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Published in: BMC Nephrology 1/2022

Open Access 01-12-2022 | Care | Study protocol

Improving chronic kidney disease detection and treatment in the United States: the chronic kidney disease cascade of care (C3) study protocol

Authors: Julio A. Lamprea-Montealegre, Priya Joshi, Abigail S. Shapiro, Erin Madden, Krista Navarra, O. Alison Potok, L. Parker Gregg, Tanya Podchiyska, Amy Robinson, Mary K. Goldstein, Carmen A. Peralta, Simerjot K. Jassal, Sankar D. Navaneethan, Dena. E. Rifkin, Virginia Wang, Michael G. Shlipak, Michelle M. Estrella

Published in: BMC Nephrology | Issue 1/2022

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Abstract

Background

There are major gaps in the implementation of guideline-concordant care for persons with chronic kidney disease (CKD). The CKD Cascade of Care (C3) initiative seeks to improve CKD care by improving detection and treatment of CKD in primary care.

Methods

C3 is a multi-modal initiative deployed in three major academic medical centers within the Department of Veterans Affairs (VA) Health Care System: San Francisco VA, San Diego VA, and Houston VA. The main objective of the first phase of C3 described in this protocol is to establish the infrastructure for universal CKD detection among primary care patients at high-risk for CKD with a triple-marker screen comprising cystatin C, creatinine, and albuminuria. Across the three sites, a comprehensive educational intervention and the integration of primary care-based clinical champions will be employed with the goal of improving CKD detection and treatment. The San Francisco VA will also implement a practice-facilitation intervention leveraging telehealth and health informatics tools and capabilities for enhanced CKD detection. Parallel formative evaluation across the three sites will assess the feasibility and acceptability of integrating cystatin C as part of routine CKD detection in primary care practice. The effectiveness of the interventions will be assessed using a pre-post observational design for change in the proportion of patients tested annually for CKD. Secondary outcomes will assess change in the initiation of cardio-kidney protective therapies and in nephrology referrals of high-risk patients.

