Weykamp Michael B, Beni Catherine E, Stern Katherine E, O'Keefe Grant E, Brakenridge Scott C, Chan Kwun C G, Robinson Bryce R H
From the Department of Surgery (M.B.W., C.E.B., G.E.O'K., S.C.B., B.R.H.R.), Harborview Medical Center, The University of Washington, Washington; Department of Surgery (K.E.S.), The University of San Francisco, East Bay, California; and Department of Health Systems and Population Health (K.C.G.C.), The University of Washington School of Public Health, Seattle, Washington.
J Trauma Acute Care Surg. 2024 Apr 1;96(4):611-617. doi: 10.1097/TA.0000000000004156. Epub 2023 Nov 20.
Best resuscitation practices in the posthemostasis 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 posthemostasis phase of care; we hypothesized that those who would require HIR could be identified, using only data available at intensive care unit (ICU) admission.
Clinical data were extracted for consecutive injured patients (2016-2019) admitted to the ICU following an immediate procedure in the operating room or angiography suite. High-intensity resuscitation thresholds were defined as the top decile of blood product (≥3 units) and/or crystalloid (≥4 L) use in the initial 12 hours of ICU care and/or vasoactive medication use between ICU hours 2 and 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.
Six hundred five patients were included. Patients were 79% male, young (median age, 39 years), severely injured (median Injury Severity Score, 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.
Intensive care unit admission data can identify subsequent HIR in the posthemostasis phase of care. Use of this model may facilitate triage, nursing ratio determination, and resource allocation.
Therapeutic/Care Management; Level IV.
止血后护理阶段的最佳复苏实践定义不明确；该护理阶段的特点是一系列生理紊乱以及用于应对这些紊乱的多种治疗方式。我们以一组到达急诊科后需要在手术室或血管造影室立即进行干预的受伤患者为研究对象，试图定义该止血后护理阶段的高强度复苏（HIR）；我们假设仅使用重症监护病房（ICU）入院时可用的数据就能识别出需要HIR的患者。
提取2016年至2019年间在手术室或血管造影室接受立即手术后入住ICU的连续受伤患者的临床数据。高强度复苏阈值定义为ICU护理最初12小时内血液制品（≥3单位）和/或晶体液（≥4升）的使用量处于最高十分位数，和/或ICU第2至12小时内血管活性药物的使用情况。主要结局HIR是这些方式中任何一种的综合情况。使用逻辑回归对HIR进行预测建模，预测变量通过最小绝对收缩和选择算子（LASSO）估计进行选择。模型使用队列的70%进行训练，并在其余30%上进行测试；使用受试者操作曲线下面积评估模型预测能力。
共纳入605例患者。患者79%为男性，较为年轻（中位年龄39岁），伤势严重（中位损伤严重度评分26），钝性伤与穿透性伤机制的比例约为3:2。共有215例（36%）需要HIR。LASSO选择的预测因素包括：休克指数、乳酸、碱缺失、血细胞比容和国际标准化比值（INR）。LASSO衍生的HIR预测模型的受试者操作曲线下面积为0.82。
ICU入院数据可识别止血后护理阶段随后的HIR。使用该模型可能有助于分诊、确定护理比例和资源分配。
治疗/护理管理；四级。
