Development and Validation of a Novel Nomogram Model for Early Diagnosis of Anastomotic Leakage After Laparoscopic Colorectal Cancer Surgery

医学 列线图 结直肠癌 腹腔镜手术 结直肠外科 外科 吻合 腹腔镜检查 普通外科 癌症 肿瘤科 内科学 腹部外科
作者
Liman Yang,Peng Zhang,Wenchang Yang,Yongzhou Huang,Jian-Bo Lv,Yuqiang Du,Weizhen Liu,Kaixiong Tao
出处
期刊:Surgical Infections [Mary Ann Liebert]
标识
DOI:10.1089/sur.2023.253
摘要

Background: This study aimed to investigate the clinical value of inflammatory factors for predicting anastomotic leakage (AL) after laparoscopic colorectal cancer surgery and establish a nomogram model to assess the probability of its occurrence. Patients and Methods: Data of 637 patients who underwent laparoscopic colorectal cancer surgery between June 2019 and June 2022 were collected. Differences in procalcitonin (PCT), C-reactive protein (CRP), and white blood cell (WBC) levels before surgery and on postoperative day (POD) 3 and 5 were compared between patients with and without AL (AL and non-AL groups, respectively). The diagnostic accuracy was determined using the area under the receiver operating characteristic curve (AUC), and a nomogram model was developed. Results: Post-operative AL occurred in 46 (7.2%) patients. Procalcitonin, CRP, and WBC levels on POD 3 and 5 were higher in the AL group than in the non-AL group. The AUCs of PCT, CRP, and WBC levels for predicting AL on POD 3 were 0.833, 0.757, and 0.756, respectively, which were better than those on POD 5 (AUC = 0.669, 0.581, and 0.588, respectively). The nomogram model for AL was developed based on five variables (PCT, CRP, WBC, American Society of Anesthesiologists [ASA] grade and comorbidities), and it had an AUC of 0.922. Calibration curves demonstrated that the nomogram had good fit. The Delong test showed that the AUC of the nomogram for predicting the probability of AL was higher than that of PCT alone (z = 2.311, p = 0.02). Conclusions: Procalcitonin measured on POD 3 seems to be a promising negative predictor of AL after laparoscopic colorectal cancer surgery. Furthermore, the nomogram model developed in our study, which utilizes a series of predictors that can be easily accessed, has demonstrated potential to further improve the prediction accuracy.
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