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IMPORTANCE: Propofol is a first-line sedative for adults receiving invasive mechanical ventilation (IMV). However, it can contribute to hemodynamic instability, especially during intubation. The magnitude, timing, risk factors, and variability of sedation-associated mean arterial pressure (MAP) changes remain poorly characterized in ICU settings. OBJECTIVES: To quantify MAP changes following propofol sedation, identify risk factors for hemodynamic instability, and characterize associated interventions. DESIGN: Retrospective cohort study. The primary outcome was MAP change within 2 hours following sedation. Secondary outcomes included vasopressor use and hypotension (MAP ≤ 60 mm Hg). Mixed-effects modeling was used to account for individual patient differences. SETTING AND PARTICIPANTS: Adults (≥ 18 yrs old) who required IMV and received greater than or equal to 6 consecutive hours of propofol infusion, between May 5th, 2018, and July 31st, 2024, in 11 ICUs across the Mayo Clinic, spanning 5 hospitals in 4 states. MAIN OUTCOMES AND MEASURES: The primary outcome was the change in MAP within 2 hours following the initiation of propofol-based sedation. RESULTS: Across 16,418 patients, 25.2% were on vasopressors before sedation initiation. Among the remaining 12,281 patients, 40.3% required vasopressors and 7.7% experienced hypotension within 2 hours of sedation. Propofol-based sedation was associated with a MAP reduction within the first 30 minutes (–6.58 mm Hg; 95% CI, –6.85 to –6.32; p < 0.001). There was substantial interpatient variability in both baseline MAP, and MAP decline after sedation (9.5 and 40.9% between-patient differences, respectively). Higher Sequential Organ Failure Assessment (SOFA) scores (–0.31 mm Hg/point), older age (–0.04 mm Hg/yr), and male sex (–0.47 mm Hg) were associated with lower MAP. Patients with higher illness severity experienced progressively greater MAP decline over time (–0.20 mm Hg/hr/SOFA point; p < 0.001). CONCLUSIONS AND RELEVANCE: Propofol-based sedation was associated with clinically significant hemodynamic effects requiring intervention in the early post-intubation period. The marked interpatient variability in hemodynamic responses highlights the importance of personalized management approaches, including risk stratification based on age, sex, and illness severity.

Purpose: To develop and validate an explainable machine learning (ML) tool to help clinicians predict the risk of propofol-associated hypertriglyceridemia in critically ill patients receiving propofol sedation. Methods: Patients from 11 intensive care units (ICUs) across five Mayo Clinic hospitals were included if they met the following criteria: a) ≥ 18 years of age, b) received propofol infusion while on invasive mechanical ventilation for ≥24 h, and c) had a triglyceride level measured. The primary outcome was hypertriglyceridemia (triglyceride >400 mg/dL) onset within 10 days of propofol initiation. Both COVID-inclusive and COVID-independent modeling pipelines were developed to ensure applicability post-pandemic. Decision thresholds were chosen to maintain model sensitivity >80%. Nested leave-one-site-out cross-validation (LOSO-CV) was used to externally evaluate pipeline performance. Model explainability was assessed using permutation importance and SHapley Additive exPlanations (SHAP). Results: Among 3922 included patients, 769 (19.6%) developed propofol-associated hypertriglyceridemia, and 879 (22.4%) had COVID-19 at ICU admission. During nested LOSO-CV, the COVID-inclusive pipeline achieved an average AUC-ROC of 0.71 (95% confidence interval [CI] 0.70–0.72), while the COVID-independent pipeline achieved an average AUC-ROC of 0.69 (95% CI 0.68–0.70). Age, initial propofol dose, and BMI were the top three most important features in both models. Conclusion: We developed an explainable ML-based tool with acceptable predictive performance for assessing the risk of propofol-associated hypertriglyceridemia in ICU patients. This tool can aid clinicians in identifying at-risk patients to guide triglyceride monitoring and optimize sedative selection.

Abstract Rationale: Propofol can increase the risk of hypertriglyceridemia and subsequent pancreatitis in critically ill patients. It is unclear whether modifying or continuing sedation regimens in patients experiencing hypertriglyceridemia while on propofol infusion influences the development of adverse events such as acute pancreatitis. This study compared the clinical outcomes of patients receiving invasive mechanical ventilation (IMV) who had their propofol regimen modified versus continued following the detection of hypertriglyceridemia. Methods: A targeted trial emulation was conducted using retrospective data from 5 Mayo Clinic hospitals between 05/05/2018 and 06/30/2023. Inclusion criteria included: 1) receiving IMV and continuous propofol infusion for &gt;24 hours, 2) experiencing triglyceride level &gt;400 mg/dL while on propofol, and 3) still requiring IMV at 24-hours after hypertriglyceridemia onset. Patients were then categorized based on whether they had their propofol regimen modified (i.e., dose reduction by ≥50% or any reduction with alternative sedatives started) versus continued (no reduction or &lt;50% reduction without alternative sedative initiation). Outcomes included 30-day ventilator-free days, successful extubation, acute pancreatitis (determined by chart review, in-duplicate), ICU and hospital length of stay, 30- and 90-days alive and at home, as well as all-cause mortality. Analyses were conducted using stabilized inverse probability of treatment weights with adjustments for covariates selected a priori. Sensitivity analyses, where only benzodiazepines or benzodiazepines and ketamine were considered to be eligible alternative sedatives, were done. Results: During the study period, 728 patients were included, of whom 422 had their propofol regimen modified (Image). The two groups had similar demographics, comorbidities, and illness severity. We identified 14 cases of acute pancreatitis possibly attributable to propofol, with 9 cases in the modified and 5 cases in the continued regimen group (2.1% versus 1.6%; P=0.63). Following adjustment for covariates, there were no significant differences in any outcomes of interest. After censoring for mortality, there was no significant difference in the 30-day cumulative incidence of successful extubation when comparing those with their regimen modified versus continued (Hazard ratio [HR] 0.95; 95% confidence intervals [CI] 0.77 to 1.18; P=0.70). There was also no significant difference in the 90-day survival rate between the two groups (HR 0.89; 95% CI 0.71 to 1.11; P=0.34). These findings remained consistent upon sensitivity analyses. Conclusion: There were no differences in outcomes among patients who had their propofol regimens modified versus continued following the detection of hypertriglyceridemia. Further prospective investigations are needed to identify the optimal strategy for managing sedative regimens in this patient population.

Abstract Rationale: Hematopoietic cell transplantation (HCT) risk scores have seen minimal updates over the past 20 years, despite advancements in clinical practice. Pulmonary function test (PFT) thresholds, initially based on NHANES reference equations, now have alternative reference standards from the Global Lung Function Initiative (GLI) 2012 and GLI-Global, which may reclassify patients and affect outcomes. This study investigates the impact of GLI reclassification on outcomes in HCT, comparing traditional fixed thresholds with continuous (z-score) risk assessment models. Methods: A retrospective review of 3,120 adult patients who underwent autologous HCT at MD Anderson Cancer Center from 1999 to 2015 was conducted. Primary endpoints were ICU admission, mechanical ventilation (IMV), and survival within 120 days post-transplant. PFT measures (FEV1, FVC, DLCO) were calculated using GLI-2012 and GLI-Global reference equations, analyzed both continuously (z-scores) and categorically by ATS/ERS guidelines. Cox proportional hazards and logistic regression models were employed, with AUROC comparisons to evaluate predictive performance. Results: The cohort had a mean age of 49 years (SD 14), with 41% female and 77% White. Of the 3,120 patients included in the study, the cohort consisted of 77% White, 5.4% Black, 3.3% Asian, and 14% identifying as other ethnicities. At 120 days, mortality was 1% (n=41), ICU admission 5% (n=169), and IMV 2% (n=70). Continuous DLCO was associated with a lower risk of mortality (HR: 0.75; p=0.03), ICU admission (OR: 0.83; p=0.01), and IMV (OR: 0.80; p=0.02). GLI-Global's FEV1 z-scores were also significant for mortality (HR: 0.70; p=0.01), ICU admission (OR: 0.74; p&lt;0.0001), and IMV (OR: 0.70; p=0.0003). Categorical analyses showed significant mortality reduction with normal DLCO (HR: 0.46; p=0.01) and ICU admission reduction with normal FEV1 GLI-2012 (OR: 0.63; p=0.02). Reclassification by race, as shown in Figure 1, indicated that race-specific equations overestimated PFT values in Black patients and underestimated them in Asian patients. Conclusion: Reclassification by GLI reference equations, particularly using continuous z-scores, significantly influences post-HCT outcomes, suggesting that continuous assessment may improve risk stratification. Including race does not improve the model's performance. These findings support integrating updated PFT references and continuous risk models into HCT prognostic tools for enhanced clinical decision-making.

