Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements
Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements
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DOI：
10.1186/s12911-019-0733-z
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发表时间：
2019-01-31
影响因子：
3.5
通讯作者：
Luo, Yuan
中科院分区：
文献类型：
--
作者：
Zimmerman, Lindsay P.;Reyfman, Paul A.;Luo, Yuan
BackgroundThe急性肾损伤（阿基）的发展在重症监护病房（ICU）入院与发病率和死亡率增加有关。MethodsOur目标是开发和验证一个数据驱动的多变量临床预测模型，用于早期检测阿基在一个大的成人重症监护患者队列。我们使用了重症监护医学信息市场III（MIMIC-III）的数据，用于所有在ICU入院后3天内测量肌酐的患者，并排除了入院时存在慢性肾脏疾病和急性肾损伤的患者。所提取的数据包括患者的年龄，性别，种族，肌酐，其他生命体征和实验室值在第一天的ICU入院，患者是否是机械通气在第一天的ICU入院，和每小时的尿量率在第一天的ICU入院results利用人口统计学，临床数据和实验室检查测量从第一天的ICU入院，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0.224mg/dL。我们证明了使用机器学习模型（多变量逻辑回归、随机森林和人工神经网络）与人口统计学和生理学特征相结合可以预测阿基发作，如当前临床指南所定义，具有竞争性AUC（通过我们的全特征逻辑回归模型，平均AUC为0.783），结论实验结果表明，我们的模型有可能帮助临床医生识别具有更大风险的患者。重症监护环境中新发阿基。需要进行独立模型训练和外部验证队列的前瞻性试验，以进一步评估这种方法的临床效用，并可能制定干预措施以降低发生阿基的可能性。
BackgroundThe development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality.MethodsOur 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 3days 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.ResultsUtilizing 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 0.224mg/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.ConclusionsExperimental 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.