PREDICTING SEPSIS-INDUCED HYPOTENSION PATIENT ATTRIBUTES FOR RESTRICTIVE VERSUS LIBERAL FLUID STRATEGY.

Background : Patients with sepsis-induced hypotension are generally treated with a combination of intravenous fluids and vasopressors. The attributes of patients receiving a liberal compared to a restrictive fluid strategy have not been fully characterized. We use machine learning (ML) techniques to identify key predictors of restrictive versus liberal fluids strategy, and the likelihood of receiving each strategy in distinct patient phenotypes. Methods: We performed a retrospective observational study of patients at Emory University Hospital from 2014 to 2021 that were hypotensive, met Sepsis-3 criteria, and received at least 1 L of intravenous crystalloid fluids. We excluded patients with nonseptic etiologies of hypotension. Supervised ML techniques were used to identify key predictors for the two strategies. Additionally, subset analyses were performed on patients with pneumonia, congestive heart failure (CHF), or chronic kidney disease (CKD). Using unsupervised ML techniques, we also identified three distinct sepsis-induced hypotension phenotypes and evaluated their likelihood of receiving either strategy. Results: We identified N = 15,292 patients and randomly split them into training (n = 12,233) and validation (n = 3,059) datasets. XGBoost was the most accurate model (AUC: 0.84) for predicting the strategies. While worse oxygenation was the strongest predictor of utilizing a restrictive fluid strategy, top predictors of a liberal fluid strategy included higher pulse and blood urea nitrogen. In subset analyses, CHF, CKD, and pneumonia were predictive of restrictive fluid strategy. We identified three distinct sepsis-induced hypotension phenotypes: 1) mild organ injury, 2) severe hypoxemia, and 3) renal dysfunction. Conclusions: We identified key predictors of restrictive versus liberal fluids strategy and distinct patient phenotypes for sepsis-induced hypotension.