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The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care: An external validation and clinical impact projection cohort study


Autoři: Rupert W. Major aff001;  David Shepherd aff001;  James F. Medcalf aff002;  Gang Xu aff002;  Laura J. Gray aff001;  Nigel J. Brunskill aff002
Působiště autorů: Department of Health Sciences, University of Leicester, Leicester, United Kingdom aff001;  John Walls Renal Unit, Leicester General Hospital, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom aff002;  Kettering General Hospital, Kettering, United Kingdom aff003;  Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom aff004
Vyšlo v časopise: The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care: An external validation and clinical impact projection cohort study. PLoS Med 16(11): e32767. doi:10.1371/journal.pmed.1002955
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pmed.1002955

Souhrn

Background

The Kidney Failure Risk Equation (KFRE) uses the 4 variables of age, sex, urine albumin-to-creatinine ratio (ACR), and estimated glomerular filtration rate (eGFR) in individuals with chronic kidney disease (CKD) to predict the risk of end stage renal disease (ESRD), i.e., the need for dialysis or a kidney transplant, within 2 and 5 years. Currently, national guideline writers in the UK and other countries are evaluating the role of the KFRE in renal referrals from primary care to secondary care, but the KFRE has had limited external validation in primary care. The study’s objectives were therefore to externally validate the KFRE’s prediction of ESRD events in primary care, perform model recalibration if necessary, and assess its projected impact on referral rates to secondary care renal services.

Methods and findings

Individuals with 2 or more Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) eGFR values < 60 ml/min/1.73 m2 more than 90 days apart and a urine ACR or protein-to-creatinine ratio measurement between 1 December 2004 and 1 November 2016 were included in the cohort. The cohort included 35,539 (5.6%) individuals (57.5% female, mean age 75.9 years, median CKD-EPI eGFR 51 ml/min/1.73 m2, median ACR 3.2 mg/mmol) from a total adult practice population of 630,504. Overall, 176 (0.50%) and 429 (1.21%) ESRD events occurred within 2 and 5 years, respectively. Median length of follow-up was 4.7 years (IQR 2.8 to 6.6). Model discrimination was excellent for both 2-year (C-statistic 0.932, 95% CI 0.909 to 0.954) and 5-year (C-statistic 0.924, 95% 0.909 to 0.938) ESRD prediction. The KFRE overpredicted risk in lower (<20%) risk groups. Reducing the model’s baseline risk improved calibration for both 2- and 5-year risk for lower risk groups, but led to some underprediction of risk in higher risk groups. Compared to current criteria, using referral criteria based on a KFRE-calculated 5-year ESRD risk of ≥5% and/or an ACR of ≥70 mg/mmol reduced the number of individuals eligible for referral who did not develop ESRD, increased the likelihood of referral eligibility in those who did develop ESRD, and referred the latter at a younger age and with a higher eGFR. The main limitation of the current study is that the cohort is from one region of the UK and therefore may not be representative of primary care CKD care in other countries.

Conclusions

In this cohort, the recalibrated KFRE accurately predicted the risk of ESRD at 2 and 5 years in primary care. Its introduction into primary care for referrals to secondary care renal services may lead to a reduction in unnecessary referrals, and earlier referrals in those who go on to develop ESRD. However, further validation studies in more diverse cohorts of the clinical impact projections and suggested referral criteria are required before the latter can be clinically implemented.

Klíčová slova:

Kidneys – Chronic kidney disease – Urine – Cardiovascular diseases – Proteinuria – Glomerular filtration rate – Primary care


Zdroje

1. Brück K, Stel VS, Gambaro G, Hallan S, Völzke H, Ärnlöv J, et al. CKD prevalence varies across the European general population. J Am Soc Nephrol. 2016;27(7):2135–47. doi: 10.1681/ASN.2015050542 26701975

2. Astor BC, Matsushita K, Gansevoort RT, Van Der Velde M, Woodward M, Levey AS, 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(12):1331–40. doi: 10.1038/ki.2010.550 21289598

3. Couser WG, Remuzzi G, Mendis S, Tonelli M. The contribution of chronic kidney disease to the global burden of major noncommunicable diseases. Kidney Int. 2011;80(12):1258–70. doi: 10.1038/ki.2011.368 21993585

4. Chronic Kidney Disease Prognosis Consortium. 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(9731):2073–81. doi: 10.1016/S0140-6736(10)60674-5 20483451

5. Kerr M, Bray B, Medcalf J, O’Donoghue DJ, Matthews B. Estimating the financial cost of chronic kidney disease to the NHS in England. Nephrol Dial Transplant. 2012;27(Suppl 3):iii73–80.

