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10513 Background: Prior reports of high frequency of BRCA1 and BRCA2 ( BRCA1/2 ) PVs among South Asian women from Bangladesh, India, Nepal, and Pakistan with breast or ovarian cancer require validation in large prospective unselected cohorts. Methods: Individual-level anonymized patient data from two prospective cohorts were pooled to evaluate the prevalence of germline PVs among South Asian women: the South Asian Regional Alliance for Hereditary Cancers (SARAH Consortium) in Bangladesh, Nepal, and Pakistan, and the previously reported Nurse-led Genetic Counseling and Awareness (NuGenA) expansion cohort in India. Both studies enrolled consecutive cases of histologically confirmed newly diagnosed breast or epithelial ovarian cancer irrespective of age at diagnosis or family history. In both studies, participants completed questionnaires on personal and family history (first-, second- or third-degree) and provided blood for germline genetic testing after written consent. PVs in cancer predisposition genes including ATM, BARD1, BRCA1, BRCA2, BRIP1, CHEK2, MLH1, MSH2, MSH6, PALB2, RAD51C, RAD51D, and TP53 were identified as part of the studies utilizing a multigene panel and the results were returned to ordering clinicians. The data cutoff date for this analysis was November 30, 2025. Results: The analysis included 1,711 South Asian women with breast (n=1,210) or ovarian (n=501) cancer. A family history of breast or ovarian cancer was noted in 177 (10.3%). Among breast cancers, 94% were invasive, 84% were ductal, 35% were triple-negative and over two-thirds presented with nodal involvement. Germline PVs across 13 predisposition genes were observed in 17.7% of breast and 35.9% of ovarian cancers. Notably, BRCA1/2 PVs accounted for most of these PV (13.6% of breast and 28.3% of ovarian cancer cases) with PVs in all genes except BRCA1/2 under 1%. The frequency of BRCA1/2 PVs was 24.4% in triple-negative breast cancer and 8.0% in ER+/HER2-. BRCA1/2 PV carriers were diagnosed with breast cancer at a younger age than non-carriers (Median age: 42 vs 46 years, P<0.001), with no age difference observed in ovarian cancer (50 vs. 51 years, P=0.79). Notably, among the 306 BRCA1/2 PV carriers identified, 229 (74.2%) did not report a family history of breast or ovarian cancer. Conclusions: We confirm a high prevalence of BRCA1/2 PVs among unselected South Asian women with either breast or ovarian cancer. The observations of younger age at cancer diagnosis, and low rates of family history support the need for population-specific germline genetic testing and risk management strategies in South Asia.

Abstract Rationale Oscillometry measures the mechanical properties of the respiratory system using the response to pressure signals superimposed on tidal breathing. The JaegerTM IOS device generates “pulse packets” of harmonic frequencies between 5 and 35 Hz in 5 Hz intervals, while the Resmon ProTM V3 FOT and Thorasys TremofloTM C-100 employ prime pseudorandom sine waveforms between 5-19 Hz and 5-37 Hz, respectively. Prior studies have raised concerns about harmonic distortion with the IOS technique, but a direct comparison of reproducibility using current devices has not been performed. Methods Healthy volunteers with normal spirometry performed weekly oscillometry following ERS technical standards using JaegerTM and ResmonTM or ThorasysTM oscillometers in random order for 10 consecutive weeks. Z scores were calculated using Berger and Oostveen normative equations. Means, pooled within-subject distributions and interquartile range, standard error (SEM), coefficient of variation (CVw%), absolute and relative minimal detectable change (MDC, MDC%), and between device and subject intraclass correlation coefficients (ICC) were calculated for R5, R10/R11, R20/R19, R5-R20/R5-R19, X5, AX, and Fres and compared. Results Fifteen participants completed 10 weekly measurement sessions with JaegerTM and ResmonTM and twelve with JaegerTM and ThorasysTM. One subject did not complete the ResmonTM analysis due to intolerance; another subject only completed