Predicting Kidney Failure, Cardiovascular Disease and Death in Advanced CKD Patients
2022; Elsevier BV; Volume: 7; Issue: 10 Linguagem: Inglês
10.1016/j.ekir.2022.08.009
ISSN2468-0249
Autores Tópico(s)Dialysis and Renal Disease Management
ResumoRisk prediction equations (RPEs) are ubiquitous in medicine. Nephrology is not immune to the phenomenon. For example, a recent systematic review found 42 articles mentioning 1 or more novel RPEs in the nephrology literature since 1986.1Ramspek C.L. de Jong Y. Dekker F.W. van Diepen M. Towards the best kidney failure prediction tool: a systematic review and selection aid.Nephrol Dial Transplant. 2020; 35: 1527-1538https://doi.org/10.1093/ndt/gfz018Crossref PubMed Scopus (40) Google Scholar The Kidney Disease Improving Global Outcomes consortium gave a rather tepid endorsement of the use of RPEs in their 2013 chronic kidney disease (CKD) management guidelines2Chapter K.D.I.G.O. Chapter 1: definition and classification of CKD.Kidney Int Suppl (2011). 2013; 3: 19-62https://doi.org/10.1038/kisup.2012.64Abstract Full Text Full Text PDF PubMed Scopus (712) Google Scholar but more robust support in a 2019 consensus conference3Lok C.E. Huber T.S. Lee T. et al.KDOQI clinical practice guideline for vascular access: 2019 update.Am J Kidney Dis. 2020; 75: S1-S164https://doi.org/10.1053/j.ajkd.2019.12.001Abstract Full Text Full Text PDF PubMed Scopus (776) Google Scholar regarding timing of vascular access placement in patients with severe and/or progressive CKD. A widely cited RPE is the Kidney Failure Risk Equation (KFRE),4Tangri N. Stevens L.A. Griffith J. et al.A predictive model for progression of chronic kidney disease to kidney failure.JAMA. 2011; 305: 1553-1559https://doi.org/10.1001/jama.2011.451Crossref PubMed Scopus (799) Google Scholar published in 2011, which currently has 620 citations listed in the Web of Science (Clarivate, London, UK). The KFRE predicts the absolute risk of kidney failure with replacement therapy (KFRT) at 1, 2, and 5 years among patients with stage G3 to G5 CKD. The 4-variable, 2-year KFRE includes age, sex, estimated glomerular filtration rate ([eGFR], using the CKD-Epidemiology Collaboration 2009 creatinine equation),5Levey A.S. Stevens L.A. Schmid C.H. et al.A new equation to estimate glomerular filtration rate.Ann Intern Med. 2009; 150: 604-612https://doi.org/10.7326/0003-4819-150-9-200905050-00006Crossref PubMed Scopus (17329) Google Scholar and log-transformed urine albumin-to-creatinine ratio as predictors. A convenient on-line calculator is available.6Kidney failure risk equation (4 variable)Calculate by QxMD.https://qxmd.com/calculate/calculator_308/kidney-failure-risk-equation-4-variableDate accessed: August 16, 2022Google Scholar The KFRE has endured some criticism since publication. It was derived and externally validated in 2 Canadian CKD cohorts, which raised concerns regarding generalizability to the general population and to other jurisdictions. Nevertheless, a large, international, and extended validation study among 31 general populations and CKD cohorts dispersed around the globe with 721,357 participants demonstrated that the KFRE had uniformly excellent discrimination but required an addition of a calibration factor for non-North-American cohorts.7Tangri N. Grams M.E. Levey A.S. et al.Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta analysis.JAMA. 2016; 315: 164-174https://doi.org/10.1001/jama.2015.18202Crossref PubMed Scopus (370) Google Scholar Another concern is that the KFRE estimates risk using predictor values obtained at a single point in time and does not consider their prior, longitudinal trajectory. Nevertheless, a study conducted by the KFRE investigators compared a dynamic RPE that did account for changes in predictors over time versus the original static KFRE using a single eGFR value and found only a slight improvement in prediction performance metrics.8Tangri N. Inker L.A. Hiebert B. et al.A dynamic predictive model for progression of CKD.Am J Kidney Dis. 2017; 69: 514-520https://doi.org/10.1053/j.ajkd.2016.07.030Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar Perhaps the biggest concern with the KFRE is that it was developed using standard Cox regression methods which did not account for the competing risk of death. This effect has been shown to result in an over-estimation of KFRT risk, particularly among older CKD patients.9Hundemer G.L. Tangri N. Sood M.M. et al.The effect of age on performance of the kidney failure risk equation in advanced CKD.Kidney Int Rep. 2021; 6: 2993-3001https://doi.org/10.1016/j.ekir.2021.09.006Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar This issue is somewhat nuanced because clinicians caring for older CKD patients cannot know their true future risk of KFRT at the time decisions are made but rather rely on predicted risk. The latter is systematically lower among older patients with CKD for 2 reasons. The first reason is that elderly patients tend to be sarcopenic resulting in creatinine-based eGFR values that overestimate measured GFR and subsequently result in lower predicted KFRT risk. This issue may be exacerbated among non-Black, older patients if new creatinine-based GFR estimating equations that do not include race as a covariate come into widespread use because they further overestimate GFR compared to the 2009 CKD-Epidemiology Collaboration creatinine equation.10Inker L.A. Eneanya N.D. Coresh J. et al.New creatinine- and cystatin C–based equations to estimate GFR without race.N Engl J Med. 2021; 385: 1737-1749https://doi.org/10.1056/nejmoa2102953Crossref PubMed Scopus (0) Google Scholar The second reason is that the hazard ratio for age in the KFRE is less than 1, leading to even lower predicted risks for older patients (Figure 1). Systematically lower predicted KFRT risk among older individuals may lead to unanticipated consequences when CKD management policy is based on KFRE thresholds. For example, in Ontario, Canada on January 1, 2016, the criterion for reimbursement of multidisciplinary team (MDT) clinics shifted from eGFR ≤ 33 ml/min to either eGFR ≤ 15 ml/min or a 4-variable, 2-year KFRE predicted risk ≥10%. The rationale is that the purpose of MDT clinics is for KFRT preparation. Therefore, patients at low predicted risk of this outcome derive no benefit from MDT care and should be excluded. This rather narrow view does not consider other goals of MDT care such as, among many others,2Chapter K.D.I.G.O. Chapter 1: definition and classification of CKD.Kidney Int Suppl (2011). 2013; 3: 19-62https://doi.org/10.1038/kisup.2012.64Abstract Full Text Full Text PDF PubMed Scopus (712) Google Scholar diagnosis and treatment of cardiovascular disease, which may contribute to the proven benefit of MDT clinics.11Wang S.M. Hsiao L.C. Ting I.W. et al.Multidisciplinary care in patients with chronic kidney disease: a systematic review and meta-analysis.Eur J Intern Med. 2015; 26: 640-645https://doi.org/10.1016/j.ejim.2015.07.002Abstract Full Text Full Text PDF PubMed Scopus (60) Google Scholar,12Jiamjariyapon T. Ingsathit A. Pongpirul K. et al.Effectiveness of integrated care on delaying progression of stage 3–4 chronic kidney disease in rural communities of Thailand (escort study): a cluster randomized controlled trial.BMC Nephrol. 2017; 18: 83https://doi.org/10.1186/s12882-016-0414-4Crossref PubMed Scopus (33) Google Scholar The newer Grams RPE published in 201813Grams M.E. Sang Y. Ballew S.H. et al.Predicting timing of clinical outcomes in patients with chronic kidney disease and severely decreased glomerular filtration rate.Kidney Int. 2018; 93: 1442-1451https://doi.org/10.1016/j.kint.2018.01.009Abstract Full Text Full Text PDF PubMed Scopus (95) Google Scholar was a welcome addition to the CKD clinician’s toolkit because it provides 2 and 4 year predictions for a broader range of outcomes among patients with stage G4 and G5 CKD, including KFRT, cardiovascular disease and death, and also predicts all 9 possible orderings (trajectories) of these events. Predictors include age, sex, race, history of cardiovascular disease, current smoking status, systolic blood pressure, diabetes status, eGFR (using the CKD-Epidemiology Collaboration 2009 creatinine equation)5Levey A.S. Stevens L.A. Schmid C.H. et al.A new equation to estimate glomerular filtration rate.Ann Intern Med. 2009; 150: 604-612https://doi.org/10.7326/0003-4819-150-9-200905050-00006Crossref PubMed Scopus (17329) Google Scholar, and urine albumin-to-creatinine ratio. The Grams RPE employed a complex derivation process. Using Fine and Gray competing risk regression,14Fine J.P. Gray R.J. A proportional hazards model for the subdistribution of a competing risk.J Am Stat Assoc. 