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首页> 外文期刊>BMC Medical Informatics and Decision Making >Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements
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Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements

机译：使用多组生理学指标对ICU入院后急性肾损伤的早期预测

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The development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality. Our objective was to develop and validate a data driven multivariable clinical predictive model for early detection of AKI among a large cohort of adult critical care patients. We utilized data form the Medical Information Mart for Intensive Care III (MIMIC-III) for all patients who had a creatinine measured for 3?days following ICU admission and excluded patients with pre-existing condition of Chronic Kidney Disease and Acute Kidney Injury on admission. Data extracted included patient age, gender, ethnicity, creatinine, other vital signs and lab values during the first day of ICU admission, whether the patient was mechanically ventilated during the first day of ICU admission, and the hourly rate of urine output during the first day of ICU admission. Utilizing the demographics, the clinical data and the laboratory test measurements from Day 1 of ICU admission, we accurately predicted max serum creatinine level during Day 2 and Day 3 with a root mean square error of 0.224?mg/dL. We demonstrated that using machine learning models (multivariate logistic regression, random forest and artificial neural networks) with demographics and physiologic features can predict AKI onset as defined by the current clinical guideline with a competitive AUC (mean AUC 0.783 by our all-feature, logistic-regression model), while previous models aimed at more specific patient cohorts. Experimental results suggest that our model has the potential to assist clinicians in identifying patients at greater risk of new onset of AKI in critical care setting. Prospective trials with independent model training and external validation cohorts are needed to further evaluate the clinical utility of this approach and potentially instituting interventions to decrease the likelihood of developing AKI.
机译：重症监护病房（ICU）入院期间发生急性肾损伤（AKI）与发病率和死亡率增加相关。我们的目标是开发和验证数据驱动的多变量临床预测模型，以在大批成人重症监护患者中早期发现AKI。我们利用ICU入院后3天测量肌酐的所有患者的重症监护医学信息中心III（MIMIC-III）数据，排除了慢性肾脏病和急性肾损伤患者。提取的数据包括在ICU入院的第一天患者的年龄，性别，种族，肌酐，其他生命体征和实验室检查值，在ICU入院的第一天是否进行了机械通气以及第一天的每小时尿量重症监护病房入院的当天。利用ICU入院第1天的人口统计学资料，临床数据和实验室测试结果，我们准确地预测了第2天和第3天的最大血清肌酐水平，均方根误差为0.224？mg / dL。我们证明了使用具有人口统计和生理特征的机器学习模型（多元Logistic回归，随机森林和人工神经网络）可以预测AKI的发作，而AKI的发作是由当前临床指南定义的具有竞争性的AUC（根据我们的全功能逻辑模型，平均AUC为0.783） -回归模型），而先前的模型则针对更具体的患者队列。实验结果表明，我们的模型有潜力协助临床医生确定在重症监护环境中发生AKI新发风险更高的患者。需要通过独立模型训练和外部验证队列进行前瞻性试验，以进一步评估该方法的临床效用，并可能采取干预措施以降低发生AKI的可能性。

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