PMID- 37675027
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230908
IS  - 2813-0626 (Electronic)
IS  - 2813-0626 (Linking)
VI  - 2
DP  - 2022
TI  - The Cardiovascular Literature-Based Risk Algorithm (CALIBRA): Predicting 
      Cardiovascular Events in Patients With Non-Dialysis Dependent Chronic Kidney 
      Disease.
PG  - 922251
LID - 10.3389/fneph.2022.922251 [doi]
LID - 922251
AB  - BACKGROUND AND OBJECTIVES: Cardiovascular (CV) disease is the main cause of 
      morbidity and mortality in patients suffering from chronic kidney disease (CKD). 
      Although it is widely recognized that CV risk assessment represents an essential 
      prerequisite for clinical management, existing prognostic models appear not to be 
      entirely adequate for CKD patients. We derived a literature-based, naive-bayes 
      model predicting the yearly risk of CV hospitalizations among patients suffering 
      from CKD, referred as the CArdiovascular, LIterature-Based, Risk Algorithm 
      (CALIBRA). METHODS: CALIBRA incorporates 31 variables including traditional and 
      CKD-specific risk factors. It was validated in two independent CKD populations: 
      the FMC NephroCare cohort (European Clinical Database, EuCliD((R))) and the German 
      Chronic Kidney Disease (GCKD) study prospective cohort. CALIBRA performance was 
      evaluated by c-statistics and calibration charts. In addition, CALIBRA 
      discrimination was compared with that of three validated tools currently used for 
      CV prediction in CKD, namely the Framingham Heart Study (FHS) risk score, the 
      atherosclerotic cardiovascular disease risk score (ASCVD), and the Individual 
      Data Analysis of Antihypertensive Intervention Trials (INDANA) calculator. 
      Superiority was defined as a DeltaAUC>0.05. RESULTS: CALIBRA showed good 
      discrimination in both the EuCliD((R)) medical registry (AUC 0.79, 95%CI 0.76-0.81) 
      and the GCKD cohort (AUC 0.73, 95%CI 0.70-0.76). CALIBRA demonstrated improved 
      accuracy compared to the benchmark models in EuCliD((R)) (FHS: DeltaAUC=-0.22, p<0.001; 
      ASCVD: DeltaAUC=-0.17, p<0.001; INDANA: DeltaAUC=-0.14, p<0.001) and GCKD (FHS: 
      DeltaAUC=-0.16, p<0.001; ASCVD: DeltaAUC=-0.12, p<0.001; INDANA: DeltaAUC=-0.04, p<0.001) 
      populations. Accuracy of the CALIBRA score was stable also for patients showing 
      missing variables. CONCLUSION: CALIBRA provides accurate and robust 
      stratification of CKD patients according to CV risk and allows score calculations 
      with improved accuracy compared to established CV risk scores also in real-world 
      clinical cohorts with considerable missingness rates. Our results support the 
      generalizability of CALIBRA across different CKD populations and clinical 
      settings.
CI  - Copyright (c) 2022 Neri, Lonati, Titapiccolo, Nadal, Meiselbach, Schmid, 
      Baerthlein, Tschulena, Schneider, Schultheiss, Barbieri, Moore, Steppan, Eckardt, 
      Stuard and Bellocchio.
FAU - Neri, Luca
AU  - Neri L
AD  - Clinical and Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care 
      Deutschland GmbH, Vaiano Cremasco, Italy.
FAU - Lonati, Caterina
AU  - Lonati C
AD  - Center for Preclinical Research, Fondazione IRCCS Ca' Granda Ospedale Maggiore 
      Policlinico, Milan, Italy.
FAU - Titapiccolo, Jasmine Ion
AU  - Titapiccolo JI
AD  - Clinical and Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care 
      Deutschland GmbH, Vaiano Cremasco, Italy.
FAU - Nadal, Jennifer
AU  - Nadal J
AD  - Department of Medical Biometry, Informatics, and Epidemiology (IMBIE), Faculty of 
      Medicine, University of Bonn, Bonn, Germany.
