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Related Experiment Video

Updated: Apr 21, 2026

An R-Based Landscape Validation of a Competing Risk Model
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Interpretable Survival Modeling for Mortality Risk Stratification in Heart Failure Using Cox Proportional Hazard

Christiana Raluca Dănciulescu1, Daniel-Robert Stănescu1, Dragoș Ovidiu Alexandru2

  • 1Doctoral School, University of Medicine and Pharmacy of Craiova, Romania.

Current Health Sciences Journal
|April 20, 2026
PubMed
Summary

Cox regression models effectively predict heart failure mortality risk using routine clinical data. Key factors include renal function, anemia, age, and ejection fraction for better patient stratification.

Keywords:
Heart failurecardiovascular prognosiscox proportional hazards modelmortality predictionrisk stratificationsurvival analysis

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Area of Science:

  • Cardiovascular Medicine
  • Biostatistics
  • Clinical Prognostics

Background:

  • Heart failure (HF) significantly contributes to patient morbidity and mortality.
  • Accurate prognostic models are crucial for clinical decision-making in HF management.
  • Statistical survival models are ideal for analyzing time-to-event data and handling censoring.

Purpose of the Study:

  • To develop and validate a clinically interpretable prognostic model for heart failure mortality.
  • To identify independent predictors of all-cause mortality in a heart failure cohort.

Main Methods:

  • Retrospective survival analysis of 299 heart failure patients.
  • Cox proportional hazards regression model for time-to-all-cause mortality analysis.
  • Internal validation using bootstrap resampling for model stability and discrimination.

Main Results:

  • 96 patients (32.1%) experienced mortality during follow-up.
  • The Cox model demonstrated moderate discriminative ability (concordance index ~0.70) post-validation.
  • Independent predictors identified: renal function (serum creatinine), anemia, age, hypertension, ejection fraction, and serum sodium.

Conclusions:

  • Cox proportional hazards regression provides a robust and interpretable method for HF mortality prediction.
  • The model, using routine variables, offers reproducible prognostic insights.
  • Supports practical risk stratification in cardiovascular research and clinical practice.