Prospective Validation of AKI Prediction Algorithm
Adam C Dziorny
Summary
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.
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…
Eligibility
- Age range
- 0–18 years
- Sex
- All
- Healthy volunteers
- No
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
Location
- Golisano Children's Hospital at StrongRochester, New York