Department of Cardiovascular Surgery, Fujian Provincial Center for Cardiovascular Medicine, Union Hospital, Fujian Medical University, Yuanjiang Road 1#, Fuzhou, 350001, People's Republic of China.
Key Laboratory of Cardio-Thoracic Surgery, Fujian Medical University, Fujian Province University, Fuzhou, 350001, People's Republic of China.
Sci Rep. 2024 Oct 28;14(1):25703. doi: 10.1038/s41598-024-76420-y.
Total thoracoscopic valve replacement (TTVR) is a minimally invasive alternative to traditional open-heart surgery. However, some patients undergoing TTVR experience prolonged mechanical ventilation (PMV). Predicting PMV risk is crucial for optimizing perioperative management and improving outcomes. We conducted a retrospective cohort study of 2,319 adult patients who underwent TTVR at a tertiary care center between January 2017 and May 2024. PMV was defined as mechanical ventilation exceeding 72 h post-surgery. A Fine-Gray competing risks regression model was developed and validated to identify predictors of PMV. Significant predictors of PMV included cardiopulmonary bypass time, ejection fraction, New York Heart Association grading, serum albumin, atelectasis, pulmonary infection, pulmonary edema, age, need for postoperative dialysis, hemoglobin levels, and PaO2/FiO2. The model demonstrated good discriminative ability, with areas under the receiver operating characteristic curves of 0.747 in the training set and 0.833 in the validation set. Calibration curves showed strong agreement between predicted and observed PMV probabilities. Decision curve analysis indicated clinical utility across a range of threshold probabilities. Our predictive model for PMV following TTVR demonstrates strong performance and clinical utility. It helps identify high-risk patients and tailor perioperative management to reduce PMV risk and improve outcomes. Further validation in diverse settings is recommended.
全胸腔镜瓣膜置换术（TTVR）是一种微创替代传统开胸心脏手术的方法。然而，一些接受 TTVR 的患者会经历长时间的机械通气（PMV）。预测 PMV 风险对于优化围手术期管理和改善预后至关重要。我们对 2017 年 1 月至 2024 年 5 月在一家三级护理中心接受 TTVR 的 2319 名成年患者进行了回顾性队列研究。PMV 定义为手术后机械通气超过 72 小时。我们开发并验证了 Fine-Gray 竞争风险回归模型，以确定 PMV 的预测因素。PMV 的显著预测因素包括体外循环时间、射血分数、纽约心脏协会分级、血清白蛋白、肺不张、肺部感染、肺水肿、年龄、术后透析需要、血红蛋白水平和 PaO2/FiO2。该模型显示出良好的判别能力，在训练集中的受试者工作特征曲线下面积为 0.747，在验证集中为 0.833。校准曲线显示预测和观察到的 PMV 概率之间具有很强的一致性。决策曲线分析表明该模型在一系列阈值概率下具有临床实用性。我们的 TTVR 后 PMV 预测模型具有良好的性能和临床实用性。它有助于识别高风险患者，并调整围手术期管理，以降低 PMV 风险并改善预后。建议在不同环境中进一步验证。
