ArticleQuantitative imaging in medicine and surgery2026
Conversion to laparotomy due to intra-abdominal adhesions during laparoscopic hernia repair: a predictive model based on computed tomography manifestations and clinical indicators.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The preoperative assessment of adhesions between the intestine and the abdominal wall in patients with ventral incisional hernias allows surgeons to assess the feasibility of laparoscopic hernia repair. This study aimed to investigate the predictive value of computed tomography (CT) images combined with clinical indicators for conversion to laparotomy due to adhesions in laparoscopic incisional hernia repair, and to develop a nomogram prediction model. Methods: This study analyzed data of 245 patients with ventral incisional hernias retrospectively. The patients were divided into laparoscopy and conversion to laparotomy groups according to whether their procedures were converted to laparotomy due to abdominal adhesions. Logistic regression was applied to identify predictive factors to establish a nomogram model for conversion to laparotomy due to adhesions. The nomogram was evaluated by receiver operator characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: Multivariate logistic regression revealed that body mass index (BMI) [odds ratio (OR) =0.868, 95% confidence interval (CI): 0.774-0.973, P=0.016], incomplete reduction of the hernia contents (OR =3.574, 95% CI: 1.278-9.995, P=0.015), localized abdominal wall thickening (OR =11.613, 95% CI: 4.907-27.482, P<0.001), asymmetric intestinal wall thickening (OR =7.508, 95% CI: 2.457-22.944, P<0.001), intestinal obstruction (OR =11.765, 95% CI: 1.969-70.284, P=0.007), and hernia defect width (OR =1.317, 95% CI: 1.130-1.536, P<0.001) were independent predictors of conversion to laparotomy due to adhesions. ROC curve showed that the nomogram model had an area under the curve of 0.890 (95% CI: 0.844-0.926, P<0.001). The concordance index (C-index) of the nomogram prediction model was 0.876, indicating that it had a satisfactory degree of discrimination. The calibration curve produced excellent calibration results. The DCA showed that the nomogram model was clinically useful when intervention was decided at a possibility threshold exceeding 4%. Conclusions: The nomogram prediction model based on abdominal CT images and clinical indicators has a high predictive value for conversion to laparotomy.
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