Usability of predictive models of kidney failure in chronic kidney disease: a scoping review
DOI:
https://doi.org/10.21615/cesmedicina.6987Keywords:
end stage renal disease, prediction models, external validation, usability, decision makingAbstract
Background: the identification of patients at higher risk of progressing to kidney failure is essential for treatment planning in chronic kidney disease, but it has not been possible to do this consistently. Predictive models could be a useful tool, however, their usability in chronic kidney disease is limited and the barriers and limitations are not well understood. Methods: a scoping review of the available literature on ESRD predictive models or prognostic rules in chronic kidney disease patients was developed. Searches were systematically executed on Cochrane, MEDLINE, and Embase. a blind and independent review was carried out by two evaluators to identify studies that reported on the development, validation, or impact assessment of a model constructed to predict the progression to an advanced stage of chronic kidney disease. A critical evaluation of the quality of the evidence provided with the GRADE system (Grading of Recommendations Assessment, Development and Evaluation) was made. Findings: of 1279 articles found, 19 studies were included for the final qualitative synthesis. Most of the studies were primary, with retrospective observational designs and a few corresponded to systematic reviews. No clinical practice guidelines were found. The qualitative synthesis showed high heterogeneity in the development of the models, as well as in the reporting of global performance measures, internal validity, and the lack of external validity in most of the studies. The evidence rating was of low overall quality, with inconsistency between studies and important methodological limitations. Conclusions: most of the available predictive models have not been adequately validated and, therefore, are of limited use to assess the individual prognosis of patients with chronic kidney disease. Therefore, additional efforts are required to focus the development and implementation of predictive models on external validity and usability and bridge the gap between generation, synthesis of evidence, and decision-making in the field of patient care.
Downloads
References
Fung E. Kurella Tamura M. Epidemiology and Public Health Concerns of CKD in Older Adults. Advances in Chronic Kidney Disease. 23(1).2016: 8-11.
Rhee CM. Kovesdy C. Spotlight on CKD deaths-increasing mortality worldwide. Lancet. 2015; 385: 117–171.
US Renal Data System. USRDS 2019 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States. Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; 2019.
Tangri N. Georgios DK. Lesley AI. Griffith J, et al. Risk Prediction Models for Patients With Chronic Kidney Disease. A Systematic Review. Ann Intern Med 2013; 158: 596-603.
Martins D, et al. Kidney Disease in Disadvantaged Populations. Hindawi Publishing Corporation International Journal of Nephrology 2012.
Coresh J, Selvin E, Stevens LA, Manzi J, Kusek JW, et al. Prevalence of chronic kidney disease in the United States. JAMA. 2007; 298: 2038–2047.
Lozano R, N. M. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet, 380: 2095–128.
Moons K. Royston P. Vergouwe Y, et al. Prognosis and prognostic research: what, why, and how? BMJ 2009; 338: 1317-1320.
Advances in Chronic Kidney Disease, Vol 23, No 1 (January), 2016: pp 8-11.
http://epistemicos.blogspot.com/2010/01/que-es-la-prediccion-cientifica.html is licensed under a Creative Commons Atribución 3.0 Unported License.
Shuttleworth M. Prediction in research. Explorable: Think outside the box. Sparta: By Explorable.com. [Acceso 2 de agosto de 2022]. https://explorable.com/es/prediction.
Hipocrates. On airs, waters, and places. In: Adams F, ed. 1 The genuine works of Hippocrates. Baltimore: Wilkins and Wilkins, 1939.
Oliver MJ, Quinn RR, Garg AX, Kim SJ, Wald R, Paterson JM. Likelihood of starting dialysis after incident fistula creation. Clin J Am Soc Nephrol. 2012; 7: 466-71.
Fletcher RH. Fletcher SW. Clinical Epidemiology: The essentials. Philadelphia: Lippincott Williams & Wilkins. 4° ed).
Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowsk N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010; 21: 128-38.
Frank E. Harrell, Jr. Regression Modeling Strategies With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York: Springer. 1st edition 2001.
ESteyerberg EW. Clinical Prediction Models. 1° ed. Bethesda-EU: Springer; 2009. P. 497.
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.
Guía Metodológica para el Observatorio de Salud Cardiovascular, Diabetes y Enfermedad Renal Crónica –OCADER- Colombia. Bogotá D.C. Colombia: Ministerio de Salud y Protección Social. 2013. Disponible en: www.minsalud.gov.co
Levey AS, Stevens LA, Coresh J. Conceptual model of CKD: applications and implications. Am J Kidney Dis 2009; 53: S4–16.
KDIGO AKI Work Group. KDIGO clinical practice guideline for acute kidney injury. Kidney inter., Suppl. 2012; 2: 1–138.
Kidney disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney inter., Suppl. 2013; 3: 1–150.
National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002; 39: S1–266.
Aromataris E, Munn Z (Editors). JBI Manual for Evidence Synthesis. JBI, 2020. Available from https://synthesismanual.jbi.global. https://doi.org/10.46658/JBIMES-20-01
Aslam S. Emmanuel P. Indian J Sex Transm Dis AIDS. 2010; 31(1): 47–50. DOI: 10.4103/0253-7184.69003.
Harrel FE. Kerry l. Daniel B. Mark. Tutorial in biostatistics multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in medicine 1996; 15: 361-387.
Justin BE. Andre P, et al. Risk Models to Predict Chronic Kidney Disease and Its Progression: A Systematic Review. PLOS Medicine 2012; 9:1-18.
R. Jaeschke, G.H. Guyatt, P. Dellinger, H. Schünemann, M.M. Levy, R. Kunz, S. Norris, J. Bion. Use of GRADE grid to reach decisions on clinical practice guidelines when consensus is elusive. BMJ. 2008; 337: a774.
Tangri N. Lesley A. Griffith SJ, et al. A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure. JAMA 2011; 305(15): 1553-1559.
Morgan E. Grams, Liang Li, Tom H. Greene, et al. Estimating time to ESRD using kidney failure risk equations: results from the African American Study of Kidney Disease and Hypertension (AASK). 2015.
Turin TC. Coresh J. Tonelli M, et al. Short-term change in kidney function and risk of end-stage renal disease. Nephrology Dialysis Transplantation 2012; 0: 1–9.
Onuigbo MA, Onuigbo NT. Variability in CKD stage in outpatients followed in two large renal clinics: implications for CKD trials and the status of current knowledge of patterns of CKD to ESRD progression. Int Urol Nephrol 2012; 44: 1589-1590.
Mafham M. Staplin N. Emberson J. Prognostic utility of estimated albumin excretion rate in chronic kidney disease: results from the Study of Heart and Renal Protection. Oxford University Press on behalf of ERA-EDTA. 2017. pubs:671507. UUID: uuid:74b18c13-7558-4876-8aba-20973064d428.
Lim CC, Chee ML, Cheng C-Y, Kwek JL, Foo M, Wong TY, et al. Simplified end-stage renal failure risk prediction model for the low-risk general population with chronic kidney disease. PLoS ONE. 2019; 14(2): e0212590. https://doi.org/10.1371/journal.pone.0212590
Toyama T, Kasama S, Sato M, Sano H, Ueda T, Sasaki T, Nakahara T, Higuchi T, Tsushima Y, Kurabayashi M. Most Important Prognostic Values to Predict Major Adverse Cardiovascular, Cerebrovascular, and Renal Events in Patients with Chronic Kidney Disease Including Hemodialysis for 2 Years. Cardiology. 2019; 142:14–23. https://doi.org/10.1159/0004963302019
Liu SY, Huang P, Zhang N. Efficacy and safety of the Fu-Zheng-Qu-Zhuo method on retarding the progress of chronic kidney disease (stage 3–4): a systematic review and meta-analysis. Annals of Translational Medicine. 2019 Mar;7(6).
Ramspek CL, de Jong Y, Dekker FW, van Diepen M. Towards the best kidney failure prediction tool: a systematic review and selection aid. Nephrology Dialysis Transplantation. 2020 Sep;35(9):1527-38.
Ren Q, Chen D, Liu X, Yang R, Yuan L, Ding M, Zhang N. Derivation and Validation of a Prediction Model of End-Stage Renal Disease in Patients With Type 2 Diabetes Based on a Systematic Review and Meta-analysis. Frontiers in endocrinology. 2022;13.
Bhachu HK, Fenton A, Cockwell P, Aiyegbusi O, Kyte D, Calvert M. Use of the kidney failure risk equation to inform clinical care of patients with chronic kidney disease: a mixed-methods systematic review. BMJ open. 2022 Jan 1;12(1): e055572.
Van Rijn MH, van de Luijtgaarden M, van Zuilen AD, Blankestijn PJ, Wetzels JF, Debray TP, van den Brand JA. Prognostic models for chronic kidney disease: a systematic review and external validation. Nephrology Dialysis Transplantation. 2021 Oct;36(10):1837-50.23.
