Factors predicting mortality in the cardiac ICU during the early phase of targeted temperature management in the treatment of post-cardiac arrest syndrome – The RAPID score

IF 2.1 Q3 CRITICAL CARE MEDICINE
Bettina Nagy , Ádám Pál-Jakab , Gábor Orbán , Boldizsár Kiss , Alexa Fekete-Győr , Gábor Koós , Béla Merkely , István Hizoh , Enikő Kovács , Endre Zima
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引用次数: 0

Abstract

Introduction

Survival rates after out-of-hospital cardiac arrest (OHCA) remain low, and early prognostication is challenging. While numerous intensive care unit scoring systems exist, their utility in the early hours following hospital admission, specifically in the targeted temperature management (TTM) population, is questionable. Our aim was to create a score system that may accurately estimate outcome within the first 12 h after admission in patients receiving TTM.

Methods

We analyzed data from 103 OHCA patients who subsequently underwent TTM between 2016 and 2022. Patient demographic data, prehospital characteristics, clinical and laboratory parameters were already available in the first 12 h after admission were collected. Following a bootstrap-based predictor selection, we constructed a nonlinear logistic regression model. Internal validation was performed using bootstrap resampling. Discrimination was described using the c-statistic, whereas calibration was characterized by the intercept and slope.

Results

According to the Akaike Information Criterion (AIC) heart rate (AIC = 9.24, p = 0.0013), age (AIC = 4.39, p = 0.0115), pH (AIC = 3.68, p = 0.0171), initial rhythm (AIC = 4.76, p = 0.0093) and right ventricular end-diastolic diameter (AIC = 2.49, p = 0.0342) were associated with 30-day mortality and were used to build our predictive model and nomogram. The area under the receiver-operating characteristics curve for the model was 0.84. The model achieved a C-statistic of 0.7974, with internally validated acceptable calibration (intercept: −0.0190, slope: 0.7772) and low error rates (mean absolute error: 0.040).

Conclusion

The model we have developed may be suitable for early risk assessment of patients receiving TTM as part of primary post-resuscitation care. The calculator needed for scoring can be accessed at the following link: https://www.rapidscore.eu/.

在治疗心脏骤停后综合征的早期阶段,预测心脏重症监护病房内定向体温管理死亡率的因素 - RAPID 评分
导言院外心脏骤停(OHCA)后的存活率仍然很低,而且早期预后的判断也很困难。虽然重症监护室有许多评分系统,但它们在入院后早期的实用性,特别是在目标体温管理(TTM)人群中的实用性,还值得怀疑。我们的目的是建立一个评分系统,以准确估计接受体温管理的患者入院后最初 12 小时内的预后。方法我们分析了 2016 年至 2022 年期间接受体温管理的 103 例 OHCA 患者的数据。我们收集了患者的人口统计学数据、院前特征、入院后 12 小时内已有的临床和实验室参数。在基于引导的预测因子选择之后,我们构建了一个非线性逻辑回归模型。使用引导重采样法进行了内部验证。结果根据 Akaike 信息标准(AIC),心率(AIC = 9.24,p = 0.0013)、年龄(AIC = 4.39,p = 0.0115)、pH 值(AIC = 3.68,p = 0.0171)、初始节律(AIC = 4.76,p = 0.0093)和右心室舒张末期直径(AIC = 2.49,p = 0.0342)与 30 天死亡率相关,并被用于建立我们的预测模型和提名图。该模型的受体运行特征曲线下面积为 0.84。该模型的 C 统计量为 0.7974,经内部验证,校准结果可接受(截距:-0.0190,斜率:0.7772),误差率较低(平均绝对误差:0.040)。评分所需的计算器可从以下链接获取:https://www.rapidscore.eu/。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Resuscitation plus
Resuscitation plus Critical Care and Intensive Care Medicine, Emergency Medicine
CiteScore
3.00
自引率
0.00%
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审稿时长
52 days
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