NCT02786277

Learning Alerts for Acute Kidney Injury

Uplift Modeling to More Narrowly Target Alerts for Acute Kidney Injury

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 2,046 (actual)
• Sponsor: Yale University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

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.

Detailed description

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 presence of AKI did not demonstrate a difference in the rate of our primary outcome of progression of AKI, dialysis, or death, despite the alert leading to some process of care changes such as measurement of creatinine and urinalysis. There was, however, substantial heterogeneity among the study sites. The proliferation of alerting systems that are ineffective can lead to the phenomenon of alert fatigue, whereby providers tend to ignore alerts in a high-alert environment, and can have deleterious effects on patient care. Further, given the highly heterogenous nature of AKI, a more personalized approach to AKI alerting may be warranted. Uplift modeling, commonly used in marketing, is a novel concept in the medical field and aims to determine phenotypic characteristics that predict a response (benefit or harm) to a given intervention. In this way, patients who are predicted to benefit most from an intervention are identified and preferentially targeted. Uplift modeling of alerting systems has the potential to both improve alert effectiveness through intelligent targeting, and reduce alert fatigue. In this study, we will expand upon our prior AKI alert trial to determine prospectively whether the use of uplift modeling to preferentially target patients expected to benefit from an AKI alert will reduce the rates of AKI progression, dialysis and death among hospitalized patients with AKI. Inpatients at 4 teaching hospitals within the YNHH system with AKI, based on the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine criteria, will be randomized to a "recommended" group (with higher scores receiving alerts and lower scores not receiving alerts as recommended) versus an "anti-recommended" group (with higher scores not receiving alerts and lower scores receiving alerts as anti-recommended). The primary outcome will be a composite of AKI progression, dialysis, or mortality within 14 days of randomization. Secondary outcomes will focus on AKI-specific process measures.

Conditions

• Acute Kidney Injury

Interventions

Type	Name	Description
OTHER	Alert	An alert informing the provider of the presence of acute kidney injury will be fired.

Timeline

• Start date: 2024-02-15
• Primary completion: 2024-08-14
• Completion: 2025-05-03
• First posted: 2016-05-30
• Last updated: 2025-10-08
• Results posted: 2025-10-08

Locations

1 site across 1 country: United States