Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions.
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...
| Publicado en: | Pediatric Nephrology Vol. 39; no. 8; pp. 2309 - 2325 |
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| Autores principales: | , , , , , , , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178064730&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178064730 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0931041X EF1 jtl: Pediatric Nephrology issn: 0931041X maglogo: N pubinfo: dt: Aug2024 vid: 39 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178064730 173214996 178064730 178064730 10.1007/s00467-023-06191-7 178064730 ppf: 2309 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions. aug: au: Raina, Rupesh Nada, Arwa Shah, Raghav Aly, Hany Kadatane, Saurav Abitbol, Carolyn Aggarwal, Mihika Koyner, Jay Neyra, Javier Sethi, Sidharth Kumar affil: Akron Nephrology Associates/Cleveland Clinic Akron General Medical Center, Akron, OH, USA sug: subj: Kidney Failure, Acute Diagnosis Kidney Failure, Acute Diagnosis Infant, Newborn Early Diagnosis Artificial Intelligence Prediction Models Machine Learning Decision Trees Risk Assessment Predictive Validity Biological Markers Child Adolescence Infant, Newborn: birth-1 month Child: 6-12 years Adolescent: 13-18 years ab: 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. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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