1999; 94: 496-509https://doi.org/10.1080/01621459.1999.10474144Crossref Scopus (9840) Google Scholar subdistribution hazard ratios were meta-analyzed across 29 CKD prognosis consortium cohorts comprising 264,296 individuals for first and second events. Fixing the meta-analyzed subdistribution hazard ratios, allowed the baseline subdistribution hazard function to be estimated within each cohort and then averaged across cohorts. A Weibull survival model was then fit to the average baseline subdistribution hazards. These and the meta-analyzed subdistribution hazard ratios were then employed to estimate time-varying transition intensities in a 5-state Markov process. The process was run for 24 or 48 months with every possible combination of covariate values to generate a large set of simulated data. Finally, a multinomial logistic meta-model was fit to the simulated data in order to produce a web-based tool.15Timing of clinical outcomes in CKD with severely decreased GFRChronic Kidney Disease Prognosis Consortium.https://ckdpcrisk.org/lowgfrevents/Date: 2018Date accessed: August 13, 2022Google Scholar Given a set of covariate values, the probabilities of each of the 9 mutually exclusive outcome trajectories are provided at each time point such that the predicted probabilities sum to one. In this issue of Kidney International Reports, Ramspeck et al.16Ramspek C.L. Boekee R. Evans M. et al.Predicting kidney failure, cardiovascular disease and death in advanced CKD patients.Kidney Int Rep. 2022; 7: 2230-2241https://doi.org/10.1016/j.ekir.2022.07.165Abstract Full Text Full Text PDF Scopus (1) Google Scholar make a valuable contribution by externally validating the Grams RPE among 1517 participating patients in the European Quality Study. Starting in 2012, The European Quality Study recruited patients 65 years or older from nephrology clinics in 6 European countries with eGFR values at baseline between 10 and 30 ml/min. Patients were followed between 4 and 8 years or until kidney transplantation. The outcomes were combinations of the trajectories considered by the Grams RPE, including “any KFRT,” “any CVD,” death, and no event at 2 and 4 years. Predicted outcomes at these time points were calculated using the Grams multinomial logistic equations by summing the predicted probabilities for trajectories that were included in a given combination. Observed probabilities were estimated using cumulative incidence functions. Performance was assessed via discrimination using a method that takes competing risks into account as well as calibration using calibration-in-the-large (predicted vs. observed probabilities) and smoothed calibration plots. The c-statistics for the Grams RPE varied according to the combined outcome being predicted. For KFRT, the c-statistics were 0.76 and 0.74 for 2 and 4 years, respectively. There was generally good calibration to observed risks with the 4-year version outperforming the 2-year RPE. The complex derivation of the Grams RPE presents a challenge to external validation. The data simulation process produced an arbitrarily large set of hypothetical individuals who could not be censored and therefore the timing and occurrence of events could be determined precisely at 2 and 4 years, hence the multinomial logistic structure of the meta-model. Of course, no real CKD cohort is free of censored observations. In the analysis by Ramspeck et al.,16Ramspek C.L. Boekee R. Evans M. et al.Predicting kidney failure, cardiovascular disease and death in advanced CKD patients.Kidney Int Rep. 2022; 7: 2230-2241https://doi.org/10.1016/j.ekir.2022.07.165Abstract Full Text Full Text PDF Scopus (1) Google Scholar the investigators