目标:目前,目前尚无可靠的术前预测切口疝修补术中成分分离(CS)的方法。通过定量测量术前计算机断层扫描(CT)成像,我们的目的是评估疝缺损大小的价值,腹壁肌肉质量,和疝体积在预测CS中的作用。
方法:回顾性分析2019年1月至2022年3月102例接受开放Rives-Stoppa后肌网修补术治疗中线切口疝的患者资料。患者被分为两组:\'\'CS组\'\'需要CS尝试筋膜闭合的患者,和\'\'非CS\'\'组患者仅需要Rives-Stoppa逆行肌松解术以实现筋膜闭合。疝缺损宽度,疝缺损角度,直肌宽度,腹壁肌肉面积和CT衰减,疝体积(HV),在CT图像上测量腹腔容积(ACV)。直肌宽度与缺损宽度比(RDR),HV/ACV,和HV/腹膜容积(PV;即,计算HV+ACV)。比较两组各项指标的差异。应用Logistic回归模型分析上述CT参数与CS的关系。生成接收器操作特征(ROC)曲线以评估CT参数在预测CS中的潜在效用。
结果:在102名患者中,非CS组69例,CS组33例。与非CS组相比,疝缺损宽度(P<0.001),疝缺损角度(P<0.001),CS组疝体积较大(P<0.001),RDR较小(P<0.001)。CS组腹壁肌面积略大于非CS组(P=0.046),两组患者腹壁肌CT衰减差异无统计学意义(P=0.089)。多因素logistic回归分析确定疝缺损宽度(OR1.815,95%CI1.428-2.308,P<0.001),RDR(OR0.018,95%CI0.003-0.106,P<0.001),疝缺损角度(OR1.077,95%CI1.042-1.114,P<0.001),疝体积(OR1.002,95%CI1.001-1.003,P<0.001),腹壁肌的CT衰减(OR0.962,95%CI0.927-0.998,P=0.037)是CS的独立预测因子。疝缺损宽度是CS的最佳预测指标,具有9.2cm的截止点和0.890的曲线下面积(AUC)。RDR的AUC,疝缺损角度,疝体积,腹壁肌CT衰减分别为0.843、0.812、0.747和0.572。
结论:定量CT测量对于CS的术前预测具有重要价值。疝缺损大小,疝体积,腹壁肌的CT衰减均为CS的术前预测指标。
Currently, there are no reliable preoperative methods for predicting component separation (CS) during incisional hernia repair. By quantitatively measuring preoperative computed tomography (CT) imaging, we aimed to assess the value of hernia defect size, abdominal wall muscle quality, and hernia volume in predicting CS.
The data of 102 patients who underwent open Rives-Stoppa retro-muscular mesh repair for midline incisional hernia between January 2019 and March 2022 were retrospectively analyzed. The patients were divided into two groups: \'\'CS group\'\' patients who required CS to attempt fascial closure, and \'\'non-CS\'\' group patients who required only Rives-Stoppa retro-muscular release to achieve fascial closure. Hernia defect width, hernia defect angle, rectus width, abdominal wall muscle area and CT attenuation, hernia volume (HV), and abdominal cavity volume (ACV) were measured on CT images. The rectus width to defect width ratio (RDR), HV/ACV, and HV/peritoneal volume (PV; i.e., HV + ACV) were calculated. Differences between the indices of the two groups were compared. Logistic regression models were applied to analyze the relationships between the above CT parameters and CS. Receiver operator characteristic (ROC) curves were generated to evaluate the potential utility of CT parameters in predicting CS.
Of the102 patients, 69 were in the non-CS group and 33 were in the CS group. Compared with the non-CS group, hernia defect width (P < 0.001), hernia defect angle (P < 0.001), and hernia volume (P < 0.001) were larger in the CS group, while RDR (P < 0.001) was smaller. The abdominal wall muscle area in the CS group was slightly greater than that in the non-CS group (P = 0.046), and there was no significant difference in the CT attenuation of the abdominal wall muscle between the two groups (P = 0.089). Multivariate logistic regression identified hernia defect width (OR 1.815, 95% CI 1.428-2.308, P < 0.001), RDR (OR 0.018, 95% CI 0.003-0.106, P < 0.001), hernia defect angle (OR 1.077, 95% CI 1.042-1.114, P < 0.001), hernia volume (OR 1.002, 95% CI 1.001-1.003, P < 0.001), and CT attenuation of abdominal wall muscle (OR 0.962, 95% CI 0.927-0.998, P = 0.037) as independent predictors of CS. Hernia defect width was the best predictor for CS, with a cut-off point of 9.2 cm and an area under the curve (AUC) of 0.890. The AUCs of RDR, hernia defect angle, hernia volume, and abdominal wall muscle CT attenuation were 0.843, 0.812, 0.747, and 0.572, respectively.
Quantitative CT measurements are of great value for preoperative prediction of CS. Hernia defect size, hernia volume, and the CT attenuation of abdominal wall muscle are all preoperative predictive indicators of CS.