Establishment and Validation of a Predictive Model for Long-Term Severe Functional Tricuspid Regurgitation after Mitral Valve Replacement

Abstract

Background: The objective was to develop and validate an individualized nomogram to predict severe functional tricuspid regurgitation (S-FTR) after mitral valve replacement (MVR) via retrospective analysis of rheumatic heart disease (RHD) patients’ pre-clinical characteristics. Methods: Between 2001-2015, 442 MVR patients of RHD were examined. Transthoracic echocardiography detected S-FTR, and logistic regression model analyzed its independent predictors. R software established a nomogram prediction model, and Bootstrap determined its theoretical probability, which subsequently was compared with the actual patient probability to calculate the area under the curve (AUC) and calibration plots. Decision curve analysis (DCA) identified its clinical utility. Results: Ninety-six patients developed S-FTR during the follow-up period. Both uni- and multivariate analyses found significant correlations between S-FTR occurrence with gender, age, atrial fibrillation (AF), pulmonary arterial hypertension (PH), left atrial diameter (LAD), and tricuspid regurgitation area (TRA). The individualized nomogram model had the AUC of 0.99 in internal verification. Calibration test indicated high agreement of predicted and actual S-FTR onset. DCA also showed that utilization of those six aforementioned factors was clinically useful. Conclusion: The nomogram for the patient characteristics of age, gender, AF, PH, LAD, and TRA found that they were highly predictive for future S-FTR onset within 5 years. This predictive ability therefore allows clinicians to optimize postoperative patient care and avoid unnecessary tricuspid valve surgeries.