Computerized Decision Support for Chronic Kidney Disease in Type 2 Diabetes

(CKD-DETECT Trial)

Overseen By Gregory Piazza, MD, MS

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Brigham and Women's Hospital

Disqualifiers: Ckd, Renal replacement therapy, others

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

Compensation: Varies

Number of Visits: Unknown

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a computerized alert system, known as an alert-based computerized decision support tool, to help doctors identify kidney issues in patients with type 2 diabetes. The goal is to catch kidney problems early so doctors can start treatment sooner, improving health outcomes and reducing costs. Individuals with type 2 diabetes who haven't had a specific urine test for kidney issues in the past year might be a good fit for this trial. Participants will either have their doctors receive alerts to remind them about this test or not, with the hope that more reminders will lead to better care. As an unphased trial, this study offers a unique opportunity to contribute to innovative research that could enhance future diabetes care.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What prior data suggests that this alert-based computerized decision support is safe for patients with type 2 diabetes and chronic kidney disease?

Research has shown that alert-based computer systems, like the one being tested, are generally safe in healthcare. These systems send reminders or alerts to healthcare providers to follow specific guidelines, such as testing for kidney disease in diabetes patients.

Previous studies found that healthcare providers handle these alerts well. They usually don't cause harm because they are just notifications on a computer screen. Instead, they help doctors remember important steps in patient care. No reports of negative effects have emerged from using these alerts, as they don't involve direct treatment to patients.

Overall, research well-supports the safety of using alert-based systems, and they are considered a safe addition to regular medical practice. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about this trial because it introduces an alert-based computerized decision support system to help manage chronic kidney disease (CKD) in patients with type 2 diabetes. Unlike current standard treatments that rely on routine clinical judgment and standard lab assessments, this system provides real-time, on-screen alerts to healthcare providers. It prompts them to evaluate patients for CKD with a specific test (UACR assessment) during regular clinical visits. This innovative approach aims to enhance early detection and management of CKD by integrating evidence-based recommendations directly into the workflow, potentially improving patient outcomes through timely interventions.

What evidence suggests that this computerized decision support is effective for chronic kidney disease in type 2 diabetes?

This trial will compare an alert-based computerized decision support system with no alert intervention. Studies have shown that computerized systems help doctors better detect and manage long-term health conditions. Research indicates that alert-based systems, such as the EPIC Best Practice Advisory (BPA), assist healthcare providers in adhering more closely to medical guidelines. Previous studies on similar systems for conditions like high blood pressure found improvements in medical care and patient health. These alerts remind doctors to check for kidney problems in patients with diabetes, helping to catch issues early. This can lead to better care and potentially improve health outcomes for patients. ^{1 2 3 6 7}

Are You a Good Fit for This Trial?

This trial is for adults over 18 with Type II Diabetes who haven't been tested for kidney disease in the past year. They should be outpatients at Brigham and Women's Hospital, receiving care in primary or specialty clinics. Those with a history of kidney transplant, known chronic kidney disease, or on dialysis are excluded.

Inclusion Criteria

You have not had a urine test to check for kidney damage in the past year.

I am over 18, have type 2 diabetes, and haven't had a UACR test in the last year.

I am 18 or older and see a doctor at BWH for primary care or a specialty like heart or diabetes care.

Exclusion Criteria

I have had a kidney transplant.

I am on dialysis for kidney failure.

I have been diagnosed with chronic kidney disease.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Participants are randomized to receive either an electronic alert for CKD assessment or no alert during outpatient clinical encounters

90 days

Ongoing outpatient visits

Follow-up

Participants are monitored for the frequency of UACR testing, referrals to nephrologists, new CKD diagnoses, and prescription of CKD-related therapies

90 days

What Are the Treatments Tested in This Trial?

Interventions

• Alert-based computerized decision support

Trial Overview The study tests an alert-based computer decision tool designed to remind doctors to check diabetic patients for chronic kidney disease using urine tests. It aims to see if this tool helps catch kidney issues earlier by following medical guidelines more closely.

How Is the Trial Designed?

