Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements

Lindsay P Zimmerman,Paul A Reyfman,Zexian Zeng,Angela D R Smith,Abel Kho,L Nelson Sanchez-Pinto,Yuan Luo

doi:10.1186/s12911-019-0733-z

Lindsay P Zimmerman, Paul A Reyfman + Show 5 more

Open Access

https://doi.org/10.1186/s12911-019-0733-z

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Abstract

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 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.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 0.224 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.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.

Highlights

The development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality
We developed SQL scripts in order to query the MIMIC-III database for all patients who had a creatinine measured at 72 h following ICU admission [27]
Note that the high bicarbonate level, which was not selected in the linear regression, but achieved a small significance level in the logistic regression, likely represents a surrogate of less acidosis, which is associated with higher severity of illness and is a risk factor of AKI

Summary

Introduction

The development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality. AKI is one of the major diagnoses among ICU patients and a leading factor associated with a prolonged hospital stay and with subsequent morbidity or early mortality post discharge [1,2,3,4]. Multiple patient and healthcare delivery related risk factors have been shown as predictors of AKI in specific patient cohorts [7, 8] Correlations between these diverse set of risk factors across heterogeneous patient cohorts are much less understood, but critical for producing effective diagnostic and treatment guidelines of AKI [2, 3], Such guidelines often need a panel of demographic, clinical physiologic, and radiologic features in order to stratify patient cohorts for targeted treatment

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