Gastroparesis post-laparoscopic cholecystectomy in diabetic patients

Clinical perspectives on post-cholecystectomy syndrome: a narrative review

INTRODUCTION

Post-cholecystectomy syndrome (PCS) is a complex condition characterized by persistent or new symptoms following gallbladder removal, affecting up to 47% of patients. Despite being recognized since 1947, there is still no consensus on its etiology, diagnosis, and treatment.

AREAS COVERED

This narrative review explores the multifactorial etiology of PCS, including biliary and extra-biliary factors, and its varied clinical manifestations. A systematic literature search was conducted using keywords like 'etiology', 'clinical manifestations', 'diagnostic challenges', and 'management strategies'. The review covers traditional diagnostic methods, recent insights into pathophysiology, and current management approaches, such as dietary modifications, pharmacological treatments, and endoscopic interventions, with a focus on patient selection.

EXPERT OPINION

PCS presents significant clinical challenges due to its diverse presentations and lack of standardized diagnostic and therapeutic protocols. Effective management starts with careful patient selection before cholecystectomy to prevent unnecessary surgeries and reduce postoperative complications. Future research should aim to refine diagnostic criteria and develop predictive models for identifying at-risk patients. Personalized management strategies incorporating genetic, biological, and clinical factors are essential for improving outcomes. An integrated, patient-centered approach is crucial for addressing PCS complexities and enhancing the quality of life for affected patients.

Risk factor analysis and nomogram for predicting gastroparesis in patients with type 2 diabetes mellitus

Purpose

The incidence of gastroparesis is higher in individuals diagnosed with type 2 diabetes mellitus (T2DM) compared to the healthy individuals. Our study aimed to explore the risk factors for gastroparesis in T2DM and to establish a clinical prediction model (nomogram).

Methods

Our study enlisted 694 patients with T2DM from two medical centers over a period of time. From January 2020 to December 2022, 347 and 149 patients were recruited from the Beilun branch of Zhejiang University's First Affiliated Hospital in the training and internal validation cohorts, respectively. The external validation cohort consisted of 198 patients who were enrolled at Nanchang University's First Affiliated Hospital from October 2020 to September 2021. We conducted univariate and multivariate logistic regression analyses to select the risk factors for gastroparesis in patients with T2DM; subsequently,we developed a nomogram model. The performance of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis(DCA).

Results

Four clinical variables, including age, regular exercise, glycated hemoglobin level(HbA1c), and Helicobacter pylori (H. pylori) infection, were identified and included in the model. The model demonstrated excellent discrimination, with an AUC of 0.951 (95% CI = 0.925-0.978) in the training group, and 0.910 (95% CI = 0.859-0.961) and 0.875 (95% CI = 0.813-0.937) in the internal and external validation groups, respectively. The calibration curve showed good consistency between prediction of the model and observed gastroparesis. The DCA also demonstrated good clinical efficacy.

Conclusion

The nomogram model developed in this study showed good performance in predicting gastroparesis in patients with T2DM.