To the Editor,
I read with great interest the article by Xiao et al., “A composite index for predicting improvement of mitral regurgitation in patients with multivalvular heart disease after transcatheter aortic valve replacement” (Front Cardiovasc Med. 2025;12:1679115). The authors present an index combining persistent atrial fibrillation, eccentric mitral regurgitation, and interventricular septal thickness to predict improvement after TAVI. While the topic is clinically relevant, several methodological issues limit the reliability and generalizability of their findings.
First, the development of a multivariable predictive model using only 53 patients creates a substantial risk of overfitting. According to TRIPOD recommendations, prognostic models should meet adequate sample-size requirements, with at least 10–20 events per variable to ensure model stability (1). In this study, the improvement event occurred in about 36 patients, which is insufficient for a multi-parameter model and strongly predisposes to optimistic estimates.
Second, the authors report an AUC of 0.909 and characterize it as “outstanding discriminative ability”. However, this value was calculated on the same dataset used for model derivation. Without internal validation techniques—such as bootstrap resampling or k-fold cross-validation—the performance is likely optimistic (2). The lack of external validation further limits confidence in the generalizability of the proposed index.
Third, the article implies potential clinical utility of the index. Yet predictive models intended for clinical decision-making must undergo rigorous validation, include calibration assessment, and be tested in independent populations (3). Without these steps, the index cannot be considered ready for clinical application and should instead be viewed as preliminary and hypothesis-generating.
Fourth, qualitative echocardiographic variables such as “eccentric MR” require strict standardization. Echocardiographic assessment of regurgitation is highly dependent on hemodynamic conditions and imaging windows, and inconsistent definitions may introduce classification bias (4).
Finally, the manuscript would benefit from stronger adherence to TRIPOD reporting standards, including transparent reporting of model development, handling of missing data, and justification of sample size (1).
In conclusion, while Xiao et al. address an important question, the composite index should be interpreted with caution due to methodological limitations intrinsic to small-sample predictive modeling without validation. Larger multicenter studies are needed before clinical implementation can be considered.
Funding Statement
The author(s) declared that financial support was not received for this work and/or its publication.
Footnotes
Edited by: Roney Orismar Sampaio, University of São Paulo, Brazil
Reviewed by: Craig Basman, Hackensack University Medical Center, United States
Sandeep Bhushan, Chengdu Second People's Hospital, China
Danilo Franco, University of Naples Federico II, Italy
Author contributions
KH: Writing – original draft, Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was used in the creation of this manuscript. Generative AI was used only for minor language polishing. No generative AI tools were used to create, analyze, interpret, or draft the scientific content of this manuscript. The author takes full responsibility for the accuracy and integrity of the submitted text.
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References
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