Computerized Decision Support for Chronic Kidney Disease in Type 2 Diabetes
(CKD-DETECT Trial)
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: Brigham and Women's Hospital
Disqualifiers: Ckd, Renal replacement therapy, others
No Placebo Group
Trial Summary
What is the purpose of this trial?While data from the National Health and Nutrition Examination Survey (NHANES) estimate that 36.9% of patients with diabetes have CKD, only approximately 10% of patients are aware of their kidney disease. In its 2020 Standards of Medical Care in Diabetes, the ADA recommends that all patients with type II diabetes (T2DM) undergo annual measurement of urine albumin-to-creatinine ratio (UACR). The National Kidney Foundation (NKF) has also proposed an update to the requirements for assessment of adults with diabetes including both an estimated glomerular filtration rate (eGFR) and uACR. The goal of accurately identifying patients with T2DM and CKD is to help providers intervene at an earlier stage of kidney impairment, improve renal outcomes, and reduce associated healthcare costs.
Failure to adopt these guideline recommendations has widespread implications, including underestimation of the burden of CKD in the T2DM population, delays in diagnosis of renal impairment, and ultimately, underutilization of therapies that could improve clinical outcomes. 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-directed assessment for CKD using UACR in patients with T2DM who have not had a UACR in the past year.
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 data supports the effectiveness of the treatment Alert-based computerized decision support for chronic kidney disease in type 2 diabetes?
Research shows that when doctors pay attention to alerts from computerized decision support systems, it can help improve the management of diabetes, as seen in better blood sugar control (HbA1C levels). This suggests that similar alert systems could be effective for managing chronic kidney disease in patients with type 2 diabetes.
12345Is the computerized decision support system safe for use in humans?
The computerized decision support system, including alert-based tools, has been used to improve medication safety in patients with kidney issues by reducing adverse drug events (harmful reactions to medications). While it helps in preventing medication errors, there is no specific mention of safety concerns for humans in the studies reviewed.
26789How is the Alert-based CDS tool treatment for chronic kidney disease in type 2 diabetes different from other treatments?
The Alert-based CDS tool is unique because it uses computerized alerts to support doctors in making timely decisions about managing chronic kidney disease in patients with type 2 diabetes, potentially improving care by integrating into the physician's workflow and promoting timely referrals.
4571011Eligibility Criteria
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.
+1 more
Exclusion Criteria
I have had a kidney transplant.
I am on dialysis for kidney failure.
I have been diagnosed with chronic kidney disease.
Trial Timeline
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
Participant Groups
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.
2Treatment groups
Experimental Treatment
Active Control
Group I: AlertExperimental Treatment1 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 AlertActive Control1 Intervention
Providers in the "No Alert" group will not receive any on-screen notification
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Brigham and Women's HospitalBoston, MA
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Who Is Running the Clinical Trial?
Brigham and Women's HospitalLead Sponsor
BayerIndustry Sponsor
References
Optimizing Best Practice Advisory alerts in electronic medical records with a multi-pronged strategy at a tertiary care hospital in Singapore. [2023]Clinical decision support (CDS) alerts can aid in improving patient care. One CDS functionality is the Best Practice Advisory (BPA) alert notification system, wherein BPA alerts are automated alerts embedded in the hospital's electronic medical records (EMR). However, excessive alerts can change clinician behavior; redundant and repetitive alerts can contribute to alert fatigue. Alerts can be optimized through a multipronged strategy. Our study aims to describe these strategies adopted and evaluate the resultant BPA alert optimization outcomes.
The Effect of Physicians' Acknowledgement of Clinical Decision Support Systems Generated Alerts on Patient Diabetes Management in a Primary Care Setting. [2023]The purpose of the study is to evaluate whether clinician's acknowledgement and adherence to Clinical Best Practice Advisories (BPA) system's alerts improves the outcome of patients with chronic diabetes. We used deidentified clinical data of elderly (65 or older) diabetes patients with hemoglobin A1C (HbA1C) >= 6.5 that were extracted from the clinical database of a multi-specialty outpatient clinic that also provides primary care services. We performed paired ttest to evaluate whether clinician's acknowledgement and adherence to BPA system's alert has any impact on patients' HbA1C management. Our findings showed that the average HbA1C values improved for patients whose alerts were acknowledged by their clinicians. For the group of patients whose BPA alerts were ignored by their clinicians, we found clinicians' acknowledgement and adherence to BPA alerts for chronic diabetes patient management did not have a significant negative effect on improvement in patient outcome.
