Electronic Decision Support for Acute Kidney Injury

(MEnD-AKI Trial)

Recruiting at 8 trial locations

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Pittsburgh

Disqualifiers: End stage renal disease, eGFR <15, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

625 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What data supports the effectiveness of the treatment Multi-hospital Electronic Decision Support for Acute Kidney Injury?

Research shows that electronic alerts and decision support tools can help identify acute kidney injury (AKI) early, which may lead to better patient outcomes by allowing timely interventions. However, the impact on clinical outcomes has been mixed, with some studies showing improvements and others not, suggesting that effectiveness may depend on how these tools are implemented and used. ^{1 2 3 4 5}

Is the electronic decision support system for acute kidney injury safe for humans?

The electronic decision support system is designed to improve medication safety by reducing errors in drug dosing for patients with kidney issues. Initial responses from healthcare providers suggest it has potential to enhance safety, but more research is needed to confirm its effectiveness and safety in different settings. ^{6 7 8 9 10}

How is the Multi-hospital Electronic Decision Support treatment for acute kidney injury different from other treatments?

The Multi-hospital Electronic Decision Support treatment is unique because it uses electronic alerts and decision support tools within electronic medical records to improve the recognition and management of acute kidney injury, potentially leading to better patient outcomes and reduced use of harmful drugs. Unlike traditional treatments, this approach focuses on early detection and intervention through real-time alerts and actionable recommendations. ^{11 12 13 14 15}

What is the purpose of this trial?

This study is a randomized controlled trial at eight hospitals within the University of Pittsburgh Medical Center-UPMC system. The project will assess the efficacy of a clinical surveillance system augmented with near real-time predictive analytics to support a pharmacist-led intervention delivered to attending physicians (primary service) to reduce the progression and complications of drug-associated acute kidney injury (D-AKI) in hospitalized (non-ICU) adults.

Research Team

Sandra L Kane-Gill, PharmD, MS

Principal Investigator

University of Pittsburgh

Azra Bihorac, MD, MS

Principal Investigator

University of Florida

Eligibility Criteria

This trial is for non-ICU hospitalized adults at risk of drug-associated acute kidney injury (D-AKI) within the UPMC system. Specific eligibility criteria are not provided, but typically include factors like age, health status, and specific medical conditions related to the study.

Inclusion Criteria

Physician-subject Inclusion: Physicians employed at UPMC hospital systems

Physician-subject Inclusion: Attending physicians of record who care for patients across multiple units outside ICU/ED

Physician-subject Inclusion: Physician cares for 1 or more patient receiving a system alert identifying high-risk for AKI

Exclusion Criteria

Physician-subject Exclusion: Physicians who primarily provide care for transplant (heart, kidney, liver, etc.) patients

Physician-subject Exclusion: Physicians who primarily provide consult services only (dermatology, rehabilitation, etc.)

Physician-subject Exclusion: Physicians of record who only care for ICU or ED patients

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Protocolized stage-based intervention delivered to the physician by a pharmacist using an automated alerting system

up to 30 days

Ongoing monitoring through EMR alerts

Usual Care

Standard care with Cerner EMR-based AKI passive alert

up to 30 days

Ongoing monitoring through EMR alerts

Follow-up

Participants are monitored for Major Adverse Kidney Events within 30 days of randomization

up to 30 days

Treatment Details

Interventions

• Multi-hospital Electronic Decision Support

Trial Overview The trial tests a clinical surveillance system with real-time analytics supporting pharmacist-led interventions aimed at reducing D-AKI progression in patients. It's a randomized controlled trial across eight hospitals where participants receive different levels of alert or intervention.

Participant Groups

Group I: Protocolized stage-based intervention Experimental Treatment 2 Interventions

The intervention uses an automated alerting system to identify patients: 1) receiving a high-risk drug or drug combination associated with AKI and at low-risk for progression to either stage 2 AKI or stage 3 AKI (Level A) and 2) patients without AKI or stage 1 AKI receiving a high-risk drug or drug combination associated with AKI and at high risk for progression to either stage 2 AKI or stage 3 AKI, and patients with AKI stage 2 or stage 3 receiving a high-risk drug or drug combination associated with AKI or a medication that requires renal dose adjustment (Level B). This patient specific risk-profile will be coupled with recommendations for medication management and delivered to the physician by a pharmacist for consideration and approval.

Group II: Usual Care Active Control 1 Intervention

A Cerner EMR-based AKI passive alert which is standard of care at UPMC.

Who Is Running the Clinical Trial?

University of Pittsburgh

Lead Sponsor

Trials

1,820

Recruited

16,360,000+

National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

Collaborator

Trials

2,513

Recruited

4,366,000+

University of Florida

Collaborator

Trials

1,428

Recruited

987,000+

University of Pittsburgh Medical Center

Collaborator

Trials

78

Recruited

77,600+

Findings from Research

In a study involving 6030 adult inpatients with acute kidney injury across six hospitals, electronic health record alerts did not significantly improve patient outcomes related to mortality, dialysis, or progression of kidney injury, with a primary outcome occurrence of 21.3% in the alert group compared to 20.9% in usual care.