Abstract RATIONALE: Long-term survival after lung transplantation is challenging, with limited research on risk and protective factors. Clinical factors such as younger age, female sex, fewer acute rejection hospitalizations, and double lung transplants are associated with better outcomes. However, the role of psychosocial health and socioeconomic status (SES) remains underexplored. This study aimed to assess the influence of pre-transplant psychosocial health and SES on long-term survival in adult lung transplant recipients. METHODS: We conducted a single-center, retrospective case-control study using the Mayo Clinic Rochester lung transplant registry. Cases (survivors ≥ 6 years) and controls (survivors &lt;6 years) were matched by transplant date (+/- 5 years) and indication for transplant, resulting in 190 subjects (63 cases, 127 controls). We assessed the impact of pre-transplant psychosocial health and SES on long-term survival using initial PACT scores (&lt;2), the HOUSES index, and the national/state area deprivation index (ADI). Additional factors included proximity to the transplant center, insurance type, marital status, primary caregiver status, and employment status. Statistical analyses used the paired Wilcoxon rank-sum test for continuous variables and McNemar's test for categorical variables, with results reported as median (IQR) or percentages. RESULTS: Donor characteristics (age, sex, smoking history, risk criteria, cause of death) and recipient characteristics (age at transplant, sex, BMI, medical condition, functional status, type of transplant) were similar between cases and controls, with no statistically significant differences (all p&gt;0.05). CMV mismatch, waitlist time, and transplant hospital length of stay were also comparable (all p&gt;0.05). Psychosocial health indicators, including proximity to the transplant center, pre-transplant initial PACT score, insurance type, marital status, primary caregiver, and employment status, did not differ significantly between groups (all p&gt;0.05). The HOUSES index was similar between cases and controls (p=0.179). However, long-term survivors had significantly higher national and state ADI scores than controls (national ADI: p=0.029; state ADI: p=0.019). CONCLUSIONS: Pre-transplant psychosocial health indicators, including proximity to the transplant center, initial PACT score, and caregiver support, did not differ significantly between long-term lung transplant survivors and those with shorter survival. Although SES measured by the HOUSES index was not associated with long-term survival, higher national and state ADI scores were linked to increased long-term survival, suggesting unique protective factors for patients from socioeconomically deprived areas. These findings highlight the complex role of socioeconomic factors in lung transplant outcomes and underscore the need for further research into how specific socioeconomic and environmental factors influence long-term survival.

Introduction Propofol is a widely used sedative-hypnotic agent for critically ill patients requiring invasive mechanical ventilation (IMV). Despite its clinical benefits, propofol is associated with increased risks of hypertriglyceridemia. Early identification of patients at risk for propofol-associated hypertriglyceridemia is crucial for optimising sedation strategies and preventing adverse outcomes. Machine-learning (ML) models offer a promising approach for predicting individualised patient risks of propofol-associated hypertriglyceridemia. Methods and analysis We propose the development of an ML model aimed at predicting the risk of propofol-associated hypertriglyceridemia in ICU patients receiving IMV. The study will use retrospective data from four Mayo Clinic sites. Nested cross validation (CV) will be employed, with a tenfold inner CV loop for model tuning and selection as well as an outer loop using leave-one-site-out CV for external validation. Feature selection will be conducted using Boruta and least absolute shrinkage and selection operator-penalised logistic regression. Data preprocessing steps include missing data imputation, feature scaling and dimensionality reduction techniques. Six ML algorithms will be tuned and evaluated. Bayesian optimisation will be used for hyperparameter selection. Global model explainability will be assessed using permutation importance, and local model explainability will be assessed using SHapley Additive exPlanations. Ethics and dissemination The proposed ML model aims to provide a reliable and interpretable tool for clinicians to predict the risk of propofol-associated hypertriglyceridemia in ICU patients. The final model will be deployed in a web-based clinical risk calculator. The model development process and performance measures obtained during nested CV will be described in a study publication to be disseminated in a peer-reviewed journal. The proposed study has received ethics approval from the Mayo Clinic Institutional Review Board (IRB #23–0 07 416).