Provider acceptance of an automated electronic alert for acute kidney injury. [2020]Clinical decision support systems, including electronic alerts, ideally provide immediate and relevant patient-specific information to improve clinical decision-making. Despite the growing capabilities of such alerts in conjunction with an expanding electronic medical record, there is a paucity of information regarding their perceived usefulness. We surveyed healthcare providers' opinions concerning the practicality and efficacy of a specific text-based automated electronic alert for acute kidney injury (AKI) in a single hospital during a randomized trial of AKI alerts.
Improvement of drug prescribing in acute kidney injury with a nephrotoxic drug alert system. [2022]Electronic alert systems have shown their capacity for improving the detection of acute kidney injury (AKI). The aim of this study was to design and implement a clinical decision support system (CDSS) for improving drug selection and reducing nephrotoxic drug use in patients with AKI.
Effects and characteristics of clinical decision support systems on the outcomes of patients with kidney disease: a systematic review. [2023]This systematic review was conducted to investigate the characteristics and effects of clinical decision support systems (CDSSs) on clinical and process-of-care outcomes of patients with kidney disease.
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]Adverse drug events (ADEs) are a leading cause of death in the United States. Patients with stage 3 and 4 chronic kidney disease (CKD) are at particular risk because many medications are cleared by the kidneys. Alerts in the electronic health record (EHR) about drug appropriateness and dosing at the time of prescription have been shown to reduce ADEs for patients with stage 3 and 4 CKD in inpatient settings, but more research is needed about the implementation and effectiveness of such alerts in outpatient settings.
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. [2021]To assess the appropriateness of medication-related clinical decision support (CDS) alerts associated with renal insufficiency and the potential/actual harm from overriding the alerts.
Real-time pharmacy surveillance and clinical decision support to reduce adverse drug events in acute kidney injury: a randomized, controlled trial. [2022]OBJECTIVES: Clinical decision support (CDS), such as computerized alerts, improves prescribing in the setting of acute kidney injury (AKI), but considerable opportunity remains to improve patient safety. The authors sought to determine whether pharmacy surveillance of AKI patients could detect and prevent medication errors that are not corrected by automated interventions. METHODS: The authors conducted a randomized clinical trial among 396 patients admitted to an academic, tertiary care hospital between June 1, 2010 and August 31, 2010 with an acute 0.5 mg/dl change in serum creatinine over 48 hours and a nephrotoxic or renally cleared medication order. Patients randomly assigned to the intervention group received surveillance from a clinical pharmacist using a web-based surveillance tool to monitor drug prescribing and kidney function trends. CDS alerting and standard pharmacy services were active in both study arms. Outcome measures included blinded adjudication of potential adverse drug events (pADEs), adverse drug events (ADEs) and time to provider modification or discontinuation of targeted nephrotoxic or renally cleared medications. RESULTS: Potential ADEs or ADEs occurred for 104 (8.0%) of control and 99 (7.1%) of intervention patient-medication pairs (p=0.4). Additionally, the time to provider modification or discontinuation of targeted nephrotoxic or renally cleared medications did not differ between control and intervention patients (33.4 hrs vs. 30.3 hrs, p=0.3). CONCLUSIONS: Pharmacy surveillance had no incremental benefit over previously implemented CDS alerts.
Tailoring of alerts substantially reduces the alert burden in computerized clinical decision support for drugs that should be avoided in patients with renal disease. [2017]Electronic alerts are often ignored by physicians, which is partly due to the large number of unspecific alerts generated by decision support systems. The aim of the present study was to analyze critical drug prescriptions in a university-based nephrology clinic and to evaluate the effect of different alerting strategies on the alert burden.
User Requirements for a Chronic Kidney Disease Clinical Decision Support Tool to Promote Timely Referral. [2018]Timely referral of patients with CKD has been associated with cost and mortality benefits, but referrals are often done too late in the course of the disease. Clinical decision support (CDS) offers a potential solution, but interventions have failed because they were not designed to support the physician workflow. We sought to identify user requirements for a chronic kidney disease (CKD) CDS system to promote timely referral.
Human-centered design of clinical decision support for management of hypertension with chronic kidney disease. [2022]Primary care providers face challenges in recognizing and controlling hypertension in patients with chronic kidney disease (CKD). Clinical decision support (CDS) has the potential to aid clinicians in identifying patients who could benefit from medication changes. This study designed an alert to control hypertension in CKD patients using an iterative human-centered design process.