three weeks of ThorasysTM prior to work assignment change. The majority (65%) were female with mean (SD) age of 37.7 (10.2) years and BMI 27 (5) kg/m2. Rrs reproducibility was excellent across devices (ICC &gt; 0.89), with more variability for X5, AX, and Fres (ICC 0.66 - 0.93) and little difference between devices (Figure 1). Small, systematic increases in mean Rrs were seen with JaegerTM compared to ResmonTM (5-10%, p &lt; 0.008) and TremofloTM (5-12%, p &lt; 0.02), with higher mean AX vs ResmonTM (44%, p = 0.002) and lower Fres vs TremofloTM (19%, p = 0.02). There was high ICC (0.97-0.99) across ResmonTM and TremofloTM Rrs comparisons with minimal bias. Intra-subject reproducibility was similar between devices. MDC% was 22.7-39.4% for Rrs values, 52.7-70.8% for X5, and 131-142% for AX. Conclusions JaegerTM, ResmonTM, and ThorasysTM oscillometers performed similarly in healthy adults over 10 weeks, with small but systematic measurement differences seen with the JaegerTM device. Rrs and Fres values demonstrated the greatest reproducibility both within and between subjects. Reactance measurements had less between-device agreement and higher MDC. Rrs, X5, and AX changes of up to 40%, 70%, and 140% over time may be due to normal subject and measurement variation. This abstract is funded by: 2024 Pulmonary, Critical Care, and Sleep Medicine Schuen Family Innovation Award

Abstract Rationale Propofol-based sedation in the intensive care unit (ICU) is associated with clinically significant hemodynamic instability, including hypotension. Previous studies have identified hypotension as a key predictor of adverse outcomes in critically ill patients. However, no validated tools currently exist to systematically predict this risk. Therefore, this study aimed to develop and externally validate machine-learning (ML) models for predicting the risk of hypotension in ICU patients receiving propofol sedation. Methods We developed ML models using retrospective data from adult, mechanically ventilated ICU patients who received propofol infusion for at least 24 hours across four Mayo Clinic sites between 2018 and 2024. The primary outcome was hypotension, defined as ≥ 2 consecutive MAP readings ≤60 mmHg within 15 minutes during the first 6 hours of propofol sedation. The MIMIC-IV dataset was used for external validation. Features were selected using a union of Boruta and LASSO-penalised logistic regression. Four algorithms (elastic net-penalised logistic regression, random forest, LightGBM, and multilayer perceptron) were tuned using Bayesian optimization to minimize cross-entropy loss during leave-one-site-out cross-validation (LOSO-CV). The algorithm with the lowest cross-entropy loss during LOSO-CV was chosen as the best-performing algorithm. Classification thresholds were selected to target sensitivity ≥80%. Model performance was evaluated using area under the receiver operating characteristic curve (AUC-ROC), calibration slope and intercept, sensitivity, and specificity. Feature importance was evaluated using SHapley Additive exPlanations (SHAP). Results The development cohort included 16,361 patients from Mayo Clinic, of whom 3,482 (21.3%) developed hypotension. The external validation cohort comprised 4,684 patients from MIMIC-IV, with 115 (2.5%) experiencing hypotension. Patient characteristics included age, admission Sequential Organ Failure Assessment (SOFA) scores, starting propofol dose, ICU admission source, and comorbidities. The best-performing algorithm was LightGBM. The average AUC-ROC during LOSO-CV was 0.70 (95% confidence interval [CI] 0.68-0.71), versus 0.64 during external validation. The average calibration slope and intercept during LOSO-CV was 1.08 (95% CI 0.89-1.27) and 0.08 (95% CI -0.25-0.41), respectively, versus 0.78 and -2.07 during external validation. The average sensitivity and specificity during LOSO-CV was 0.81 (95% CI 0.79-0.83) and 0.47 (95% CI 0.42-0.52), respectively, versus 0.54 and 0.65 during external validation. Conclusions We developed and externally validated an ML-based classifier that demonstrates moderate discrimination for predicting propofol-induced hypotension in mechanically ventilated ICU patients. This tool may facilitate early identification of high-risk patients and enable proactive hemodynamic management strategies. Further prospective validation and assessment of clinical impact are warranted. This abstract is funded by: None

