Dataset Information

Modified Colon Leakage Score to Predict Anastomotic Leakage in Patients Who Underwent Left-Sided Colorectal Surgery.

ABSTRACT: Colon leakage score (CLS) was introduced as a clinical tool to predict anastomotic leakage (AL) in patients who underwent left-sided colorectal surgery, but its clinical validity has not been widely studied. We evaluated the clinical utility of CLS and developed a modified CLS (m-CLS). In total, 566 patients who underwent left-sided colorectal surgery were enrolled and categorized into training (n = 396) and validation (n = 170) sets via random sampling. Using CLS variables, the least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection and predictive signature building in the training set. The model's performance was validated in the validation set. The predictive powers of m-CLS and CLS were compared by the area under the receiver operating characteristic (AUROC) curve in the overall group. Twenty-three AL events (4.1%) were noted. The AL group had a significantly higher mean CLS than the No Leakage group (12.5 vs. 9.6, p = 0.001). Five clinical variables were selected and used to generate m-CLS. The predictive performance of m-CLS was similar in training and validation sets (AUROC 0.838 vs. 0.803, p = 0.724). In the overall set, m-CLS was significantly predictive of AL and performed better than CLS (AUROC 0.831 vs. 0.701, p = 0.008). In conclusion, LASSO-model-generated m-CLS could predict AL more accurately than CLS.

SUBMITTER: Yang SU

PROVIDER: S-EPMC6780090 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Modified Colon Leakage Score to Predict Anastomotic Leakage in Patients Who Underwent Left-Sided Colorectal Surgery.

Yang Seung Up SU Park Eun Jung EJ Baik Seung Hyuk SH Lee Kang Young KY Kang Jeonghyun J

Journal of clinical medicine 20190912 9

Colon leakage score (CLS) was introduced as a clinical tool to predict anastomotic leakage (AL) in patients who underwent left-sided colorectal surgery, but its clinical validity has not been widely studied. We evaluated the clinical utility of CLS and developed a modified CLS (m-CLS). In total, 566 patients who underwent left-sided colorectal surgery were enrolled and categorized into training (<i>n</i> = 396) and validation (<i>n</i> = 170) sets via random sampling. Using CLS variables, the le ...[more]

PMID: 31547283

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Project description:BackgroundAnastomotic leakage (AL) is a severe complication after colorectal surgery. This study aimed to investigate a method for the early diagnosis of AL after surgical resection by analysing inflammatory factors (IFs) in peritoneal drainage fluid.MethodsAbdominal drainage fluid of patients with colorectal cancer who underwent resection between April 2017 and April 2018, were prospectively collected in the postoperative interval. Six IFs, including interleukin (IL)-1β, IL-6, IL-10, tumour necrosis factor (TNF)-α, matrix metalloproteinase (MMP)2, and MMP9, in drainage were determined by multiplex immunoassay to investigate AL (in patients undergoing resection and anastomosis) and pelvic collection (in patients undergoing abdominoperineal resection). Sparreboom and colleagues' prediction model was first evaluated for AL/pelvic collection, followed by a new IF-based score system (AScore) that was developed by a least absolute shrinkage and selection operator (LASSO) regression, for the same outcomes. The model performance was tested for the area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV).ResultsOut of 123 patients eligible, 119 patients were selected, including 12 patients with AL/pelvic collection. Sparreboom and colleagues' prediction model was documented with the best diagnostic efficacy on postoperative day 3 (POD3), with an AUC of 0.77. After optimization, AScore on POD3 increased the AUC to 0.83 and on POD1 showed the best diagnostic efficiency, with an AUC of 0.88. Based on the Youden index, the cut-off value of AScore on POD1 was set as -2.46 to stratify patients into low-risk and high-risk groups for AL/pelvic collection. The model showed 90.0 per cent sensitivity, 69.7 per cent specificity, 98.4 per cent NPV, and 25.0 per cent PPV.ConclusionsThe early determination of IFs in abdominal drainage fluid of patients undergoing colorectal surgery could be useful to predict AL or pelvic collection.

