Validation of an online risk calculator for the prediction of anastomotic leak after colon cancer surgery and preliminary exploration of artificial intelligence-based analytics

Sammour, Tarik; Cohen, Lauren; Karunatillake, A. I.; Lewis, Mark; Lawrence, Matthew; Hunter, Andrew; Moore, James W.; Thomas, Michelle

doi:10.1007/s10151-017-1701-1

Cited by 33 publications

(29 citation statements)

References 31 publications

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“…Previous studies have reported on data collected during the ANACO project: one study used as an end‐point the risk of leakage in all types of colonic resections , from which an online calculator has been developed and validated ; another study reported on factors associated with complications after right colectomy . Left colectomies without diversion ileostomy need separate assessment due to the anatomical and technical issues associated with left‐sided colon cancer.…”

Section: Discussionmentioning

confidence: 99%

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Predictors of complications and mortality following left colectomy with primary stapled anastomosis for cancer: results of a multicentric study with 1111 patients

et al. 2018

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Section: Discussionmentioning

confidence: 99%

“…Perforation was not significantly higher after stent placement (3.1% vs 4.8%, no stent vs stent, P = 0.48). The median number of nodes isolated from the specimen was 14 (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20).…”

Section: Pathology and Perioperative Outcomementioning

confidence: 99%

Predictors of complications and mortality following left colectomy with primary stapled anastomosis for cancer: results of a multicentric study with 1111 patients

et al. 2018

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“…The accuracy of CLS was initially reported to be quite high [AUROC, 0.95 (95% CI, 0.89–1.00)] [31]. Two studies performed subsequently to validate the CLS gave relatively good results, with the AUROC being 0.965 (95% CI, 0.913–1.00) and 0.80 (95% CI, 0.618–0.982) [32,33]. In this study, although the mean CLS was significantly higher in the AL group than in the No Leakage group, the predictive strength of CLS (AUROC 0.701) was not as high as was anticipated.…”

Section: Discussionmentioning

confidence: 99%

“…Based on the comparison using ROC curves, we were able to confirm that the newly developed AL classifier (m-CLS) has a better predictive power than the CLS. Several AL risk prediction models have been developed for patients with colorectal cancer [33,47,48,49,50]. These predictive models would ultimately help surgeons decide whether to perform a diversion indirectly, by estimating a possibility of AL.…”

Section: Discussionmentioning

confidence: 99%

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

Yang

Park

Baik

et al. 2019

JCM

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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.

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Prediction of symptomatic anastomotic leak after rectal cancer surgery: A machine learning approach

Shen,

Huang,

et al. 2023

Journal of Surgical Oncology

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IntroductionAnastomotic leakage (AL) remains the most dreaded and unpredictable major complication after low anterior resection for mid‐low rectal cancer. The aim of this study is to identify patients with high risk for AL based on the machine learning method.MethodsPatients with mid‐low rectal cancer undergoing low anterior resection were enrolled from West China Hospital between January 2008 and October 2019 and were split by time into training cohort and validation cohort. The least absolute shrinkage and selection operator (LASSO) method and stepwise method were applied for variable selection and predictive model building in the training cohort. The area under the receiver operating characteristic curve (AUC) and calibration curves were used to evaluate the performance of the models.ResultsThe rate of AL was 5.8% (38/652) and 7.2% (15/208) in the training cohort and validation cohort, respectively. The LASSO‐logistic model selected almost the same variables (hypertension, operating time, cT4, tumor location, intraoperative blood loss) compared to the stepwise logistic model except for tumor size (the LASSO‐logistic model) and American Society of Anesthesiologists score (the stepwise logistic model). The predictive performance of the LASSO‐logistics model was better than the stepwise‐logistics model (AUC: 0.790 vs. 0.759). Calibration curves showed mean absolute error of 0.006 and 0.013 for the LASSO‐logistics model and stepwise‐logistics model, respectively.ConclusionOur study developed a feasible predictive model with a machine‐learning algorithm to classify patients with a high risk of AL, which would assist surgical decision‐making and reduce unnecessary stoma diversion. The involved machine learning algorithms provide clinicians with an innovative alternative to enhance clinical management.

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Validation of an online risk calculator for the prediction of anastomotic leak after colon cancer surgery and preliminary exploration of artificial intelligence-based analytics

Abstract: The anastomotic leak risk calculator is significantly predictive of anastomotic leak after colon cancer resection. Wider investigation of artificial intelligence-based analytics for risk prediction is warranted.

Cited by 33 publications

References 31 publications

Predictors of complications and mortality following left colectomy with primary stapled anastomosis for cancer: results of a multicentric study with 1111 patients

Predictors of complications and mortality following left colectomy with primary stapled anastomosis for cancer: results of a multicentric study with 1111 patients

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

Prediction of symptomatic anastomotic leak after rectal cancer surgery: A machine learning approach

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