Abstract Rationale Propofol sedation in mechanically ventilated patients often causes clinically significant hypotension. While we concurrently developed and validated a machine-learning (ML) classifier to predict hypotension risk, accurately estimating the timing of hemodynamic deterioration could enable more precise, proactive interventions. This study aimed to develop and externally validate ML models that predict not only whether hypotension will occur, but also when it is likely to develop after propofol initiation. Methods ML models were developed using retrospective data from adult, mechanically ventilated ICU patients who received propofol infusion for ≥24 hours across four Mayo Clinic sites from 2018 to 2024 and developed hypotension (≥2 consecutive MAP readings ≤60 mmHg within 15 minutes) during the first 6 hours of sedation. The MIMIC-IV dataset was used for external validation. Features were selected using a union of Boruta and LASSO-penalised quantile regression. Two quantile algorithms (LASSO-penalised quantile regression and LightGBM) were trialed to predict the conditional median and interquartile range (IQR) of time to hypotension. In addition, two probabilistic algorithms (NGBoost and Mixture Density Network) were trialed to predict the full conditional distribution. The quantile models were tuned via Bayesian optimisation to minimise pinball loss during leave-one-site-out cross-validation (LOSO-CV), whereas probabilistic models were tuned to minimise the Continuous Ranked Probability Score. The algorithm with the lowest average pinball loss was deemed the best-performing algorithm. Model performance was evaluated using median absolute error (MAE), IQR coverage, and IQR width. Feature importance was evaluated using SHapley Additive exPlanations (SHAP). Results We included 3482 patients from the Mayo Clinic for model development, with 115 patients from the MIMIC-IV dataset used for external validation. Patient characteristics in the modeling dataset included age, sex, admission SOFA scores, weight, ICU admission source, and initial propofol dose. The best-performing algorithm was LightGBM-based quantile regression. The average MAE during LOSO-CV was 4.11 hours (95% confidence interval [CI] 3.98-4.23), versus 4.83 hours during external validation. The average IQR coverage and width during LOSO-CV was 0.49 (95% CI 0.48-0.49) and 7.12 (6.89-7.35), respectively, versus 0.07 and 6.51 during external validation. Conclusions We developed and externally validated a quantile regression model that predicts both the timing and uncertainty of propofol-induced hypotension in mechanically ventilated ICU patients. This model provides clinicians with actionable time estimates that may facilitate preemptive hemodynamic interventions and optimize sedation management. Prospective validation, expanded predictors and clinical impact assessment are warranted. This abstract is funded by: None

Abstract Rationale Lung-protective ventilation protocols use predicted body weight (PBW) to set tidal volumes. However, biases exist in these formulas, with overestimation of lung capacity in older, shorter, female, and non-White patients—groups that experience higher mortality rates during invasive mechanical ventilation (IMV). This study investigated associations between pre-admission forced vital capacity (FVC), driving pressure, and patient outcomes. Methods This multicenter retrospective study included adults (18-95 years old) with pre-admission spirometry who required IMV between 01/01/2016 and 05/31/2025 across the Mayo Clinic, spanning 11 ICUs within 5 hospitals in 4 states. Patients were stratified by FVC/PBW ratio quartiles. The primary outcome was 28-day mortality. Trends were assessed using linear or logistic regression, with FVC/PBW as a continuous variable. Mediation analysis