Predicting high-intensity resuscitation needs in injured patients in the post-hemostasis phase of care following intervention

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