Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care

Autor: David Inwald, Byung-Gu Cho, Binod Thapa-Chhetry, Ting Feng, Vinay Vaidya, Christopher J. L. Newth, Junzi Dong, Tunu Shum
Rok vydání: 2021
Předmět:
Male
Adolescent
Critical Care
medicine.medical_treatment
Critical Care and Intensive Care Medicine
Machine learning
computer.software_genre
urologic and male genital diseases
Intensive Care Units
Pediatric
Pediatrics
Severity of Illness Index
Cohort Studies
Machine Learning
chemistry.chemical_compound
Young Adult
AKI
Early prediction
medicine
Contextual information
Humans
Computer Simulation
Renal replacement therapy
Stage (cooking)
Child
Creatinine
business.industry
RC86-88.9
urogenital system
Research
Acute kidney injury
Infant
Newborn
Infant
Medical emergencies. Critical care. Intensive care. First aid
Acute Kidney Injury
medicine.disease
female genital diseases and pregnancy complications
Pediatric critical care
chemistry
ROC Curve
Predictive model
Area Under Curve
Child
Preschool
Kidney Impairment
Female
Artificial intelligence
business
computer
Zdroj: Critical Care
Critical Care, Vol 25, Iss 1, Pp 1-8 (2021)
ISSN: 1466-609X
Popis: Background Acute kidney injury (AKI) in pediatric critical care patients is diagnosed using elevated serum creatinine, which occurs only after kidney impairment. There are no treatments other than supportive care for AKI once it has developed, so it is important to identify patients at risk to prevent injury. This study develops a machine learning model to learn pre-disease patterns of physiological measurements and predict pediatric AKI up to 48 h earlier than the currently established diagnostic guidelines. Methods EHR data from 16,863 pediatric critical care patients between 1 month to 21 years of age from three independent institutions were used to develop a single machine learning model for early prediction of creatinine-based AKI using intelligently engineered predictors, such as creatinine rate of change, to automatically assess real-time AKI risk. The primary outcome is prediction of moderate to severe AKI (Stage 2/3), and secondary outcomes are prediction of any AKI (Stage 1/2/3) and requirement of renal replacement therapy (RRT). Predictions generate alerts allowing fast assessment and reduction of AKI risk, such as: “patient has 90% risk of developing AKI in the next 48 h” along with contextual information and suggested response such as “patient on aminoglycosides, suggest check level and review dose and indication”. Results The model was successful in predicting Stage 2/3 AKI prior to detection by conventional criteria with a median lead-time of 30 h at AUROC of 0.89. The model predicted 70% of subsequent RRT episodes, 58% of Stage 2/3 episodes, and 41% of any AKI episodes. The ratio of false to true alerts of any AKI episodes was approximately one-to-one (PPV 47%). Among patients predicted, 79% received potentially nephrotoxic medication after being identified by the model but before development of AKI. Conclusions As the first multi-center validated AKI prediction model for all pediatric critical care patients, the machine learning model described in this study accurately predicts moderate to severe AKI up to 48 h in advance of AKI onset. The model may improve outcome of pediatric AKI by providing early alerting and actionable feedback, potentially preventing or reducing AKI by implementing early measures such as medication adjustment.
Databáze: OpenAIRE
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