examined pathways between FVC/PBW and mortality, with driving pressure (cm H2O, standardized) as the mediator, and adjusted for covariates including age, sex, SOFA score, and BMI. Absolute risk differences and number needed to harm (NNH) were estimated for key thresholds (e.g. FVC/PBW ≥45 mL/kg, PFTs within 2 years). Results Among 4,839 patients (38.7% female, mean (±SD) age 64.5±13.5 years), FVC/PBW ratio demonstrated substantial individual variation, with a mean of 48.3 ± 13.4 mL/kg. There was systematic variation by demographics (Figure): females had significantly lower ratios than males (45.4 versus 50.2 mL/kg, P &lt; 0.001), and ratios were lower with older age from 52.5 mL/kg in patients &lt;65 to 43.2 mL/kg in those ≥85 years (P &lt; 0.001 for trend). Patients in the lowest FVC/PBW quartile had higher 28-day mortality compared to the highest quartile (26.0% versus 17.0%, P &lt; 0.001) and higher driving pressures (14.0 versus 10.0 cm H2O, P &lt; 0.001; Figure). In mediation analysis (n = 4,147), driving pressure mediated the relationship between FVC and mortality. Each 1-SD increase in FVC/PBW was associated with 0.364-SD lower driving pressure (P &lt; 0.001) and 2.4% lower mortality. The adjusted indirect effect through driving pressure was significant (ACME -0.024, 95% CI: -0.029 to -0.019), accounting for the mortality effect. Sensitivity analyses across binary thresholds, age quartiles, outlier exclusion, and obstructive pathology confirmed robust mediation. Patients with FVC/PBW &lt;45 mL/kg (N = 1,644; 39.6%) had 28-day mortality of 17.8% versus 13.5% for ≥45 mL/kg (risk difference 4.3%, 95% CI 1.8-6.8%; NNH: 23 patients). Conclusions Pre-admission lung function was strongly associated with mortality in adults receiving IMV through driving pressure-mediated pathways. Driving pressure is a modifiable target, especially in patients with smaller lung capacities at highest risk for ventilator-induced lung injury. Further prospective investigation is needed to confirm these findings. This abstract is funded by: National Heart, Lung, and Blood Institute (NHLBI) of the National Institute of Health (NIH) Grant Number K23HL151671

Abstract Introduction Solid tumors modify local angiogenesis and co-opt surrounding vasculature to maintain energy supply required for ongoing growth. This process is often complicated by metastasis and hemorrhage. Pulmonary tumors can rarely create intrapulmonary shunt physiology and hypoxemia in this manner; this has been described most often in bronchoalveolar carcinoma. We present a case of malignant intrapulmonary shunt occurring with spindle cell sarcoma, which resolved with arterial embolization. Case Presentation A 72-year-old male with a history of stage IV spindle cell sarcoma with abdominal, vertebral, and pulmonary metastases presented to an outpatient appointment with three weeks of fatigue and decreased exercise tolerance. He had no history of parenchymal lung disease nor any home oxygen requirement. In clinic, he was found to require 6L per minute of supplemental oxygen to maintain saturation of 88%. He was directly admitted to the oncology service. Initial laboratory evaluation was unremarkable. CT imaging demonstrated normal lung parenchyma, no pulmonary embolism, and an 11cm vascular tumor involving proximal branches of the local pulmonary artery (Figure). Transthoracic echocardiography (TTE) with agitated saline revealed a large intrapulmonary shunt. As the patient had limited response to oxygen therapy, this intra-tumoral shunt was deemed the most likely cause of his hypoxemia. Surgical resection was considered but was deemed to confer limited clinical benefit in the setting of multiple metastases. Embolization of the local pulmonary artery was therefore pursued with Interventional Radiology. The patient tolerated the procedure well with complete resolution of hypoxemia post-procedure. His energy and mobility improved, and he was discharged home. Three months later, he continued to require no supplemental oxygen. Discussion While shunt physiology is commonly part of the differential for hypoxemia, it is rarely described as