Low Adherence to Kidney Disease: Improving Global Outcomes 2012 CKD Clinical Practice Guidelines Despite Clear Evidence of Utility

Glen James, Juan Jose Garcia Sanchez, Juan Jesus Carrero, Supriya Kumar, Roberto Pecoits-Filho, Hiddo J L Heerspink, Stephen Nolan, Carolyn S P Lam, Hungta Chen, Eiichiro Kanda, Naoki Kashihara, Matthew Arnold, Mikhail N Kosiborod, Mitja Lainscak, Carol Pollock, David C Wheeler, Glen James, Juan Jose Garcia Sanchez, Juan Jesus Carrero, Supriya Kumar, Roberto Pecoits-Filho, Hiddo J L Heerspink, Stephen Nolan, Carolyn S P Lam, Hungta Chen, Eiichiro Kanda, Naoki Kashihara, Matthew Arnold, Mikhail N Kosiborod, Mitja Lainscak, Carol Pollock, David C Wheeler

Abstract

Introduction: Kidney Disease: Improving Global Outcomes (KDIGO) 2012 guidelines classify chronic kidney disease (CKD) risk or prognosis using estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (UACR). We assessed patient characteristics and outcomes according to the KDIGO classification, using data from DISCOVER CKD (NCT04034992).

Methods: Data were extracted from the US integrated Limited Claims and Electronic Health Record Dataset and TriNetX databases, and the UK Clinical Practice Research Datalink linked to Hospital Episode Statistics and Office for National Statistics databases. Eligible patients were aged ≥18 years with CKD, and identified by 2 consecutive eGFR measures (5 to <75 ml/min/1.73 m2; ≥90 days apart [maximum 730]) from January 2008. Index date was the second eGFR measurement; patients were categorized using the UACR measure closest to the index. Outcomes included patient characteristics, eGFR or UACR measurement frequency, and clinical outcomes per baseline KDIGO classification.

Results: Across databases, only 8.6% of patients with 2 eGFR measures had ≥1 UACR measures. Among 123,807 eligible patients, prevalence of heart failure, hypertension, and type 2 diabetes increased with increasing albuminuria. Incidence rates of mortality and adverse cardiovascular and renal outcomes increased with declining baseline eGFR, and particularly with increasing albuminuria. Median number of eGFR and UACR tests per year post-index ranged from 1.6 to 2.5 and 0.5 to 0.6, respectively, across databases; there was no clear increase in UACR testing frequency following the KDIGO 2012 guidelines.

Conclusion: Albuminuria monitoring is critical for optimal risk stratification in CKD, and our findings highlight an imperative for more regular UACR testing in clinical practice.

Keywords: DISCOVER CKD; Kidney Disease: Improving Global Outcomes; chronic kidney disease; estimated glomerular filtration rate; retrospective; urinary albumin-to-creatinine ratio.

© 2022 International Society of Nephrology. Published by Elsevier Inc.

Figures

Graphical abstract
Graphical abstract
Figure 1
Figure 1
Patient flow diagram. CKD, chronic kidney disease; CPRD, Clinical Practice Research Datalink; eGFR, estimated glomerular filtration rate; LCED, Limited Claims and Electronic Health Record Dataset; RRT, renal replacement therapy; UACR, urinary albumin-to-creatinine ratio.
Figure 2
Figure 2
Frequency of (a) eGFR and (b) UACR testing during follow-up. Numbers of tests inferred from the number of days with a test result during follow-up post-index. CPRD, Clinical Practice Research Datalink; eGFR, estimated glomerular filtration rate; IQR, interquartile range; LCED, Limited Claims and Electronic Health Record Dataset; UACR, urinary albumin-to-creatinine ratio.
Figure 3
Figure 3
Impact of KDIGO 2012 guidelines on frequency of UACR testing during follow-up. Numbers of tests inferred from the number of days with a test result during follow-up post-index. UACR data were not available from before 2012 in LCED. Data are rates with 95% confidence intervals, calculated according to the method reported by Ulm, K. CPRD, Clinical Practice Research Datalink; KDIGO, Kidney Disease: Improving Global Outcomes; LCED, Limited Claims and Electronic Health Record Dataset; UACR, urinary albumin-to-creatinine ratio.
Figure 4
Figure 4
Patient categorization and baseline characteristics by KDIGO category at index. Color coding is based on odds ratio quartile (Q) for each outcome within each database: green = Q1 and below; yellow = Q1 to Q2; orange = Q2 to Q3; red = Q3 and above. CPRD, Clinical Practice Research Datalink; eGFR, estimated glomerular filtration rate; KDIGO, Kidney Disease: Improving Global Outcomes; LCED, Limited Claims and Electronic Health Record Dataset; UACR, urinary albumin-to-creatinine ratio.
Figure 5
Figure 5
Incidence rates per 100 PY of clinical outcomes during follow-up by KDIGO category. Data are incidence rate (95% CI), calculated as the number of specific events that occur during patient follow-up time at risk (time in the study until first event or loss to follow-up). Color coding is based on odds ratio quartile (Q) for each outcome within each database: green = Q1 and below; yellow = Q1 to Q2; orange = Q2 to Q3; red = Q3 and above. Kidney failure was defined as progression to CKD stage 5 (sustained eGFR ≤15 ml/min/1.73 m2) or initiation of chronic RRT for >30 days (2 dialysis codes 30–365 days apart) or kidney transplant. Mortality data were not available for US LCED, US TriNetX reports only in-hospital deaths, and incidence rates for some outcomes were not available where there were low patient or event numbers (e.g., LCED eGRF <15 ml/min/1.73 m2). CI, confidence interval; CPRD, Clinical Practice Research Datalink; CV, cardiovascular; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; hHF, hospitalization for heart failure; KDIGO, Kidney Disease: Improving Global Outcomes; LCED, Limited Claims and Electronic Health Record Dataset; MI, myocardial infarction; NA, not available (owing to low patient/event numbers); PY, patient-years; RRT, renal replacement therapy; UACR, urinary albumin-to-creatinine ratio.
Figure 5
Figure 5
Incidence rates per 100 PY of clinical outcomes during follow-up by KDIGO category. Data are incidence rate (95% CI), calculated as the number of specific events that occur during patient follow-up time at risk (time in the study until first event or loss to follow-up). Color coding is based on odds ratio quartile (Q) for each outcome within each database: green = Q1 and below; yellow = Q1 to Q2; orange = Q2 to Q3; red = Q3 and above. Kidney failure was defined as progression to CKD stage 5 (sustained eGFR ≤15 ml/min/1.73 m2) or initiation of chronic RRT for >30 days (2 dialysis codes 30–365 days apart) or kidney transplant. Mortality data were not available for US LCED, US TriNetX reports only in-hospital deaths, and incidence rates for some outcomes were not available where there were low patient or event numbers (e.g., LCED eGRF <15 ml/min/1.73 m2). CI, confidence interval; CPRD, Clinical Practice Research Datalink; CV, cardiovascular; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; hHF, hospitalization for heart failure; KDIGO, Kidney Disease: Improving Global Outcomes; LCED, Limited Claims and Electronic Health Record Dataset; MI, myocardial infarction; NA, not available (owing to low patient/event numbers); PY, patient-years; RRT, renal replacement therapy; UACR, urinary albumin-to-creatinine ratio.

