Abstract
Background: The development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality. Methods: 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. Results: 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. Conclusions: 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.
| Original language | English (US) |
|---|---|
| Article number | 16 |
| Journal | BMC Medical Informatics and Decision Making |
| Volume | 19 |
| DOIs | |
| State | Published - Jan 31 2019 |
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Keywords
- Acute kidney injury
- Artificial neural networks
- Intensive care unit
- Multivariate logistic regression
- Physiological measurements
- Predictive modeling
- Random forest
ASJC Scopus subject areas
- Health Policy
- Health Informatics
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Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements. / Zimmerman, Lindsay P.; Reyfman, Paul Andrew; Smith, Angela D.R.; Zeng, Zexian; Kho, Abel N; Sanchez-Pinto, Lazaro Nelson; Luo, Yuan.
In: BMC Medical Informatics and Decision Making, Vol. 19, 16, 31.01.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements
AU - Zimmerman, Lindsay P.
AU - Reyfman, Paul Andrew
AU - Smith, Angela D.R.
AU - Zeng, Zexian
AU - Kho, Abel N
AU - Sanchez-Pinto, Lazaro Nelson
AU - Luo, Yuan
PY - 2019/1/31
Y1 - 2019/1/31
N2 - Background: The development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality. Methods: 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. Results: 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. Conclusions: 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.
AB - Background: The development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality. Methods: 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. Results: 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. Conclusions: 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.
KW - Acute kidney injury
KW - Artificial neural networks
KW - Intensive care unit
KW - Multivariate logistic regression
KW - Physiological measurements
KW - Predictive modeling
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=85060861835&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060861835&partnerID=8YFLogxK
U2 - 10.1186/s12911-019-0733-z
DO - 10.1186/s12911-019-0733-z
M3 - Article
C2 - 30700291
AN - SCOPUS:85060861835
VL - 19
JO - BMC Medical Informatics and Decision Making
JF - BMC Medical Informatics and Decision Making
SN - 1472-6947
M1 - 16
ER -