Gastroparesis: A Multidisciplinary Approach to Management

Factor analysis of postsurgical gastroparesis syndrome after right hemicolectomy for colon cancer

The present study aimed to investigate factors influencing postsurgical gastroparesis syndrome (PGS) in patients with right-sided colon cancer. In total, 260 patients who underwent complete mesocolic excision for right-sided colon cancer were included in the present analysis. Among the included patients, 69 underwent open radical right-sided colon resection, 175 underwent laparoscopic radical right-sided colon resection and 16 underwent robot-assisted radical right-sided colon resection. The occurrence of PGS was observed, and both the χ2 test and multivariate regression analysis were conducted to identify influencing factors. Among the 260 patients, 32 experienced PGS, with an incidence rate of 12.3%. Univariate analysis demonstrated that age, perioperative blood glucose levels, self-rated anxiety scale scores and surgical approach were significantly associated with PGS (P<0.05), whereas sex, surgical duration, diabetes and perioperative albumin levels were not significant factors (P>0.05). Multivariate logistic regression analysis showed that age >70 years, perioperative blood glucose ≥11.1 mmol/l, a self-rating anxiety scale score ≥50 and radical extended right-sided colon resection were risk factors for PGS occurrence. In conclusion, the occurrence of PGS in patients with right-sided colon cancer was revealed to be associated with age, perioperative blood glucose levels, self-rated anxiety scale scores and surgical approach.

Predicting the risk of gastroparesis in critically ill patients after CME using an interpretable machine learning algorithm - a 10-year multicenter retrospective study

Background

Gastroparesis following complete mesocolic excision (CME) can precipitate a cascade of severe complications, which may significantly hinder postoperative recovery and diminish the patient's quality of life. In the present study, four advanced machine learning algorithms-Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and k-nearest neighbor (KNN)-were employed to develop predictive models. The clinical data of critically ill patients transferred to the intensive care unit (ICU) post-CME were meticulously analyzed to identify key risk factors associated with the development of gastroparesis.

Methods

We gathered 34 feature variables from a cohort of 1,097 colon cancer patients, including 87 individuals who developed gastroparesis post-surgery, across multiple hospitals, and applied a range of machine learning algorithms to construct the predictive model. To assess the model's generalization performance, we employed 10-fold cross-validation, while the receiver operating characteristic (ROC) curve was utilized to evaluate its discriminative capacity. Additionally, calibration curves, decision curve analysis (DCA), and external validation were integrated to provide a comprehensive evaluation of the model's clinical applicability and utility.

Results

Among the four predictive models, the XGBoost algorithm demonstrated superior performance. As indicated by the ROC curve, XGBoost achieved an area under the curve (AUC) of 0.939 in the training set and 0.876 in the validation set, reflecting exceptional predictive accuracy. Notably, in the k-fold cross-validation, the XGBoost model exhibited robust consistency across all folds, underscoring its stability. The calibration curve further revealed a favorable concordance between the predicted probabilities and the actual outcomes of the XGBoost model. Additionally, the DCA highlighted that patients receiving intervention under the XGBoost model experienced significantly greater clinical benefit.

Conclusion

The onset of postoperative gastroparesis in colon cancer patients remains an elusive challenge to entirely prevent. However, the prediction model developed in this study offers valuable assistance to clinicians in identifying key high-risk factors for gastroparesis, thereby enhancing the quality of life and survival outcomes for these patients.

Machine learning-based prediction of gastroparesis risk following complete mesocolic excision

BACKGROUND

Gastroparesis is a major complication following complete mesocolic excision (CME) and significantly impacts patient outcomes. This study aimed to create a machine learning model to pinpoint key risk factors before, during, and after surgery, effectively predicting the risk of gastroparesis after CME.

METHODS

The study involved 1146 patients with colon cancer, out of which 95 developed gastroparesis. Data were collected on 34 variables, including demographics, chronic conditions, pre-surgery test results, types of surgery, and intraoperative details. Four machine learning techniques were employed: extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). The evaluation involved k-fold cross-validation, receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA), and external validation.

RESULTS

XGBoost excelled in its performance for predictive models. ROC analysis showed high accuracy for XGBoost, with area under the curve (AUC) scores of 0.976 for the training set and 0.906 for the validation set. K-fold cross-validation confirmed the model's stability, and calibration curves indicated high predictive accuracy. Additionally, DCA highlighted XGBoost's superior patient benefits for intervention treatments. An AUC of 0.77 in external validation demonstrated XGBoost's strong generalization ability.

CONCLUSION

The XGBoost-fueled predictive model for post-surgery colon cancer patients proved highly effective. It underlined gastroparesis as a significant post-operative issue, associated with advanced age, prolonged surgeries, extensive intraoperative blood loss, surgical techniques, low serum protein levels, anemia, diabetes, and hypothyroidism.

When the Stomach Takes a Vacation: The Unseen Battles of Gastroparesis

Gastroparesis is a syndrome characterised by delayed gastric emptying that is usually idiopathic, diabetic, or iatrogenic. This underdiagnosed disease has a substantial influence on the quality of life of its patients. We present the case of an 86-year-old man with dementia, benign prostatic hyperplasia, and gastroesophageal reflux disease who developed symptoms of gastroparesis during a lengthy hospital stay. Computed tomography (CT) and upper digestive endoscopy demonstrated gastric distention and pyloric stenosis. Despite cautious treatment and eventual pyloric dilation, the patient died from aspiration due to refractory respiratory failure. This example emphasises the need for early detection and thorough examination of gastroparesis to optimise patient outcomes and reduce morbidity and mortality.

Using an Endoluminal Functional Lumen Imaging Probe (EndoFLIP™) to Compare Pyloric Function in Patients with Gastroparesis to Patients After Esophagectomy

BACKGROUND

Gastroparesis (GP) occurs in patients after upper gastrointestinal surgery, in patients with diabetes or systemic sclerosis and in idiopathic GP patients. As pyloric dysfunction is considered one of the underlying mechanisms, measuring this mechanism with EndoFLIP™ can lead to a better understanding of the disease.

METHODS

Between November 2021 and March 2022, we performed a retrospective single-centre study of all patients who had non-surgical GP, post-surgical GP and no sign of GP after esophagectomy and who underwent our post-surgery follow-up program with surveillance endoscopies and further exams. EndoFLIP™ was used to perform measurements of the pylorus, and distensibility was measured at 40 ml, 45 ml and 50 ml balloon filling.

RESULTS

We included 66 patients, and successful application of the EndoFLIP™ was achieved in all interventions (n = 66, 100%). We identified 18 patients suffering from non-surgical GP, 23 patients suffering from GP after surgery and 25 patients without GP after esophagectomy. At 40, 45 and 50 ml balloon filling, the mean distensibility in gastroparetic patients was 8.2, 6.2 and 4.5 mm²/mmHg; 5.4, 5.1 and 4.7 mm²/mmHg in post-surgical patients suffering of GP; and 8.5, 7.6 and 6.3 mm²/mmHg in asymptomatic post-surgical patients. Differences between symptomatic and asymptomatic patients were significant.

CONCLUSION

Measurement with EndoFLIP™ showed that asymptomatic post-surgery patients seem to have a higher pyloric distensibility. Pyloric distensibility and symptoms of GP seem to correspond.