Heybati, Kiyan

ABSTRACT Introduction Patients often receive propofol for sedation and clevidipine for hypertension in the intensive care unit. Both agents are formulated as a lipid emulsion, which may increase the risk of hypertriglyceridemia and acute pancreatitis with their coadministration. We aimed to determine the incidence of hypertriglyceridemia and acute pancreatitis with concurrent administration of propofol and clevidipine and identify patient risk factors for hypertriglyceridemia. Methods This was a multicenter retrospective study of adult patients (≥ 18 years old) receiving concurrent propofol and clevidipine infusions for ≥ 6 h. The primary outcome was the frequency of triglyceride levels greater than 400 mg/dL and acute pancreatitis. The secondary outcome was risk factors associated with hypertriglyceridemia during concurrent propofol and clevidipine infusions until both infusions had been discontinued. Univariable and multivariable logistic regression analyses were used to assess risk factors of hypertriglyceridemia in patients receiving concurrent propofol and clevidipine infusions. Results Across 1,226 patients, the median duration of infusion overlap was 12.4 [interquartile range (IQR) 8.3, 22.7] hours. The mean age of the cohort was 59.6 [standard deviation (SD) 14.9] years, and 59.4% were male. Assuming those without a triglyceride level drawn did not have hypertriglyceridemia, hypertriglyceridemia after initiation of propofol and clevidipine infusion was found in 7.7% [95% confidence interval (CI) 6.3% to 9.3%] of the entire cohort. Twenty‐six patients (2.1%) had a diagnosis of pancreatitis after initiation of lipid infusions, of which 12 cases were possibly infusion‐related, and one case was likely infusion‐related. After calculating the Naranjo Scale, all 13 cases of pancreatitis were possibly related to propofol or clevidipine infusions. Based on a multivariable logistic regression of those with a triglyceride level available, risk factors for hypertriglyceridemia included pancreatitis within the past 5 years [adjusted odds ratio (aOR) 2.77, 95% CI, 1.23 to 6.23] and statin use (aOR 1.94, 95% CI, 1.13 to 3.34). Conclusions In our cohort of patients receiving concurrent propofol and clevidipine, the frequency of hypertriglyceridemia and acute pancreatitis was low. Further comparative studies are warranted to assess the risk of pancreatitis with the addition of clevidipine to propofol.

Background Cardiac involvement by extranodal natural killer/T-cell lymphoma (ENKTL) and aggressive NK-cell leukaemia (ANKL) is exceedingly rare and presents with heterogeneous manifestations, often leading to diagnostic uncertainty and delay. We contribute three additional biopsy- or autopsy-proven cases and place them in the context of a literature review of previously reported cases classified according to the 5th edition of the World Health Organization (WHO) Classification of Hematolymphoid Tumors. Case series Three men (ages 58–72) presented with diverse cardiac manifestations. A 72-year-old with progressive dyspnoea and cardiogenic shock had cMRI findings interpreted as myocarditis on an infiltrative substrate; endomyocardial biopsy confirmed cardiac infiltration by ENKTL. Despite brentuximab vedotin (BV)-based therapy he died shortly thereafter. A 60-year-old with encephalopathy, biventricular dysfunction, and widespread nodal/multiorgan disease had nodal pathology consistent with EBV-negative ANKL according to the criteria of the 5th edition of the WHO Classification of Hematolymphoid Tumors; he declined chemotherapy and died within six days, with autopsy confirming multiorgan (including myocardial and bone marrow) infiltration. A 58-year-old with prior sinonasal ENKTL treated with four cycles of SMILE developed a new right atrial mass one month later; the mass was diagnosed by cMRI/PET-CT concordance and an extracardiac (EBUS-lung) tissue confirmation of the same recurrence. BV followed by gemcitabine/oxaliplatin failed to control progression and he died approximately one month after cardiac spread. Across cases, CD56 was positive in all, EBER in two, and CD30 was expressed variably. Conclusions Cardiac ENKTL/ANKL is a rapidly fatal entity with protean features. Multimodal imaging aids early recognition and tissue targeting, but definitive diagnosis rests on tissue confirmation. L-asparaginase-based chemotherapy remains the treatment backbone; CD30- and PD-L1-guided agents such as BV, PD-1 blockade, and daratumumab may offer transient control after biomarker-guided selection but lack randomized data in cardiac disease. Across the reported experience, survival after cardiac involvement was uniformly short, typically days to a few months. Our three cases add to this literature by illustrating the diversity of cardiac presentations, the variable CD30 expression relevant to brentuximab vedotin use, and the contribution of multimodal imaging to diagnosis. Findings from our series and the pooled literature are descriptive rather than constituting a formal survival analysis, and underscore the need for earlier diagnosis and prospective investigation.

