ReviewInternational journal of colorectal disease2024
Development and validation of a preoperative systemic inflammation-based nomogram for predicting surgical site infection in patients with colorectal cancer.
Review in International journal of colorectal disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Association between 12 composite inflammatory indices and postoperative complications in colorectal cancer surgery: a two-center retrospective study.Frontiers in oncology · 2026Article
- Reply to "Disregarding C-reactive protein in an inflammation-based nomogram?"International journal of colorectal disease · 2025Article
- Disregarding C-reactive protein in an inflammation-based nomogram?International journal of colorectal disease · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundSurgical site infection (SSI) represents a significant postoperative complication in colorectal cancer (CRC). Identifying associated factors is therefore critical. We evaluated the predictive value of clinicopathological features and inflammation-based prognostic scores (IBPSs) for SSI occurrence in CRC patients.
methodsWe retrospectively analyzed data from 1445 CRC patients who underwent resection surgery at Wuhan Union Hospital between January 2015 and December 2018. We applied two algorithms, least absolute shrinkage and selector operation (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), to identify key predictors. Participants were randomly divided into training (n = 1043) and validation (n = 402) cohorts. A nomogram was constructed to estimate SSI risk, and its performance was assessed by calibration, discrimination, and clinical utility.
resultsCombining the 30 clinicopathological features identified by LASSO and SVM-RFE, we pinpointed seven variables as optimal predictors for a pathology-based nomogram: obstruction, dNLR, ALB, HGB, ALT, CA199, and CA125. The model demonstrated strong calibration and discrimination, with an area under the curve (AUC) of 0.838 (95% CI 0.799-0.876) in the training cohort and 0.793 (95% CI 0.732-0.865) in the validation cohort. Decision curve analysis (DCA) showed that our models provided greater predictive benefit than individual clinical markers.
conclusionThe model based on simplified clinicopathological features in combination with IBPSs is useful in predicting SSI for CRC patients.
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