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TRI-SCORE: a single-centre validation study.

Alessandra Sala1, Davide Carino1, Roberto Lorusso2,3

  • 1Vita-Salute San Raffaele University, Department of Cardiac Surgery, IRCCS San Raffaele Hospital, Milan, Italy.

Interdisciplinary Cardiovascular and Thoracic Surgery
|May 22, 2023
PubMed
Summary

The TRI-SCORE effectively predicts both in-hospital and long-term mortality in patients undergoing isolated tricuspid valve surgery (ITVS). This external validation confirms its robust performance for risk stratification in ITVS patients.

Keywords:
Isolated tricuspid valve surgeryRisk scoresTRI-SCORETricuspid regurgitationTricuspid valve disease

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Area of Science:

  • Cardiovascular Surgery
  • Surgical Risk Assessment

Background:

  • The TRI-SCORE is a novel risk assessment tool for isolated tricuspid valve surgery (ITVS).
  • External validation is crucial to confirm its predictive accuracy in diverse patient populations.

Purpose of the Study:

  • To externally validate the TRI-SCORE for predicting in-hospital mortality after ITVS.
  • To assess the TRI-SCORE's ability to predict long-term mortality following ITVS.

Main Methods:

  • Retrospective analysis of 176 patients undergoing ITVS between March 1997 and March 2021.
  • TRI-SCORE calculation, receiver operating characteristic curve analysis for discrimination, and Brier score for accuracy.
  • COX regression analysis to evaluate the association between TRI-SCORE and long-term mortality.

Main Results:

  • The TRI-SCORE demonstrated high discrimination (AUC 0.82) and accuracy (Brier score 0.054) for in-hospital mortality.
  • The score also showed excellent performance for long-term mortality prediction (HR 1.47, P < 0.001) with high discrimination and accuracy at 10 years.
  • A TRI-SCORE cutoff of 5 was identified for increased risk in ITVS.

Conclusions:

  • The TRI-SCORE is a validated tool for predicting in-hospital mortality in ITVS patients.
  • The TRI-SCORE also demonstrates strong performance in predicting long-term mortality after ITVS, aiding clinical decision-making.