Yadav, H

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 > 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 < 0.008) and TremofloTM (5-12%, p < 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 < 0.001), and ratios were lower with older age from 52.5 mL/kg in patients <65 to 43.2 mL/kg in those ≥85 years (P < 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 < 0.001) and higher driving pressures (14.0 versus 10.0 cm H2O, P < 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 < 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 <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