Abstract Rationale Propofol is one of the first-line sedative-hypnotic agents for critically ill adults requiring mechanical ventilation. Although propofol can elevate triglyceride levels, and the latter is a risk factor for pancreatitis, the association between propofol and acute pancreatitis is unclear. Objectives We sought to determine the clinical impact and potential associations between propofol infusion, hypertriglyceridemia, and acute pancreatitis. Methods This is an observational multicenter study of adults (⩾18 yr old) who were admitted to an intensive care unit, who required mechanical ventilation and received continuous propofol infusion for at least 24 hours. The primary outcomes were the frequency of hypertriglyceridemia (&gt;400 mg/dl) and acute pancreatitis. Further analyses were done to determine the clinical impact of elevated triglyceride levels (i.e., sedation changes) and risk factors for pancreatitis development. Results Of 11,828 patients included, 33.2% (n = 3,922) had triglyceride levels measured, of whom 21.7% (n = 851) had hypertriglyceridemia at 4.5 days (SD = 6.8) after propofol initiation. Of those still requiring sedation, 70.4% (n = 576/818) received alternative sedatives after developing hypertriglyceridemia. Pancreatitis occurred in 1.2% of patients (n = 47/3,922) and was more frequent in those with hypertriglyceridemia (3.2%, 27/851; vs. 0.7%, 20/3,071; P &lt; 0.001). After adjustment for potential confounding variables, each 100 mg/dl increase in triglyceride levels was associated with an 11% increase in risk of pancreatitis. Propofol dose was not associated with pancreatitis development. Conclusions Acute pancreatitis is uncommon in patients receiving propofol infusion, and it occurs over a wide range of triglyceride levels, indicating a multifactorial pathophysiology. Hypertriglyceridemia frequently prompts the use of alternative sedatives. Further study is needed to determine how to best monitor and treat hypertriglyceridemia in critically ill patients receiving propofol infusion.

Abstract Rationale Pulmonary complications contribute significantly to nonrelapse mortality following hematopoietic stem cell transplantation (HCT). Identifying patients at high risk can help enroll such patients into clinical studies to better understand, prevent, and treat posttransplantation respiratory failure syndromes. Objectives To develop and validate a prediction model to identify those at increased risk of acute respiratory failure after HCT. Methods Patients underwent HCT between January 1, 2019, and December 31, 2021, at one of three institutions. Those treated in Rochester, MN, formed the derivation cohort, and those treated in Scottsdale, AZ, or Jacksonville, FL, formed the validation cohort. The primary outcome was the development of acute respiratory distress syndrome (ARDS), with secondary outcomes including the need for invasive mechanical ventilation (IMV) and/or noninvasive ventilation (NIV). Predictors were based on prior case-control studies. Measurements and Main Results Of 2,450 patients undergoing stem cell transplantation, there were 1,718 hospitalizations (888 patients) in the training cohort and 1,005 hospitalizations (470 patients) in the test cohort. A 22-point model was developed, with 11 points from prehospital predictors and 11 points from posttransplantation or early (&lt;24-h) in-hospital predictors. The model performed well in predicting ARDS (C-statistic, 0.905; 95% confidence interval [CI], 0.870–0.941) and the need for IMV and/or NIV (C-statistic, 0.863; 95% CI, 0.828–0.898). The test cohort differed markedly in demographic, medical, and hematologic characteristics. The model also performed well in this setting in predicting ARDS (C-statistic, 0.841; 95% CI, 0.782–0.900) and the need for IMV and/or NIV (C-statistic, 0.872; 95% CI, 0.831–0.914). Conclusions A novel prediction model incorporating data elements from the pretransplantation, posttransplantation, and early in-hospital domains can reliably predict the development of post-HCT acute respiratory failure.