Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning.
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ABSTRACT: Background
Acute kidney injury (AKI) carries a poor prognosis. Its incidence is increasing in the intensive care unit (ICU). Our purpose in this study is to develop and externally validate a model for predicting AKI in the ICU using patient data present prior to ICU admission.Methods
We used data of 98 472 adult ICU admissions at Mayo Clinic between 1 January 2005 and 31 December 2017 and 51 801 encounters from Medical Information Mart for Intensive Care III (MIMIC-III) cohort. A gradient-boosting model was trained on 80% of the Mayo Clinic cohort using a set of features to predict AKI acquired in the ICU.Results
AKI was identified in 39 307 (39.9%) encounters in the Mayo Clinic cohort. Patients who developed AKI in the ICU were older and had higher ICU and in-hospital mortality compared to patients without AKI. A 30-feature model yielded an area under the receiver operating curve of 0.690 [95% confidence interval (CI) 0.682-0.697] in the Mayo Clinic cohort set and 0.656 (95% CI 0.648-0.664) in the MIMIC-III cohort.Conclusions
Using machine learning, AKI among ICU patients can be predicted using information available prior to admission. This model is independent of ICU information, making it valuable for stratifying patients at admission.
SUBMITTER: Shawwa K
PROVIDER: S-EPMC8087133 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe progression from acute kidney injury (AKI) to chronic kidney disease (CKD) is not well understood in children.ObjectivesWe aimed to develop a pediatric CKD definition using administrative data and use it to evaluate the association between AKI in critically ill children and CKD 5 years after hospital discharge.DesignRetrospective cohort study using chart collection and administrative data.SettingTwo-center study in Montreal, Canada.PatientsChildren (?18 years old) admitted to two pediatric intensive care units (ICUs) between 2003 and 2005. We a priori excluded patients with end-stage renal disease or no health care number. Only the first admission during the study period was included. We excluded patients who could not be linked to administrative data, did not survive hospitalization, or had preexisting renal disease.MeasurementsAcute kidney injury was defined using Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Patients were defined as having CKD 5 years post-discharge if they had ?1 CKD diagnostic code or ?1 CKD-specific medication prescription.MethodsChart data used to define the exposure (AKI) were merged with provincial administrative data used to define the outcome (CKD). Cox regression was used to evaluate the AKI-CKD association.ResultsA total of 2235 (56% male) patients were included, and the median admission age was 3.7 years. A total of 464 (21%) patients developed AKI during pediatric ICU admission. At 5 years post-discharge, 43 (2%) patients had a CKD diagnosis. Patients with both stage 1 and stage 2-3 AKI had increased risk of a CKD diagnosis, with the adjusted hazard ratios (95% confidence intervals) of 2.2 (1.1-4.5) and 2.5 (1.1-5.7), respectively (P < .001).LimitationsResults may not be generalizable to non-ICU patients. We were not able to control for post-discharge variables; future research should try to explore these additional potential risk factors further.ConclusionsAcute kidney injury is associated with 5-year post-discharge CKD diagnosis defined by administrative health care data.
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