IMPORTANCE: Phenobarbital use in ICU alcohol withdrawal syndrome (AWS) may reflect severe or refractory withdrawal and treatment escalation, making multicenter data important to understand its use within ICU practice and its relationship with clinical outcomes. OBJECTIVES: To evaluate phenobarbital use in real-world ICU AWS as a marker of severe or refractory withdrawal and treatment escalation. We also examined its association with time-to-discharge and safety outcomes compared with nonphenobarbital regimens. DESIGN, SETTING, AND PARTICIPANTS: Multicenter retrospective cohort study using multiple imputation and propensity score matching. Adults with AWS, defined as Clinical Institute Withdrawal Assessment for Alcohol, revised (CIWA-Ar) score greater than or equal to 10, were included if they received a phenobarbital-based or nonphenobarbital-based regimen in ICUs across a large academic health system from June 30, 2017, to July 1, 2024. Postoperative ICU admissions were excluded. MAIN OUTCOMES AND MEASURES: Primary outcomes were time to ICU and hospital discharge alive from treatment initiation. Safety outcomes were hypotension, new intubation, and aspiration within 48 hours. RESULTS: Of 1216 patients, 345 (28.4%) received phenobarbital. Before matching, phenobarbital-treated patients had higher CIWA-Ar, Acute Physiology and Chronic Health Evaluation III, and pre-ICU benzodiazepine exposure, consistent with more severe or refractory withdrawal. In adjusted time-to-discharge models, phenobarbital use was not significantly associated with time to hospital discharge alive (discharge rate ratio [DRR], 0.91; 95% CI, 0.74–1.12; p = 0.370) or ICU discharge alive (DRR, 0.77; 95% CI, 0.59–1.01; p = 0.057). Hypotension and intubation were similar, and aspiration was less frequent with phenobarbital. CONCLUSIONS: In a contemporary ICU cohort, phenobarbital use reflected treatment escalation in patients with more severe or refractory AWS. After adjustment, time to ICU and hospital discharge alive did not differ significantly, hypotension and intubation were similar, and aspiration was less frequent with phenobarbital. Future studies should define optimal timing, dosing, and patient selection.

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

Abstract Rationale Major advances in precision medicine and immunotherapy have led to improved outcomes for patients with metastatic cancer, but have also introduced new, severe toxicities that can necessitate ICU admission. The overall mortality remains substantial; however, prior studies have often been single centered with small sample sizes. Therefore, we sought to provide an overview of the current literature with pooled estimates of short- and long-term mortality. Methods This study was registered a priori on Open Science Framework (10.17605/OSF.IO/438RY) and conducted per the Cochrane Handbook. A medical librarian searched MEDLINE, Embase, PubMed, Cochrane Central Register for Controlled Trials, supplemented by hand searching. Eligible studies included randomized and observational designs with >10 patients; studies were included if ≥ 70% of participants had metastatic disease when subgroup data were unavailable. The primary outcome was ICU mortality. Other outcomes included hospital, 30-day, 3-month, 6-month, and 12-month mortality. Pooled prevalence and 95% CIs were estimated using a random-effects model with Freeman-Tukey transformation. Risk of bias was assessed using the validated 10-item tool for prevalence studies. Results Of 4752 unique citations, 49 were included (N = 168,209). All studies were rated as having low risk of bias with the main threat to external validity being single-center design. Fourteen studies were multi-centered and 3 were multi-national, with the rest being single center. Across 23 studies (N = 2,918), the ICU mortality was 32% (95% CI: 25-39%; I2 = 89%; Figure 1). Across 5 studies (N = 400) with a focus on lung cancer, there was significantly higher ICU mortality (53% [43-64%]; I2 = 75% versus 26% [20-34%]; I2 = 86%; PQ = <0.01). Among 5 studies (N = 474) focusing on acute respiratory failure (ARF), ICU mortality was numerically higher but not statistically significant (49% [33-66%]; I2 = 91% versus 26% [20-34%]; I2 = 85%; PQ=0.07). Hospital mortality was 50% (44-56%; I2 = 99%; N = 157,682), 30-day mortality was 48% (39-57%; I2 = 84%; N = 1,067), 3-month mortality was 69% (62-74%; I2 = 80%; N = 1,296), 6-month mortality was 70% (59-79%; I2 = 91%; N = 1,310), and 12-month mortality was 84% (71-91%; I2 = 95%; N = 1,446; Figure 1). Conclusions Among critically ill adults with metastatic cancer, about one-third died in the ICU, half survived to hospital discharge, and 12-month mortality was 84%. There was considerable heterogeneity in mortality estimates, with worse outcomes in lung cancer. Given that ICU admission criteria and illness severity may differ, large multi-center studies are needed to clarify temporal trends and identify management strategies to improve outcomes. This abstract is funded by: None

