A New Severity of Illness Scale Using a Subset of Acute Physiology and Chronic Health Evaluation Data Elements Shows Comparable Predictive Accuracy
A New Severity of Illness Scale Using a Subset of Acute Physiology and Chronic Health Evaluation Data Elements Shows Comparable Predictive Accuracy
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DOI：
10.1097/ccm.0b013e31828a24fe
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发表时间：
2013-07-01
影响因子：
8.8
通讯作者：
Clifford, Gari D.
中科院分区：
文献类型：
--
作者：
Johnson, Alistair E. W.;Kramer, Andrew A.;Clifford, Gari D.
目的：疾病严重程度评分在预测预后（如死亡率和住院时间）中的应用引起了人们的极大兴趣。最复杂的评分系统需要收集大量的生理测量结果，这使得它们的实时使用变得困难。基于几个可以电子捕获的参数的疾病严重程度评分将是非常有益的。使用称为粒子群优化的机器学习技术，我们试图减少急性生理学、年龄和慢性健康评估IV系统中收集的生理参数的数量，而不损失预测准确性。设计：2007年至2011年ICU入院的回顾性队列研究。设置：美国49家医院的86个ICU，其中安装了急性生理学、年龄和慢性健康评估IV系统。患者：81，087例入院，其中72，474例没有任何缺失值。干预措施：无。测量和主要结果：机器学习算法被用来提出能够产生准确的疾病严重程度评分的最小变量集：牛津急性疾病严重程度评分。使用牛津急性疾病严重程度评分的ICU死亡率预测模型是在2007-2009年期间的入院时开发的，并在2010-2011年期间的入院时进行了验证。最简约的牛津急性疾病严重程度评分包括7项生理指标、择期手术、年龄和既往住院时间。ICU死亡率预测模型使用牛津急性疾病严重程度评分达到了0.88的接收器工作特征曲线下的面积和calibratedwell.Conclusions：减少疾病评分的严重程度的歧视和校准相当于更复杂的现有模型。这在很大程度上是使用机器学习算法完成的，机器学习算法可以有效地解释生理参数和结果之间的非线性关联。
Objectives:Severity of illness scores have gained considerable interest for their use in predicting outcomes such as mortality and length of stay. The most sophisticated scoring systems require the collection of numerous physiologic measurements, making their use in real-time difficult. A severity of illness score based on a few parameters that can be captured electronically would be of great benefit. Using a machine-learning technique known as particle swarm optimization, we attempted to reduce the number of physiologic parameters collected in the Acute Physiology, Age, and Chronic Health Evaluation IV system without losing predictive accuracy.Design:Retrospective cohort study of ICU admissions from 2007 to 2011.Setting:Eighty-six ICUs at 49 U.S. hospitals where an Acute Physiology, Age, and Chronic Health Evaluation IV system had been installed.Patients:81,087 admissions, of which 72,474 did not have any missing values.Interventions:None.Measurements and Main Results:Machine-learning algorithms were used to come up with the minimal set of variables that were capable of yielding an accurate severity of illness score: the Oxford Acute Severity of Illness Score. Predictive models of ICU mortality using Oxford Acute Severity of Illness Score were developed on admissions during 2007-2009 and validated on admissions during 2010-2011. The most parsimonious Oxford Acute Severity of Illness Score consisted of seven physiologic measurements, elective surgery, age, and prior length of stay. Predictive models of ICU mortality using Oxford Acute Severity of Illness Score achieved an area under the receiver operating characteristic curve of 0.88 and calibrated well.Conclusions:A reduced severity of illness score had discrimination and calibration equivalent to more complex existing models. This was accomplished in large part using machine-learning algorithms, which can effectively account for the nonlinear associations between physiologic parameters and outcome.