A novel method to predict mortality and length of stay after transfemoral transcatheter aortic valve implantation

  • Background and Objectives: We tested if a novel combination of predictors could improve the accuracy of outcome prediction after transfemoral transcatheter aortic valve implantation (TAVI). Materials and Methods: This prospective study recruited 169 participants (49% female; median age 81 years). The primary endpoint was midterm mortality; secondary endpoints were acute Valve Academic Research Consortium (VARC)-3 complication rate and post-TAVI in-hospital length of stay (LoS). EuroSCORE II (ESII), comorbidities (e.g., coronary artery disease), eGFR (estimated glomerular filtration rate; based on cystatin C), hemoglobin, creatinine, N-Terminal pro-Brain Natriuretic Peptide (NTproBNP) levels and patient-reported outcome measures (PROMs, namely EuroQol-5-Dimension-5-Levels, EQ5D5L; Kansas City Cardiomyopathy Questionnaire, KCCQ; clinical frailty scale, CFS) at baseline were tested as predictors. Regression (uni- and multi-variate Cox; linear; binary logistic) and receiver operating characteristic (ROC)-curve analysis were applied. Results: Within a median follow-up of 439 (318–585) days, 12 participants died (7.1%). Independent predictors of mortality using multivariate Cox regression were baseline eGFR (p = 0.001) and KCCQ (p = 0.037). Based on these predictors, a Linear Prediction Score (LPS1) was calculated. The LPS1-area under the curve (AUC)-value (0.761) was significantly higher than the ESII-AUC value (0.597; p = 0.035). Independent predictors for LoS > 6 days (the median LoS) were eGFR (p = 0.028), NTproBNP (p = 0.034), and EQ5D5L values (p = 0.002); a respective calculated LPS2 provided an AUC value of 0.677 (p < 0.001). Eighty participants (47.3%) experienced complications. Male sex predicted complications only in the univariate analysis. Conclusions: The combination of KCCQ and eGFR can better predict midterm mortality than ES II alone. Combining eGFR, NTproBNP, and EQ5D5L can reliably predict LoS after TAVI. This novel method improves personalized TAVI risk stratification and hence may help reduce post-TAVI risk.

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Author:Maria ZisiopoulouORCiDGND, Alexander BerkowitschGND, Philipp SeppeltORCiDGND, Andreas M. ZeiherORCiDGND, Mariuca Vasa-NicoteraORCiDGND
URN:urn:nbn:de:hebis:30:3-825227
DOI:https://doi.org/10.3390/medicina57121332
ISSN:1648-9144
Parent Title (English):Medicina
Publisher:Kaunas Univ. of Medicine
Place of publication:Kaunas
Document Type:Article
Language:English
Date of Publication (online):2021/12/06
Date of first Publication:2021/12/06
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2024/02/27
Tag:EQ5D5L; KCCQ; TAVI; aortic valve stenosis; biomarkers; outcomes; personalized; prediction
Volume:57
Issue:12, art. 1332
Article Number:1332
Page Number:11
First Page:1
Last Page:11
HeBIS-PPN:517874601
Institutes:Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International