Clinical trial NCT02786277: Learning Alerts for Acute Kidney Injury
Official title: Uplift Modeling to More Narrowly Target Alerts for Acute Kidney Injury
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Acute Kidney Injury
Lead sponsor: Yale University
Sponsor class: OTHER
Collaborators: name: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
First posted: 2016-05-30
Enrollment: 4092
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: TREATMENT

Brief 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.

Eligibility criteria: 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

Primary outcomes:
measure: Proportion of patients with progression to a higher stage of AKI OR Dialysis OR Death
timeFrame: Within 14 days from randomization
Secondary outcomes:
measure: 14-day Mortality
timeFrame: Assessed from point of randomization to date of death within 14 days of randomization
measure: Inpatient mortality
timeFrame: Assessed from point of randomization to date of death from any cause, up to one year post-randomization
measure: 14-day dialysis
timeFrame: Assessed from point of randomization to date of first documented dialysis order, within 14 days of randomization
measure: Inpatient dialysis
timeFrame: Assess from point of randomization to date of first documented dialysis order during index hospitalization, up to one year post-randomization
measure: Discharge on dialysis
timeFrame: Assessed at point of discharge from index hospitalization, up to one year post-randomization
measure: Progression to stage 2 AKI
timeFrame: Assessed from the date of randomization to 14 days post randomization
measure: Progression to stage 3 AKI
timeFrame: Assessed from the date of randomization to 14 days post randomization
measure: Duration of AKI
timeFrame: Assessed from the date of randomization to the cessation of AKI during index hospitalization, up to one year
measure: 30 day readmission rate
timeFrame: Assessed from discharge date of index hospitalization to 30 days post discharge date
measure: Index hospitalization cost
timeFrame: Assessed from point of randomization to date of discharge from index hospitalization, up to one year
measure: Chart documentation of AKI
timeFrame: Assessed from date of randomization to date of discharge from index hospitalization, up to one year
measure: Proportion of AKI "Best Practices" Achieved Per Subject During Index Hospitalization
timeFrame: 24 hours from randomization to discharge, up to one year post randomization
Other outcomes:
Locations:
city: New Haven
country: United States
facility: Yale New Haven Hospital
state: Connecticut
Study officials:
affiliation: Yale University
name: Francis P Wilson, MD MSCE
role: PRINCIPAL_INVESTIGATOR
References: