IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0278729.html
   My bibliography  Save this article

Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance

Author

Listed:
  • Fangyue Chen
  • Piyawat Kantagowit
  • Tanawin Nopsopon
  • Arisa Chuklin
  • Krit Pongpirul

Abstract

Chronic Kidney disease (CKD) is an important yet under-recognized contributor to morbidity and mortality globally. Machine-learning (ML) based decision support tools have been developed across many aspects of CKD care. Notably, algorithms developed in the prediction and diagnosis of CKD development and progression may help to facilitate early disease prevention, assist with early planning of renal replacement therapy, and offer potential clinical and economic benefits to patients and health systems. Clinical implementation can be affected by the uncertainty surrounding the methodological rigor and performance of ML-based models. This systematic review aims to evaluate the application of prognostic and diagnostic ML tools in CKD development and progression. The protocol has been prepared using the Preferred Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. The systematic review protocol for CKD prediction and diagnosis have been registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022356704, CRD42022372378). A systematic search will be undertaken of PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), the Web of Science, and the IEEE Xplore digital library. Studies in which ML has been applied to predict and diagnose CKD development and progression will be included. The primary outcome will be the comparison of the performance of ML-based models with non-ML-based models. Secondary analysis will consist of model use cases, model construct, and model reporting quality. This systematic review will offer valuable insight into the performance and reporting quality of ML-based models in CKD diagnosis and prediction. This will inform clinicians and technical specialists of the current development of ML in CKD care, as well as direct future model development and standardization.

Suggested Citation

  • Fangyue Chen & Piyawat Kantagowit & Tanawin Nopsopon & Arisa Chuklin & Krit Pongpirul, 2023. "Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-10, February.
    • Handle: RePEc:plo:pone00:0278729
    DOI: 10.1371/journal.pone.0278729
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0278729
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0278729&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0278729?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
    ---><---

