Ruttimann U E, Pollack M M, Fiser D H
Department of Pediatrics, George Washington University School of Medicine, Washington, DC, USA.
Crit Care Med. 1996 Jan;24(1):78-85. doi: 10.1097/00003246-199601000-00014.
To develop a method based on admission day data for predicting patient outcome status as independently functional, compromised functional, or dead.
Prospectively acquired development and validation samples.
A pediatric intensive care unit located in a tertiary care center.
Consecutive admissions (n = 1,663) for predictor development, and consecutive admissions (n = 1,153) for predictor validation.
Pediatric Risk of Mortality score, baseline Pediatric Overall Performance Category score, age, operative status, and primary diagnosis classified into ten organ systems and nine etiologies were recorded at the time of intensive care unit admission. Predictor was developed by stepwise polychotomous logistic regression analysis for the outcome functional, compromised, and dead. Model fit was evaluated by chi-square statistics; prediction performance was measured by the area under the receiver operating characteristic curve, and classification table analysis of observed vs. predicted outcomes.
The resulting predictor included Pediatric Risk of Mortality, baseline Pediatric Overall Performance Category, operative status, age, and diagnostic factors from four systems (cardiovascular, respiratory, neurologic, gastrointestinal), and six etiologies (infection, trauma, drug overdose, allergy/immunology, diabetes, miscellaneous/undetermined). Its application to the validation sample yielded good agreement between the total number expected and the observed outcomes for each state (chi-square = 3.16, 2 degrees of freedom, p = .206), with area indices of 0.96 +/- 0.01 for discrimination of fully functional vs. the combination of the two poor outcome states (compromised or death), and 0.94 +/- 0.02 for discrimination of fully or compromised functional vs. death. The 3 x 3 classification resulted in correct classification rates of 83.2%, 74.4%, and 81.3%, for the outcomes functional, compromised, and death, respectively.
Prediction of three outcome states using physiologic status, baseline functional level, and broad-based diagnostic groupings at admission is feasible and may improve the relevance of quality of care assessment.
开发一种基于入院当天数据的方法，用于预测患者的结局状态，即独立功能状态、功能受损状态或死亡状态。
前瞻性获取的开发样本和验证样本。
位于三级医疗中心的儿科重症监护病房。
连续入院患者（n = 1663）用于预测指标开发，连续入院患者（n = 1153）用于预测指标验证。
在重症监护病房入院时记录小儿死亡风险评分、基线小儿总体表现类别评分、年龄、手术状态，以及分为十个器官系统和九种病因的主要诊断。通过逐步多分类逻辑回归分析开发预测指标，以得出功能、受损和死亡的结局。通过卡方统计评估模型拟合度；通过受试者操作特征曲线下面积测量预测性能，并对观察到的与预测的结局进行分类表分析。
最终的预测指标包括小儿死亡风险、基线小儿总体表现类别、手术状态、年龄，以及来自四个系统（心血管、呼吸、神经、胃肠道）的诊断因素和六种病因（感染、创伤、药物过量、过敏/免疫、糖尿病、其他/未确定）。将其应用于验证样本时，每个状态的预期总数与观察到的结局之间具有良好的一致性（卡方 = 3.16，自由度为2，p = 0.206），区分完全功能状态与两种不良结局状态（受损或死亡）组合的面积指数为0.96±0.01，区分完全或受损功能状态与死亡的面积指数为0.94±0.02。3×3分类得出功能、受损和死亡结局的正确分类率分别为83.2%、74.4%和81.3%。
利用入院时的生理状态、基线功能水平和广泛的诊断分组来预测三种结局状态是可行的，并且可能提高护理质量评估的相关性。
