Learning Alerts for Acute Kidney Injury

Yale University

Status: Completed

Conditions: Acute Kidney Injury

Treatments: Other: Alert

Study type: Interventional

Funder types: Other; NIH

Identifiers

NCT02786277

YALEAKIALERTLEARN

1R01DK113191-01A1 (U.S. NIH Grant/Contract)

Details and patient eligibility

About

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.

Full 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.

Enrollment: 2,046 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

Primary purpose: Treatment

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Triple Blind

---

2,046 participants in 2 patient groups

Recommended

Experimental group

Description:

Those whose uplift score represents a probability of benefit greater than 0.5 will generate an alert, while those whose uplift score represents a probability of benefit less than 0.5 will not generate an alert.

Treatment:

Other: Alert

Anti-recommended

Experimental group

Description:

Those whose uplift score represents a probability of benefit greater than 0.5 will not generate an alert, while those whose uplift score represents a probability of benefit less than 0.5 will generate an alert.

Treatment:

Other: Alert