    References listed on IDEAS

    as
    1. Trishan Panch & Erin Duralde & Heather Mattie & Gopal Kotecha & Leo Anthony Celi & Melanie Wright & Felix Greaves, 2022. "A distributed approach to the regulation of clinical AI," PLOS Digital Health, Public Library of Science, vol. 1(5), pages 1-8, May.
    2. Nathan R Hill & Samuel T Fatoba & Jason L Oke & Jennifer A Hirst & Christopher A O’Callaghan & Daniel S Lasserson & F D Richard Hobbs, 2016. "Global Prevalence of Chronic Kidney Disease – A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    3. Karel G M Moons & Joris A H de Groot & Walter Bouwmeester & Yvonne Vergouwe & Susan Mallett & Douglas G Altman & Johannes B Reitsma & Gary S Collins, 2014. "Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist," PLOS Medicine, Public Library of Science, vol. 11(10), pages 1-12, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Santiago Ferrière-Steinert & Joaquín Valenzuela Jiménez & Sebastián Heskia Araya & Thomas Kouyoumdjian & José Ramos-Rojas & Abraham I J Gajardo, 2024. "Early high-sensitivity troponin elevation in predicting short-term mortality in sepsis: A protocol for a systematic review with meta-analysis," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-10, October.
    2. Gabriela Ortiz, 2023. "New meta-analysis (MA) features in Stata 18: MA for prevalence and multilevel MA," Canadian Stata Conference 2023 06, Stata Users Group.
    3. William P Martin & Neil G Docherty & Carel W Le Roux, 2017. "Bariatric Surgery for the Treatment of Diabetic Kidney Disease," Current Research in Diabetes & Obesity Journal, Juniper Publishers Inc., vol. 3(5), pages 1-4, August.
    4. V Boima & K Agyabeng & V Ganu & D Dey & E Yorke & M B Amissah-Arthur & A A Wilson & A E Yawson & C C Mate-Kole & J Nonvignon, 2020. "Willingness to pay for kidney transplantation among chronic kidney disease patients in Ghana," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-15, December.
    5. Jiaxin Li & Zijun Zhou & Jianyu Dong & Ying Fu & Yuan Li & Ze Luan & Xin Peng, 2021. "Predicting breast cancer 5-year survival using machine learning: A systematic review," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-23, April.
    6. Wanting Zu & Xuemiao Huang & Tianxin Xu & Lin Du & Yiming Wang & Lisheng Wang & Wenbo Nie, 2023. "Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review," PLOS ONE, Public Library of Science, vol. 18(6), pages 1-14, June.
    7. Lukas Higi & Angela Lisibach & Patrick E Beeler & Monika Lutters & Anne-Laure Blanc & Andrea M Burden & Dominik Stämpfli, 2021. "External validation of the PAR-Risk Score to assess potentially avoidable hospital readmission risk in internal medicine patients," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-14, November.
    8. Christian T. Litchepah & Issidor. Noumba & Mohammadou. Nourou, 2022. "Does reducing violence against women improve children’s health? The case of Cameroon," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 6(6), pages 187-194, June.
    9. Anna M van Boekel & Siri L van der Meijden & Sesmu M Arbous & Rob G H H Nelissen & Karin E Veldkamp & Emma B Nieswaag & Kim F T Jochems & Jeroen Holtz & Annekee van IJlzinga Veenstra & Jeroen Reijman , 2024. "Systematic evaluation of machine learning models for postoperative surgical site infection prediction," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-17, December.
    10. Qingqing Cai & Louise H Dekker & Stephan J L Bakker & Martin H de Borst & Gerjan Navis, 2019. "Intraregional differences in renal function in the Northern Netherlands: The Lifelines Cohort Study," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-15, October.
    11. Fazel, Seena & Burghart, Matthias & Fanshawe, Thomas & Gil, Sharon Danielle & Monahan, John & Yu, Rongqin, 2022. "The predictive performance of criminal risk assessment tools used at sentencing: Systematic review of validation studies," Journal of Criminal Justice, Elsevier, vol. 81(C).
    12. Fisaha Haile Tesfay & Kathryn Backholer & Christina Zorbas & Steven J. Bowe & Laura Alston & Catherine M. Bennett, 2022. "The Magnitude of NCD Risk Factors in Ethiopia: Meta-Analysis and Systematic Review of Evidence," IJERPH, MDPI, vol. 19(9), pages 1-19, April.
    13. Gabriela Ortíz, "undated". "New meta-analysis (MA) features in Stata 18: MA for prevalence and multilevel MA," Spanish Stata Conference 2023 03, Stata Users Group.
    14. Shamil D. Cooray & Lihini A. Wijeyaratne & Georgia Soldatos & John Allotey & Jacqueline A. Boyle & Helena J. Teede, 2020. "The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal," IJERPH, MDPI, vol. 17(9), pages 1-20, April.
    15. Helder Novais Bastos & Nuno S Osório & António Gil Castro & Angélica Ramos & Teresa Carvalho & Leonor Meira & David Araújo & Leonor Almeida & Rita Boaventura & Patrícia Fragata & Catarina Chaves & Pat, 2016. "A Prediction Rule to Stratify Mortality Risk of Patients with Pulmonary Tuberculosis," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-14, September.
    16. Antonio Palazón-Bru & María José Prieto-Castelló & David Manuel Folgado-de la Rosa & Ana Macanás-Martínez & Emma Mares-García & María de los Ángeles Carbonell-Torregrosa & Vicente Francisco Gil-Guillé, 2020. "Development, and Internal, and External Validation of a Scoring System to Predict 30-Day Mortality after Having a Traffic Accident Traveling by Private Car or Van: An Analysis of 164,790 Subjects and ," IJERPH, MDPI, vol. 17(24), pages 1-13, December.
    17. Paulien Van Acker & Wim Van Biesen & Evi V Nagler & Muguet Koobasi & Nic Veys & Jill Vanmassenhove, 2021. "Risk prediction models for acute kidney injury in adults: An overview of systematic reviews," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-14, April.
    18. John Pastor Ansah & Shawn Tan Yi Wei & Tessa Lui Shi Min, 2020. "An evaluation of the impact of aggressive diabetes and hypertension management on chronic kidney diseases at the population level: a simulation analysis," System Dynamics Review, System Dynamics Society, vol. 36(4), pages 497-522, October.
    19. Hayfa Almutary & Nahla Tayyib, 2021. "Translation, Adaptation, and Validation of Chronic Kidney Disease Self-Management and Knowledge Instruments for People at Pre-Dialysis Stage in the Arab World," Clinical Nursing Research, , vol. 30(8), pages 1231-1240, November.
    20. Sara J Baart & Veerle Dam & Luuk J J Scheres & Johanna A A G Damen & René Spijker & Ewoud Schuit & Thomas P A Debray & Bart C J M Fauser & Eric Boersma & Karel G M Moons & Yvonne T van der Schouw & on, 2019. "Cardiovascular risk prediction models for women in the general population: A systematic review," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-14, January.

    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:pone00:0278729. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.