Impact of Kidney Failure Risk Prediction Clinical Decision Support on Monitoring and Referral in Primary Care Management of CKD: A Randomized Pragmatic Clinical Trial

Lipika Samal, John D D'Amore, Michael P Gannon, John L Kilgallon, Jean-Pierre Charles, Devin M Mann, Lydia C Siegel, Kelly Burdge, Shimon Shaykevich, Stuart Lipsitz, Sushrut S Waikar, David W Bates, Adam Wright, Lipika Samal, John D D'Amore, Michael P Gannon, John L Kilgallon, Jean-Pierre Charles, Devin M Mann, Lydia C Siegel, Kelly Burdge, Shimon Shaykevich, Stuart Lipsitz, Sushrut S Waikar, David W Bates, Adam Wright

Abstract

Rationale & objective: To design and implement clinical decision support incorporating a validated risk prediction estimate of kidney failure in primary care clinics and to evaluate the impact on stage-appropriate monitoring and referral.

Study design: Block-randomized, pragmatic clinical trial.

Setting & participants: Ten primary care clinics in the greater Boston area. Patients with stage 3-5 chronic kidney disease (CKD) were included. Patients were randomized within each primary care physician panel through a block randomization approach. The trial occurred between December 4, 2015, and December 3, 2016.

Intervention: Point-of-care noninterruptive clinical decision support that delivered the 5-year kidney failure risk equation as well as recommendations for stage-appropriate monitoring and referral to nephrology.

Outcomes: The primary outcome was as follows: Urine and serum laboratory monitoring test findings measured at one timepoint 6 months after the initial primary care visit and analyzed only in patients who had not undergone the recommended monitoring test in the preceding 12 months. The secondary outcome was nephrology referral in patients with a calculated kidney failure risk equation value of >10% measured at one timepoint 6 months after the initial primary care visit.

Results: The clinical decision support application requested and processed 569,533 Continuity of Care Documents during the study period. Of these, 41,842 (7.3%) documents led to a diagnosis of stage 3, 4, or 5 CKD by the clinical decision support application. A total of 5,590 patients with stage 3, 4, or 5 CKD were randomized and included in the study. The link to the clinical decision support application was clicked 122 times by 57 primary care physicians. There was no association between the clinical decision support intervention and the primary outcome. There was a small but statistically significant difference in nephrology referral, with a higher rate of referral in the control arm.

Limitations: Contamination within provider and clinic may have attenuated the impact of the intervention and may have biased the result toward null.

Conclusions: The noninterruptive design of the clinical decision support was selected to prevent cognitive overload; however, the design led to a very low rate of use and ultimately did not improve stage-appropriate monitoring.

Funding: Research reported in this publication was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under award K23DK097187.

Trial registration: ClinicalTrials.gov Identifier: NCT02990897.

Keywords: CKD awareness; CKD diagnosis; CKD management; Chronic kidney disease (CKD); clinical decision support (CDS); electronic health record (EHR); nephrology referral; primary care; primary care physician (PCP); randomized clinical trial (RCT); risk stratification.

© 2022 The Authors.

Figures

Graphical abstract
Graphical abstract
Figure 1
Figure 1
Clinical decision support application flow for patient screening, enrollment, randomization, and delivery of intervention. Abbreviations: CCD, Continuity of Care Document; CDS, clinical decision support; CKD, chronic kidney disease; CO2, carbon dioxide; eGFR, estimated glomerular filtration rate; PCP, primary care physician.
Figure 2
Figure 2
Consolidated Standards of Reporting Trials (CONSORT) flowchart: participant enrollment, allocation, exclusion, and analysis. Abbreviations: KFRE, kidney failure risk equation.
Figure 3
Figure 3
Partners electronic health record patient summary page showing clinical decision support hyperlink and rollover text.
Figure 4
Figure 4
Comparison of the proportion of patients with completion of tests or referrals. (A) Primary outcome: completion of tests necessary for calculation of the kidney failure risk equation in the intervention arm versus in the control arm patients. (B) Secondary outcome: completion of urinary albumin-to-creatinine ratio test in the intervention arm versus in the control arm patients. (C) Secondary outcome: completion of nephrology consultation for patients with a risk estimate of >10% in the intervention arm versus in the control arm patients.

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