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Multiple Biomarker Panel to Screen for Severe Aortic Stenosis: Results from the CASABLANCA Study

Open Heart

November 2018

Highlights & Key Findings

  • Calcific aortic stenosis (AS) is the most common cause of valvular heart disease in the Western world, present in >20% of older adults. In the US alone, more than five million Americans are diagnosed with heart valve disease each year.  In those who have AS symptoms, without repair, the chance of death at five years is about 50% and at 10 years is about 90%.  Studies show that severe AS is undertreated.


  • Of 1244 subjects enrolled in the CASABLANCA Study (Clinical Trials.Gov NCT00842868), 80 (6.4%) had severe AS (defined as aortic valve area [AVA] <1.0 cm2).


  • Machine Learning (Subset of AI) & Big Data. A panel of 109 proteins was measured in blood obtained at the time of the procedure. Statistical learning methods were used to identify biomarkers and clinical parameters that associate with severe AS. A diagnostic model incorporating clinical and biomarker results was developed and evaluated using Monte Carlo cross-validation. The ultimate result of this process was a final test panel and scoring system.


  • Final Test Panel has 1 clinical variable (Age) and 3 biomarkers (N-terminal pro-B-type natriuretic peptide, von Willebrand Factor, and fetuin-A).  The model had good discrimination for severe AS (OR=5.9, 95%CI 3.5-10.1, P<0.001) with an area under the curve (AUC) of 0.76.  At optimal model cut-off, the results were:  76% sensitivity; 65% specificity; 13% positive predictive value; and 98% negative predictive value.


  • In a prospective cohort study, a model was developed and validated that identified severe AS.  The model had good discrimination for severe AS and robust sensitivity, specificity, and negative predictive value.

  • This model, composed of age and three circulating biomarkers, may supplement currently available diagnostic methods to facilitate the diagnosis of severe AS.

  • Clinically, severe aortic valve stenosis (AS) progresses via insidious processes and can be challenging to correctly diagnose.

  • Conclusions and Relevance:  a novel, multiple-biomarker approach has been described for diagnostic evaluation of severe AS.

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