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Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
Risk prediction models for patients with chronic kidney disease: a systematic review
Navdeep Tangri1, Georgios D Kitsios, Lesley Ann Inker
1Seven Oaks General Hospital, 2PD-13, 2300 McPhillips Street, Winnipeg, Manitoba R2V 3M3, Canada. ntangri@sogh.mb.ca
Insights
Accurate models for predicting kidney failure in chronic kidney disease (CKD) patients are available. Further research is needed to improve models for cardiovascular events and all-cause mortality in CKD.
Area of Science:
- Nephrology
- Epidemiology
- Biostatistics
Background:
- Patients with chronic kidney disease (CKD) face elevated risks of kidney failure, cardiovascular events, and mortality.
- Accurate prediction models are crucial for managing individual patient risk in CKD.
Purpose of the Study:
- To systematically review existing risk prediction models for kidney failure, cardiovascular events, and mortality in CKD patients.
- Evaluate the performance and clinical utility of these models.
Main Methods:
- A comprehensive MEDLINE search was conducted for English-language articles from 1966 to November 2012.
- Included were cohort studies of adults with any stage of CKD (non-dialysis, non-transplant) with at least one year of follow-up.
- Data extraction focused on study design, population, modeling methods, performance metrics, risk of bias, and clinical usefulness.
Main Results:
- Thirteen studies described 23 risk prediction models: 11 for kidney failure, 6 for all-cause mortality, and 6 for cardiovascular events.
- Common predictors included estimated glomerular filtration rate (eGFR) or serum creatinine (17 models) and proteinuria (15 models).
- Only 4 models (from 2 studies) met criteria for clinical usefulness, with 3 models showing clinically useful risk categories.
Conclusions:
- Validated models for predicting kidney failure in CKD patients are available for clinical testing.
- A need exists for validated risk-of-bias tools and comparative performance analyses of models within the same validation populations.
- Further development is required for robust prediction models targeting cardiovascular events and all-cause mortality in CKD.
Background:
Patients with chronic kidney disease (CKD) are at increased risk for kidney failure, cardiovascular events, and all-cause mortality. Accurate models are needed to predict the individual risk for these outcomes.
Purpose:
To systematically review risk prediction models for kidney failure, cardiovascular events, and death in patients with CKD.
Data Sources:
MEDLINE search of English-language articles published from 1966 to November 2012.
Study Selection:
Cohort studies that examined adults with any stage of CKD who were not receiving dialysis and had not had a transplant; had at least 1 year of follow-up; and reported on a model that predicted the risk for kidney failure, cardiovascular events, or all-cause mortality.
Data Extraction:
Reviewers extracted data on study design, population characteristics, modeling methods, metrics of model performance, risk of bias, and clinical usefulness.
Data Synthesis:
Thirteen studies describing 23 models were found. Eight studies (11 models) involved kidney failure, 5 studies (6 models) involved all-cause mortality, and 3 studies (6 models) involved cardiovascular events. Measures of estimated glomerular filtration rate or serum creatinine level were included in 10 studies (17 models), and measures of proteinuria were included in 9 studies (15 models). Only 2 studies (4 models) met the criteria for clinical usefulness, of which 1 study (3 models) presented reclassification indices with clinically useful risk categories.
Limitation:
A validated risk-of-bias tool and comparisons of the performance of different models in the same validation population were lacking.
Conclusion:
Accurate, externally validated models for predicting risk for kidney failure in patients with CKD are available and ready for clinical testing. Further development of models for cardiovascular events and all-cause mortality is needed.
Primary Funding Source:
None.
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