Affiliation:
1. Department of Surgery, Harborview Medical Center, The University of Washington, Washington
2. Department of Surgery, The University of San Francisco – East Bay, California
3. Department of Health Systems and Population Health, The University of Washington School of Public Health, Washington
Abstract
Abstract
Background
Best resuscitation practices in the post-hemostasis phase of care are poorly defined; this phase of care is characterized by a range of physiologic derangements and multiple therapeutic modalities used to address them. Using a cohort of injured patients who required an immediate intervention in the operating room or angiography suite following arrival to the emergency department, we sought to define high-intensity resuscitation (HIR) in this post-hemostasis phase of care; we hypothesized that those who would require HIR could be identified, using only data available at ICU admission.
Methods
Clinical data was extracted for consecutive injured patients (2016-19) admitted to the ICU following an immediate procedure in the operating room or angiography suite. HIR thresholds were defined as the top decile of blood product (≥3 units) and/or crystalloid (≥4 Liters) use in the initial twelve hours of ICU care and/or vasoactive medication use between ICU hours 2-12. The primary outcome, HIR, was a composite of any of these modalities. Predictive modeling of HIR was performed using logistic regression with predictor variables selected using Least Absolute Shrinkage and Selection Operator (LASSO) estimation. Model was trained using 70% of the cohort and tested on the remaining 30%; model predictive ability was evaluated using area under receiver operator curves.
Results
Six-hundred-and-five patients were included. Patients were 79% male, young (median age: 39 years), severely injured (median ISS: 26), and an approximately 3:2 ratio of blunt to penetrating mechanisms of injury. A total of 215 (36%) required HIR. Predictors selected by LASSO included: shock index, lactate, base deficit, hematocrit, and INR. The area under receiver operator curve for the LASSO-derived HIR prediction model was 0.82.
Conclusions
ICU admission data can identify subsequent HIR in the post-hemostasis phase of care. Use of this model may facilitate triage, nursing ratio determination, and resource allocation.
Level of Evidence
Retrospective Cohort, Level IV
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Critical Care and Intensive Care Medicine,Surgery