Interestingly, in non-teaching hospitals, alerts were associated with worse outcomes, including a higher risk of death, suggesting that the effectiveness of alert systems may vary significantly by hospital type and warrants further investigation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial. Wilson, FP., Martin, M., Yamamoto, Y., et al.[2021]

In a study of 267 predialysis patients with chronic kidney disease (CKD), 69.3% experienced at least one adverse safety event, highlighting the high risk of complications in this population.

The most common adverse events reported were hypoglycemia in diabetic patients and hyperkalemia (high potassium levels), with significant co-occurrences of these events, indicating a need for better safety monitoring in CKD patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Patient-reported and actionable safety events in CKD. Ginsberg, JS., Zhan, M., Diamantidis, CJ., et al.[2021]

Patients with stage 3 and 4 chronic kidney disease (CKD) are at high risk for adverse drug events (ADEs) due to the kidneys' role in medication clearance, highlighting the need for effective monitoring systems.

Interviews with clinics revealed significant differences in electronic decision support practices and organizational culture, which are crucial factors to consider when implementing electronic drug-disease alerts to reduce ADEs in outpatient settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using the diffusion of innovations theory to assess socio-technical factors in planning the implementation of an electronic health record alert across multiple primary care clinics. Lin, CP., Guirguis-Blake, J., Keppel, GA., et al.[2018]

A clinical decision support system was developed to monitor and alert healthcare providers about potential early-onset acute kidney injury related to nephrotoxic or renally cleared drugs, aiming to reduce dosing errors.

Initial responses from providers indicate that this medication safety intervention may improve patient safety, with potential for future enhancements to further increase its effectiveness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A system to improve medication safety in the setting of acute kidney injury: initial provider response. McCoy, AB., Peterson, JF., Gadd, CS., et al.[2016]

In a study of 300 adult patients with chronic kidney disease (CKD), 40.7% experienced adverse drug events (ADEs), highlighting the high prevalence of medication-related complications in this population.

Out of the 159 reported ADEs, 30.2% were deemed preventable, suggesting that the involvement of clinical pharmacists in monitoring medication use could significantly reduce these events.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Adverse drug events in hospitalized patients with chronic kidney disease. Hassan, Y., Al-Ramahi, RJ., Aziz, NA., et al.[2022]

The implementation of a clinical decision support system (CDSS) significantly improved the response to nephrotoxic drug alerts, with 29% of alerts leading to changes in medication compared to 20% before the system was in place.

The CDSS contributed to a slight increase in the detection of acute kidney injury (AKI) cases, suggesting that real-time alerts can enhance patient safety by promoting more careful drug selection in at-risk patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Improvement of drug prescribing in acute kidney injury with a nephrotoxic drug alert system. Arias Pou, P., Aquerreta Gonzalez, I., Idoate García, A., et al.[2022]

References

1. England pubmed.ncbi.nlm.nih.gov

Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial. [2021]

2. Switzerland pubmed.ncbi.nlm.nih.gov

Acute Kidney Injury Electronic Alert for Nephrologist: Reactive versus Proactive? [2018]

3. England pubmed.ncbi.nlm.nih.gov

Impact of a computerized decision support tool deployed in two intensive care units on acute kidney injury progression and guideline compliance: a prospective observational study. [2021]

4. England pubmed.ncbi.nlm.nih.gov

The potential for artificial intelligence to predict clinical outcomes in patients who have acquired acute kidney injury during the perioperative period. [2021]

5. United States pubmed.ncbi.nlm.nih.gov

Automated acute kidney injury alerts. [2019]

6. United States pubmed.ncbi.nlm.nih.gov

Patient-reported and actionable safety events in CKD. [2021]

7. United States pubmed.ncbi.nlm.nih.gov

Impact of vendor computerized physician order entry on patients with renal impairment in community hospitals. [2015]

8. England pubmed.ncbi.nlm.nih.gov

Using the diffusion of innovations theory to assess socio-technical factors in planning the implementation of an electronic health record alert across multiple primary care clinics. [2018]

9. United States pubmed.ncbi.nlm.nih.gov

A system to improve medication safety in the setting of acute kidney injury: initial provider response. [2016]

10. Germany pubmed.ncbi.nlm.nih.gov

Adverse drug events in hospitalized patients with chronic kidney disease. [2022]

11. England pubmed.ncbi.nlm.nih.gov

Effect of clinical decision support systems on clinical outcome for acute kidney injury: a systematic review and meta-analysis. [2022]

12. England pubmed.ncbi.nlm.nih.gov

Development and initial implementation of electronic clinical decision supports for recognition and management of hospital-acquired acute kidney injury. [2021]

13. England pubmed.ncbi.nlm.nih.gov

Computerized clinical decision support for the early recognition and management of acute kidney injury: a qualitative evaluation of end-user experience. [2020]

14. England pubmed.ncbi.nlm.nih.gov

Improvement of drug prescribing in acute kidney injury with a nephrotoxic drug alert system. [2022]

15. England pubmed.ncbi.nlm.nih.gov

Applications for detection of acute kidney injury using electronic medical records and clinical information systems: workgroup statements from the 15(th) ADQI Consensus Conference. [2022]