Uplift Modeling to More Narrowly Target Alerts for Acute Kidney Injury

Description

The primary objective of this study is to determine whether the use of uplift (also known as Conditional Average Treatment Effect - CATE) modeling to empirically identify patients expected to benefit the most from AKI alerting and to target AKI alerts to these patients will reduce the rates of AKI progression, dialysis, and mortality.Acute kidney injury (AKI) carries a significant, independent risk of mortality among hospitalized patients, but despite its association with poor clinical outcomes, AKI is asymptomatic and frequently overlooked by clinicians, with fewer than half of all AKI patients with documentation of the syndrome in the electronic medical record, which was associated with decreased rates of AKI clinical best practices. Our research group recently conducted a large-scale multicenter randomized controlled trial of electronic alerts for AKI throughout the Yale New Haven Health System from 2018 to 2020 (ELAIA-1). Our study showed that, overall, alerting physicians to the

Trial Eligibility

Inclusion Criteria: 1. Adults ≥ 18 years 2. Admitted to a participating hospital 3. Has AKI as defined by creatinine criteria: * 0.3 mg/dl increase in inpatient serum creatinine over 48 hours OR * 50% relative increase in inpatient serum creatinine over 7 days Exclusion Criteria: 1. Dialysis order prior to AKI onset 2. Initial creatinine ≥ 4.0 mg/dl 3. Prior admission in which patient was randomized 4. Admission to hospice service or comfort measures only order 5. ESKD diagnosis code 6. Kidney transplant within six months 7. Opted out of electronic health record research

Study Info

Interventions

Locations Recruiting

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