University of Pittsburgh, Department of Emergency Medicine, PA, USA.
Resuscitation. 2011 Nov;82(11):1399-404. doi: 10.1016/j.resuscitation.2011.06.024. Epub 2011 Jul 5.
Illness severity scores are commonly employed in critically ill patients to predict outcome. To date, prior scores for post-cardiac arrest patients rely on some event-related data. We developed an early, novel post-arrest illness severity score to predict survival, good outcome and development of multiple organ failure (MOF) after cardiac arrest.
Retrospective review of data from adults treated after in-hospital or out-of-hospital cardiac arrest in a single tertiary care facility between 1/1/2005 and 12/31/2009. In addition to clinical data, initial illness severity was measured using serial organ function assessment (SOFA) scores and full outline of unresponsiveness (FOUR) scores at hospital or intensive care unit arrival. Outcomes were hospital mortality, good outcome (discharge to home or rehabilitation) and development of multiple organ failure (MOF). Single-variable logistic regression followed by Chi-squared automatic interaction detector (CHAID) was used to determine predictors of outcome. Stepwise multivariate logistic regression was used to determine the independent association between predictors and each outcome. The Hosmer-Lemeshow test was used to evaluate goodness of fit. The n-fold method was used to cross-validate each CHAID analysis and the difference between the misclassification risk estimates was used to determine model fit.
Complete data from 457/495 (92%) subjects identified distinct categories of illness severity using combined FOUR motor and brainstem subscales, and combined SOFA cardiovascular and respiratory subscales: I. Awake; II. Moderate coma without cardiorespiratory failure; III. Moderate coma with cardiorespiratory failure; and IV. Severe coma. Survival was independently associated with category (I: OR 58.65; 95% CI 27.78, 123.82; II: OR 14.60; 95% CI 7.34, 29.02; III: OR 10.58; 95% CI 4.86, 23.00). Category was also similarly associated with good outcome and development of MOF. The proportion of subjects in each category changed over time.
Initial illness severity explains much of the variation in cardiac arrest outcome. This model provides prognostic information at hospital arrival and may be used to stratify patients in future studies.
疾病严重程度评分常用于危重症患者以预测预后。迄今为止，既往针对心搏骤停后患者的评分依赖于一些与事件相关的数据。我们开发了一种新的早期心搏骤停后疾病严重程度评分，以预测心搏骤停后患者的生存、良好结局和多器官衰竭（MOF）的发生。
对 2005 年 1 月 1 日至 2009 年 12 月 31 日期间在一家三级医疗中心治疗的院内或院外心搏骤停成年患者的数据进行回顾性分析。除了临床数据外，初始疾病严重程度还通过入院时的序贯器官衰竭评估（SOFA）评分和昏迷程度评估（FOUR）评分进行测量。结局包括院内死亡率、良好结局（出院回家或康复）和多器官衰竭（MOF）的发生。采用单变量逻辑回归和卡方自动交互检测（CHAID）确定预后的预测因素。采用逐步多变量逻辑回归确定预测因素与每种结局之间的独立关联。Hosmer-Lemeshow 检验用于评估拟合优度。n 折交叉验证用于对每个 CHAID 分析进行交叉验证，通过比较两种模型的错误分类风险估计值来确定模型拟合情况。
对 457/495（92%）例患者的完整数据进行分析，根据联合 FOUR 运动和脑干子量表以及联合 SOFA 心血管和呼吸系统子量表，确定了不同的疾病严重程度类别：I.清醒；II.无心肺衰竭的中度昏迷；III.有心肺衰竭的中度昏迷；和 IV.重度昏迷。生存与类别独立相关（I 类：OR 58.65；95%CI 27.78，123.82；II 类：OR 14.60；95%CI 7.34，29.02；III 类：OR 10.58；95%CI 4.86，23.00）。类别也与良好结局和 MOF 的发生相似相关。各类别中的患者比例随时间发生变化。
初始疾病严重程度解释了心搏骤停结局的大部分差异。该模型在入院时提供预后信息，可用于未来研究中对患者进行分层。