sensibly use cumulative incidence functions to estimate observed 2-year and 4-year observed risks but it should be pointed out that these are not the same as multinomial probabilities. This may account for some of the difference between predicted and observed risks. The Grams RPE predicts 8 possible outcome trajectories and 1 nonevent trajectory. Assessing discrimination and calibration with a large number of possible outcomes is difficult. Understandably, the investigators have combined disease trajectories into the 4 groups listed above in order to have sufficient numbers of patients per group. These groupings are no longer mutually exclusive but this would not be expected to meaningfully impair the ability to externally validate the Grams RPE. The investigators employ decision curve analysis to consider the clinical effect of decision-making with respect to initiation of KFRT preparation using the Grams and KFRE RPEs and a simpler rule based solely on eGFR.16Ramspek C.L. Boekee R. Evans M. et al.Predicting kidney failure, cardiovascular disease and death in advanced CKD patients.Kidney Int Rep. 2022; 7: 2230-2241https://doi.org/10.1016/j.ekir.2022.07.165Abstract Full Text Full Text PDF Scopus (1) Google Scholar Decision curve analysis is a validated approach but some readers may be unfamiliar with it. The basic idea is to consider positive tests (i.e., predicted KFRT risks above a given threshold). A lower threshold would increase the proportion of true positives ([TP], individuals above the threshold who did experience KFRT) but also the proportion of false positives ([FP], individuals above the threshold who did not experience KFRT), whereas a higher threshold would reduce both TP and false positive. Decision curve analysis attempts to “convert” false positives into TP-equivalents using a harm-to-benefit ratio (the harm of a false positive divided by the benefit of a TP). The net benefit is then calculated as difference between the proportion of TPs and the TP-equivalents that are “lost” because of the diagnostic test characteristics and threshold. Because a harm-to-benefit ratio is difficult to estimate, it is more useful to examine net benefit across a range of ratios. The investigators show that at low ratios, using the Grams predicted 2-year KFRT risk above 20%, and at higher ratios a Grams predicted risk above 40% yielded higher net benefits than an eGFR threshold of ≤ 15 ml/min. Net benefits for KFRE-derived thresholds were similar to Grams thresholds. Ramspeck et al.16Ramspek C.L. Boekee R. Evans M. et al.Predicting kidney failure, cardiovascular disease and death in advanced CKD patients.Kidney Int Rep. 2022; 7: 2230-2241https://doi.org/10.1016/j.ekir.2022.07.165Abstract Full Text Full Text PDF Scopus (1) Google Scholar have taken on the difficult but important task of externally validating a potentially more useful RPE for patients with stage G4 and G5 CKD. Decision-making, with respect to access to MDT care and initiation of planning for KFRT, are fraught in this population making the insights from RPEs potentially useful. Work remains to be done with respect to the development and validation of RPEs in CKD as changes to measurement and estimation evolve such as the wider use of cystatin-C as a filtration marker and wider use of the CKD-EPI 2021 equation that does not consider race as a covariate. Ultimately, the utility of RPEs will need to be proven in cluster randomized trials. Given their wide diffusion, however, that horse may have already left the barn. The author declared no competing interests. Predicting Kidney Failure, Cardiovascular Disease and Death in Advanced CKD PatientsKidney International ReportsVol. 7Issue 10PreviewPredicting the timing and occurrence of kidney replacement therapy (KRT), cardiovascular events, and death among patients with advanced chronic kidney disease (CKD) is clinically useful and relevant. We aimed to externally validate a recently developed CKD G4+ risk calculator for these outcomes and to assess its potential clinical impact in guiding vascular access placement. 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