ArticleHernia : the journal of hernias and abdominal wall surgery2023
CT-measured hernia parameters can predict component separation: a cross-sectional study from China.
Article in Hernia : the journal of hernias and abdominal wall surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.
- From imaging to outcomes: evaluating the predictive value of preoperative CT for intra and postoperative outcomes in ventral hernia repair - a systematic review.Hernia : the journal of hernias and abdominal wall surgery · 2025Pooled it
- Predictive factors for complexity in abdominal wall hernias: a literature scope review.Revista do Colegio Brasileiro de Cirurgioes · 2024Pooled it
- Article
- Computed tomography measurements to predict need for robotic transversus abdominis release: a single institution analysis.Hernia : the journal of hernias and abdominal wall surgery · 2024Article
- Computed tomography for evaluation of abdominal wall hernias-what is the value of the Valsalva maneuver?Hernia : the journal of hernias and abdominal wall surgery · 2024Article
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Authors and funding
7 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeCurrently, 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.
methodsThe 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.
resultsOf 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.
conclusionQuantitative 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.
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