Evidence map›Paper›PMID 37675027›Full record

ArticleFrontiers in nephrology2022

The Cardiovascular Literature-Based Risk Algorithm (CALIBRA): Predicting Cardiovascular Events in Patients With Non-Dialysis Dependent Chronic Kidney Disease.

Luca Neri, Caterina Lonati, Jasmine Ion Titapiccolo, Jennifer Nadal, Heike Meiselbach, Matthias Schmid, Barbara Baerthlein, Ulrich Tschulena, Markus P Schneider, Ulla T Schultheiss and 6 more

Open access · diamondAbstract read
In one paragraph

Article in Frontiers in nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 7 institutions in 2 countries.

Luca NeriClinical and Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, Vaiano Cremasco, Italy.
Caterina LonatiCenter for Preclinical Research, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Jasmine Ion TitapiccoloClinical and Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, Vaiano Cremasco, Italy.
Jennifer NadalDepartment of Medical Biometry, Informatics, and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany.
Heike MeiselbachDepartment of Nephrology and Hypertension, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnber, Erlangen, Germany.
Matthias SchmidDepartment of Medical Biometry, Informatics, and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany.
Barbara BaerthleinMedical Centre for Information and Communication Technology (MIK), University Hospital Erlangen, Erlangen, Germany.
Ulrich TschulenaFresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany.
Markus P SchneiderDepartment of Nephrology and Hypertension, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnber, Erlangen, Germany.
Ulla T SchultheissInstitute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.
Carlo BarbieriFresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany.
Christoph MooreFresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany.
Sonia SteppanFresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany.
Kai-Uwe EckardtDepartment of Nephrology and Hypertension, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnber, Erlangen, Germany.
Stefano StuardFresenius Medical Care, Deutschland GmbH, Bad Homburg, Germany.
Francesco BellocchioClinical and Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, Vaiano Cremasco, Italy.
Fresenius Medical Care (Germany) · DEUniversitätsklinikum Erlangen · DECharité - Universitätsmedizin Berlin · DEFondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico · ITInstitut für Medizinische Biometrie, Informatik und Epidemiologie · DEUniversity of Bonn · DEUniversity of Freiburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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, naïve-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 Results: CALIBRA showed good discrimination in both the EuCliD 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.

Indexed as

cardiovascular eventscardiovascular risk scorechronic kidney diseasehospitalizationmachine learningpersonalized medicine

Identifiers

PMID37675027
PMCPMC10479593
OpenAlexW4285090659

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.