Group I: Alert Experimental Treatment 1 Intervention

For patients randomly assigned to the BPA intervention group (alert group), an on-screen electronic alert will be issued during the outpatient clinical encounter that notifies the responsible provider that his or her T2DM patient should be evaluated for CKD with UACR assessment. The provider then will be given on-screen options to either order a UACR assessment or follow a link to learn more about CKD assessment in T2DM. Should the alert-recipient elect to omit an order for UACR assessment and decline to follow a link to learn more about CKD assessment in T2DM, the provider will be able to continue on with clinic visit-related EHR documentation but will need to select an acknowledge reason (rationale) for not following the evidence-based clinical practice recommendation highlighted in the alert.

Group II: No Alert Active Control 1 Intervention

Providers in the "No Alert" group will not receive any on-screen notification

Who Is Running the Clinical Trial?

Brigham and Women's Hospital

Lead Sponsor

Trials

1,694

Recruited

14,790,000+

Bayer

Industry Sponsor

Trials

2,291

Recruited

25,560,000+

Founded

1863

Headquarters

Leverkusen, Germany

Known For

Pharmaceutical Innovations

Top Products

Aspirin, Aleve, Yaz, Nexavar

Bill Anderson

Bayer

Chief Executive Officer since 2023

BSc in Chemical Engineering from the University of Texas, MSc in Chemical Engineering and Management from MIT

Michael Devoy

Bayer

Chief Medical Officer since 2014

MD, PhD

Published Research Related to This Trial

The study involved 20 participants and utilized a human-centered design process to create a clinical decision support (CDS) alert aimed at improving hypertension management in patients with chronic kidney disease (CKD).

User feedback highlighted the importance of aligning alert recommendations with clinical expectations and workflow, leading to iterative design changes that enhanced the alert's usability and relevance for primary care providers.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Human-centered design of clinical decision support for management of hypertension with chronic kidney disease. Garabedian, PM., Gannon, MP., Aaron, S., 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]

A systematic review of 11 studies found that clinical decision support systems (CDSSs) significantly improved 45.5% of process-of-care outcomes, particularly in medication appropriateness for patients with kidney disease.

However, the review did not find any statistically significant effects of CDSSs on clinical outcomes, indicating that while they may enhance care processes, their impact on actual health results remains unclear.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Effects and characteristics of clinical decision support systems on the outcomes of patients with kidney disease: a systematic review. Mirpanahi, N., Nabovati, E., Sharif, R., et al.[2023]

During a study of medication-related clinical decision support (CDS) alerts for renal insufficiency, 100% of alerts were overridden, indicating a significant issue with alert appropriateness.

Only a small percentage of alerts were deemed appropriate, with 12.5% for 'dose change' and 29.6% for 'avoid medication', leading to 5 adverse drug events, 4 of which were linked to inappropriate overrides, highlighting the need for better alert design and monitoring.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Renal medication-related clinical decision support (CDS) alerts and overrides in the inpatient setting following implementation of a commercial electronic health record: implications for designing more effective alerts. Shah, SN., Amato, MG., Garlo, KG., et al.[2021]

In a randomized trial involving 396 patients with acute kidney injury, pharmacy surveillance did not significantly reduce the occurrence of potential adverse drug events compared to standard computerized decision support alerts.

The time taken for healthcare providers to modify or discontinue nephrotoxic or renally cleared medications was similar between the intervention and control groups, indicating that the added pharmacy surveillance did not enhance patient safety beyond existing automated systems.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Real-time pharmacy surveillance and clinical decision support to reduce adverse drug events in acute kidney injury: a randomized, controlled trial. McCoy, AB., Cox, ZL., Neal, EB., et al.[2022]

In a study analyzing 1012 medication regimens in a nephrology clinic, more specific alerting strategies significantly reduced the number of alerts generated, with the most advanced strategy (D) resulting in alerts for only 7.81% of regimens compared to 87.5% with the least specific strategy (A).

The findings suggest that using patient and drug-specific information in alert systems can effectively minimize alert fatigue among physicians, potentially improving the safety of prescribing medications to patients with renal impairment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Tailoring of alerts substantially reduces the alert burden in computerized clinical decision support for drugs that should be avoided in patients with renal disease. Czock, D., Konias, M., Seidling, HM., et al.[2017]

The study at Jurong Health Campus showed a 59.6% reduction in interruptive Best Practice Advisory (BPA) alerts after implementing optimization strategies, which significantly improved clinician response rates to alerts.

