IDEAS home Printed from https://ideas.repec.org/a/plo/pmed00/1001344.html
   My bibliography  Save this article

Risk Models to Predict Chronic Kidney Disease and Its Progression: A Systematic Review

Author

Listed:
  • Justin B Echouffo-Tcheugui
  • Andre P Kengne

Abstract

A systematic review of risk prediction models conducted by Justin Echouffo-Tcheugui and Andre Kengne examines the evidence base for prediction of chronic kidney disease risk and its progression, and suitability of such models for clinical use. Background: Chronic kidney disease (CKD) is common, and associated with increased risk of cardiovascular disease and end-stage renal disease, which are potentially preventable through early identification and treatment of individuals at risk. Although risk factors for occurrence and progression of CKD have been identified, their utility for CKD risk stratification through prediction models remains unclear. We critically assessed risk models to predict CKD and its progression, and evaluated their suitability for clinical use. Methods and Findings: We systematically searched MEDLINE and Embase (1 January 1980 to 20 June 2012). Dual review was conducted to identify studies that reported on the development, validation, or impact assessment of a model constructed to predict the occurrence/presence of CKD or progression to advanced stages. Data were extracted on study characteristics, risk predictors, discrimination, calibration, and reclassification performance of models, as well as validation and impact analyses. We included 26 publications reporting on 30 CKD occurrence prediction risk scores and 17 CKD progression prediction risk scores. The vast majority of CKD risk models had acceptable-to-good discriminatory performance (area under the receiver operating characteristic curve>0.70) in the derivation sample. Calibration was less commonly assessed, but overall was found to be acceptable. Only eight CKD occurrence and five CKD progression risk models have been externally validated, displaying modest-to-acceptable discrimination. Whether novel biomarkers of CKD (circulatory or genetic) can improve prediction largely remains unclear, and impact studies of CKD prediction models have not yet been conducted. Limitations of risk models include the lack of ethnic diversity in derivation samples, and the scarcity of validation studies. The review is limited by the lack of an agreed-on system for rating prediction models, and the difficulty of assessing publication bias. Conclusions: The development and clinical application of renal risk scores is in its infancy; however, the discriminatory performance of existing tools is acceptable. The effect of using these models in practice is still to be explored. Background: Chronic kidney disease (CKD)—the gradual loss of kidney function—is increasingly common worldwide. In the US, for example, about 26 million adults have CKD, and millions more are at risk of developing the condition. Throughout life, small structures called nephrons inside the kidneys filter waste products and excess water from the blood to make urine. If the nephrons stop working because of injury or disease, the rate of blood filtration decreases, and dangerous amounts of waste products such as creatinine build up in the blood. Symptoms of CKD, which rarely occur until the disease is very advanced, include tiredness, swollen feet and ankles, puffiness around the eyes, and frequent urination, especially at night. There is no cure for CKD, but progression of the disease can be slowed by controlling high blood pressure and diabetes, both of which cause CKD, and by adopting a healthy lifestyle. The same interventions also reduce the chances of CKD developing in the first place. Why Was This Study Done?: CKD is associated with an increased risk of end-stage renal disease, which is treated with dialysis or by kidney transplantation (renal replacement therapies), and of cardiovascular disease. These life-threatening complications are potentially preventable through early identification and treatment of CKD, but most people present with advanced disease. Early identification would be particularly useful in developing countries, where renal replacement therapies are not readily available and resources for treating cardiovascular problems are limited. One way to identify people at risk of a disease is to use a “risk model.” Risk models are constructed by testing the ability of different combinations of risk factors that are associated with a specific disease to identify those individuals in a “derivation sample” who have the disease. The model is then validated on an independent group of people. In this systematic review (a study that uses predefined criteria to identify all the research on a given topic), the researchers critically assess the ability of existing CKD risk models to predict the occurrence of CKD and its progression, and evaluate their suitability for clinical use. What Did the Researchers Do and Find?: The researchers identified 26 publications reporting on 30 risk models for CKD occurrence and 17 risk models for CKD progression that met their predefined criteria. The risk factors most commonly included in these models were age, sex, body mass index, diabetes status, systolic blood pressure, serum creatinine, protein in the urine, and serum albumin or total protein. Nearly all the models had acceptable-to-good discriminatory performance (a measure of how well a model separates people who have a disease from people who do not have the disease) in the derivation sample. Not all the models had been calibrated (assessed for whether the average predicted risk within a group matched the proportion that actually developed the disease), but in those that had been assessed calibration was good. Only eight CKD occurrence and five CKD progression risk models had been externally validated; discrimination in the validation samples was modest-to-acceptable. Finally, very few studies had assessed whether adding extra variables to CKD risk models (for example, genetic markers) improved prediction, and none had assessed the impact of adopting CKD risk models on the clinical care and outcomes of patients. What Do These Findings Mean?: These findings suggest that the development and clinical application of CKD risk models is still in its infancy. Specifically, these findings indicate that the existing models need to be better calibrated and need to be externally validated in different populations (most of the models were tested only in predominantly white populations) before they are incorporated into guidelines. The impact of their use on clinical outcomes also needs to be assessed before their widespread use is recommended. Such research is worthwhile, however, because of the potential public health and clinical applications of well-designed risk models for CKD. Such models could be used to identify segments of the population that would benefit most from screening for CKD, for example. Moreover, risk communication to patients could motivate them to adopt a healthy lifestyle and to adhere to prescribed medications, and the use of models for predicting CKD progression could help clinicians tailor disease-modifying therapies to individual patient needs. Additional Information: Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001344.