an oncologic complication. This appears to be the first report of such pathology in a sarcoma patient, and the first to report shunt resolution through arterial embolization rather than surgical resection or systemic anti-tumor therapy. In addition, previous cases typically describe shunt detection via selective pulmonary arterial occlusion by Swan-Ganz catheter, whereas here the noninvasive TTE with agitated saline was sufficient. While the few existing reports highlight good response to lobectomy or pneumonectomy, this case highlights the potential for minimally invasive vascular intervention. Despite the metastatic nature of this patient’s disease, he received significant, rapid physiologic and functional benefit from a procedure with less morbidity. Maintaining suspicion of intra-tumoral shunt physiology is appropriate for patients with vascular pulmonary masses and hypoxemia. This abstract is funded by: None

Abstract Introduction Drug-induced pneumonitis is an important mimic of disseminated infection in immunocompromised hosts. Lenalidomide, an immunomodulatory agent widely used in multiple myeloma, has been associated with organizing and eosinophilic pneumonitis, and occasionally sarcoid-like granulomatous inflammation. The coexistence of acute fibrinous injury and granulomatous inflammation is exceptionally rare. We present a case of lenalidomide-associated acute fibrinous and granulomatous pneumonitis presenting with a miliary pattern following autologous stem cell transplant. Case Presentation A 44-year-old woman with kappa light-chain multiple myeloma, chronic kidney disease stage V, presented with 10 days of fever (to 105 °F), dry cough, hemoptysis, dyspnea and hypoxia requiring high flow oxygen. She had been on maintenance therapy with lenalidomide, daratumumab, and weekly dexamethasone after undergoing an autologous stem cell transplant for multiple myeloma, last administered one week before symptom onset.. CT chest (Figure 1-1) showed innumerable randomly distributed micronodules with ground-glass opacities and septal thickening, suggesting disseminated infection. Laboratory results revealed eosinophilia (950/µL), neutropenia, and profound hypogammaglobulinemia. Empiric piperacillin-tazobactam, doxycycline, and fluconazole were started for possible bacterial and/or fungal infection (history of positive Coccidioides IgM). Two bronchoscopies demonstrated multifocal hypervascular airway lesions. BAL differential showed 15 % eosinophils and &gt; 30 % lymphocytes; cultures, Fungitell, and comprehensive testing for bacterial, fungal, mycobacterial, viral, and parasitic pathogens were negative. Cryobiopsy revealed acute fibrinous and granulomatous pneumonitis with negative GMS and acid-fast stains. Following withdrawal of lenalidomide and daratumumab, the patient’s symptoms and CT findings improved without further antimicrobial therapy within the same hospitalization (Figure 1-2). She received an 8-week prednisone taper with inhaled pentamidine prophylaxis. Follow-up CT showed complete clinical and radiographic resolution (Figure 1-3), confirming a grade 3 drug-induced pneumonitis. A bronchoscopy will be repeated to assess the endobronchial vascular lesions. Discussion Lenalidomide related lung injury typically manifests as organizing or eosinophilic pneumonitis. The presence of both fibrinous and granulomatous inflammation suggests a mixed hypersensitivity and immune-complex reaction possibly potentiated by immune reconstitution after autologous transplant. The miliary pattern and BAL eosinophilia closely mimic disseminated fungal or mycobacterial infection. A Naranjo Adverse Drug Reaction Probability Scale score of 7 supported a highly probable causal relationship. Awareness of this pattern is essential, as prompt drug cessation and corticosteroid therapy are curative and prevent progression to fibrotic lung injury. This abstract is funded by: None

