Zimmerman Jack E, Kramer Andrew A, McNair Douglas S, Malila Fern M
George Washington University, Washington, DC , USA.
Crit Care Med. 2006 May;34(5):1297-310. doi: 10.1097/01.CCM.0000215112.84523.F0.
To improve the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) method for predicting hospital mortality among critically ill adults and to evaluate changes in the accuracy of earlier APACHE models.
: Observational cohort study.
A total of 104 intensive care units (ICUs) in 45 U.S. hospitals.
A total of 131,618 consecutive ICU admissions during 2002 and 2003, of which 110,558 met inclusion criteria and had complete data.
None.
We developed APACHE IV using ICU day 1 information and a multivariate logistic regression procedure to estimate the probability of hospital death for randomly selected patients who comprised 60% of the database. Predictor variables were similar to those in APACHE III, but new variables were added and different statistical modeling used. We assessed the accuracy of APACHE IV predictions by comparing observed and predicted hospital mortality for the excluded patients (validation set). We tested discrimination and used multiple tests of calibration in aggregate and for patient subgroups. APACHE IV had good discrimination (area under the receiver operating characteristic curve = 0.88) and calibration (Hosmer-Lemeshow C statistic = 16.9, p = .08). For 90% of 116 ICU admission diagnoses, the ratio of observed to predicted mortality was not significantly different from 1.0. We also used the validation data set to compare the accuracy of APACHE IV predictions to those using APACHE III versions developed 7 and 14 yrs previously. There was little change in discrimination, but aggregate mortality was systematically overestimated as model age increased. When examined across disease, predictive accuracy was maintained for some diagnoses but for others seemed to reflect changes in practice or therapy.
APACHE IV predictions of hospital mortality have good discrimination and calibration and should be useful for benchmarking performance in U.S. ICUs. The accuracy of predictive models is dynamic and should be periodically retested. When accuracy deteriorates they should be revised and updated.
提高急性生理学与慢性健康状况评估（APACHE）方法预测危重症成年患者医院死亡率的准确性，并评估早期APACHE模型准确性的变化。
观察性队列研究。
美国45家医院的104个重症监护病房（ICU）。
2002年至2003年期间共有131618例连续入住ICU的患者，其中110558例符合纳入标准并拥有完整数据。
无。
我们利用ICU第1天的信息和多变量逻辑回归程序开发了APACHE IV，以估计从数据库中随机选取的占60%的患者的医院死亡概率。预测变量与APACHE III中的相似，但增加了新变量并采用了不同的统计建模方法。我们通过比较排除患者（验证集）的观察到的和预测的医院死亡率来评估APACHE IV预测的准确性。我们测试了区分度，并对校准进行了总体以及针对患者亚组的多次测试。APACHE IV具有良好的区分度（受试者工作特征曲线下面积 = 0.88）和校准度（Hosmer-Lemeshow C统计量 = 16.9，p = 0.08）。对于116种ICU入院诊断中的90%，观察到的与预测的死亡率之比与1.0无显著差异。我们还使用验证数据集将APACHE IV预测的准确性与使用7年和14年前开发的APACHE III版本的预测准确性进行比较。区分度变化不大，但随着模型使用年限增加，总体死亡率被系统性高估。按疾病进行检查时，某些诊断的预测准确性得以维持，但其他诊断的准确性似乎反映了实践或治疗方面的变化。
APACHE IV对医院死亡率的预测具有良好的区分度和校准度，应有助于美国ICU的绩效基准评估。预测模型的准确性是动态的，应定期重新测试。当准确性下降时，应进行修订和更新。
