Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data
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RESEARCH
Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data Pete Yeh1, Yiheng Pan2, L. Nelson Sanchez‑Pinto3,4* and Yuan Luo4*
From 10th International Workshop on Biomedical and Health Informatics San Diego, CA, USA. 18-20 November 2019
Abstract Background: Increased chloride in the context of intravenous fluid chloride load and serum chloride levels (hyper‑ chloremia) 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 super‑ vised learning model for the prediction of hyperchloremia in ICU patients. Methods: We assessed hyperchloremia and chloride load and their associations with several outcomes (ICU mortal‑ ity, new acute kidney injury [AKI] by day 7, and multiple organ dysfunction syndrome [MODS] on day 7) using regres‑ sion 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. Results: 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. Conclusions: 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. Keywords: Biomedical informatics, Decision support systems, Machine learning, Predictive models
*Correspondence: lazaro.sanchez‑[email protected]; yuan. [email protected] 4 Department of Preventive Medicine (Health and Biomedical Informatics), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA Full list of author information is available at the end of the article
Background Intravenous (IV) fluids are commonplace in the critical care setting for good reason—they are low-risk, go-to interventions for patients with fluid deficits and electrolyte imbalances. Recent studies have reexamined the effects of these fluids, however, and mounting evidence cautions that aggressive doses that are still within reference therapeutic ranges may lead to adverse outcomes ranging from organ damage to in-hospital mortality [1]. Particular concern has been raised regarding chloride,
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