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| Name | Class |
|---|---|
| Bayer | INDUSTRY |
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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.
The study is a 400-patient single-center Quality Improvement Initiative in the form of a randomized controlled trial of an EPIC EHR BPA. The allocation ratio will be 1:1 for an EPIC BPA versus no notification with randomization by Attending Physician of Record, using odd or even provider ID number, to minimize cluster-effect. While the study will randomize patients by Attending Physician of Record, the observational unit will be the patient.
The study will use a BPA within EPIC that will integrate with the EHR medical history, visit diagnoses, and problem list to identify outpatients with a diagnosis of T2DM and without an established diagnosis of CKD. The BPA will then search the laboratory results section of the EHR for a UACR result within the last year. If the patient with T2DM has not had a UACR result within the past year, he or she will be randomly assigned to the BPA intervention group (alert group) or the control (no alert) group according the Attending Physician of Record's provider ID number. 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. The provider then will be given on-screen options to either order a UACR or follow a link to learn more about CKD assessment in T2DM.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Alert | Experimental | 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. |
|
| No Alert | No Intervention | Providers in the "No Alert" group will not receive any on-screen notification |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Alert-based computerized decision support | Other | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Frequency of UACR testing order for CKD in patients with T2DM who have not had such testing within the past year | Review the order entry section of the Electronic Health Record (EPIC) to make this determination | 90 days |
| Measure | Description | Time Frame |
|---|---|---|
| Frequency of new clinical diagnosis of CKD in patients with T2DM who have not had UACR assessment within the past year | Review the medical history and problem list sections of the Electronic Health Record (EPIC) to make this determination | 90 days |
| Measure | Description | Time Frame |
|---|---|---|
| Frequency of prescription of medical therapy for CKD in patients with T2DM, including GLP-1 receptor agonists, renin-angiotensin system antagonists (ACEi or ARB), SGLT2 inhibitors, statins, and emerging anti-inflammatory/anti-fibrotic agents | Review the order entry system for new prescriptions | 90 days |
| Frequency of referral to a nephrologist |
Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Brigham and Women's Hospital | Boston | Massachusetts | 02115 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 42031191 | Derived | Goldhaber SZ, Rashedi S, Pfeferman M, Leyva H, Bacare B, Chopard R, Robinson ES, Lan Z, Bikdeli B, Piazza G; CKD-DETECT investigators. Electronic alert-based computerized decision support for detection of chronic kidney disease in type 2 diabetes mellitus: CKD-DETECT. Am J Med. 2026 Apr 22:S0002-9343(26)00297-4. doi: 10.1016/j.amjmed.2026.04.022. Online ahead of print. |
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| ID | Term |
|---|---|
| D051436 | Renal Insufficiency, Chronic |
| D003920 | Diabetes Mellitus |
| ID | Term |
|---|---|
| D051437 | Renal Insufficiency |
| D007674 | Kidney Diseases |
| D014570 | Urologic Diseases |
| D052776 | Female Urogenital Diseases |
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1:1 allocation ratio, randomized controlled trial
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double-blind
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Review the order entry system for new referrals |
| 90 days |
| D005261 |
| Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D052801 | Male Urogenital Diseases |
| D002908 | Chronic Disease |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D004700 | Endocrine System Diseases |