Despite increasing the number of unique BPAs from 54 to 360, the optimized alerts led to a 74% reduction in alerts from seven specific BPAs, saving an estimated 3600 hours of provider time annually and enhancing overall alert compliance.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Optimizing Best Practice Advisory alerts in electronic medical records with a multi-pronged strategy at a tertiary care hospital in Singapore. Ng, HJH., Kansal, A., Abdul Naseer, JF., et al.[2023]

In a study of elderly diabetes patients (65 or older) with HbA1C levels of 6.5 or higher, clinicians' acknowledgment of Clinical Best Practice Advisories (BPA) alerts led to improved management of HbA1C levels.

Patients whose BPA alerts were ignored did not experience any significant negative effects on their HbA1C management, suggesting that while acknowledgment is beneficial, ignoring alerts does not worsen patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Effect of Physicians' Acknowledgement of Clinical Decision Support Systems Generated Alerts on Patient Diabetes Management in a Primary Care Setting. Faysel, MA., Miller, T., Singer, J., et al.[2023]

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 survey of 98 healthcare providers revealed that 69% approved of the electronic alert system for acute kidney injury (AKI), indicating a generally positive reception among physicians, pharmacists, and non-physician providers.

Approval of the alert system was strongly linked to the belief that it improved patient care, but notably, approval decreased by 20% for every additional 30 days of trial duration, highlighting the importance of perceived efficacy over time.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Provider acceptance of an automated electronic alert for acute kidney injury. Oh, J., Bia, JR., Ubaid-Ullah, M., et al.[2020]

A study involving 12 primary care physicians identified 21 key user requirements for a clinical decision support (CDS) system aimed at promoting timely referrals for chronic kidney disease (CKD), emphasizing the need for integration into physician workflows.

The requirements included support for referral processes, management of CKD risk factors, and user-centered design features, which are essential for creating an effective CDS system that can enhance patient care and outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

User Requirements for a Chronic Kidney Disease Clinical Decision Support Tool to Promote Timely Referral. Gulla, J., Neri, PM., Bates, DW., et al.[2018]

Citations

1. clinicaltrials.gov clinicaltrials.gov/study/NCT05342545

Decision Support for Detection of Chronic Kidney Disease ... This single-center, 400-patient, randomized controlled trial will assess the impact of an EPIC Best Practice Advisory (BPA; alert-based CDS tool) on guideline- ...

2. sciencedirect.com sciencedirect.com/org/science/article/pii/S1929074825003087

Implementation and Evaluation of a Best Practice Advisory ... Compared with manual approaches, computer-based CDS systems are more effective in improving clinical practice and patient outcomes [29,30]. Evidence supports ...

3. withpower.com withpower.com/trial/computerized-decision-support-for-chronic-kidney-disease-in-type-2-diabetes-43e10

Computerized Decision Support for Chronic Kidney ... This single-center, 400-patient, randomized controlled trial will assess the impact of an EPIC Best Practice Advisory (BPA; alert-based CDS tool) on guideline- ...

4. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC12221691/

User Actions within a Clinical Decision Support Alert for the ... This study aimed to examine user actions within a clinical decision support (CDS) alert addressing hypertension (HTN) in chronic kidney disease (CKD).

5. jamanetwork.com jamanetwork.com/journals/jamainternalmedicine/fullarticle/2816065

Clinical Decision Support for Hypertension Management in ... This randomized clinical trial evaluates a computerized clinical decision support system for the management of uncontrolled hypertension in ...

6. bmcmedinformdecismak.biomedcentral.com bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-022-01962-y

Human-centered design of clinical decision support for ... ... Epic EHR, and years in practice ranged from one to ... clinical decision support tool prototype embedded in an electronic medical record.

7. jmir.org jmir.org/2023/1/e51024/

Applications of Clinical Decision Support Systems in ... Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence–Enabled Prescription Advisory Tool for Type 2 Diabetes ...