Project description:The difference between left- and right-sided colon cancer has become the focus of global attention, and researchers have found differences in the morbidity, molecular biological characteristics, and response to targeted drug therapy between left- and right-sided colon cancer. Therefore, the identification of more effective predictive indicators is critical for providing guidance to future clinical work. We collected samples from different colon sites and regions and analyzed the identities and distributions of differentially expressed species in the microbiota in the left and right sides of the colon to better explore the pathogenesis of colon cancer and provided a basis for individualized drug therapy. We collected samples from different regions in the body of 40 patients with colon cancer, including stool and tissues. The Subjects were classified into four groups, and this classification was mainly based on the colon cancer distribution. The microbiota composition of the left-sided and right-sided colon samples was assessed by specifically amplifying the V3-V4 region of the 16S rDNA gene from DNA extracts from the samples. These amplicons were examined by Illumina HiSeq 2500 sequencing. The microbial taxa in the left-sided colon samples are more abundant than those in the right-sided colon samples. The flora in the left-sided colon samples, such as Clostridium perfringens and Fusobacterium nucleatum, might be associated with VEGF expression and are more likely to promote colon cancer. The microbiota distribution in the right-sided colon samples is less invasive and harmful and particularly rich in Bifidobacterium dentium. In addition, Streptococcus, which is the target of EGFR, was found to be expressed in both the left- and right-sided colon samples but was found at a higher level in the left-sided colon samples. Additionally, the differential pathways involved in the left-sided colon samples mainly mediate DNA damage, methylation, and histone modifications, whereas those in the right-sided colon samples are dominated by DNA synthesis. The comparison of only the geographical differences revealed a significant difference in the distribution of the microbial population. The adherent microbiota composition and structural changes between the left- and right-sided colon samples might contribute to the development of colon cancer, lead to different morbidities, and further affect the prognosis of patients and their sensitivity to targeted drugs. Therefore, the identification of the differential flora in the colon could be used as an indicator for predicting the occurrence and development of colon cancer, which is also beneficial for future individualized drug therapy.

Project description:BackgroundAnastomotic leakage (AL) has always been one of the most serious complications of esophagectomy with gastric conduit reconstruction. There are many strong risk factors for AL in clinical practice. Notably, the tension at the esophagogastric anastomosis and the blood supply to the gastric conduit directly affect the integrity of the anastomosis. However, there has been a lack of quantitative research on the tension and blood supply of the gastric conduit. Changes in extracellular matrix collagen reflect tension and blood supply, which affect the quality of the anastomosis. This study aimed to establish a quantitative collagen score to describe changes in the collagen structure in the extracellular matrix and to identify patients at high risk of postoperative AL.MethodsA retrospective study of 213 patients was conducted. Clinical and pathological data were collected at baseline. Optical imaging of the "donut" specimen at the anastomotic gastric end and collagen feature extraction were performed. Least absolute shrinkage and selection operator (LASSO) regression models were used to select the significant collagen features, compute collagen scores, and validate the predictive efficacy of the collagen scores for ALs.ResultsLASSO regression analysis revealed three collagen-related parameters in the gastric donuts: histogram mean, histogram variance, and histogram energy. Based on this analysis, we established a formula to calculate the collagen score. The results of the univariate analysis revealed significant differences in the preoperative low albumin values (P=0.002) and collagen scores between the AL and non-AL groups (P=0.001), while the results of the multivariate analysis revealed significant differences in the collagen scores between the AL and non-AL groups (P=0.002). The areas under the curve (AUCs) of the experimental and validation cohorts were 0.978 [95% confidence interval (CI): 0.931-0.996] and 0.900 (95% CI: 0.824-0.951), respectively.ConclusionsThe collagen score established herein was shown to be related to AL and can be used to predict AL in patients who underwent esophagectomy.

Dataset Information

Modified Colon Leakage Score to Predict Anastomotic Leakage in Patients Who Underwent Left-Sided Colorectal Surgery.

Publications

Modified Colon Leakage Score to Predict Anastomotic Leakage in Patients Who Underwent Left-Sided Colorectal Surgery.

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