Acute Kidney Injury
Conditions
Keywords
Machine Learning, Pediatrics, Critical Care
Brief 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.
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.
Interventions
None listed
Sponsors
Adam C Dziorny
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
30 Days to 18 Years
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Acute Kidney Injury (AKI) within the first 72 hours of ICU admission | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Prediction Accuracy and Timeliness of AKI Risk using a Predictive Model | 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. |
| Risk of Mortality in Patients with Acute Kidney Injury (AKI) | 28 Days following ICU admission | Investigators will assess for AKI independent risk of mortality at 28 days after adjusting for confounders. |
Countries
United States
Outcome results
None listed