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)

Francesco Bellocchio, Caterina Lonati, Jasmine Ion Titapiccolo, Jennifer Nadal, Heike Meiselbach, Matthias Schmid, Barbara Baerthlein, Ulrich Tschulena, Markus Schneider, Ulla T. Schultheiss, Carlo Barbieri, Christoph Moore, Sonja Steppan, Kai Uwe Eckardt, Stefano Stuard, Luca Neri

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number12649
JournalInternational Journal of Environmental Research and Public Health
Volume18
Issue number23
DOIs
Publication statusPublished - Dec 1 2021

Keywords

  • Artificial intelligence
  • Chronic kidney disease (CKD)
  • End-stage kidney disease (ESKD)
  • Kidney replacement therapy (KRT)
  • Machine learning
  • Naïve Bayes classifiers
  • Precision medicine
  • Risk prediction

ASJC Scopus subject areas

  • Pollution
  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

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