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Colonial Wig Pancreaticojejunostomy
Published on: March 12, 2019
The Charlson Comorbidity Index Predicts Clinically Relevant Postoperative Pancreatic Fistula in Patients Undergoing
Hiroki Imamura1, Yoshito Tomimaru1, Shogo Kobayashi1
1Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan.
Background:
Postoperative pancreatic fistula (POPF) is a severe complication after pancreatectomy. The preoperative prediction of POPF would benefit patients by providing postoperative management tailored to each patient based on the risk for POPF. The Charlson Comorbidity Index (CCI), which assesses the severity of patient comorbidities, has been associated with postoperative complications in various surgeries. However, its relationship with POPF remains unclear. This study investigates the impact of CCI on the development of POPF.
Methods:
This retrospective study reviewed 597 patients who underwent pancreatectomy from 2010 to 2020, of whom 219 underwent distal pancreatectomy (DP) and 378 underwent pancreaticoduodenectomy (PD). Significant factors were assessed in association with clinically relevant POPF (CR-POPF) using a logistic regression model. K-means clustering was employed based on the body mass index, pancreatic thickness, and CCI score to stratify patients by the risk for CR-POPF.
Results:
Higher CCI scores were significantly associated with an increased incidence of CR-POPF, particularly in patients undergoing DP, whereas such association was not observed in patients undergoing PD. Multivariate analysis identified male sex, BMI > 25.95 kg/m2, pancreatic thickness > 9.01 mm, and CCI score > 4 as independent predictors of CR-POPF in the DP group. A predictive model incorporating these factors demonstrated moderate accuracy (AUC = 0.6750) in stratifying patients into high- and low-risk groups for CR-POPF.
Conclusion:
CCI is a significant predictor of CR-POPF, especially in patients undergoing DP. By integrating CCI with other factors, it was feasible to develop a predictive model with high diagnostic accuracy.
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