Discussion

The first phase of C3 is a multi-facility multi-modal initiative that aims to improve CKD care by implementing a triple-marker screen for enhanced CKD detection in primary care.
Literature
1.
go back to reference GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709–33.CrossRef GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709–33.CrossRef
3.
go back to reference Kidney disease: Improving global outcomes (KDIGO) CKD work group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2013;3(1):1–150. Kidney disease: Improving global outcomes (KDIGO) CKD work group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2013;3(1):1–150.
4.
go back to reference Kidney Disease: Improving Global Outcomes (KDIGO) Diabetes Work Group. KDIGO 2020 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease. Kidney Int. 2020;98(4S):S1–S115. Kidney Disease: Improving Global Outcomes (KDIGO) Diabetes Work Group. KDIGO 2020 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease. Kidney Int. 2020;98(4S):S1–S115.
6.
go back to reference United States Renal Data System. 2019 USRDS annual data report: Epidemiology of Kidney disease in the United States. National Institutes of Health, National Institutes of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2019. United States Renal Data System. 2019 USRDS annual data report: Epidemiology of Kidney disease in the United States. National Institutes of Health, National Institutes of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2019.
7.
go back to reference Diamantidis CJ, Hale SL, Wang V, Smith VA, Scholle SH, Maciejewski ML. Lab-based and diagnosis-based chronic kidney disease recognition and staging concordance. BMC Nephrol. 2019;20(1):357.CrossRef Diamantidis CJ, Hale SL, Wang V, Smith VA, Scholle SH, Maciejewski ML. Lab-based and diagnosis-based chronic kidney disease recognition and staging concordance. BMC Nephrol. 2019;20(1):357.CrossRef
8.
go back to reference Sperati CJ, Soman S, Agrawal V, et al. Primary care physicians’ perceptions of barriers and facilitators to management of chronic kidney disease: a mixed methods study. PLoS ONE. 2019;14:e0221325.CrossRef Sperati CJ, Soman S, Agrawal V, et al. Primary care physicians’ perceptions of barriers and facilitators to management of chronic kidney disease: a mixed methods study. PLoS ONE. 2019;14:e0221325.CrossRef
9.
go back to reference Greer RC, Crews DC, Boulware LE. Challenges perceived by primary care providers to educating patients about chronic kidney disease. J Ren Care. 2012;38:174–81.CrossRef Greer RC, Crews DC, Boulware LE. Challenges perceived by primary care providers to educating patients about chronic kidney disease. J Ren Care. 2012;38:174–81.CrossRef
10.
go back to reference Peralta CA, Frigaard M, Rolon L, et al. Screening for CKD To Improve Processes of Care among Nondiabetic Veterans with Hypertension: A Pragmatic Cluster-Randomized Trial. Clin J Am Soc Nephrol. 2020;15(2):174–81.CrossRef Peralta CA, Frigaard M, Rolon L, et al. Screening for CKD To Improve Processes of Care among Nondiabetic Veterans with Hypertension: A Pragmatic Cluster-Randomized Trial. Clin J Am Soc Nephrol. 2020;15(2):174–81.CrossRef
12.
go back to reference Shlipak MG, Tummalapalli SL, Boulware LE, Grams ME, Ix JH, Jha V, Kengne AP, Madero M, Mihaylova B, Tangri N, Cheung M, Jadoul M, Winkelmayer WC, Zoungas S. The case for early identification and intervention of chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. 2020;S0085–2538:31. Shlipak MG, Tummalapalli SL, Boulware LE, Grams ME, Ix JH, Jha V, Kengne AP, Madero M, Mihaylova B, Tangri N, Cheung M, Jadoul M, Winkelmayer WC, Zoungas S. The case for early identification and intervention of chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. 2020;S0085–2538:31.
13.
go back to reference Lees JS, Welsh C, Celis-Morales CA, Mackay D, Lewsey J, Gray SR, et al. Glomerular filtration rate by differing measures, albuminuria and prediction of cardiovascular disease, mortality and end-stage kidney disease. Nat Med. 2019;25:1753–60. Lees JS, Welsh C, Celis-Morales CA, Mackay D, Lewsey J, Gray SR, et al. Glomerular filtration rate by differing measures, albuminuria and prediction of cardiovascular disease, mortality and end-stage kidney disease. Nat Med. 2019;25:1753–60.
14.
go back to reference Shlipak MG, Matsushita K, Ärnlöv J, Inker LA, Katz R, Polkinghorne KR, et al. Cystatin C versus creatinine in determining risk based on kidney function. N Engl J Med. 2013;369(10):932–43.CrossRef Shlipak MG, Matsushita K, Ärnlöv J, Inker LA, Katz R, Polkinghorne KR, et al. Cystatin C versus creatinine in determining risk based on kidney function. N Engl J Med. 2013;369(10):932–43.CrossRef
15.
16.
