IMR Press / RCM / Volume 23 / Issue 2 / DOI: 10.31083/j.rcm2302062
Open Access Original Research
Prediction of one- and two-year mortality after transcatheter aortic valve implantation: proposal of a fast sum-score system integrating a novel biomarker of cardiac extracellular matrix accumulation and fibrosis
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1 Department of Internal Medicine I, University Hospital Jena, Friedrich-Schiller-University Jena, 07740 Jena, Germany
2 Department of Cardiothoracic Surgery, University Hospital Jena, Friedrich-Schiller-University Jena, 07740 Jena, Germany
3 Department of Internal Medicine III, University Hospital Jena, Friedrich-Schiller-University Jena, 07740 Jena, Germany
4 Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, University of Düsseldorf, 40225 Düsseldorf, Germany
*Correspondence: Marcus.Franz@med.uni-jena.de (Marcus Franz)
Academic Editor: Giuseppe Santarpino
Rev. Cardiovasc. Med. 2022, 23(2), 62; https://doi.org/10.31083/j.rcm2302062
Submitted: 22 November 2021 | Revised: 4 January 2022 | Accepted: 11 January 2022 | Published: 14 February 2022
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
Abstract

Background: Prediction of long-term mortality in patients with severe symptomatic aortic valve stenosis undergoing transcatheter aortic valve implantation (TAVI) is still challenging but of great impact with respect to the selection of treatment strategy. Whereas most of the established scores address perioperative risk and/or short-term mortality, the aim of our current study was the integrative investigation of a multitude of patients’ characteristics including novel biomarkers of cardiovascular remodeling with respect to their value for the prediction of long-term mortality. Methods: In a first subset of patients (n = 122, identification group) a wide range of baseline characteristics were assigned to three clusters with 4 to 10 items each (classical clinical parameters; risk assessment scores; novel biomarkers of cardiovascular remodeling) and tested with respect to their predictive value for one-year mortality. Thereby, a sum-score system (Jena Mortality Score, JMS) was defined and tested in a larger collective of TAVI patients (n = 295, validation group) with respect to one- and two-year mortality prediction. Results: In the identification cohort, binary logistic regression analysis, with one-year mortality as dependent variable and the items per cluster as cofounders, revealed atrial fibrillation (Afib; odds ratio [OR] 7.583, 95% confidence interval [95% CI]: 2.051–28.040, p = 0.002), clinical frailty scale (CFS; OR 2.258, 95% CI: 1.262–4.039, p = 0.006) and Tissue-Inhibitor of Metalloproeinase-1 (TIMP-1; OR 1.006, 95% CI: 1.001–1.011, p = 0.019) as independent predictors of one-year mortality. These 3 parameters were integrated into a simplified sum-score as follows: presence of Afib (no = 0, yes = 1); dichotomized CFS (1 to 4 = 0; 5 to 9 = 1); TIMP-1 range (cut-off value 187.2 ng/mL; below = 0, above = 1). The resulting sum-score (JMS) ranged from 0 to 3. By binary logistic regression analysis in the validation cohort with one- and two-year mortality as dependent variable and Society of Thoracic Surgeons (STS) score (STS), staging of extra-valvular cardiac damage (stage), presence of high gradient aortic stenosis (HGAS), EQ visual analogue scale score (EQ-VAS) and JMS as cofounders, besides STS score, only JMS could be proven to serve as independent predictor of both, one-year (OR 1.684, 95% CI: 1.094–2.592, p = 0.018) and two-year (OR 1.711, 95% CI: 1.136–2.576, p = 0.010) mortality. After dichotomization of patients into a low-risk and a high-risk group according to JMS, Kaplan-Meier survival analysis displayed a significant survival benefit for the low-risk group after one and two years (p < 0.001). Conclusion: JMS, including TIMP-1 as a novel biomarker of cardiac extracellular matrix accumulation and fibrosis, could serve as a novel simple tool to assess long-term mortality risk after TAVI and might thereby contribute to a more precise stratification of individual risk.

Keywords
TAVI
risk prediction
mortality
biomarker
TIMP-1
Figures
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