TY - JOUR
T1 - Validation of a novel predictive algorithm for kidney failure in patients suffering from chronic kidney disease
T2 - The prognostic reasoning system for chronic kidney disease (PROGRES-CKD)
AU - Bellocchio, Francesco
AU - Lonati, Caterina
AU - Titapiccolo, Jasmine Ion
AU - Nadal, Jennifer
AU - Meiselbach, Heike
AU - Schmid, Matthias
AU - Baerthlein, Barbara
AU - Tschulena, Ulrich
AU - Schneider, Markus
AU - Schultheiss, Ulla T.
AU - Barbieri, Carlo
AU - Moore, Christoph
AU - Steppan, Sonja
AU - Eckardt, Kai Uwe
AU - Stuard, Stefano
AU - Neri, Luca
N1 - Funding Information:
Conflicts of Interest: The results presented in this paper have not been published previously in whole or part, except in abstract format. L.N., J.I.T., F.B., S.S. (Sonja Steppan), S.S. (Stefano Stuard), C.M., C.B., U.T. are full time employees at Fresenius Medical Care. C.L. provided medical writing services on behalf of Fresenius Medical Care. H.M. reports grants from KfH Foundation of Preventive Medicine, and grants from German ministry of Education and Research. M.S. (Matthias Schmid) reports grants from Fresenius Medical Care during the conduct of the study. B.B. reports grants from the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung (www.bmbf.de), FKZ 01ER 0804, 01ER 0818, 01ER 0819, 01ER 0820 und 01ER 0821), and grants from Foundation for Preventive Medicine of the KfH (Kuratorium für Heimdialyse und Nierentransplantation e.V.– Stiftung Präventivmedizin; www.kfh-stiftung-praeventivmedizin.de). MSchneider reports grants from Fresenius Medical Care outside the submitted work. K.-U.E. reports grants from: Astra Zeneca, Bayer, Fresenius Medical Care, Vifor, and Amgen during the conduct of the study; personal fees from Akebia, Astellas, Astra Zeneca, Bayer, and Boehringer Ingelheim; and grants from Genzyme, Shire, and Vifor outside the submitted work. J.N. has no conflicts of interest to disclose. U.T.S. has no conflicts of interest to disclose.
Funding Information:
Acknowledgments: The GCKD study was supported by the German Ministry of Education and Research (Bundesministerium für Bildung und Forschung, FKZ 01ER 0804, 01ER 0818, 01ER 0819, 01ER 0820, and 01ER 0821), KfH Foundation for Preventive Medicine, Innovative Medicines Initiative 2 Joint Undertaking (BEAt-DKD, grant number 115974), and corporate sponsors (www.gckd.org).
Funding Information:
Funding: This research was funded by Fresenius Medical Care Deutschland GmbH.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Chronic kidney disease (CKD)
KW - End-stage kidney disease (ESKD)
KW - Kidney replacement therapy (KRT)
KW - Machine learning
KW - Naïve Bayes classifiers
KW - Precision medicine
KW - Risk prediction
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U2 - 10.3390/ijerph182312649
DO - 10.3390/ijerph182312649
M3 - Article
AN - SCOPUS:85120052392
VL - 18
JO - International Journal of Environmental Research and Public Health
JF - International Journal of Environmental Research and Public Health
SN - 1661-7827
IS - 23
M1 - 12649
ER -