References

    1. KDIGO KDIGO 2012 Clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2012;3:1–150.
    1. Bikbov B., Purcell C.A., Levey A.S. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395:709–733. doi: 10.1016/S0140-6736(20)30045-3.
    1. Jager K.J., Kovesdy C., Langham R., et al. A single number for advocacy and communication-worldwide more than 850 million individuals have kidney diseases. Nephrol Dial Transplant. 2019;34:1803–1805. doi: 10.1093/ndt/gfz174.
    1. Chen T.K., Knicely D.H., Grams M.E. Chronic kidney disease diagnosis and management: a review. JAMA. 2019;322:1294–1304. doi: 10.1001/jama.2019.14745.
    1. Rysz J., Gluba-Brzozka A., Franczyk B., et al. Novel biomarkers in the diagnosis of chronic kidney disease and the prediction of its outcome. Int J Mol Sci. 2017;18 doi: 10.3390/ijms18081702.
    1. Murton M., Goff-Leggett D., Bobrowska A., et al. Burden of chronic kidney disease by KDIGO categories of glomerular filtration rate and albuminuria: a systematic review. Adv Ther. 2021;38:180–200. doi: 10.1007/s12325-020-01568-8.
    1. Polkinghorne K.R. Estimated glomerular filtration rate versus albuminuria in the assessment of kidney function: what’s more important? Clin Biochem Rev. 2014;35:67–73.
    1. Astor B.C., Matsushita K., Gansevoort R.T., et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts. Kidney Int. 2011;79:1331–1340. doi: 10.1038/ki.2010.550.
    1. Matsushita K., van der Velde M., Astor B.C., et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010;375:2073–2081. doi: 10.1016/S0140-6736(10)60674-5.
    1. van der Velde M., Matsushita K., Coresh J., et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts. Kidney Int. 2011;79:1341–1352. doi: 10.1038/ki.2010.536.
    1. Gansevoort R.T., Matsushita K., van der Velde M., et al. Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts. Kidney Int. 2011;80:93–104. doi: 10.1038/ki.2010.531.
    1. Mace-Brickman T., Eddeen A.B., Carrero J.J., et al. The risk of stroke and stroke type in patients with atrial fibrillation and chronic kidney disease. Can J Kidney Health Dis. 2019;6 doi: 10.1177/2054358119892372.
    1. Tuttle K.R., Alicic R.Z., Duru O.K., et al. Clinical characteristics of and risk factors for chronic kidney disease among adults and children: an analysis of the CURE-CKD Registry. JAMA Netw Open. 2019;2 doi: 10.1001/jamanetworkopen.2019.18169.
    1. Saran R., Robinson B., Abbott K.C., et al. US Renal Data System 2019 annual data report: epidemiology of kidney disease in the United States. Am J Kidney Dis. 2020;75:A6–A7. doi: 10.1053/j.ajkd.2019.09.003.
    1. Szczech L.A., Stewart R.C., Su H.L., et al. Primary care detection of chronic kidney disease in adults with type-2 diabetes: the ADD-CKD Study (awareness, detection and drug therapy in type 2 diabetes and chronic kidney disease) PLoS One. 2014;9 doi: 10.1371/journal.pone.0110535.
    1. Olufade T., Lamerato L., Sanchez J.J.G., et al. Clinical outcomes and healthcare resource utilization in a real-world population reflecting the DAPA-CKD Trial participants. Adv Ther. 2021;38:1352–1363. doi: 10.1007/s12325-020-01609-2.
    1. Fraser S.D., Parkes J., Culliford D., et al. Timeliness in chronic kidney disease and albuminuria identification: a retrospective cohort study. BMC Fam Pract. 2015;16:18. doi: 10.1186/s12875-015-0235-8.
    1. Darlington O., Dickerson C., Evans M., et al. Costs and healthcare resource use associated with risk of cardiovascular morbidity in patients with chronic kidney disease: evidence from a systematic literature review. Adv Ther. 2021;38:994–1010. doi: 10.1007/s12325-020-01607-4.
    1. Garcia Sanchez J.J., Thompson J., Scott D.A., et al. Treatments for chronic kidney disease: A systematic literature review of randomized controlled trials. Adv Ther. 2021;39:193–220. doi: 10.1007/s12325-021-02006-z.
    1. Pecoits-Filho R., James G., Carrero J.J., et al. Methods and rationale of the DISCover CKD global observational study. Clin Kidney J. 2021;14:1570–1578. doi: 10.1093/ckj/sfab046.
