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. 2024 Jan 23;19(1):24.
doi: 10.1186/s13019-024-02479-3.

Navigating the challenges of minimally invasive mitral valve surgery: a risk analysis and learning curve evaluation

Affiliations

Navigating the challenges of minimally invasive mitral valve surgery: a risk analysis and learning curve evaluation

Nestoras Papadopoulos et al. J Cardiothorac Surg. .

Abstract

Background: This study aimed to report the risk and learning curve analysis of a minimally invasive mitral valve surgery program performed through a right mini-thoracotomy at a single institution.

Methods: From January 2013 through December 2019, 266 consecutive patients underwent minimally invasive mitral valve surgery in our department and were included in the current study. Multiple logistic regression analysis was used for the adverse event outcome. Distribution over time of perioperative complications, defined as clinical endpoints in the Valve Academic Research Consortium-2 (VARC-2) consensus document, as well as CUSUM charts for assessment of cardiopulmonary bypass and aortic cross-clamping duration over time, has been performed for learning curve assessment.

Results: Overall incidences of postoperative stroke (1.1%), myocardial infarction (1.1%), and thirty-day mortality (1.5%) were low. The mitral valve reconstruction rate in our series was 95%. Multivariable analysis revealed that concomitant tricuspid valve surgery (OR 4.44; 95%CI 1.61-11.80; p = 0.003) was significantly associated with adverse event outcomes. Despite a trend towards adverse event outcomes in patients with preexisting active mitral valve endocarditis (OR 2.69; 95%CI 0.81-7.87; p = 0.082), mitral valve pathology did not significantly impact postoperative morbidity and mortality. Distribution over time of perioperative complications, defined as clinical endpoints in the VARC-2 consensus document, showed a trend towards an improved complication rate after the initial 65-100 procedures.

Conclusions: Mitral valve surgery via right-sided mini-thoracotomy can be implemented safely with low perioperative morbidity and mortality rates. Careful patient selection regarding isolated mitral valve surgery in the presence of degenerative mitral valve disease may represent a significant safety issue during the learning curve.

Trial registration: The cantonal ethics commission of Zurich approved the study (registration ID 2020-00752, date of approval 24 April 2020).

Keywords: Minimally invasive cardiac surgery; Mitral valve reconstruction; Mitral valve surgery; Surgical education.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Kaplan- Meier analysis revealing overall survival of 96.56 ± 1.2% at 3 years follow-up
Fig. 2
Fig. 2
Assessment of our learning curve by evaluation of the rate of perioperative complications defined as Valve Academic Research Consortium-2 (VARC-2) clinical endpoints. The number of minimally invasive (MIV) mitral valve procedures performed per year is shown in blue bars, and the number of perioperative complications defined as VARC-2 clinical points per years is shown in red bars. VARC-2 clinical endpoints include death, stroke, myocardial infarction, bleeding, vascular complications, acute kidney injury, conduction disturbances and valve related complications
Fig. 3
Fig. 3
CUSUM chart for aortic cross-clamping time (A) in isolated mitral valve repair reveals higher cross-clamping times (CUSUM curve above upper decision limit) for cases 12–55 (year 2013–2016). Afterward (cases 56–103) reduction towards the center of group statistics with lower cross-clamping times (CUSUM curve below lower decision limit) for cases 104–111 (year 2018), case 126 (year 2019), and cases 130–145 (year 2019) was detected. Higher CPB times were seen for mitral valve repair (B) for cases 11–40. Afterward, there was a reduction towards the group's center with lower CBP times for cases 105–107 and 142–145
Fig. 4
Fig. 4
CUSUM charts for aortic cross-clamping and CBP-time in isolated mitral valve replacement illustrate all values within upper and lower detection limits as depicted in A and B

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