Nagy Bettina, Pál-Jakab Ádám, Orbán Gábor, Kiss Boldizsár, Fekete-Győr Alexa, Koós Gábor, Merkely Béla, Hizoh István, Kovács Enikő, Zima Endre
Semmelweis University Heart and Vascular Center, Budapest, Hungary.
Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom.
Resusc Plus. 2024 Aug 16;19:100732. doi: 10.1016/j.resplu.2024.100732. eCollection 2024 Sep.
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.
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.
According to the Akaike Information Criterion (AIC) heart rate (AIC = 9.24, = 0.0013), age (AIC = 4.39, = 0.0115), pH (AIC = 3.68, = 0.0171), initial rhythm (AIC = 4.76, = 0.0093) and right ventricular end-diastolic diameter (AIC = 2.49, = 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).
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/.
院外心脏骤停(OHCA)后的生存率仍然很低,早期预后评估具有挑战性。虽然存在许多重症监护病房评分系统,但它们在入院后的最初几个小时,特别是在目标温度管理(TTM)人群中的效用值得怀疑。我们的目标是创建一个评分系统,该系统可以准确估计接受TTM治疗的患者入院后12小时内的预后。
我们分析了2016年至2022年间103例随后接受TTM治疗的OHCA患者的数据。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。在基于自助法的预测变量选择之后,我们构建了一个非线性逻辑回归模型。使用自助重采样进行内部验证。使用c统计量描述辨别力,而校准则由截距和斜率来表征。
根据赤池信息准则(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)、初始心律(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,并用于构建我们的预测模型和列线图。该模型的受试者工作特征曲线下面积为0.84。该模型的C统计量为0.7974,具有内部验证的可接受校准(截距:-0.0190,斜率:0.7772)和低错误率(平均绝对误差:0.040)。
我们开发的模型可能适用于对接受TTM作为初始复苏后护理一部分的患者进行早期风险评估。评分所需的计算器可通过以下链接访问:https://www.rapidscore.eu/ 。