Zhao J, Zhang Y, Qiu J, Zhang X, Wei F, Feng J, Chen C, Zhang K, Feng S, Li WD. An early prediction model for chronic kidney disease. Scientific reports. 2022 Feb 17;12(1):1-9.
Weiner DE, Tighiouart H, Elsayed EF, Griffith JL, Salem DN, Levey AS, et al. The Framingham Predictive Instrument in Chronic Kidney Disease. J Am Coll Cardiol. 2007; 50 (3): 217–24.
Rucci P, Mandreoli M, Gibertoni D, Zuccala A, Fantini MP, Lenzi J, Santoro A. A clinical stratification tool for chronic kidney disease progression rate based on classification tree analysis. Nephrol Dial Transplant. 2014;29(3):603–10. https://doi.org/10.1093/ndt/gft444 Epub 2013/11/30. PubMed PMID: 24286974.
Morgan E Grams, Liang Li, Tom H Greene, Adrienne Tin, Yingying Sang, W H Linda Kao, Michael S Lipkowitz. Estimating time to ESRD using kidney failure risk equations: results from the African American Study of Kidney Disease and Hypertension (AASK). Am J Kidney Dis. 2015; 65(3):394-402. doi: 10.1053/j.ajkd.2014.07.026. Epub 2014 Oct 14.
Whaley-Connell AT, Kurella Tamura M, Jurkovitz CT, Kosiborod M, McCullough PA. Advances in CKD detection and determination of prognosis: executive summary of the National Kidney Foundation-Kidney Early Evaluation Program (KEEP) 2012 annual data report. American Journal of Kidney Diseases: the Official Journal of the National Kidney Foundation. 2013 Apr;61(4 Suppl 2): S1-3. DOI: 10.1053/j.ajkd.2013.01.006. PMID: 23507265; PMCID: PMC3608929.
Kaushal A, Naimark D, Tangri N. Use of the kidney failure risk equation to reduce uncertainty in predicting time to ESRD. Am J Kidney Dis. 2015;65(3):369–71.
Peeters MJ, Van Zuilen AD, Van den Brand JA, Bots ML, Blankestijn PJ, Wetzels JF. Validation of the kidney failure risk equation in European CKD patients. Nephrol Dial Transplant 2013; 28: 1773-1779.
Chukwukadibia MA, Agbasi ON. Chronic kidney disease prediction is an inexact science: The concept of “progressors” and “nonprogressors”. World J Nephrol. 2014; 3(3): 31-49.
Walser M, Drew HH, LaFrance ND. Reciprocal creatinine slopes often give erroneous estimates of the progression of chronic renal failure. Kidney Int. 1989; 27: S81-S85.
Altman DG, Vergouwe Y, Royston P, Moons K. Prognosis and prognostic research: validating a prognostic model. BMJ. 2009; 338: b605.
Moons K, Altman DG, Vergouwe Y, Royston P. Prognosis, and prognostic research: application and impact of prognostic models in clinical practice. BMJ. 2009; 338: b606.
Karel G.M. Douglas G.A. Johannes BR, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and Elaboration. Annals of Internal Medicine; 2015. 162(1): W1-W73.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 CES Medicina
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Derechos de reproducción (copyright)
Cada manuscrito se acompañará de una declaración en la que se especifique que los materiales son inéditos, que no han sido publicados anteriormente en formato impreso o electrónico y que no se presentarán a ningún otro medio antes de conocer la decisión de la revista. En todo caso, cualquier publicación anterior, sea en forma impresa o electrónica, deberá darse a conocer a la redacción por escrito.
Plagios, duplicaciones totales o parciales, traduccones del original a otro idioma son de responsabilidad exclusiva de los autores el envío.
Los autores adjuntarán una declaración firmada indicando que, si el manuscrito se acepta para su publicación, los derechos de reproducción son propiedad exclusiva de la Revista CES Medicina.
Se solicita a los autores que proporcionen la información completa acerca de cualquier beca o subvención recibida de una entidad comercial u otro grupo con intereses privados, u otro organismo, para costear parcial o totalmente el trabajo en que se basa el artículo.
Los autores tienen la responsabilidad de obtener los permisos necesarios para reproducir cualquier material protegido por derechos de reproducción. El manuscrito se acompañará de la carta original que otorgue ese permiso y en ella debe especificarse con exactitud el número del cuadro o figura o el texto exacto que se citará y cómo se usará, así como la referencia bibliográfica completa.
| Article metrics | |
|---|---|
| Abstract views | |
| Galley vies | |
| PDF Views | |
| HTML views | |
| Other views | |
