Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data.
Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data.
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危重患者的高氯血症：与结果的关联以及使用电子健康记录数据的预测。
DOI：
10.1186/s12911-020-01326-4
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发表时间：
2020-12-15
影响因子：
3.5
通讯作者：
Luo Y
中科院分区：
文献类型：
--
作者：
Yeh P;Pan Y;Sanchez-Pinto LN;Luo Y
在静脉内液体氯化物负荷和血清氯化物水平（高氯血症）的背景下，氯化物增加先前与重症监护室（ICU）患者（例如脓毒症患者）的选定亚群的发病率和死亡率增加相关。在这里，我们研究了重症监护医学信息市场III（MIMIC-III）数据库的一般ICU人群，以证实这些关联，并提出了一个监督学习模型来预测ICU患者的高血糖症。我们使用回归分析评估了高血压和氯负荷及其与几种结局（ICU死亡率、第7天新发急性肾损伤[阿基]和第7天多器官功能障碍综合征[MODS]）的相关性。四个预测监督学习分类器被训练成使用来自成人ICU停留的前24小时的临床记录的代表性特征来预测高血糖症。高血糖与ICU死亡率、第7天新发阿基和第7天MODS的几率增加有独立相关性。高氯化物负荷也与ICU死亡率增加有关。我们表现最好的监督学习模型预测了第二天的高血糖，AUC为0.76，需要警报的数量（NNA）为7-临床可行的比率。我们的研究结果支持使用预测模型来帮助临床医生监测和预防高危患者的高血糖症，并提供了改善患者预后的机会。
Increased chloride in the context of intravenous fluid chloride load and serum chloride levels (hyperchloremia) have previously been associated with increased morbidity and mortality in select subpopulations of intensive care unit (ICU) patients (e.g patients with sepsis). Here, we study the general ICU population of the Medical Information Mart for Intensive Care III (MIMIC-III) database to corroborate these associations, and propose a supervised learning model for the prediction of hyperchloremia in ICU patients. We assessed hyperchloremia and chloride load and their associations with several outcomes (ICU mortality, new acute kidney injury [AKI] by day 7, and multiple organ dysfunction syndrome [MODS] on day 7) using regression analysis. Four predictive supervised learning classifiers were trained to predict hyperchloremia using features representative of clinical records from the first 24h of adult ICU stays. Hyperchloremia was shown to have an independent association with increased odds of ICU mortality, new AKI by day 7, and MODS on day 7. High chloride load was also associated with increased odds of ICU mortality. Our best performing supervised learning model predicted second-day hyperchloremia with an AUC of 0.76 and a number needed to alert (NNA) of 7—a clinically-actionable rate. Our results support the use of predictive models to aid clinicians in monitoring for and preventing hyperchloremia in high-risk patients and offers an opportunity to improve patient outcomes.
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DOI：
10.1109/bibm.2018.8621574
发表时间：
2018-12
期刊：
Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
影响因子：
--
作者：
Li Y;Yao L;Mao C;Srivastava A;Jiang X;Luo Y
通讯作者：
Luo Y
影响因子：
3
作者：
Garcia-Gallo, J. E.;Fonseca-Ruiz, N. J.;Duitama-Munoz, J. F.
通讯作者：
Duitama-Munoz, J. F.
影响因子：
3.7
作者：
Lin, Yu-Wei;Zhou, Yuqian;Campbell, Roy H.
通讯作者：
Campbell, Roy H.
影响因子：
8.8
作者：
Raghunathan, Karthik;Shaw, Andrew;Lindenauer, Peter K.
通讯作者：
Lindenauer, Peter K.
影响因子：
3.5
作者：
Zimmerman, Lindsay P.;Reyfman, Paul A.;Luo, Yuan
通讯作者：
Luo, Yuan