Predicting Risk of RRT in Patients with CKD

Grams, Morgan E.; Coresh, Josef

doi:10.2215/cjn.11841116

Cited by 7 publications

(7 citation statements)

References 15 publications

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“…Additional work and validation of outcome prediction in patients with CKD may help inform delivery of “personalized medicine”; however, we acknowledge that the precision of such models will depend on the amount of variation explained by known covariates, and that such models may be much more accurate in the short-term. 15,31,39 …”

Section: Discussionmentioning

confidence: 99%

Risks of Adverse Events in Advanced CKD: The Chronic Renal Insufficiency Cohort (CRIC) Study

Grams

Yang

Rebholz

et al. 2017

American Journal of Kidney Diseases

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Background People with advanced chronic kidney disease (CKD) are at risk for the development of end-stage renal disease (ESRD), but also many other adverse outcomes, including cardiovascular disease (CVD) events and death. Determination of risk factors that explain the variability in prognosis and timing of these adverse outcomes can aid patient counseling and medical decision-making. Study Design Prospective research cohort. Setting & Participants 1,798 participants with eGFR <30 ml/min/1.73 m2 in the CRIC Study were followed up for a median of 5.5 years. Predictors Age, race, sex, eGFR, proteinuria, diabetes mellitus, body mass index, ejection fraction, systolic blood pressure, history of cardiovascular disease, and history. Outcomes ESRD, CVD (congestive heart failure, stroke, myocardial infarction, peripheral artery disease), and death. Results Baseline age of the cohort was 60 years; 46% were women, and 46% were African American. While 52.3% of participants progressed to ESRD during follow-up, the path by which this occurred was variable. For example, the predicted 1-year probabilities for a hypothetical 60-year-old white woman with eGFR 30 ml/min/1.73 m2, 1.8 g/d of proteinuria, and no diabetes or CVD (risk characteristics similar to the average participant), were 3.3%, 4.1%, and 0.3%, for first developing CVD, ESRD, and death, respectively. For a 40-year-old African-American man with similar characteristics but higher systolic blood pressure, the corresponding 1-year probabilities were 2.4%, 13.2%, and 0.1%. For all participants, the development of ESRD or CVD increased the risk of subsequent mortality, with no differences by patient race or body-mass index. Limitations The CRIC population was specifically recruited for kidney disease, and the vast majority had seen a nephrologist. Conclusions The prognosis and timing of adverse outcomes in CKD varies by patient characteristics. These results may help guide the development of personalized approaches for managing patients with advanced CKD.

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Section: Discussionmentioning

confidence: 99%

Risks of Adverse Events in Advanced CKD: The Chronic Renal Insufficiency Cohort (CRIC) Study

Grams

Yang

Rebholz

et al. 2017

American Journal of Kidney Diseases

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“…Conversely, most patients at low risk of renal failure but high risk of death (nodes 6 and 7) will require greater attention in the treatment of death risk factors, in particular modifiable cardiovascular risk factors. Introducing the CT-PIRP prediction tool into clinical practice can facilitate a more personalized therapeutic approach [35].…”

Section: Discussionmentioning

confidence: 99%

Temporal validation of the CT-PIRP prognostic model for mortality and renal replacement therapy initiation in chronic kidney disease patients

