Dataset Information

Development and validation of a nomogram to predict anastomotic leakage in colorectal cancer based on CT body composition.

ABSTRACT:

Background

Anastomotic leakage (AL) is one of the most serious postoperative complications. This study aimed to investigate the predictive value of preoperative body composition for AL in patients with colorectal cancer (CRC).

Methods

We first reviewed data from 3,681 patients who underwent radical CRC resection 2013-2021 in our hospital, and 60 patients were diagnosed with AL after surgery. We designed a nested case-control study and two controls were randomly selected for each case according to the time and position of surgery. Body composition was measured at the level of the third lumbar vertebra based on computed tomography (CT) images. The risk factors of AL were analyzed by univariate and multivariate analysis. Nomogram was built using binary regression analysis and assessed for clinical usefulness, calibration, and discrimination.

Results

In the multivariate analysis, gender, blood glucose, nutrition risk screening (NRS), skeletal muscle area (SMA) and visceral fat area (VFA) were independent risk factors for developing anastomotic leakage after surgery. The prognostic model had an area under the receiver operating characteristic curve of 0.848 (95% CI, 0.781-0.914). The calibration curve showed good consistency between the predicted and observed outcomes. Decision curve analysis indicated that patients with colorectal cancer can benefit from the prediction model.

Conclusions

The nomogram that combined with gender, blood glucose, NRS, SMA, and VFA had good predictive accuracy and reliability to AL. It may be conveniently for clinicians to predict AL preoperatively and be useful for guiding treatment decisions.

SUBMITTER: Xiang S

PROVIDER: S-EPMC9490075 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a nomogram to predict anastomotic leakage in colorectal cancer based on CT body composition.

Xiang Shuai S Yang Yong-Kang YK Wang Tong-Yu TY Yang Zhi-Tao ZT Lu Yun Y Liu Shang-Long SL

Frontiers in nutrition 20220907

<h4>Background</h4>Anastomotic leakage (AL) is one of the most serious postoperative complications. This study aimed to investigate the predictive value of preoperative body composition for AL in patients with colorectal cancer (CRC).<h4>Methods</h4>We first reviewed data from 3,681 patients who underwent radical CRC resection 2013-2021 in our hospital, and 60 patients were diagnosed with AL after surgery. We designed a nested case-control study and two controls were randomly selected for each c ...[more]

PMID: 36159450

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Project description:BackgroundPituitary adenomas (PAs) are the most common tumor of the sellar region. PA resection is the preferred treatment for patients with clear indications for surgery. Intraoperative cerebrospinal fluid (iCSF) leakage is a major complication of PA resection surgery. Risk factors for iCSF leakage have been studied previously, but a predictive nomogram has not yet been developed. We constructed a nomogram for preoperative prediction of iCSF leakage in endoscopic pituitary surgery.MethodsA total of 232 patients who underwent endoscopic PA resection at the Department of Neurosurgery in Jinling Hospital between January of 2018 and October of 2020 were enrolled in this retrospective study. Patients treated by a board-certified neurosurgeon were randomly classified into a training cohort or a validation cohort 1. Patients treated by other qualified neurosurgeons were included in validation cohort 2. A range of demographic, clinical, radiological, and laboratory data were acquired from the medical records. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and uni- and multivariate logistic regression were utilized to analyze these features and develop a nomogram model. We used a receiver operating characteristic (ROC) curve and calibration curve to evaluate the predictive performance of the nomogram model.ResultsVariables were comparable between the training cohort and validation cohort 1. Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. The calibration curve showed satisfactory homogeneity between the predicted probability and actual observations. Nomogram performance was stable in the subgroup analysis.ConclusionsTumor height and albumin were the independent risk factors for iCSF leakage. The prediction model developed in this study is the first nomogram developed as a practical and effective tool to facilitate the preoperative prediction of iCSF leakage in endoscopic pituitary surgery, thus optimizing treatment decisions.

Project description:BACKGROUND:Clinical and experimental evidence suggests that colorectal mucosal microbiota changes during colorectal carcinogenesis and may impair colorectal anastomotic wound healing. Thus, we hypothesized that amounts of colorectal cancer-associated microbes in colorectal tissue might be associated with anastomotic leakage after resection for colorectal carcinoma. METHODS:We analyzed 256 fresh frozen tissues of colorectal cancer from patients who underwent elective colorectal resection and anastomosis. Amounts of colorectal cancer-associated microbes, including Fusobacterium nucleatum, Escherichia coli possessing the polyketide synthase (pks) gene cluster, Enterococcus faecalis, and Bifidobacterium genus, in colorectal cancer tissues were measured by quantitative polymerase chain reaction assay; we equally dichotomized positive cases (high versus low). Multivariable logistic regression analysis was conducted to assess associations of these microbes with anastomotic leakage, adjusting for patient and tumor characteristics, and surgery-related factors. RESULTS:Fusobacterium nucleatum, pks-positive Escherichia coli, Enterococcus faecalis, and Bifidobacterium genus were detected in colorectal carcinoma tissue in 140 (54%), 94 (36%), 193 (75%), and 89 (35%) of 256 cases, respectively. Compared with Bifidobacterium genus-negative cases, Bifidobacterium genus-high cases were associated with an increased risk of anastomotic leakage (multivariable odds ratio, 3.96; 95% confidence interval, 1.50 to 10.51; Ptrend = 0.004). The association of Fusobacterium nucleatum, pks-positive Escherichia coli, or Enterococcus faecalis with anastomotic leakage was not statistically significant. CONCLUSIONS:The amount of Bifidobacterium genus in colorectal tissue is associated with an increased risk of anastomotic leakage after resection for colorectal cancer. These findings need to be validated to target gastrointestinal microflora for the prevention of anastomotic leakage after colorectal resection.

Dataset Information

Development and validation of a nomogram to predict anastomotic leakage in colorectal cancer based on CT body composition.

Background

Methods

Results

Conclusions

Publications

Development and validation of a nomogram to predict anastomotic leakage in colorectal cancer based on CT body composition.

Similar Datasets

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