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Risk Prediction Models for Patients With Chronic Kidney Disease: A Systematic Review

Navdeep Tangri, MD, PhD; Georgios D. Kitsios, MD, PhD, MS; Lesley Ann Inker, MD, MS; John Griffith, PhD; David M. Naimark, MD, MSc; Simon Walker, BSc(Hons); Claudio Rigatto, MD, MSc; Katrin Uhlig, MD, MS; David M. Kent, MD, MS; and Andrew S. Levey, MD
[+] Article and Author Information

From University of Manitoba, Winnipeg, Manitoba, Canada; Tufts Medical Center, Boston, Massachusetts; and Sunnybrook Hospital, University of Toronto, Toronto, Ontario, Canada.

Note: Drs. Tangri, Naimark, Levey, Inker, and Griffith were part of the team that developed one of the predictive models reviewed in this paper.

Financial Support: By the William B. Schwartz Research Fund, Division of Nephrology, Tufts Medical Center.

Potential Conflicts of Interest: Dr. Levey: Board membership (money to institution): National Kidney Foundation; Grants/grants pending (money to institution): National Kidney Foundation, National Institutes of Health, Amgen, Pharmalink; Payment for lectures including service on speakers bureaus: Multiple universities; Travel/accommodations/meeting expenses unrelated to activities listed (money to institution): Multiple universities. All other authors have no disclosures. Disclosures can be also viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M12-2411.

Requests for Single Reprints: Navdeep Tangri, MD, PhD, Seven Oaks General Hospital, 2PD-13, 2300 McPhillips Street, Winnipeg, Manitoba R2V 3M3, Canada; e-mail, ntangri@sogh.mb.ca.

Current Author Addresses: Drs. Tangri and Rigatto and Mr. Walker: Seven Oaks General Hospital, 2PD-13, 2300 McPhillips Street, Winnipeg, Manitoba R2V 3M3, Canada.

Drs. Inker, Uhlig, Kent, and Levey: Tufts Medical Center, 800 Washington Street, Box 391, Boston, MA 02111.

Dr. Kitsios: Graduate Medical Education, Internal Medicine, Lahey Clinic Medical Center, 41 Mall Road, Burlington, MA 01805.

Dr. Griffith: Northeastern University, 110 Behrakis Health Sciences Center, 360 Huntington Avenue, Boston, MA 02115.

Dr. Naimark: University of Toronto, Room A139, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada.

Author Contributions: Conception and design: N. Tangri, G.D. Kitsios, K. Uhlig, D.M. Kent, A.S. Levey.

Analysis and interpretation of the data: N. Tangri, G.D. Kitsios, L.A. Inker, J. Griffith, S. Walker, K. Uhlig, D.M. Kent, A.S. Levey.

Drafting of the article: N. Tangri, G.D. Kitsios, D.M. Naimark, C. Rigatto.

Critical revision of the article for important intellectual content: N. Tangri, G.D. Kitsios, L.A. Inker, D.M. Naimark, C. Rigatto, K. Uhlig, D.M. Kent, A.S. Levey.

Final approval of the article: N. Tangri, G.D. Kitsios, L.A. Inker, D.M. Naimark, C. Rigatto, K. Uhlig, D.M. Kent, A.S. Levey.

Provision of study materials or patients: N. Tangri.

Statistical expertise: N. Tangri, G.D. Kitsios, J. Griffith.

Obtaining of funding: N. Tangri, A.S. Levey.

Administrative, technical, or logistic support: D.M. Kent, A.S. Levey.

Collection and assembly of data: N. Tangri, G.D. Kitsios, S. Walker, C. Rigatto.


Ann Intern Med. 2013;158(8):596-603. doi:10.7326/0003-4819-158-8-201304160-00004
Text Size: A A A

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.

Figures

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Appendix Figure.

Summary of evidence search and selection.

CKD = chronic kidney disease.

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Figure.

Risk of bias and clinical usefulness of prediction models.

Risk-of-bias items were evaluated as low risk (−), high risk (+), or unknown risk (?). Clinical usefulness was evaluated as yes (+) or no (−).

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Chronic Kidney Disease and Risk Prediction
Posted on May 10, 2013
Shayan Shirazian, MD, Candace Grant, MD, and Joseph Mattana, MD
Winthrop University Hospital, Mineola, NY
Conflict of Interest: None Declared

In their systematic review, Tangri et al. (1) have thoroughly analyzed 13 studies with 23 existing models of risk prediction for major adverse clinical outcomes in patients with chronic kidney disease (CKD). We commend the authors for highlighting the importance of accurate and individualized risk prediction in patients with CKD and the need for more research in this field. As they point out, a key issue in risk prediction is to be able to better identify the patient at high risk of developing end stage renal disease (ESRD) so that aggressive treatment can be directed to them, while also identifying the low risk patient who can be managed more conservatively.

Of note, age was a variable in both models of kidney failure prediction that met the criteria for clinical usefulness. We would like to emphasize that age may have a substantial impact on predicting an individual’s risk of developing ESRD, and models that do not incorporate age may be misleading and potentially result in inappropriate treatment.For younger patients, CKD risk prediction models that do not take age into account may underestimate the risk of developing ESRD. For example, a 40 year old woman with an estimated glomerular filtration rate between 30 to 44 mL per minute per 1.73m2 BSA would have a short term risk of progression to ESRD that is far less than her lifetime risk, the latter being approximately 30 percent in one recent study (2). In contrast, for the older patient with CKD, models that don’t take age into account may overestimate their risk of developing ESRD. For any stage of CKD ranging from mild to severe, the risk of ESRD goes down dramatically with age, and is progressively exceeded by the risk of death (2,3). Hence for the young patient, failure to account for age may falsely suggest that the risk for ESRD is low and lead to less aggressive treatment to slow progression.

In some older patients, failure to account for age may exaggerate the likelihood of developing ESRD, leading to over-treatment including unnecessary dialysis preparation and counseling and its attendant risk for psychological harm to patients and families as well as physical harm from attempts at vascular access creation and medication toxicity. Hopefully incorporation of the validated age-based CKD risk models reviewed by the authors will lead to better individualization of CKD care and an improvement in outcomes.

References

1) Tangri N, Kitsios GD, Inker LA, Griffith J, Naimark DM, Walker S, et al. Risk prediction models for patients with chronic kidney disease: a systematic review. Ann Intern Med. 2013;158:596-603.

2) Turin TC, Tonelli M, Manns BJ, Ahmed SB, Ravani P, James M, et al. Lifetime risk of ESRD. J Am Soc Nephrol. 2012;23:1569-78.3) O'Hare AM, Choi AI, Bertenthal D, Bacchetti P, Garg AX, Kaufman JS, et al. Age affects outcomes in chronic kidney disease. J Am Soc Nephrol. 2007;18:2758-65.

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