (NPQ); 6 of these NPQs were due to cardiovascular causes. Of 22 cardiac medical evacuations during the study period, 21 involved individuals without prior advanced cardiovascular screening. One screened individual was evacuated for symptoms subsequently found to have no cardiac cause. Conclusions: Risk calculators disagree substantially in the fitness-selected population. We recommend PREVENT as the primary risk-stratification tool as it is the most contemporary derivation, incorporates renal function, is race-agnostic, extends to heart failure, applies from age 30, and agrees closely with FRS, easing transition. Targeted imaging, including coronary artery calcium scoring, may supplement intermediate-risk candidates.

Background. Coronary CT angiography (CCTA) and calcium scoring detect subclinical coronary atherosclerosis, but the results of community based screening in asymptomatic adults with cardiovascular risk factors are poorly described. Objective. To describe the results of the first phase of a county-funded CT screening program and their variation by demographics, health system, time, and ZIP code. Methods. Retrospective analysis of aggregate reports from the Broward County Florida Heart Project (May 2023 to September 2025). Eligible residents were those with insurance coverage, age 45 to 70 years (limited exceptions), free of known cardiovascular disease and symptoms, with at least one risk factor. Examinations at six hospitals in four health systems were classified by the program team as normal (Category 1), abnormal (Category 2), or very abnormal (Category 3), with a separate flag for other non coronary abnormality. We used Wilson confidence intervals and chi square, trend, and Mantel Haenszel tests. Results. Among 7,135 participants (58.3% men; 79.0% White, 8.0% Black, 17.2% Hispanic), 43.2% had Category 1, 45.4% Category 2, and 11.4% Category 3 results; 56.8% (95% CI, 55.7% to 58.0%) had abnormal or very abnormal results. The proportion rose from 39.3% at 45 to 55 years to 78.5% at 71 or older (trend P<0.001) and was higher in men than women (68.9% vs 39.8%; odds ratio [OR], 3.36; 95% CI, 3.05 to 3.71). Rates were lower in Black than White participants (38.9% vs 58.6%; hospital adjusted OR, 0.45; 95% CI, 0.38 to 0.54) and in Hispanic than non Hispanic participants (51.0% vs 57.8%). Abnormal results ranged from 39.5% to 60.3% across health systems (P<0.001). Variation across the 50 highest volume ZIP codes did not exceed chance (P=0.53). Conclusions. More than half of insured, asymptomatic adults with risk factors screened in this community program had abnormal results, with steep age and sex gradients and unexplained variation across health systems. Black, Hispanic, and female residents were under-represented relative to the county

Background: CPR is the primary intervention for cardiac arrest, and the mortality trends before and after the pandemic remain are not fully understood. Methods: This cross-sectional study used the National Inpatient Sample (NIS) database from 2016-2022 and International Classification of Diseases, Tenth Revision (ICD-10) codes to evaluate the mortality trend for adult patients undergoing CPR after in-hospital cardiac arrest (IHCA). Primary exposures were calendar year, demographics, socioeconomic factors, and hospital characteristics. Multivariable weighted logistic regression was performed to identify predictors of mortality after CPR for IHCA. Results: Among 612,930 weighted adult hospitalizations undergoing CPR for IHCA between 2016 and 2022, overall in-hospital mortality was 67.8%. Risk-adjusted mortality declined through 2019 before increasing sharply during the COVID-19 pandemic, peaking in 2021 and remaining above pre-pandemic levels in 2022. Compared with 2016, admission in 2020 (aOR 1.18, 95% CI 1.12-1.25; p<0.001) and 2021 (aOR 1.26, 95% CI 1.19-1.33; p<0.001) was independently associated with higher mortality, whereas admission in 2019 was associated with lower mortality (aOR 0.89, 95% CI 0.84-0.95; p<0.001). Higher mortality was also associated with increasing age (aOR 1.01 per year; p<0.001), female sex (aOR 1.05, 95% CI 1.02-1.08; p<0.001), Black (aOR 1.14, 95% CI 1.10-1.18; p<0.001) and Hispanic race/ethnicity (aOR 1.20, 95% CI 1.15-1.26; p<0.001), and self-pay insurance (aOR 1.62, 95% CI 1.51-1.74; p<0.001). Protective factors included private insurance (aOR 0.91, 95% CI 0.87-0.94; p<0.001), residence in the highest income quartile (aOR 0.91, 95% CI 0.87-0.94; p<0.001), and treatment at private, non-profit hospitals (aOR 0.89, 95% CI 0.85-0.94; p<0.001). Conclusion: This large nationwide patient database analysis demonstrates that mortality following CPR for IHCA declined before the COVID-19 pandemic but increased during 2020, remaining above pre-pandemic levels through 2022. Independent predictors of mortality reveal socioeconomic disparities in mortality outcomes.