Comparison of outcomes between minimally invasive and median sternotomy for double and triple valve surgery: A meta-analysis

Background

Limited data exists demonstrating the efficacy of minimally invasive surgery (MIS) compared to median sternotomy (MS) for multiple valvular disease (MVD). This systematic review and meta-analysis aims to compare operative and peri-operative outcomes of MIS vs MS in MVD.

Methods

PubMed, Ovid, and Embase were searched from inception until August 2019 for randomized and observational studies comparing MIS and MS in patients with MVD. Clinical outcomes of intra- and postoperative times, reoperation for bleeding and surgical site infection were evaluated.

Results

Five observational studies comparing 340 MIS vs 414 MS patients were eligible for qualitative and quantitative review. The quality of evidence assessed using the Newcastle-Ottawa scale was good for all included studies. Meta-analysis demonstrated increased cardiopulmonary bypass time for MIS patients (weighted mean difference [WMD], 0.487; 95% confidence interval [CI], 0.365-0.608; P < .0001). Similarly, aortic cross-clamp time was longer in patients undergoing MIS (WMD, 0.632; 95% CI, 0.509-0.755; P < .0001). No differences were found in operative mortality, reoperation for bleeding, surgical site infection, or hospital stay.

Conclusions

MIS for MVD have similar short-term outcomes compared to MS. This adds value to the use of minimally invasive methods for multivalvular surgery, despite conferring longer operative times. However, the paucity in literature and learning curve associated with MIS warrants further evidence, ideally randomized control trials, to support these findings.

     

基于剂量组学预测肺癌患者放射性肺炎发生的研究

目的 探讨剂量组学在预测肺癌根治性放疗患者放射性肺炎发生中的应用潜能。方法 回顾性收集行根治性放疗的314例肺癌患者的临床资料、放疗剂量文件、定位及随访CT图像,根据临床资料及影像学随访资料对放射性肺炎进行分级,提取全肺的剂量组学特征,构建机器学习模型。应用1000次自助抽样法（bootstrap）的最小绝对值收敛和选择算子嵌套逻辑回归（LASSO‐LR）及1000次bootstrap的赤池信息量准则（AIC）向后法筛选与放射性肺炎相关的剂量组学特征,随机按照7∶3划分为训练集及验证集,应用逻辑回归建立预测模型,并应用ROC曲线及校正曲线评价模型的性能。结果 共提取120个剂量组学特征,经LASSO‐LR降维筛选得到12个特征进入“特征池”,再经过AIC向后法筛选,最终筛选出6个剂量组学特征进行模型构建,训练集AUC为0.77（95%CI为0.65~0.87）,独立验证集AUC为0.72（95%CI为0.64~0.81）。结论 利用剂量组学建立的预测模型具有预测放射性肺炎发生的潜力,但仍需继续纳入多中心数据及前瞻性数据进一步挖掘剂量组学的应用潜能。