Gibertoni

Rucci

Mandreoli³

et al. 2019

BMC Nephrol

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Background A classification tree model (CT-PIRP) was developed in 2013 to predict the annual renal function decline of patients with chronic kidney disease (CKD) participating in the PIRP (Progetto Insufficienza Renale Progressiva) project, which involves thirteen Nephrology Hospital Units in Emilia-Romagna (Italy). This model identified seven subgroups with specific combinations of baseline characteristics that were associated with a differential estimated glomerular filtration rate (eGFR) annual decline, but the model’s ability to predict mortality and renal replacement therapy (RRT) has not been established yet. Methods Survival analysis was used to determine whether CT-PIRP subgroups identified in the derivation cohort ( n = 2265) had different mortality and RRT risks. Temporal validation was performed in a matched cohort ( n = 2051) of subsequently enrolled PIRP patients, in which discrimination and calibration were assessed using Kaplan-Meier survival curves, Cox regression and Fine & Gray competing risk modeling. Results In both cohorts mortality risk was higher for subgroups 3 (proteinuric, low eGFR, high serum phosphate) and lower for subgroups 1 (proteinuric, high eGFR), 4 (non-proteinuric, younger, non-diabetic) and 5 (non-proteinuric, younger, diabetic). Risk of RRT was higher for subgroups 3 and 2 (proteinuric, low eGFR, low serum phosphate), while subgroups 1, 6 (non-proteinuric, old females) and 7 (non-proteinuric, old males) showed lower risk. Calibration was excellent for mortality in all subgroups while for RRT it was overall good except in subgroups 4 and 5. Conclusions The CT-PIRP model is a temporally validated prediction tool for mortality and RRT, based on variables routinely collected, that could assist decision-making regarding the treatment of incident CKD patients. External validation in other CKD populations is needed to determine its generalizability.

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“…40 In addition to its use for predicting referral from primary care to nephrology services, the KFRE may also be used to improve timing of referral for permanent vascular access creation and kidney transplantation. 41 An examination of data collected at state level and analysed against the KFRE could assist in identifying or mapping the distribution of state-wide CKD and assessing the usefulness of the tool to support health service planning at the state and local level.…”

Section: Ckd: Prediction and Referralmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Referral patterns, disease progression and impact of the kidney failure risk equation (KFRE) in a Queensland Chronic Kidney Disease Registry (CKD.QLD) cohort: a study protocol

et al. 2022

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IntroductionChronic kidney disease (CKD) is a rapidly increasing and global phenomenon which carries high morbidity and mortality. Although timely referral from primary care to secondary care confers favourable outcomes, it is not possible for every patient with CKD to be managed at secondary care. With 1 in 10 Australians currently living with markers of CKD against a workforce of about 600 nephrology specialists, a risk stratification strategy is required that will reliably identify individuals whose kidney disease is likely to progress.Methods and analysisThis study will undertake a retrospective secondary analysis of the Chronic Kidney Disease Queensland Registry (CKD.QLD) data of consented adults to examine the referral patterns to specialist nephrology services from primary care providers and map the patient trajectory and outcomes to inform the optimal referral timing for disease mitigation. Patient data over a 5-year period will be examined to determine the impact of the kidney failure risk equation-based risk stratification on the referral patterns, disease progression and patient outcomes. The results will inform considerations of a risk stratification strategy that will ensure adequate predialysis management and add to the discussion of the time interval between referral and initiation of kidney replacement therapy or development of cardiovascular events.Ethics and disseminationThis protocol was approved by the Ethics Committee of the Royal Brisbane and Women’s Hospital in January 2021 (LNR/2020/QRBW/69707 14/01/2021). The HREC waived the requirement for patient consent as all patients had consented for the use of their data for the purpose of research on recruitment into CKD.QLD Registry. The results will be presented as a component of a PhD study with The University of Queensland. It is anticipated that the results will be presented at health-related conferences (local, national and possibly international) and via publication in peer-reviewed academic journals.

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Predicting Risk of RRT in Patients with CKD

Cited by 7 publications

References 15 publications

Risks of Adverse Events in Advanced CKD: The Chronic Renal Insufficiency Cohort (CRIC) Study

Risks of Adverse Events in Advanced CKD: The Chronic Renal Insufficiency Cohort (CRIC) Study

Temporal validation of the CT-PIRP prognostic model for mortality and renal replacement therapy initiation in chronic kidney disease patients

Referral patterns, disease progression and impact of the kidney failure risk equation (KFRE) in a Queensland Chronic Kidney Disease Registry (CKD.QLD) cohort: a study protocol

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