Estimation of the degree of surgical difficulty anticipated for pancreatoduodenectomy: Preoperative and intraoperative factors

Kosaka, Hisashi; Satoi, Sohei; Kono, Yumiko; Yamamoto, Tomohisa; Hirooka, Satoshi; Yamaki, So; Hashimoto, Daisuke; Sakaguchi, Tatsuma; Sekimoto, Mitsugu

doi:10.1002/jhbp.1052

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…Accurate preoperative assessment of surgical difficulty is crucial to the success of the surgery and patient safety. By predicting the difficulty of the surgical procedure before surgery, surgeons can better prepare for the surgery, optimize the surgical plan, and ensure patient safety during the operation ( 27 , 28 ). Su et al.…”

Section: Discussionmentioning

confidence: 99%

Automated machine learning to predict the difficulty for endoscopic resection of gastric gastrointestinal stromal tumor

et al. 2023

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BackgroundAccurate preoperative assessment of surgical difficulty is crucial to the success of the surgery and patient safety. This study aimed to evaluate the difficulty for endoscopic resection (ER) of gastric gastrointestinal stromal tumors (gGISTs) using multiple machine learning (ML) algorithms.MethodsFrom December 2010 to December 2022, 555 patients with gGISTs in multi-centers were retrospectively studied and assigned to a training, validation, and test cohort. A difficult case was defined as meeting one of the following criteria: an operative time ≥ 90 min, severe intraoperative bleeding, or conversion to laparoscopic resection. Five types of algorithms were employed in building models, including traditional logistic regression (LR) and automated machine learning (AutoML) analysis (gradient boost machine (GBM), deep neural net (DL), generalized linear model (GLM), and default random forest (DRF)). We assessed the performance of the models using the areas under the receiver operating characteristic curves (AUC), the calibration curve, and the decision curve analysis (DCA) based on LR, as well as feature importance, SHapley Additive exPlanation (SHAP) Plots and Local Interpretable Model Agnostic Explanation (LIME) based on AutoML.ResultsThe GBM model outperformed other models with an AUC of 0.894 in the validation and 0.791 in the test cohorts. Furthermore, the GBM model achieved the highest accuracy among these AutoML models, with 0.935 and 0.911 in the validation and test cohorts, respectively. In addition, it was found that tumor size and endoscopists’ experience were the most prominent features that significantly impacted the AutoML model’s performance in predicting the difficulty for ER of gGISTs.ConclusionThe AutoML model based on the GBM algorithm can accurately predict the difficulty for ER of gGISTs before surgery.

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Section: Discussionmentioning

confidence: 99%

Automated machine learning to predict the difficulty for endoscopic resection of gastric gastrointestinal stromal tumor

et al. 2023

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“…7 This undertaking is complicated by the fact that PD is not a uniform procedure due to anatomic variations in liver supply and branching pattern of superior mesenteric vessels, 8 the need to tailor the amount of retroperitoneal dissection based on disease and/or tumor type, 9 variability in digestive reconstruction techniques, 10 and different levels of technical difficulty. 11,12 Minimally invasive PD is associated with additional technical challenges that may sometimes result in major intraoperative adverse events requiring emergency conversion 13 and potentially increasing the risk of death. 14,15 The experience of the Pittsburgh group 16 and the LAELAPS-3 training program in the Netherlands 17 have shown that structured training programs permit safe implementation of RPD and reduce the learning curve for new adopters.…”

Section: Introductionmentioning

confidence: 99%

“…20 Also, in open PD, there is just 1 difficulty score, developed in 99 procedures without external validation. 12 We herein provide a new difficulty score for RPD, the PD-ROBOSCORE, developed within the International Consortium on Minimally Invasive Pancreatic Surgery (I-MIPS) (www.i-mips.com).…”

Section: Introductionmentioning

confidence: 99%

The PD-ROBOSCORE: A difficulty score for robotic pancreatoduodenectomy

Napoli

Cacace

Kauffmann

et al. 2023

Surgery

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“…Pancreatoduodenectomy (PD) is a demanding procedure that, among gastrointestinal surgeries, requires a high level of skill and appropriate perioperative management 1–5 . Pancreatic cancer accounts for the majority of diseases requiring pancreatectomy.…”

Section: Introductionmentioning

confidence: 99%

“…Pancreatoduodenectomy (PD) is a demanding procedure that, among gastrointestinal surgeries, requires a high level of skill and appropriate perioperative management. 1 , 2 , 3 , 4 , 5 Pancreatic cancer accounts for the majority of diseases requiring pancreatectomy. In recent years, advances in multidisciplinary treatment for pancreatic cancer have led to an increase in the 5‐year survival rate of pancreatic cancer patients and an increase in the number of patients who survive for a long period.…”

Section: Introductionmentioning

confidence: 99%

Novel choledochojejunostomy technique “T‐shaped anastomosis” for preventing the development of postoperative cholangitis in pancreatoduodenectomy: A propensity score matching analysis

Kimura,

Igarashi,

Murotani

et al. 2023

Annals of Gastroent Surgery

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BackgroundThere have been few studies of countermeasures against postoperative cholangitis, a serious complication after pancreaticoduodenectomy (PD) that impairs quality of life.ObjectiveTo evaluate our recently developed, novel method of choledochojejunostomy with a larger anastomotic diameter, the “T‐shaped anastomosis.”MethodsThe study included 261 cases of PD. The T‐shaped choledochojejunostomy technique was performed with an additional incision for a distance greater than half the diameter of the bile duct at the anterior wall of the bile duct and the anterior wall of the elevated jejunum. To compensate for potential confounding biases between the standard anastomosis group (n = 206) and the T‐shaped anastomosis group (n = 55), we performed propensity score matching (PSM). The primary endpoint was the incidence of medium‐term postoperative cholangitis adjusted for PSM.ResultsIn the PSM analysis, 54 patients in each group were matched, and the median bile duct diameter measured by preoperative CT was 8.8 mm versus 9.3 mm, the rate of preoperative biliary drainage was 31% versus 37%, the incidence of cholangitis within 1 month before surgery was 9% versus 13%, and the incidence of postoperative bile leakage was 2% versus 2%, with no significant differences. The incidence of medium‐term postoperative cholangitis was 15% versus 4%, and multivariate logistic regression revealed that T‐shaped choledochojejunostomy was an independent predictor of a reduced incidence of cholangitis (odds ratio, 0.17, 95% CI 0.02–0.81; p = 0.024).ConclusionsThe T‐shaped choledochojejunostomy technique was shown to be effective with a significant reduction in the incidence of medium‐term postoperative cholangitis. Clinical trial identification: UMIN000050990.

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Estimation of the degree of surgical difficulty anticipated for pancreatoduodenectomy: Preoperative and intraoperative factors

Cited by 9 publications

References 26 publications

Automated machine learning to predict the difficulty for endoscopic resection of gastric gastrointestinal stromal tumor

Automated machine learning to predict the difficulty for endoscopic resection of gastric gastrointestinal stromal tumor

The PD-ROBOSCORE: A difficulty score for robotic pancreatoduodenectomy

Novel choledochojejunostomy technique “T‐shaped anastomosis” for preventing the development of postoperative cholangitis in pancreatoduodenectomy: A propensity score matching analysis

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