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. 2018 Nov 1;5(2):e000916.
doi: 10.1136/openhrt-2018-000916. eCollection 2018.

Multiple biomarker panel to screen for severe aortic stenosis: results from the CASABLANCA study

Sammy Elmariah  1   2 , Cian McCarthy  3 , Nasrien Ibrahim  3 , Deborah Furman  1 , Renata Mukai  1 , Craig Magaret  4 , Rhonda Rhyne  4 , Grady Barnes  4 , Roland R J van Kimmenade  5   6 , James L Januzzi Jr  1   2
Affiliations

Multiple biomarker panel to screen for severe aortic stenosis: results from the CASABLANCA study

Sammy Elmariah et al. Open Heart. .

Abstract

Objective: Severe aortic valve stenosis (AS) develops via insidious processes and can be challenging to correctly diagnose. We sought to develop a circulating biomarker panel to identify patients with severe AS.

Methods: We enrolled study participants undergoing coronary or peripheral angiography for a variety of cardiovascular diseases at a single academic medical centre. A panel of 109 proteins were 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.

Results: Of 1244 subjects (age 66.4±11.5 years, 28.7% female), 80 (6.4%) had severe AS (defined as aortic valve area (AVA) <1.0 cm2). A final model included age, 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 to 10.1, p<0.001) with an area under the curve of 0.76 insample and 0.74 with cross-validation. A diagnostic score was generated. Higher prevalence of severe AS was noted in those with higher scores, such that 1.6% of those with a score of 1 had severe AS compared with 15.3% with a score of 5 (p<0.001), and score values were inversely correlated with AVA (r=-0.35; p<0.001). At optimal model cut-off, we found 76% sensitivity, 65% specificity, 13% positive predictive value and 98% negative predictive value.

Conclusions: We describe a novel, multiple biomarker approach for diagnostic evaluation of severe AS.

Trial registration number: NCT00842868.

Keywords: aortic stenosis; biomarkers; valvular heart disease.

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Conflict of interest statement

Competing interests: SE receives institutional research support from Siemens and Boehringer Ingelheim Pharmaceuticals and consulting fees from Medtronic and Edwards Lifesciences. The remaining authors have no relationships to disclose. JLJ has received grant support from Abbott Diagnostics, Roche Diagnostics, Singulex and Prevencio; consulting income from Roche Diagnostics, Critical Diagnostics, Philips and Novartis; and participates in clinical endpoint committees/data safety monitoring boards for Novartis, Amgen, AbbVie, Pfizer, Janssen and Boehringer Ingelheim. CM is a consultant to Prevencio. RR and GB are employees of Prevencio. Siemens Diagnostics had no involvement in the present study design, collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. All other authors have reported that they have no relationships relevant to the content of this paper to disclose.

Figures

Figure 1
Figure 1
Distribution of NT-proBNP, von Willebrand factor and fetuin-A in patients with and without aortic stenosis. Levels of circulating biomarkers are shown in patients without significant AS, moderate AS and severe AS. Dark lines indicate median values and grey bar the IQR. Error bars indicate the range of the rest of the distribution to 1.5 times the IQR. AS, aortic stenosis; NT-proBNP, N-terminal pro-B-type natriuretic peptide.
Figure 2
Figure 2
Prevalence of severe aortic stenosis (AS) within a quintile-based five-level diagnostic score. Increasing prevalence of severe AS is noted at higher scores, such that 1.6% of those with a score of 1 (lowest risk) had severe AS compared with 15.3% with a score of 5 (highest risk) (p<0.001).
Figure 3
Figure 3
Receiver operating characteristic curve for aortic stenosis (AS) score. The AS score demonstrated good discrimination with an insample area under the curve (AUC) of 0.76. The sensitivity, specificity, positive predictive value and negative predictive value at the optimal cut-off are depicted.
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