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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)

Author

Listed:
  • Francesco Bellocchio

    (Clinical & Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, 26020 Vaiano Cremasco, Italy)

  • Caterina Lonati

    (Center for Preclinical Research, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy)

  • Jasmine Ion Titapiccolo

    (Clinical & Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, 26020 Vaiano Cremasco, Italy)

  • Jennifer Nadal

    (Department of Medical Biometry, Informatics, and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, 53113 Bonn, Germany)

  • Heike Meiselbach

    (Department of Nephrology and Hypertension, Friedrich-Alexander University of Erlangen-Nürnberg, 91054 Erlangen, Germany)

  • Matthias Schmid

    (Department of Medical Biometry, Informatics, and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, 53113 Bonn, Germany)

  • Barbara Baerthlein

    (Medical Centre for Information and Communication Technology (MIK), University Hospital Erlangen, 91054 Erlangen, Germany)

  • Ulrich Tschulena

    (Fresenius Medical Care, Deutschland GmbH, 61352 Bad Homburg, Germany)

  • Markus Schneider

    (Department of Medical Biometry, Informatics, and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, 53113 Bonn, Germany)

  • Ulla T. Schultheiss

    (Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79085 Freiburg, Germany
    Department of Medicine IV–Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79085 Freiburg, Germany)

  • Carlo Barbieri

    (Fresenius Medical Care, Deutschland GmbH, 61352 Bad Homburg, Germany)

  • Christoph Moore

    (Fresenius Medical Care, Deutschland GmbH, 61352 Bad Homburg, Germany)

  • Sonja Steppan

    (Fresenius Medical Care, Deutschland GmbH, 61352 Bad Homburg, Germany)

  • Kai-Uwe Eckardt

    (Department of Nephrology and Hypertension, Friedrich-Alexander University of Erlangen-Nürnberg, 91054 Erlangen, Germany
    Department of Nephrology and Medical Intensive Care, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany)

  • Stefano Stuard

    (Fresenius Medical Care, Deutschland GmbH, 61352 Bad Homburg, Germany)

  • Luca Neri

    (Clinical & Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, 26020 Vaiano Cremasco, Italy)

Abstract

Current equation-based risk stratification algorithms for kidney failure (KF) may have limited applicability in real world settings, where missing information may impede their computation for a large share of patients, hampering one from taking full advantage of the wealth of information collected in electronic health records. To overcome such limitations, we trained and validated the Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD), a novel algorithm predicting end-stage kidney disease (ESKD). PROGRES-CKD is a naïve Bayes classifier predicting ESKD onset within 6 and 24 months in adult, stage 3-to-5 CKD patients. PROGRES-CKD trained on 17,775 CKD patients treated in the Fresenius Medical Care (FMC) NephroCare network. The algorithm was validated in a second independent FMC cohort ( n = 6760) and in the German Chronic Kidney Disease (GCKD) study cohort ( n = 4058). We contrasted PROGRES-CKD accuracy against the performance of the Kidney Failure Risk Equation (KFRE). Discrimination accuracy in the validation cohorts was excellent for both short-term (stage 4–5 CKD, FMC: AUC = 0.90, 95%CI 0.88–0.91; GCKD: AUC = 0.91, 95% CI 0.86–0.97) and long-term (stage 3–5 CKD, FMC: AUC = 0.85, 95%CI 0.83–0.88; GCKD: AUC = 0.85, 95%CI 0.83–0.88) forecasting horizons. The performance of PROGRES-CKD was non-inferior to KFRE for the 24-month horizon and proved more accurate for the 6-month horizon forecast in both validation cohorts. In the real world setting captured in the FMC validation cohort, PROGRES-CKD was computable for all patients, whereas KFRE could be computed for complete cases only (i.e., 30% and 16% of the cohort in 6- and 24-month horizons). PROGRES-CKD accurately predicts KF onset among CKD patients. Contrary to equation-based scores, PROGRES-CKD extends to patients with incomplete data and allows explicit assessment of prediction robustness in case of missing values. PROGRES-CKD may efficiently assist physicians’ prognostic reasoning in real-life applications.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12649-:d:692274
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Tim Hulsen, 2022. "Data Science in Healthcare: COVID-19 and Beyond," IJERPH, MDPI, vol. 19(6), pages 1-4, March.

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