Goto Yoshikazu, Maeda Tetsuo, Nakatsu-Goto Yumiko
Crit Care. 2014 Jun 27;18(3):R133. doi: 10.1186/cc13951.
At hospital arrival, early prognostication for children after out-of-hospital cardiac arrest (OHCA) might help clinicians formulate strategies, particularly in the emergency department. In this study, we aimed to develop a simple and generally applicable bedside tool for predicting outcomes in children after cardiac arrest.
We analyzed data of 5,379 children who had undergone OHCA. The data were extracted from a prospectively recorded, nationwide, Utstein-style Japanese database. The primary endpoint was survival with favorable neurological outcome (Cerebral Performance Category (CPC) scale categories 1 and 2) at 1 month after OHCA. We developed a decision tree prediction model by using data from a 2-year period (2008 to 2009, n = 3,693), and the data were validated using external data from 2010 (n = 1,686).
Recursive partitioning analysis for 11 predictors in the development cohort indicated that the best single predictor for CPC 1 and 2 at 1 month was the prehospital return of spontaneous circulation (ROSC). The next predictor for children with prehospital ROSC was an initial shockable rhythm. For children without prehospital ROSC, the next best predictor was a witnessed arrest. Use of a simple decision tree prediction model permitted stratification into four outcome prediction groups: good (prehospital ROSC and initial shockable rhythm), moderately good (prehospital ROSC and initial nonshockable rhythm), poor (prehospital non-ROSC and witnessed arrest) and very poor (prehospital non-ROSC and unwitnessed arrest). By using this model, we identified patient groups ranging from 0.2% to 66.2% for 1-month CPC 1 and 2 probabilities. The validated decision tree prediction model demonstrated a sensitivity of 69.7% (95% confidence interval (CI) = 58.7% to 78.9%), a specificity of 95.2% (95% CI = 94.1% to 96.2%) and an area under the receiver operating characteristic curve of 0.88 (95% CI = 0.87 to 0.90) for predicting 1-month CPC 1 and 2.
With our decision tree prediction model using three prehospital variables (prehospital ROSC, initial shockable rhythm and witnessed arrest), children can be readily stratified into four groups after OHCA. This simple prediction model for evaluating children after OHCA may provide clinicians with a practical bedside tool for counseling families and making management decisions soon after patient arrival at the hospital.
在医院接诊时，对院外心脏骤停（OHCA）后的儿童进行早期预后评估可能有助于临床医生制定策略，尤其是在急诊科。在本研究中，我们旨在开发一种简单且普遍适用的床边工具，用于预测心脏骤停后儿童的预后。
我们分析了5379例经历过OHCA的儿童的数据。这些数据取自一个前瞻性记录的、全国性的、符合Utstein模式的日本数据库。主要终点是OHCA后1个月时具有良好神经功能预后（脑功能分类（CPC）量表1级和2级）的存活情况。我们使用2008年至2009年两年期间的数据（n = 3693）开发了一个决策树预测模型，并使用2010年的外部数据（n = 1686）对该数据进行了验证。
对开发队列中的11个预测因素进行递归划分分析表明，1个月时CPC 1级和2级的最佳单一预测因素是院前自主循环恢复（ROSC）。院前ROSC儿童的下一个预测因素是初始可电击心律。对于院前无ROSC的儿童，下一个最佳预测因素是目击心脏骤停。使用简单的决策树预测模型可将其分为四个预后预测组：良好（院前ROSC和初始可电击心律）、中等良好（院前ROSC和初始不可电击心律）、差（院前无ROSC且目击心脏骤停）和非常差（院前无ROSC且未目击心脏骤停）。通过使用该模型，我们确定了1个月时CPC 1级和2级概率在0.2%至66.2%之间的患者组。经过验证的决策树预测模型在预测1个月时CPC 1级和2级方面显示出敏感性为69.7%（95%置信区间（CI）= 58.7%至78.9%），特异性为95.2%（95%CI = 94.1%至96.2%），受试者工作特征曲线下面积为0.88（95%CI = 0.87至0.90）。
通过我们使用三个院前变量（院前ROSC、初始可电击心律和目击心脏骤停）的决策树预测模型，OHCA后的儿童可很容易地分为四组。这种用于评估OHCA后儿童的简单预测模型可能为临床医生提供一种实用的床边工具，以便在患者入院后不久为家属提供咨询并做出管理决策。
