Xie, Guohao

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 >24 hours, 2) experiencing triglyceride level >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 <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 Propofol is the first-line sedative for critically-ill, mechanically-ventilated patients, but it can increase serum triglyceride levels. Machine-learning (ML) offers a promising approach for individualized risk stratification of propofol-associated hypertriglyceridemia, enabling proactive triglyceride monitoring and sedation optimization. This study aims to develop and validate ML models to predict hypertriglyceridemia risks in critically-ill patients receiving propofol. METHODS We analyzed retrospective data from adult ICU patients on mechanical ventilation receiving continuous propofol infusion for ≥24 hours across four Mayo Clinic sites from May 2018 to June 2023. The primary outcome was hypertriglyceridemia, defined as serum triglycerides >400 mg/dL within 10 days of starting propofol. Ten-fold cross-validation and leave-one-site-out nested cross-validation (LOSO-CV) were used for model tuning and external validation of our modeling process, respectively. Feature selection was conducted through a union of Boruta and least absolute shrinkage and selection operator (LASSO). While COVID status was a strong predictor of hypertriglyceridemia, we concurrently developed non-COVID models to ensure future relevance in non-pandemic settings. Six ML algorithms (logistic regression, support vector machines, random forests, LightGBM, XGBoost, and multilayer perceptrons) were tuned using Bayesian optimization to minimize cross-entropy loss. Classification thresholds were set to target sensitivity ≥80%. Model performance was evaluated using AUC-ROC, Brier score, sensitivity, and specificity. Models with the highest Youden's Index were designated the best-performing model. Model explainability was assessed using permutation importance and SHapley Additive exPlanations (SHAP). RESULTS We included 3922 eligible patients with triglyceride measurements. Of these, 3042 patients were COVID-negative and used for non-COVID modeling. The best-performing non-COVID models achieved an average AUC-ROC of 0.69 (95% confidence interval [CI] 0.68-0.70), Brier score of 0.12 (95% CI 0.09-0.14), sensitivity of 0.79 (95% CI 0.75-0.83), and specificity of 0.48 (95% CI 0.44-0.52) during LOSO-CV. The best-performing models that included COVID status achieved an average AUC-ROC of 0.71 (95% CI 0.70-0.72), Brier score of 0.13 (95% CI 0.10-0.16), sensitivity of 0.78 (95% CI 0.71-0.84), and specificity of 0.51 (95% CI 0.45 to 0.56) during LOSO-CV. The permutation importance results of the final non-COVID model are shown in the figure. CONCLUSION We have developed sensitive ML models for predicting hypertriglyceridemia risks in critically-ill patients receiving propofol. Although developed during the COVID-19 pandemic, the non-COVID models are more likely to have future relevance, and they have similar performance as models including COVID status. These models will be integrated into a web-based clinical calculator and undergo further validation in prospective studies.