Skip to content
#small language model Open access

Residual Mitral Regurgitation After Surgical Mitral Valve Repair and Long-Term Recurrence and Reintervention

Oct 2026 · Open Science Framework
Cardiac Valve Diseases and Treatments

Abstract

Surgical mitral valve repair is the preferred treatment for degenerative mitral regurgitation (MR) and is also widely performed for MR of other etiologies; however, recurrent MR after repair is not uncommon and is associated with adverse left ventricular remodeling and increased risks of mitral valve reintervention and death. At the completion of repair, a small amount of residual MR is frequently detected on intraoperative transesophageal echocardiography or predischarge transthoracic echocardiography, and whether mild residual MR is acceptable remains clinically controversial. Existing evidence derives largely from single-center studies with limited sample sizes, and the timing of residual MR assessment, grading thresholds, and outcome definitions vary considerably across studies, limiting the generalizability of their conclusions. We therefore plan to conduct a systematic review and meta-analysis. PubMed/MEDLINE, Embase, Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL) will be systematically searched for English-language studies of adults undergoing surgical mitral valve repair for MR of any etiology that report residual MR grade and long-term follow-up outcomes. The primary outcomes are recurrent MR (≥ moderate MR during follow-up) and mitral valve reintervention (surgical or transcatheter). In the primary analysis, adjusted hazard ratios will be pooled using random-effects models; unadjusted estimates will be pooled separately as supplementary analyses and will not be combined with adjusted estimates. Pooled estimates will be obtained separately for different residual MR thresholds (≥ mild vs. none/trace; > mild vs. ≤ mild). Subgroup analyses will be performed according to MR etiology (degenerative vs. other or mixed etiologies) and timing of residual MR assessment (intraoperative vs. early postoperative/predischarge vs. not specified), and the association of residual MR with long-term mortality will also be explored. Because death precludes subsequent reintervention, we will record whether included studies accounted for competing risks, and cause-specific and subdistribution hazard ratios will be pooled together in the primary analysis, with the measure type examined as a subgroup factor and a sensitivity analysis restricted to cause-specific hazard ratios. These analyses will be complemented by risk-of-bias assessment, sensitivity analyses (including recurrent MR defined as ≥ moderate-to-severe MR and reintervention restricted to surgical reoperation), and grading of the certainty of evidence. This study is expected to clarify the prognostic value of residual MR, particularly mild residual MR, thereby providing evidence to inform whether surgeons should aim for no or trace residual MR intraoperatively, whether a second pump run for re-repair is warranted, and how high-risk patients should be stratified for postoperative surveillance, as well as offering a reference for standardizing the definition and reporting of residual MR in future studies.

View source

Similar papers

#small language model Dataset Open access Oct 2026

Socratic guiding questions in synthetic arithmetic data: matched LoRA runs (revision v2)

Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...

O'Grady, Jake, Gürhan, Asena Isik, Chee, Fong Ting et al. · 465 citations
#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6

Related blog posts

MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.