A model based on preoperative nutrition-inflammation score for predicting mucocutaneous separation after enterostomy in colorectal cancer patients - Jie Yuan; Yali Hu; Jiaying Wang; Le Chang; Ting Guan; Xiaochao Fu; Wangwang Sheng; Jingyu Nian; Bing Zhu; Jifu E; Cheng Peng (Scientific Reports)
Abstract Mucocutaneous separation (MCS) is a common early complication after colorectal cancer (CRC) surgery. The aim of the present study is to investigate the predictive value of preoperative nutrition-inflammation markers for MCS and establish a novel predictive model. The internal cohort composed of CRC patients admitted to Changhai Hospital Affiliated to Naval Medical University was randomly divided into a training cohort and an internal validation cohort at a ratio of 7:3, while patients from the First Affiliated Hospital of Bengbu Medical University and 72nd Group Army Hospital formed an external cohort. The clinical variables were retrospectively analyzed to establish a scoring system for evaluating preoperative nutrition-inflammation status. In the training cohort, the independent factors for MCS were identified through univariate and multivariate Logistics regression analysis. The predictive model was drawn with a nomogram, which was verified in the two validation cohorts by receiver operating characteristic (ROC) curves, calibration curves and decision curve analyses (DCA). 359 and 145 eligible patients were included in the internal and external cohorts, respectively, including 47 and 30 patients suffering MCS in their respective cohorts. Old age, overweight, colostomy, end stoma, and peripheral blood markers were associated with MCS. The ROC curve showed that the nutrition-inflammation score (NIS) composed of GLR, SII and PNI predicted the differentiation of MCS well, and the area under the curve (AUC) was 0.752. Univariate and multivariate analyses showed that old age, end stoma and NIS (= 3) were independent risk factors for MCS. The AUC of the nomogram model based on these factors in the training cohort and validation cohorts were 0.850, 0.909, and 0.906, respectively. The calibration curves showed no significant difference between the predicted value of the model and the actual observed value, and the DCA curves showed that the model had good clinical application value. The risk score of this model was correlated with healing time of MCS, postoperative hospital stays, and chemotherapy intervals of CRC patients. The prediction model of MCS based on preoperative peripheral NIS had good accuracy and demonstrated promising performance of predicting the risk of postoperative MCS in CRC patients.