Prospective Validation of AKI Prediction

Adam C Dziorny

Status: Not yet enrolling

Conditions: Acute Kidney Injury

Study type: Observational

Funder types: Other; NIH

Identifiers

NCT06804200

STUDY00008612

K23DK138299 (U.S. NIH Grant/Contract)

Details and patient eligibility

About

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.

Full 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.

Enrollment: 800 estimated patients

Sex: All

Ages: 30 days to 18 years old

Volunteers: No Healthy Volunteers

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

Trial design

800 participants in 1 patient group

Pediatric ICU Admissions

Description:

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.