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Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions

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Abstract

Acute kidney injury (AKI) has a significant impact on the short-term and long-term clinical outcomes of pediatric and neonatal patients, and it is imperative in these populations to mitigate the pathways leading to AKI and be prepared for early diagnosis and treatment intervention of established AKI. Recently, artificial intelligence (AI) has provided more advent predictive models for early detection/prediction of AKI utilizing machine learning (ML). By providing strong detail and evidence from risk scores and electronic alerts, this review outlines a comprehensive and holistic insight into the current state of AI in AKI in pediatric/neonatal patients. In the pediatric population, AI models including XGBoost, logistic regression, support vector machines, decision trees, naïve Bayes, and risk stratification scores (Renal Angina Index (RAI), Nephrotoxic Injury Negated by Just-in-time Action (NINJA)) have shown success in predicting AKI using variables like serum creatinine, urine output, and electronic health record (EHR) alerts. Similarly, in the neonatal population, using the “Baby NINJA” model showed a decrease in nephrotoxic medication exposure by 42%, the rate of AKI by 78%, and the number of days with AKI by 68%. Furthermore, the “STARZ” risk stratification AI model showed a predictive ability of AKI within 7 days of NICU admission of AUC 0.93 and AUC of 0.96 in the validation and derivation cohorts, respectively. Many studies have reported the superiority of using biomarkers to predict AKI in pediatric patients and neonates as well. Future directions include the application of AI along with biomarkers (NGAL, CysC, OPN, IL-18, B2M, etc.) in a Labelbox configuration to create a more robust and accurate model for predicting and detecting pediatric/neonatal AKI.

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Authors and Affiliations

  1. Akron Nephrology Associates/Cleveland Clinic Akron General Medical Center, Akron, OH, USA

    Rupesh Raina & Raghav Shah

  2. Department of Nephrology, Akron Children’s Hospital, Akron, OH, USA

    Rupesh Raina

  3. Le Bonheur Children’s Hospital & St. Jude Research Hospital, The University of Tennessee Health Science Center, Memphis, TN, USA

    Arwa Nada

  4. Department of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA

    Rupesh Raina, Raghav Shah & Saurav Kadatane

  5. Department of Neonatology, Cleveland Clinic Children’s, Cleveland, OH, USA

    Hany Aly

  6. Department of Pediatrics, Division of Pediatric Nephrology, University of Miami Miller School of Medicine/Holtz Children’s Hospital, Miami, FL, USA

    Carolyn Abitbol

  7. Paediatric Nephrology & Paediatric Kidney Transplantation, Kidney and Urology Institute, Medanta, The Medicity Hospital, Gurgaon, India

    Mihika Aggarwal & Sidharth Kumar Sethi

  8. Section of Nephrology, Department of Medicine, University of Chicago, Pritzker School of Medicine, Chicago, IL, USA

    Jay Koyner

  9. Department of Medicine, Division of Nephrology, University of Alabama at Birmingham, Birmingham, AL, USA

    Javier Neyra

Authors
  1. Rupesh Raina
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  2. Arwa Nada
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  3. Raghav Shah
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  4. Hany Aly
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  5. Saurav Kadatane
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  6. Carolyn Abitbol
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  7. Mihika Aggarwal
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  8. Jay Koyner
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  9. Javier Neyra
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  10. Sidharth Kumar Sethi
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All authors contributed equally to this manuscript.

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Correspondence to Rupesh Raina.

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Raina, R., Nada, A., Shah, R. et al. Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions. Pediatr Nephrol (2023). https://doi.org/10.1007/s00467-023-06191-7

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  • DOI: https://doi.org/10.1007/s00467-023-06191-7

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