Abstract Rationale Alpha-1 antitrypsin deficiency (AATD) continues to be underdiagnosed. The estimated prevalence of the most common severe deficient genotype, PI*ZZ, is ∼1:3500 or 100,000 Americans but only ∼10,000 such individuals have been recognized. Diagnostic delays persist and these delays are associated with worse disease. High-resolution CT (HRCT) imaging may contain latent phenotypic AATD signatures that are not visibly apparent to clinicians. We hypothesized a deep learning model could identify imaging patterns predictive of AATD from inspiratory HRCT scans. Methods Confirmed genotypes and HRCT scans were pooled from COPDGene, SPIROMICS, and GRADS, yielding 350 MM, 102 MZ, and 69 ZZ participants. Only scans reconstructed with smooth kernels (GE STANDARD, Philips B, Siemens B31f/B35f) and slice thickness &lt; =1.25mm were used. The cohort was selected to have similar distributions of quantitative emphysema, sex, and age by matching five MZ and two MM subjects to each ZZ subject (&lt;10-year age difference, same sex, and &lt;3% difference in %LAA). Tobacco smoke-exposure was more prevalent among the MM/MZ groups given these were predominantly from the tobacco smoke-enriched COPDGene and SPIROMICS cohorts. A DenseNet121 convolutional neural network (CNN) was trained using the MONAI platform to predict genotype by outputting probabilities for MM, MZ, and ZZ. Input to the CNN was a three-channel LAA-910, LAA-950, and LAA-970 map downsampled to 2.0mm3 resolution to remove bias due to differences in scanner and image reconstruction parameters. Five-fold cross-validation was performed. Area under the curve (AUC) was calculated for classification of ZZ vs other genotypes (MM/MZ) and MZ vs MM, with statistical significance assessed at p &lt; 0.05. Results The three groups were similar across age (mean±SD years: MM 57.9±8.8, MZ: 57.5±8.9, ZZ: 55.0±10.7), sex (% female: MM: 48.2%, MZ: 46.7%, ZZ: 47.6), and emphysema (LAA-950%: MM: 13.0±12.2, MZ: 12.4±11.8, ZZ: 13.1±11.9). There was a higher prevalence of ever smokers among MM (100%) and MZ (91.7%), compared to ZZ (47.6%). The model achieved an AUC of 0.806 (p &lt; 0.001) for classification of ZZ vs other genotypes, demonstrating robust discriminatory ability for homozygous AATD. Classification of MZ vs other genotypes did not achieve an AUC significantly greater than 0.5. Conclusions This proof-of-concept study demonstrates that deep learning applied to routine inspiratory HRCT can accurately identify homozygous AATD. These findings support the potential role of imaging-based machine learning as a non-invasive screening tool for undiagnosed ZZ individuals. Future work will focus on cohort enrichment, integration with clinical parameters, and explainability of model-derived imaging features. This abstract is funded by: NIH NHLBI K23HL173570

ABSTRACT Genomic instability is a defining hallmark of breast cancer, yet the mechanisms by which tumors tolerate persistent DNA damage remain poorly understood. We performed a comprehensive, multi-cohort analysis of breast cancer datasets to define how DNA damage response (DDR) and replication stress tolerance (RST) networks are rewired in genomically unstable tumors. Using fraction of genome altered (FGA) as a chromosomal instability metric, we show that BRCA-mutant tumors exhibit elevated genomic instability coupled with increased expression of homologous recombination, Fanconi anemia, mismatch repair, base excision repair, and alternative end-joining pathways. Strikingly, heightened pathway activity correlates with increased genome alteration, supporting a model of damage tolerance rather than repair restoration. RST programs, including fork remodeling, protection, and single strand DNA gap suppression, further contribute to tumor fitness under replication stress. These adaptive states are enriched in aggressive subtypes, intensified with progression, and associate with pathway-specific mutational burden. Co-occurrence and mutual exclusivity mapping uncovered non-random subtype-relevant genetic interactions states among major drivers and DDR genes, nominating context-specific synthetic lethal opportunities. Our findings identify compensatory genome-maintenance programs as central drivers of tumor resilience and highlight pathway-specific vulnerabilities for targeted therapeutic intervention.