Suggested Citation

  • Justin B Echouffo-Tcheugui & Andre P Kengne, 2012. "Risk Models to Predict Chronic Kidney Disease and Its Progression: A Systematic Review," PLOS Medicine, Public Library of Science, vol. 9(11), pages 1-18, November.
  • Handle: RePEc:plo:pmed00:1001344
    DOI: 10.1371/journal.pmed.1001344
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001344
    Download Restriction: no

    File URL: https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1001344&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pmed.1001344?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chin-Chuan Shih & Chi-Jie Lu & Gin-Den Chen & Chi-Chang Chang, 2020. "Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals," IJERPH, MDPI, vol. 17(14), pages 1-11, July.
    2. Chin-Chuan Shih & Ssu-Han Chen & Gin-Den Chen & Chi-Chang Chang & Yu-Lin Shih, 2021. "Development of a Longitudinal Diagnosis and Prognosis in Patients with Chronic Kidney Disease: Intelligent Clinical Decision-Making Scheme," IJERPH, MDPI, vol. 18(23), pages 1-13, December.
    3. Francesco Bellocchio & Caterina Lonati & Jasmine Ion Titapiccolo & Jennifer Nadal & Heike Meiselbach & Matthias Schmid & Barbara Baerthlein & Ulrich Tschulena & Markus Schneider & Ulla T. Schultheiss , 2021. "Validation of a Novel Predictive Algorithm for Kidney Failure in Patients Suffering from Chronic Kidney Disease: The Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD)," IJERPH, MDPI, vol. 18(23), pages 1-18, November.
    4. Samantha M. Bomotti & Jennifer A. Smith & Alicia L. Zagel & Jacquelyn Y. Taylor & Stephen T. Turner & Sharon L. R. Kardia, 2013. "Epigenetic Markers of Renal Function in African Americans," Nursing Research and Practice, Hindawi, vol. 2013, pages 1-9, December.
    5. Liang Li & Sheng Luo & Bo Hu & Tom Greene, 2017. "Dynamic Prediction of Renal Failure Using Longitudinal Biomarkers in a Cohort Study of Chronic Kidney Disease," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 357-378, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pmed00:1001344. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosmedicine (email available below). General contact details of provider: https://journals.plos.org/plosmedicine/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.