ArticlePediatric research2017
Electronic health record-based predictive models for acute kidney injury screening in pediatric inpatients.
Article in Pediatric research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Incidence of acute kidney injury-associated mortality in hospitalized children: a systematic review and meta-analysis.BMC nephrology · 2025Pooled it
- Development and bootstrap-based internal validation of a dynamic model for predicting acute kidney injury after day-3 reassessment in children with severe community-acquired pneumonia.Translational pediatrics · 2026Article
- Current status and future directions for the use of artificial intelligence in pediatric critical care nephrology.Pediatric nephrology (Berlin, Germany) · 2026Review
- Using health systems evidence to study and predict long-term outcomes of childhood acute kidney injury.Pediatric nephrology (Berlin, Germany) · 2026Article
- External Validation, Recalibration, and Extension of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Multicenter Data.Critical care explorations · 2026Article
- Leveraging ICT Tools to Improve Kidney Health: A Comprehensive Review of Innovations in Nephrology.Healthcare (Basel, Switzerland) · 2026Review
- Acute kidney injury is associated with elevated urinary endotrophin.American journal of physiology. Renal physiology · 2025Article
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Pediatric acute kidney injury is associated with impairment in nicotinamide adenine dinucleotide (NAD+) metabolism.Pediatric nephrology (Berlin, Germany) · 2025Observational
- Development and validation of a real-time risk prediction model for acute kidney injury in hospitalized pediatric patients.World journal of pediatrics : WJP · 2025Article
- Machine learning in lymphocyte and immune biomarker analysis for childhood thyroid diseases in China.BMC pediatrics · 2025Article
- The challenges for developing prognostic prediction models for acute kidney injury in hospitalized children: A systematic review.Pediatric investigation · 2025Review
- Development and validation of a deep learning algorithm for the prediction of serum creatinine in critically ill patients.JAMIA open · 2024Article
- Evaluation of Vancomycin Dose Needed to Achieve 24-Hour Area Under the Concentration-Time Curve to Minimum Inhibitory Concentration Ratio Greater Than or Equal to 400 Using Pharmacometric Approaches in Pediatric Intensive Care Patients.Critical care explorations · 2024Article
- Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study.EClinicalMedicine · 2024Article
- Clinical Characteristics and Outcomes of Drug-Induced Acute Kidney Injury Cases.Kidney international reports · 2023Article
- Phenotype Risk Score but Not Genetic Risk Score Aids in Identifying Individuals With Systemic Lupus Erythematosus in the Electronic Health Record.Arthritis & rheumatology (Hoboken, N.J.) · 2023Article
- Expert Consensus on the Nephrotoxic Potential of 195 Medications in the Non-intensive Care Setting: A Modified Delphi Method.Drug safety · 2023Article
- Fallacy of Median Door-to-ECG Time: Hidden Opportunities for STEMI Screening Improvement.Journal of the American Heart Association · 2022Article
- Estimating baseline creatinine values to define acute kidney injury in critically ill pediatric patients.Kidney research and clinical practice · 2022Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
BackgroundAcute kidney injury (AKI) is common in pediatric inpatients and is associated with increased morbidity, mortality, and length of stay. Its early identification can reduce severity.MethodsTo create and validate an electronic health record (EHR)-based AKI screening tool, we generated temporally distinct development and validation cohorts using retrospective data from our tertiary care children's hospital, including children aged 28 days through 21 years with sufficient serum creatinine measurements to determine AKI status. AKI was defined as 1.5-fold or 0.3 mg/dl increase in serum creatinine. Age, medication exposures, platelet count, red blood cell distribution width, serum phosphorus, serum transaminases, hypotension (ICU only), and pH (ICU only) were included in AKI risk prediction models.ResultsFor ICU patients, 791/1,332 (59%) of the development cohort and 470/866 (54%) of the validation cohort had AKI. In external validation, the ICU prediction model had a c-statistic=0.74 (95% confidence interval 0.71-0.77). For non-ICU patients, 722/2,337 (31%) of the development cohort and 469/1,474 (32%) of the validation cohort had AKI, and the prediction model had a c-statistic=0.69 (95% confidence interval 0.66-0.72).ConclusionsAKI screening can be performed using EHR data. The AKI screening tool can be incorporated into EHR systems to identify high-risk patients without serum creatinine data, enabling targeted laboratory testing, early AKI identification, and modification of care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.