ArticleJournal of minimal access surgery2024
Establishment of a nomogram model in predicting risk factors of post-operative complications after laparoscopic anterior resection for rectal cancer.
Article in Journal of minimal access surgery, 2024. 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
objectiveWe aimed to analyse the risk factors of complications after laparoscopic anterior resection of rectal cancer, and to establish a nomogram prediction model and evaluate its accuracy. PATIENTS AND
methodsWe retrospectively analysed the clinical data of 180 patients undergoing laparoscopic anterior resection of rectal cancer. Univariate analysis and multivariate logistic regression analysis were used to screen the potential risk factors of post-operative complications of Grade II and establish a nomogram model. The receiver operating characteristic (ROC) curve and Hosmer-Lemeshow goodness-of-fit test were used to evaluate the discrimination and coincidence of the model, and the calibration curve was used to internally verify.
resultsA total of 53 patients (29.4%) with rectal cancer had Grade II post-operative complications. Multivariate logistic regression analysis showed that age (odds ratio [OR] =1.085, P < 0.001), body mass index ≥24 kg/m 2 (OR = 2. 763, P = 0. 008), tumour diameter ≥5 cm (OR = 3. 572, P = 0.002), tumour distance from anal margin ≤6 cm (OR = 2.729, P = 0.012) and operation time ≥180 min (OR = 2.243, P = 0.032) were independent risk factors for Grade II post-operative complications. The area under the ROC was 0.782 (95% confidence interval: 0.706-0.858, sensitivity: 66.0%, specificity: 76.4%) in the nomogram prediction model. Hosmer-Lemeshow goodness-of-fit test showed χ2 = 9.350, P = 0.314.
conclusionBased on five independent risk factors, the nomogram prediction model has a good predictive performance for post-operative complications after laparoscopic anterior resection of rectal cancer, which is helpful to early identify high-risk people and formulate clinical intervention measures.
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