Novel risk prediction model for major adverse cardiovascular events in minimally invasive mitral valve surgery: a retrospective study.

Background: Minimally invasive mitral valve surgery (MIMVS) has become the standard procedure for treating mitral valve pathologies. However, the existing cardiac risk model fails to consider the distinctive perfusion and ventilation techniques of MIMVS, leading to inaccurate prediction of perioperative risks. This study aimed to identify the perioperative risk factors for major adverse cardiovascular events (MACEs) in MIMVS and develop a predictive model based on these factors.

Methods: This single-center retrospective study recruited 480 patients undergoing MIMVS at Beijing Anzhen Hospital between April 2010 and May 2024 and collected data on 79 perioperative clinical variables. The primary outcome was MACE within 30 days postoperatively. Univariate Cox regression analysis was used to analyze the associations between variables and outcomes, whereas elastic net regression was used to develop a risk prediction model (CompliMit Score) for MACE. The model was validated using 200 bootstrap replicates.

Results: The 30-day MACE rate was 12%, and 31 clinical variables significantly correlated with MACE: 13 preoperatively, 9 intraoperatively, and 9 postoperatively. From these, we developed the CompliMit Score, which included 14 risk factors identified through elastic net regression. The CompliMit Score identified more high-risk patients for MACE than the European System for Cardiac Operative Risk Evaluation II {area under the curve: 0.92 [95% confidence interval (CI): 0.88-0.96] vs. 0.67 (95% CI: 0.59-0.75)}, and internal validation confirmed its superior predictive performance.

Conclusions: Factors influencing MIMVS prognosis included preoperative, intraoperative, and postoperative variables. The newly developed CompliMit Score effectively identified patients who are at high risk of perioperative MACE, thus facilitating targeted postoperative care and resource allocation.