go back to reference Proctor E, Silmere H, Raghavan R, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38(2):65–76.CrossRef Proctor E, Silmere H, Raghavan R, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38(2):65–76.CrossRef
17.
go back to reference Noy C. Sampling Knowledge: The Hermeneutics of Snowball Sampling in Qualitative Research. Int J Soc Res Methodol. 2008;11(4):327–44.CrossRef Noy C. Sampling Knowledge: The Hermeneutics of Snowball Sampling in Qualitative Research. Int J Soc Res Methodol. 2008;11(4):327–44.CrossRef
19.
go back to reference Averill JB. Matrix analysis as a complementary analytic strategy in qualitative inquiry. Qual Health Res. 2002;12(6):855–66.CrossRef Averill JB. Matrix analysis as a complementary analytic strategy in qualitative inquiry. Qual Health Res. 2002;12(6):855–66.CrossRef
21.
go back to reference Bitton A, Pereira AG, Smith CS. The EFECT framework for interprofessional education in the patient centered medical home. Healthcare. 2013;1:63–8.CrossRef Bitton A, Pereira AG, Smith CS. The EFECT framework for interprofessional education in the patient centered medical home. Healthcare. 2013;1:63–8.CrossRef
22.
go back to reference Tso GJ, Tu SW, Oshiro C, Martins S, Ashcraft M, Yuen KW, et al. Automating Guidelines for Clinical Decision Support: Knowledge Engineering and Implementation. AMIA Annu Symp Proc. 2016;2016:1189–98.PubMed Tso GJ, Tu SW, Oshiro C, Martins S, Ashcraft M, Yuen KW, et al. Automating Guidelines for Clinical Decision Support: Knowledge Engineering and Implementation. AMIA Annu Symp Proc. 2016;2016:1189–98.PubMed
23.
go back to reference Tu SW, Martins S, Oshiro C, Yuen K, Wang D, Robinson A, et al. Automating Performance Measures and Clinical Practice Guidelines: Differences and Complementarities. AMIA Annu Symp Proc. 2016;2016:1199–208.PubMed Tu SW, Martins S, Oshiro C, Yuen K, Wang D, Robinson A, et al. Automating Performance Measures and Clinical Practice Guidelines: Differences and Complementarities. AMIA Annu Symp Proc. 2016;2016:1199–208.PubMed
25.
go back to reference Baskervile NB, Liddy C, Hogg W. Systematic review and meta-analysis of practice facilitation within primary care settings. Ann Fam Med. 2012;10:63–74.CrossRef Baskervile NB, Liddy C, Hogg W. Systematic review and meta-analysis of practice facilitation within primary care settings. Ann Fam Med. 2012;10:63–74.CrossRef
26.
go back to reference Stuart E, Duckworth K, Simmons J. Using propensity scores in difference-in-difference models to estimate the effects of a policy change. Health Serv Outcomes Res Methodol. 2014;14:166–82.CrossRef Stuart E, Duckworth K, Simmons J. Using propensity scores in difference-in-difference models to estimate the effects of a policy change. Health Serv Outcomes Res Methodol. 2014;14:166–82.CrossRef
27.
go back to reference Kalantar-Zadeh K, Jafar TH, Nitsch D, Neuen BL, Perkovic V. Chronic Kidney Disease. Lancet. 2021;398:786–802.CrossRef Kalantar-Zadeh K, Jafar TH, Nitsch D, Neuen BL, Perkovic V. Chronic Kidney Disease. Lancet. 2021;398:786–802.CrossRef
28.
go back to reference Seale MT, McGuire TG, Zhang W. Time Allocation in Primary Care Office Visits. Health Serv Res. 2007;42:1871–94.CrossRef Seale MT, McGuire TG, Zhang W. Time Allocation in Primary Care Office Visits. Health Serv Res. 2007;42:1871–94.CrossRef
29.
go back to reference Lamprea-Montealegre JA, Shlipak MG, Estrella MM. Chronic kidney disease detection, staging and treatment in cardiovascular disease prevention. Heart. 2021;107:1282–8.CrossRef Lamprea-Montealegre JA, Shlipak MG, Estrella MM. Chronic kidney disease detection, staging and treatment in cardiovascular disease prevention. Heart. 2021;107:1282–8.CrossRef
30.
go back to reference Delgado C, Baweja M, Crews D, Eneanya N, Gadegbeku C, Inker L, et al. A unifying approach for GFR estimation: recommendations of the NKF-ASN task force on reassessing the inclusion of race in diagnosing kidney disease. Am J Kidney Dis. 2021;79(2):268–88. Delgado C, Baweja M, Crews D, Eneanya N, Gadegbeku C, Inker L, et al. A unifying approach for GFR estimation: recommendations of the NKF-ASN task force on reassessing the inclusion of race in diagnosing kidney disease. Am J Kidney Dis. 2021;79(2):268–88.
Metadata
Title
Improving chronic kidney disease detection and treatment in the United States: the chronic kidney disease cascade of care (C3) study protocol
Authors
Julio A. Lamprea-Montealegre
Priya Joshi
Abigail S. Shapiro
Erin Madden
Krista Navarra
O. Alison Potok
L. Parker Gregg
Tanya Podchiyska
Amy Robinson
Mary K. Goldstein
Carmen A. Peralta
Simerjot K. Jassal
Sankar D. Navaneethan
Dena. E. Rifkin
Virginia Wang
Michael G. Shlipak
Michelle M. Estrella
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Nephrology / Issue 1/2022
Electronic ISSN: 1471-2369
DOI
https://doi.org/10.1186/s12882-022-02943-z

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