PDF(Pubmed)
Abstract：
UNASSIGNED: During cardiopulmonary resuscitation (CPR), end-tidal carbon dioxide (EtCO2) is primarily determined by pulmonary blood flow, thereby reflecting the blood flow generated by CPR. We aimed to develop an EtCO2 trajectory-based prediction model for prognostication at specific time points during CPR in patients with out-of-hospital cardiac arrest (OHCA).
UNASSIGNED: We screened patients receiving CPR between 2015-2021 from a prospectively collected database of a tertiary-care medical center. The primary outcome was survival to hospital discharge. We used group-based trajectory modeling to identify the EtCO2 trajectories. Multivariable logistic regression analysis was used for model development and internally validated using bootstrapping. We assessed performance of the model using the area under the receiver operating characteristic curve (AUC).
UNASSIGNED: The primary analysis included 542 patients with a median age of 68.0 years. Three distinct EtCO2 trajectories were identified in patients resuscitated for 20 minutes (min): low (average EtCO2 10.0 millimeters of mercury [mm Hg]; intermediate (average EtCO2 26.5 mm Hg); and high (average EtCO2: 51.5 mm Hg). Twenty-min EtCO2 trajectory was fitted as an ordinal variable (low, intermediate, and high) and positively associated with survival (odds ratio 2.25, 95% confidence interval [CI] 1.07-4.74). When the 20-min EtCO2 trajectory was combined with other variables, including arrest location and arrest rhythms, the AUC of the 20-min prediction model for survival was 0.89 (95% CI 0.86-0.92). All predictors in the 20-min model remained statistically significant after bootstrapping.
UNASSIGNED: Time-specific EtCO2 trajectory was a significant predictor of OHCA outcomes, which could be combined with other baseline variables for intra-arrest prognostication. For this purpose, the 20-min survival model achieved excellent discriminative performance in predicting survival to hospital discharge.
UNASSIGNED: We screened patients receiving CPR between 2015-2021 from a prospectively collected database of a tertiary-care medical center. The primary outcome was survival to hospital discharge. We used group-based trajectory modeling to identify the EtCO2 trajectories. Multivariable logistic regression analysis was used for model development and internally validated using bootstrapping. We assessed performance of the model using the area under the receiver operating characteristic curve (AUC).
UNASSIGNED: The primary analysis included 542 patients with a median age of 68.0 years. Three distinct EtCO2 trajectories were identified in patients resuscitated for 20 minutes (min): low (average EtCO2 10.0 millimeters of mercury [mm Hg]; intermediate (average EtCO2 26.5 mm Hg); and high (average EtCO2: 51.5 mm Hg). Twenty-min EtCO2 trajectory was fitted as an ordinal variable (low, intermediate, and high) and positively associated with survival (odds ratio 2.25, 95% confidence interval [CI] 1.07-4.74). When the 20-min EtCO2 trajectory was combined with other variables, including arrest location and arrest rhythms, the AUC of the 20-min prediction model for survival was 0.89 (95% CI 0.86-0.92). All predictors in the 20-min model remained statistically significant after bootstrapping.
UNASSIGNED: Time-specific EtCO2 trajectory was a significant predictor of OHCA outcomes, which could be combined with other baseline variables for intra-arrest prognostication. For this purpose, the 20-min survival model achieved excellent discriminative performance in predicting survival to hospital discharge.
摘要：
■在心肺复苏（CPR）期间，呼气末二氧化碳(EtCO2)主要由肺血流量决定，从而反映由CPR产生的血流。我们旨在开发一种基于EtCO2轨迹的预测模型，用于院外心脏骤停（OHCA）患者在CPR期间特定时间点的预测。
■我们从三级医疗中心前瞻性收集的数据库中筛选了2015-2021年间接受CPR的患者。主要结局是生存至出院。我们使用基于组的轨迹建模来识别EtCO2轨迹。多变量逻辑回归分析用于模型开发，并使用自举进行内部验证。我们使用接受者工作特征曲线下面积(AUC)评估模型的性能。
■主要分析包括542例患者，中位年龄为68.0岁。在复苏20分钟（分钟）的患者中发现了三种不同的EtCO2轨迹：低（平均EtCO210.0mmHg[mmHg]；中等（平均EtCO226.5mmHg）；和高（平均EtCO2：51.5mmHg）。将20分钟的EtCO2轨迹拟合为序数变量(低，中间,和高）并与生存率呈正相关（比值比2.25，95%置信区间[CI]1.07-4.74）。当20分钟的EtCO2轨迹与其他变量相结合时，包括逮捕地点和逮捕节奏,生存20分钟预测模型的AUC为0.89(95%CI0.86-0.92).20分钟模型中的所有预测因子在引导后仍具有统计学意义。
■特定时间的EtCO2轨迹是OHCA结果的重要预测因子，这可以与其他基线变量相结合，用于停搏内预测。为此,20分钟生存模型在预测生存至出院时取得了优异的判别性能.
■我们从三级医疗中心前瞻性收集的数据库中筛选了2015-2021年间接受CPR的患者。主要结局是生存至出院。我们使用基于组的轨迹建模来识别EtCO2轨迹。多变量逻辑回归分析用于模型开发，并使用自举进行内部验证。我们使用接受者工作特征曲线下面积(AUC)评估模型的性能。
■主要分析包括542例患者，中位年龄为68.0岁。在复苏20分钟（分钟）的患者中发现了三种不同的EtCO2轨迹：低（平均EtCO210.0mmHg[mmHg]；中等（平均EtCO226.5mmHg）；和高（平均EtCO2：51.5mmHg）。将20分钟的EtCO2轨迹拟合为序数变量(低，中间,和高）并与生存率呈正相关（比值比2.25，95%置信区间[CI]1.07-4.74）。当20分钟的EtCO2轨迹与其他变量相结合时，包括逮捕地点和逮捕节奏,生存20分钟预测模型的AUC为0.89(95%CI0.86-0.92).20分钟模型中的所有预测因子在引导后仍具有统计学意义。
■特定时间的EtCO2轨迹是OHCA结果的重要预测因子，这可以与其他基线变量相结合，用于停搏内预测。为此,20分钟生存模型在预测生存至出院时取得了优异的判别性能.
