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The Cardiovascular Literature-Based Risk Algorithm (CALIBRA): Predicting Cardiovascular Events in Patients With

Luca Neri1, Caterina Lonati2, Jasmine Ion Titapiccolo1

  • 1Clinical and Data Intelligence Systems-Advanced Analytics, Fresenius Medical Care Deutschland GmbH, Vaiano Cremasco, Italy.

Frontiers in Nephrology
|September 7, 2023
PubMed

Insights

The new CArdiovascular, LIterature-Based, Risk Algorithm (CALIBRA) accurately predicts cardiovascular risk in chronic kidney disease (CKD) patients. CALIBRA outperforms existing models, offering improved risk stratification for better clinical management.

Area of Science:

  • Nephrology
  • Cardiology
  • Biostatistics

Background:

  • Cardiovascular disease (CVD) is the leading cause of mortality in chronic kidney disease (CKD) patients.
  • Current risk prediction models are often inadequate for this population.
  • Accurate CVD risk assessment is crucial for effective clinical management of CKD.

Purpose of the Study:

  • To develop and validate a novel, literature-based risk prediction model for cardiovascular (CV) hospitalizations in CKD patients.
  • To assess the performance of the new model, named CArdiovascular, LIterature-Based, Risk Algorithm (CALIBRA), against existing CV risk scores.

Main Methods:

  • A literature-based, naïve-bayes model (CALIBRA) was developed incorporating 31 traditional and CKD-specific risk factors.
  • CALIBRA was validated in two independent CKD cohorts: the FMC NephroCare (EuCliD®) and the German Chronic Kidney Disease (GCKD) study.
  • Model performance was evaluated using c-statistics and calibration, with comparisons to Framingham Heart Study (FHS), ASCVD, and INDANA risk scores.

Main Results:

  • CALIBRA demonstrated good discrimination in both validation cohorts (AUC 0.79 in EuCliD®, 0.73 in GCKD).
  • CALIBRA showed significantly improved accuracy over FHS, ASCVD, and INDANA in both cohorts (ΔAUC ranging from -0.04 to -0.22).
  • The model maintained accuracy even with missing data, indicating robustness in real-world settings.

Conclusions:

  • CALIBRA offers accurate and robust cardiovascular risk stratification for CKD patients.
  • The model provides superior predictive accuracy compared to established CV risk scores.
  • CALIBRA's generalizability across diverse CKD populations and clinical settings is supported by the findings.
Abstract

Keywords:
cardiovascular eventscardiovascular risk scorechronic kidney diseasehospitalizationmachine learningpersonalized medicine

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