Prospective Validation of AKI Prediction

April 7, 2026 updated by: Adam C Dziorny

Prospective Validation of AKI Prediction Algorithm

The purpose of this prospective observational study is to implement, deploy, and quantify accuracy of an existing Pediatric Early AKI Risk Score algorithm. The implementation will be facilitated using a Health Level 7 (HL7) Fast Healthcare Interoperability Resource (FHIR)-based architecture. Investigators will deploy this model and store results in a manner not viewable to the clinical team caring for the patient. To determine the accuracy of the implemented prediction model, Investigators will prospectively identify patients with AKI at 72 hours following ICU admission. Investigators hypothesize that this model will prospectively detect AKI with a sensitivity >70% and a positive predictive value >20%, both chosen a priori as 10% improvement over the initial Pediatric AKI Risk Score tool.

Study Overview

Status

Recruiting

Conditions

Detailed Description

This is a single-center prospective observational study validating an AKI predictive model. Each model feature will be mapped to an appropriate FHIR-based resource. To mitigate the latency issues seen in other distributed CDS systems, Investigators have developed an asynchronous design where algorithm calculations are performed offline (e.g., not within the EHR) and risk scores are subsequently written back to the EHR. Importantly, in this deployment, model output and resulting clinical risk score will not be communicated to the treating clinicians.

During the study period, Investigators will review charts daily for all patients admitted to the Golisano Children's Hospital PICU, a 12-bed facility adjacent to our 15-bed Pediatric Cardiac Intensive Care Unit (PCICU). Using a standard protocol to screen and identify patients by chart review, Investigators will generate a list of patients who meet AKI KDIGO criteria by SCr and urine output, along with recorded clinical information about these patients. At the conclusion of the study period, this list will be used as the "gold standard" and compared to the automated screening tool to determine the tool's test characteristics.

Model assessment outcomes include sensitivity, positive predictive value (PPV), and number needed to alert (NNA) to prospectively identify AKI in a population of critically ill children. Additional outcomes include timeliness of identification based on model implementation (e.g., measured timestamps of algorithm prediction compared to manual, prospectively identified AKI development). Additionally, Investigators will report interventions and clinical outcomes of the prospectively identified patients with AKI, stratified by those predicted early by the model (within 24 hours of admission) versus not.

Study Type

Observational

Enrollment (Estimated)

800

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

    • New York
      • Rochester, New York, United States, 14642
        • Recruiting
        • Golisano Children's Hospital at Strong

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

All medical and surgical patients admitted to the pediatric intensive care unit and pediatric cardiac intensive care unit.

Description

Inclusion Criteria:

  • Patients admitted to the Pediatric Intensive Care Unit or Pediatric Cardiac Intensive Care Unit
  • Age > 30 days and < 18 years

Exclusion Criteria:

  • Discharged less than 12 hours after admission
  • AKI at 12 hours of admission by KDIGO Serum Criteria

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Pediatric ICU Admissions
The study subjects are all patients admitted to the pediatric intensive care unit and the pediatric cardiac care unit at Golisano Children's Hospital at Strong. By virtue of admission to a pediatric hospital bed, all subjects will be children. The rationale for focusing on this population is that the developed algorithm focuses on identifying acute kidney injury among critically ill pediatric patients. The gender and age of subjects will not influence enrollment. To accurately determine algorithm test characteristics (sensitivity, PPV, NNA) pragmatically, Investigators must include all admitted patients. Missing patients may bias our results and invalidate the study.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Acute Kidney Injury (AKI) within the first 72 hours of ICU admission
Time Frame: Within 72 hours following ICU Admission
Acute Kidney Injury (AKI) defined by KDIGO stages 1, 2, or 3, based on changes in serum creatinine levels or urine output (UOP), assessed within the first 72 hours of ICU admission. Stage 1 is defined by a 1.5 to 1.9 times baseline serum creatinine or an increase of ≥0.3 mg/dL. Stage 2 is a 2.0 to 2.9 times baseline increase, and Stage 3 is a 3.0 times baseline increase or a serum creatinine ≥4.0 mg/dL.
Within 72 hours following ICU Admission

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Prediction Accuracy and Timeliness of AKI Risk using a Predictive Model
Time Frame: Within 12 hours of ICU Admission
Predictive model results generated prospectively (at 12 hours following admission) will be used to generate a 2x2 confusion matrix with prospectively identified AKI by serum creatinine or urine output changes based on KDIGO criteria. Investigators will calculate sensitivity, PPV, and NNA for the prospective identification of AKI. Investigators will report the time of AKI prediction compared to admission date and the onset date of AKI.
Within 12 hours of ICU Admission
Risk of Mortality in Patients with Acute Kidney Injury (AKI)
Time Frame: 28 Days following ICU admission
Investigators will assess for AKI independent risk of mortality at 28 days after adjusting for confounders.
28 Days following ICU admission

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Collaborators

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

June 25, 2025

Primary Completion (Estimated)

June 1, 2026

Study Completion (Estimated)

June 1, 2026

Study Registration Dates

First Submitted

January 27, 2025

First Submitted That Met QC Criteria

January 27, 2025

First Posted (Actual)

February 3, 2025

Study Record Updates

Last Update Posted (Actual)

April 8, 2026

Last Update Submitted That Met QC Criteria

April 7, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Other Study ID Numbers

  • STUDY00008612
  • K23DK138299 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

IPD Plan Description

Non-PHI data elements may be made available by request, per data sharing plan.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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