The Reverse Calcification Technique (RCT): A Quantitative Parametric Model of Aortic Stenosis Progression

Abstract

Abstract Introduction Aortic stenosis (AS) develops from calcific aortic valve disease (CAVD), which narrows the aortic valve opening as leaflet stiffness increases due to calcium deposition. This study extends the Reverse Calcification Technique (RCT) by incorporating a time dimension to develop a quantitative parametric model for patient-specific prediction of CAVD progression from sequential CT scans. Methods Seventeen pre-transcatheter aortic valve replacement (TAVR) patients underwent sequential CT scans (1.2–6.5 years); baseline aortic valve calcification (AVC) volumes: $$\approx$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>≈</mml:mo> </mml:math> 250 to $$\approx$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>≈</mml:mo> </mml:math> 1,600 mm 3 (cohort mean $$\approx$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>≈</mml:mo> </mml:math> 730 mm 3 at the first scan and $$\approx$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>≈</mml:mo> </mml:math> 920 mm 3 at the follow-up scan). A parametric model was developed using two approaches: forward prediction (mild to severe stages) and backward reconstruction (from severe to moderate stages). 34 test cases were assessed through alternating calibration and verification, with performance evaluated using Bland–Altman analysis, paired t-tests, and relative error calculations. Results For scan intervals under 3 years, forward prediction achieved a mean absolute error of 77 mm 3 (7.0% relative to a mean target volume of 918 mm 3 ) and backward reconstruction achieved 53 mm 3 (8.4% relative to 542 mm 3 ) within the inherent CT measurement uncertainty of 8–12%. For longer intervals (&gt; 3 years), relative errors increased to 16.8–21.6%. Individual errors ranged from 0.02% to 36.8%, with no systematic bias detected. Conclusion The quantitative parametric RCT model demonstrates feasibility for patient-specific estimation of CAVD progression and shows promise for optimizing follow-up intervals. External validation in larger independent cohorts and incorporation of patient-specific risk factors are required before clinical implementation.