Introduction Angina with no obstructive coronary artery disease (ANOCA) affects millions and is frequently under-recognised because diagnostic pathways and risk tools predominantly target obstructive coronary artery disease (CAD). This protocol describes shared methods for two machine-learning (ML) studies: (1) differentiating ANOCA from stable angina with obstructive CAD and (2) predicting long-term mortality among patients with ANOCA and obstructive CAD. Methods and analysis We will develop and cross-site validate ML classification models using a multicentre retrospective cohort drawn from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease registry and institutional datasets from the University of Ottawa Heart Institute and the University Health Network. Eligible participants are adults (≥18 years) undergoing initial cardiac catheterisation for chest pain/anginal equivalents since 1995, excluding prior revascularisation, major structural heart disease and predefined non-anginal indications. Outcomes are (1) ANOCA (0% to <50% stenosis) versus obstructive CAD (≥50% stenosis) and (2) 1, 3 and 5-year mortality, modelled separately for ANOCA and obstructive CAD. Model development will use nested cross-validation with stratified k-fold inner-loop tuning and leave-one-site-out cross-validation for repeated external validation. Candidate predictors will be harmonised across sites, filtered for missingness and refined using expert/directed acyclic graph-guided selection plus Boruta and Least Absolute Shrinkage and Selection Operator. Preprocessing includes appropriate encoding, missing-data imputation (multivariate imputation by chained equations) and feature scaling. Algorithms will include elastic-net logistic regression, random forest, LightGBM and multilayer perceptron models; hyperparameters will be optimised via Bayesian optimisation. Performance and threshold tuning will be reported. Explainability and subgroup fairness will be assessed using SHapley Additive exPlanations. Final models will be deployed as a web-based clinical risk calculator. Ethics and dissemination Ethics approval has been obtained from the University of Calgary and the University Health Network (#24-5916). Analyses will use deidentified data in secure environments; only aggregate results will be reported. Findings will be disseminated via peer-reviewed publications, conferences and a web-based calculator.

Objective: To evaluate the associations between surgeon-patient sex concordance, long-term clinical outcomes and healthcare costs. Background Data: Emerging data suggest surgeon-patient sex concordance may improve short-term outcomes, especially for female patients, yet its long-term effects remain unknown. Methods: We conducted a population-based retrospective cohort study of adults undergoing common surgeries in Ontario, Canada (2007-2019). Patients were followed for 90 and 365 days. Surgeon-patient sex concordance was categorized into four dyads. Multivariate generalized estimating equations clustered by procedure type evaluated composite and individual (complications, readmission, mortality) clinical outcomes and healthcare costs. Results: A total of 1,165,711 patients were included. Female patients treated by male surgeons, compared with female surgeons, were more likely to experience an adverse composite event (90 d: aOR 1.12, 95% CI 1.08–1.17, P <0.001; 365 d: aOR 1.09, 95% CI 1.05–1.14, P <0.001), with no association among male patients (90 d: aOR 1.00, 95% CI 0.94–1.05, P =0.89; 365 d: 0.99, 95% CI 0.92–1.07, P =0.87). Long-term total healthcare costs were greater for patients treated by male surgeons - an effect seen in both male patients (90 d: aRR 1.08, 95% CI 1.03-1.14, P =0.002 and 365 d: aRR 1.08, 95% CI 1.02-1.14, P =0.007) and female patients (90 d: aRR 1.10, 95% CI 1.05-1.14, P <0.001, 365 d: aRR 1.09, 95% CI 1.05-1.14, P <0.001). Conclusions: Surgeon-patient sex concordance was associated with improved long-term outcomes for female, but not male, patients, and female surgeons’ patients had lower healthcare costs. Given these benefits, further investigation into the mechanisms driving these outcomes is warranted.