    1. Harrison P.J., Luciano S., Colbourne L. Rates of delirium associated with calcium channel blockers compared to diuretics, renin-angiotensin system agents and beta-blockers: an electronic health records network study. J Psychopharmacol. 2020;34:848–855. doi: 10.1177/0269881120936501.
    1. The TriNetX global health research network . 2022. TriNetX.
    1. Topaloglu U., Palchuk M.B. Using a federated network of real-world data to optimize clinical trials operations. JCO Clin Cancer Inform. 2018;2:1–10. doi: 10.1200/CCI.17.00067.
    1. Herrett E., Gallagher A.M., Bhaskaran K., et al. Data resource profile: Clinical Practice Research Datalink (CPRD) Int J Epidemiol. 2015;44:827–836. doi: 10.1093/ije/dyv098.
    1. Clinical Practice Research Datalink . 2022. CPRD.
    1. Ulm K. A simple method to calculate the confidence interval of a standardized mortality ratio (SMR) Am J Epidemiol. 1990;131:373–375. doi: 10.1093/oxfordjournals.aje.a115507.
    1. Kidney Disease: Improving Global Outcomes (KDIGO) Blood Pressure Work Group KDIGO 2021 Clinical Practice Guideline for the Management of Blood Pressure in Chronic Kidney Disease. Kidney Int. 2021;99:S1–S87.
    1. American Diabetes Association 11. Microvascular complications and foot care: standards of medical care in diabetes-2021. Diabetes Care. 2021;44(suppl 1):S151–S167. doi: 10.2337/dc21-S011.
    1. Staruschenko A., Bhalla V., Rangaswami J. SGLT2 inhibitors: diabetic kidney disease and beyond. Am J Physiol Ren Physiol. 2020;319:F780–F781. doi: 10.1152/ajprenal.00518.2020.
    1. Heerspink H.J.L., Stefansson B.V., Correa-Rotter R., et al. Dapagliflozin in patients with chronic kidney disease. N Engl J Med. 2020;383:1436–1446. doi: 10.1056/NEJMoa2024816.
    1. Foley R.N., Murray A.M., Li S., et al. Chronic kidney disease and the risk for cardiovascular disease, renal replacement, and death in the United States Medicare population, 1998 to 1999. J Am Soc Nephrol. 2005;16:489–495. doi: 10.1681/ASN.2004030203.
    1. Tonelli M., Wiebe N., Culleton B., et al. Chronic kidney disease and mortality risk: a systematic review. J Am Soc Nephrol. 2006;17:2034–2047. doi: 10.1681/ASN.2005101085.
    1. Hallan S.I., Matsushita K., Sang Y., et al. Age and association of kidney measures with mortality and end-stage renal disease. JAMA. 2012;308:2349–2360. doi: 10.1001/jama.2012.16817.
    1. Carrero J.J., Grams M.E., Sang Y., et al. Albuminuria changes are associated with subsequent risk of end-stage renal disease and mortality. Kidney Int. 2017;91:244–251. doi: 10.1016/j.kint.2016.09.037.
    1. Lees J.S., Welsh C.E., Celis-Morales C.A., et al. Glomerular filtration rate by differing measures, albuminuria and prediction of cardiovascular disease, mortality and end-stage kidney disease. Nat Med. 2019;25:1753–1760. doi: 10.1038/s41591-019-0627-8.
    1. Chronic kidney disease in adults: assessment and management. NICE; 2014.
    1. Perkins R.M., Chang A.R., Wood K.E., et al. Incident chronic kidney disease: trends in management and outcomes. Clin Kidney J. 2016;9:432–437. doi: 10.1093/ckj/sfw044.
    1. Feakins B., Oke J., McFadden E., et al. Trends in kidney function testing in UK primary care since the introduction of the quality and outcomes framework: a retrospective cohort study using CPRD. BMJ Open. 2019;9 doi: 10.1136/bmjopen-2018-028062.
    1. Inker L.A., Astor B.C., Fox C.H., et al. KDOQI US commentary on the 2012 KDIGO clinical practice guideline for the evaluation and management of CKD. Am J Kidney Dis. 2014;63:713–735. doi: 10.1053/j.ajkd.2014.01.416.
    1. Sultan A.A., James G., Wang X., et al. Incidence of uncommon clinical events in USA patients with dialysis-dependent and nondialysis-dependent chronic kidney disease: analysis of electronic health records from TriNetX. Nephron. 2021;145:462–473. doi: 10.1159/000516280.

Source: PubMed

3
Subscribe