6. Moynihan R, Glassock R, Doust J. Chronic kidney disease controversy: how expanding definitions are unnecessarily labelling many people as diseased. BMJ. 2013;347:f4298. doi: 10.1136/bmj.f4298 23900313

7. Kale MS, Korenstein D. Overdiagnosis in primary care: framing the problem and finding solutions. BMJ. 2018;362:k2820. doi: 10.1136/bmj.k2820 30108054

8. Phillips L, Donovan K, Phillips AO. Renal quality outcomes framework and eGFR: impact on secondary care. QJM. 2009;102(6):415–23. doi: 10.1093/qjmed/hcp030 19349287

9. Royston P, Altman DG. External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol. 2013;13(1):33.

10. Riley RD, Ensor J, Snell KI, Debray TP, Altman DG, Moons KG, et al. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ. 2016;353:i3140. doi: 10.1136/bmj.i3140 27334381

11. Steyerberg EW, Moons KG, van der Windt DA, Hayden JA, Perel P, Schroter S, et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10(2):e1001381. doi: 10.1371/journal.pmed.1001381 23393430

12. Damen JA, Hooft L, Schuit E, Debray TP, Collins GS, Tzoulaki I, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016;353:i2416. doi: 10.1136/bmj.i2416 27184143

13. Tangri N, Stevens LA, Griffith J, Tighiouart H, Djurdjev O, Naimark D, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA. 2011;305(15):1553–9. doi: 10.1001/jama.2011.451 21482743

14. Tangri N, Grams ME, Levey AS, Coresh J, Appel LJ, Astor BC, et al. Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta-analysis. JAMA. 2016;315(2):164–74. doi: 10.1001/jama.2015.18202 26757465

15. National Institute for Health and Clinical Excellence. Surveillance report 2017—chronic kidney disease (stage 4 or 5): management of hyperphosphataemia (2013) NICE guideline CG157, chronic kidney disease in adults: assessment and management (2014) NICE guideline CG182 and chronic kidney disease: managing anaemia (2015) NICE guideline NG8. London: National Institute for Health and Clinical Excellence; 2017 [cited 2019 Oct 18]. Available from: https://www.nice.org.uk/guidance/cg182/resources/surveillance-report-2017-chronic-kidney-disease-stage-4-or-5-management-of-hyperphosphataemia-2013-nice-guideline-cg157-chronic-kidney-disease-in-adults-assessment-and-management-2014-nice-guideline—4429248445/chapter/Surveillance-decision?tab=evidence.

16. Hingwala J, Wojciechowski P, Hiebert B, Bueti J, Rigatto C, Komenda P, et al. Risk-based triage for nephrology referrals using the Kidney Failure Risk Equation. Can J Kidney Health Dis. 2017;4:2054358117722782. doi: 10.1177/2054358117722782 28835850

17. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann Int Med. 1999;130(6):461–70. doi: 10.7326/0003-4819-130-6-199903160-00002 10075613

18. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Int Med. 2009;150(9):604–12. doi: 10.7326/0003-4819-150-9-200905050-00006 19414839

19. National Institute for Health and Clinical Excellence. Chronic kidney disease in adults: assessment and management. Clinical guideline [CG182]. London: National Institute for Health and Clinical Excellence; 2015 [cited 2019 Oct 15]. Available from: https://www.nice.org.uk/guidance/cg182.

20. Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med. 2016;35(2):214–26. doi: 10.1002/sim.6787 26553135

21. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMC Med. 2015;13(1):1.

22. Major R. Data for Kidney Failure Risk Equation, Major et al PLOS Medicine 2019. Figshare. 2019 [cited 2019 Sep 19]. Available from: https://doi.org/10.25392/leicester.data.9860807.v1

23. Moons KG, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ. 2009;338:b606. doi: 10.1136/bmj.b606 19502216

24. Calvert M, Shankar A, McManus RJ, Lester H, Freemantle N. Effect of the quality and outcomes framework on diabetes care in the United Kingdom: retrospective cohort study. BMJ. 2009;338:b1870. doi: 10.1136/bmj.b1870 19474024

25. Falaschetti E, Chaudhury M, Mindell J, Poulter N. Continued improvement in hypertension management in England: results from the Health Survey for England 2006. Hypertension. 2009;53(3):480–6. doi: 10.1161/HYPERTENSIONAHA.108.125617 19204180

26. Grams ME, Sang Y, Ballew SH, Carrero JJ, Djurdjev O, Heerspink HJ, et al. Predicting timing of clinical outcomes in patients with chronic kidney disease and severely decreased glomerular filtration rate. Kidney Int. 2018;93(6):1442–51. doi: 10.1016/j.kint.2018.01.009 29605094

27. Herrett E, Shah AD, Boggon R, Denaxas S, Smeeth L, van Staa T, et al. Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study. BMJ. 2013;346:f2350. doi: 10.1136/bmj.f2350 23692896

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Interné lekárstvo

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PLOS Medicine


2019 Číslo 11
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