FAU - Meiselbach, Heike
AU  - Meiselbach H
AD  - Department of Nephrology and Hypertension, Universitatsklinikum Erlangen, 
      Friedrich-Alexander Universitat Erlangen-Nurnber, Erlangen, Germany.
FAU - Schmid, Matthias
AU  - Schmid M
AD  - Department of Medical Biometry, Informatics, and Epidemiology (IMBIE), Faculty of 
      Medicine, University of Bonn, Bonn, Germany.
FAU - Baerthlein, Barbara
AU  - Baerthlein B
AD  - Medical Centre for Information and Communication Technology (MIK), University 
      Hospital Erlangen, Erlangen, Germany.
FAU - Tschulena, Ulrich
AU  - Tschulena U
AD  - Fresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany.
FAU - Schneider, Markus P
AU  - Schneider MP
AD  - Department of Nephrology and Hypertension, Universitatsklinikum Erlangen, 
      Friedrich-Alexander Universitat Erlangen-Nurnber, Erlangen, Germany.
FAU - Schultheiss, Ulla T
AU  - Schultheiss UT
AD  - Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - 
      University of Freiburg, Freiburg, Germany.
AD  - Department of Medicine IV - Nephrology and Primary Care, Faculty of Medicine and 
      Medical Center - University of Freiburg, Freiburg, Germany.
FAU - Barbieri, Carlo
AU  - Barbieri C
AD  - Fresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany.
FAU - Moore, Christoph
AU  - Moore C
AD  - Fresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany.
FAU - Steppan, Sonia
AU  - Steppan S
AD  - Fresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany.
FAU - Eckardt, Kai-Uwe
AU  - Eckardt KU
AD  - Department of Nephrology and Hypertension, Universitatsklinikum Erlangen, 
      Friedrich-Alexander Universitat Erlangen-Nurnber, Erlangen, Germany.
AD  - Department of Nephrology and Medical Intensive Care, Charite Universitatsmedizin 
      Berlin, Berlin, Germany.
FAU - Stuard, Stefano
AU  - Stuard S
AD  - Fresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany.
FAU - Bellocchio, Francesco
AU  - Bellocchio F
AD  - Clinical and Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care 
      Deutschland GmbH, Vaiano Cremasco, Italy.
LA  - eng
PT  - Journal Article
DEP - 20220712
PL  - Switzerland
TA  - Front Nephrol
JT  - Frontiers in nephrology
JID - 9918469487906676
PMC - PMC10479593
OTO - NOTNLM
OT  - cardiovascular events
OT  - cardiovascular risk score
OT  - chronic kidney disease
OT  - hospitalization
OT  - machine learning
OT  - personalized medicine
COIS- LN, JT, FB, SoS, StS, CM, CB, and UT are full time employees at Fresenius Medical 
      Care. CL provided medical writing services on behalf of Fresenius Medical Care. 
      HM reports grants from KfH Foundation of Preventive Medicine, and grants from 
      German ministry of Education and Research. MatS reports grants from Fresenius 
      Medical Care during the conduct of the study. BB reports grants from the Federal 
      Ministry of Education and Research (Bundesministerium fur 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 fur 
      Heimdialyse und Nierentransplantation e.V.-Stiftung Praventivmedizin; 
      www.kfh-stiftung-praeventivmedizin.de). MarS reports grants from Fresenius 
      Medical Care outside the submitted work. K-UE 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. 
      The remaining authors declare that the research was conducted in the absence of 
      any commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2022/07/12 00:00
MHDA- 2022/07/12 00:01
PMCR- 2022/07/12
CRDT- 2023/09/07 04:13
PHST- 2022/04/17 00:00 [received]
PHST- 2022/05/20 00:00 [accepted]
PHST- 2022/07/12 00:01 [medline]
PHST- 2022/07/12 00:00 [pubmed]
PHST- 2023/09/07 04:13 [entrez]
PHST- 2022/07/12 00:00 [pmc-release]
AID - 10.3389/fneph.2022.922251 [doi]
PST - epublish
SO  - Front Nephrol. 2022 Jul 12;2:922251. doi: 10.3389/fneph.2022.922251. eCollection 
      2022.