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Cited by 52Acute Kidney Injury ResearchSepsis Diagnosis and TreatmentChronic Kidney Disease and Diabetes

The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.

Development of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Electronic Health Record Data

Sanchez-Pinto, L. Nelson, Khemani, Robinder G.·2016·Pediatric Critical Care Medicine
DOI 10.1097/pcc.0000000000000750
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Abstract

Objective: Acute kidney injury is independently associated with poor outcomes in critically ill children. However, the main biomarker of acute kidney injury, serum creatinine, is a late marker of injury and can cause a delay in diagnosis. Our goal was to develop and validate a data-driven multivariable clinical prediction model of acute kidney injury in a general PICU using electronic health record data. Design: Derivation and validation of a prediction model using retrospective data. Patients: All patients 1 month to 21 years old admitted between May 2003 and March 2015 without acute kidney injury at admission and alive and in the ICU for at least 24 hours. Setting: A multidisciplinary, tertiary PICU. Intervention: The primary outcome was early acute kidney injury, which was defined as new acute kidney injury developed in the ICU within 72 hours of admission. Multivariable logistic regression was performed to derive the Pediatric Early AKI Risk Score using electronic health record data from the first 12 hours of ICU stay. Measurements and Main Results: A total of 9,396 patients were included in the analysis, of whom 4% had early acute kidney injury, and these had significantly higher mortality than those without early acute kidney injury (26% vs 3.3%; p < 0.001). Thirty-three candidate variables were tested. The final model had seven predictors and had good discrimination (area under the curve 0.84) and appropriate calibration. The model was validated in two validation sets and maintained good discrimination (area under the curves, 0.81 and 0.86). Conclusion: We developed and validated the Pediatric Early AKI Risk Score, a data-driven acute kidney injury clinical prediction model that has good discrimination and calibration in a general PICU population using only electronic health record data that is objective, available in real time during the first 12 hours of ICU care and generalizable across PICUs. This prediction model was designed to be implemented in the form of an automated clinical decision support system and could be used to guide preventive, therapeutic, and research strategies.

Overview

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Registry

Crossref · 10.1097/pcc.0000000000000750

Cites

37 of 37 references verified0 citation errors · 0 not checkable

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Claims other papers make about this one

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  • Many research groups have tackled early prediction of AKI using electronic health records data, but no model so far explains the rationale behind specific predictions despite a clear need for explainable and actionable predictions.

    “Many research groups have tackled early prediction of AKI using electronic health records (EHR) data [8–11], but no model so far explains the rationale behind specific predictions despite a clear need for explainable and actionable predictions [6, 12].”

    From Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care (2021) · cites this paper as [8]

Citations

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Research works printed but not linked: 2

  1. 2012, KDIGO clinical practice guideline for acute kidney injury., Kidney Int Suppl, 2, 1
  2. Therneau, 2010, rpart: Recursive partitioning., R package version, 3, 1

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Metadata

Authors
Sanchez-Pinto, L. N., & Khemani, R. G.
Affiliations
  • University of Chicago (US)
  • University of Southern California (US)
ORCID
Venue
Pediatric Critical Care Medicine
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2016-06
Volume
17
Issue
6
Pages
508-515
ISSN
1529-7535 (ISSN-L)
DOI
10.1097/pcc.0000000000000750
OpenAlex
W2342731602
Cited by
52
References
37
Citations by year
2026: 4 · 2025: 8 · 2024: 10 · 2023: 2 · 2022: 6 · 2021: 7 · 2020: 4 · 2019: 4 · 2018: 3 · 2017: 4
FWCI
3.69
Type
journal article
Language
en
Open access
closed
Locations
  • Pediatric Critical Care Medicine (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
  • Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
  • Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
Keywords
acute kidney injury, pediatric intensive care, early risk prediction, clinical prediction model, electronic health records, critically ill children, mortality, clinical decision support, serum creatinine
MeSH terms
Adolescent, Child, Child, Preschool, Decision Support Techniques, Female, Humans, Infant, Male, Retrospective Studies, Severity of Illness Index, Intensive Care Units, Pediatric, Multivariate Analysis, Logistic Models, Critical Illness, Risk Assessment, Young Adult, Electronic Health Records, Acute Kidney Injury

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