دورية أكاديمية
أعرض في EDS

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).

التفاصيل البيبلوغرافية
العنوان: 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).
المؤلفون: Bellocchio F; Clinical & Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, 26020 Vaiano Cremasco, Italy., Lonati C; Center for Preclinical Research, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy., Ion Titapiccolo J; Clinical & Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, 26020 Vaiano Cremasco, Italy., Nadal J; Department of Medical Biometry, Informatics, and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, 53113 Bonn, Germany., Meiselbach H; Department of Nephrology and Hypertension, Friedrich-Alexander University of Erlangen-Nürnberg, 91054 Erlangen, Germany., Schmid M; Department of Medical Biometry, Informatics, and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, 53113 Bonn, Germany., Baerthlein B; Medical Centre for Information and Communication Technology (MIK), University Hospital Erlangen, 91054 Erlangen, Germany., Tschulena U; Fresenius Medical Care, Deutschland GmbH, 61352 Bad Homburg, Germany., Schneider M; Department of Medical Biometry, Informatics, and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, 53113 Bonn, Germany., Schultheiss UT; 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., Barbieri C; Fresenius Medical Care, Deutschland GmbH, 61352 Bad Homburg, Germany., Moore C; Fresenius Medical Care, Deutschland GmbH, 61352 Bad Homburg, Germany., Steppan S; Fresenius Medical Care, Deutschland GmbH, 61352 Bad Homburg, Germany., Eckardt KU; 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., Stuard S; Fresenius Medical Care, Deutschland GmbH, 61352 Bad Homburg, Germany., Neri L; Clinical & Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, 26020 Vaiano Cremasco, Italy.
المصدر: International journal of environmental research and public health [Int J Environ Res Public Health] 2021 Nov 30; Vol. 18 (23). Date of Electronic Publication: 2021 Nov 30.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101238455 Publication Model: Electronic Cited Medium: Internet ISSN: 1660-4601 (Electronic) Linking ISSN: 16604601 NLM ISO Abbreviation: Int J Environ Res Public Health Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Basel : MDPI, c2004-
مواضيع طبية MeSH: Kidney Failure, Chronic*/diagnosis , Renal Insufficiency* , Renal Insufficiency, Chronic*/diagnosis , Renal Insufficiency, Chronic*/epidemiology, Algorithms ; Bayes Theorem ; Disease Progression ; Humans ; Prognosis ; Risk Assessment
مستخلص: 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.
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فهرسة مساهمة: 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
تواريخ الأحداث: Date Created: 20211210 Date Completed: 20211214 Latest Revision: 20231108
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC8656741
DOI: 10.3390/ijerph182312649
PMID: 34886378
قاعدة البيانات: MEDLINE
الوصف
تدمد:1660-4601
DOI:10.3390/ijerph182312649