Literature DB >> 35210045

Solving the Pulmonary Hypertension Paradox in Patients With Severe Tricuspid Regurgitation by Employing Artificial Intelligence.

Vera Fortmeier1, Mark Lachmann2, Maria I Körber3, Matthias Unterhuber4, Moritz von Scheidt5, Elena Rippen2, Gerhard Harmsen6, Muhammed Gerçek1, Kai Peter Friedrichs1, Fabian Roder1, Tanja K Rudolph1, Shinsuke Yuasa7, Michael Joner5, Karl-Ludwig Laugwitz2, Stephan Baldus3, Roman Pfister3, Philipp Lurz7, Volker Rudolph8.   

Abstract

OBJECTIVES: This study aimed to improve echocardiographic assessment of pulmonary hypertension (PH) in patients presenting with severe tricuspid regurgitation (TR).
BACKGROUND: Echocardiographic assessment of PH in patients with severe TR carries several pitfalls for underestimation, hence concealing the true severity of PH in very sick patients in particular, and ultimately obscuring the impact of PH on survival after transcatheter tricuspid valve intervention (TTVI).
METHODS: All patients in this study underwent TTVI for severe TR between 2016 and 2020. To predict the mean pulmonary artery pressure (mPAP) solely based on echocardiographic parameters, we trained an extreme gradient boosting (XGB) algorithm. The derivation cohort was constituted by 116 out of 162 patients with both echocardiography and right heart catheterization data, preprocedurally obtained, from a bicentric registry. Moreover, 142 patients from an independent institution served for external validation.
RESULTS: Systolic pulmonary artery pressure was consistently underestimated by echocardiography in comparison to right heart catheterization (40.3 ± 15.9 mm Hg vs 44.1 ± 12.9 mm Hg; P = 0.0066), and the assessment was most discrepant among patients with severe defects of the tricuspid valve and impaired right ventricular systolic function. Using 9 echocardiographic parameters as input variables, an XGB algorithm could reliably predict mPAP levels (R = 0.96, P < 2.2 × 10-16). Moreover, patients with elevations in predicted mPAP levels ≥29.9 mm Hg showed significantly reduced 2-year survival after TTVI (58.3% [95% CI: 41.7%-81.6%] vs 78.8% [95% CI: 68.7%-90.5%]; P = 0.026). Importantly, the poor prognosis associated with elevation in predicted mPAP levels was externally confirmed (HR for 2-year mortality: 2.9 [95% CI: 1.5-5.7]; P = 0.002).
CONCLUSIONS: PH in patients with severe TR can be reliably assessed based on echocardiographic parameters in conjunction with an XGB algorithm, and elevations in predicted mPAP levels translate into increased mortality after TTVI.
Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  artificial intelligence; pulmonary hypertension; transcatheter tricuspid valve intervention; tricuspid regurgitation

Mesh:

Year:  2022        PMID: 35210045     DOI: 10.1016/j.jcin.2021.12.043

Source DB:  PubMed          Journal:  JACC Cardiovasc Interv        ISSN: 1936-8798            Impact factor:   11.195


  1 in total

1.  Artificial intelligence-enabled phenotyping of patients with severe aortic stenosis: on the recovery of extra-aortic valve cardiac damage after transcatheter aortic valve replacement.

Authors:  Mark Lachmann; Elena Rippen; Tibor Schuster; Erion Xhepa; Moritz von Scheidt; Teresa Trenkwalder; Costanza Pellegrini; Tobias Rheude; Amelie Hesse; Anja Stundl; Gerhard Harmsen; Shinsuke Yuasa; Heribert Schunkert; Adnan Kastrati; Karl-Ludwig Laugwitz; Michael Joner; Christian Kupatt
Journal:  Open Heart       Date:  2022-10
  1 in total

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