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Potievskiy MB, Petrov LO, Ivanov SA, Sokolov PV, Trifanov VS, Grishin NA, Moshurov RI, Shegai PV, Kaprin AD. Machine learning for modeling and identifying risk factors of pancreatic fistula. World J Gastrointest Oncol 2025; 17:100089. [DOI: 10.4251/wjgo.v17.i4.100089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2024] [Revised: 12/05/2024] [Accepted: 02/05/2025] [Indexed: 03/25/2025] Open
Abstract
BACKGROUND Pancreatic fistula is the most common complication of pancreatic surgeries that causes more serious conditions, including bleeding due to visceral vessel erosion and peritonitis.
AIM To develop a machine learning (ML) model for postoperative pancreatic fistula and identify significant risk factors of the complication.
METHODS A single-center retrospective clinical study was conducted which included 150 patients, who underwent pancreatoduodenectomy. Logistic regression, random forest, and CatBoost were employed for modeling the biochemical leak (symptomless fistula) and fistula grade B/C (clinically significant complication). The performance was estimated by receiver operating characteristic (ROC) area under the curve (AUC) after 5-fold cross-validation (20% testing and 80% training data). The risk factors were evaluated with the most accurate algorithm, based on the parameter “Importance” (Im), and Kendall correlation, P < 0.05.
RESULTS The CatBoost algorithm was the most accurate with an AUC of 74%-86%. The study provided results of ML-based modeling and algorithm selection for pancreatic fistula prediction and risk factor evaluation. From 14 parameters we selected the main pre- and intraoperative prognostic factors of all the fistulas: Tumor vascular invasion (Im = 24.8%), age (Im = 18.6%), and body mass index (Im = 16.4%), AUC = 74%. The ML model showed that biochemical leak, blood and drain amylase level (Im = 21.6% and 16.4%), and blood leukocytes (Im = 11.2%) were crucial predictors for subsequent fistula B/C, AUC = 86%. Surgical techniques, morphology, and pancreatic duct diameter less than 3 mm were insignificant (Im < 5% and no correlations detected). The results were confirmed by correlation analysis.
CONCLUSION This study highlights the key predictors of postoperative pancreatic fistula and establishes a robust ML-based model for individualized risk prediction. These findings contribute to the advancement of personalized perioperative care and may guide targeted preventive strategies.
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Affiliation(s)
- Mikhail B Potievskiy
- Center for Clinical Trials, Center for Innovative Radiological and Regenerative Technologies, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
| | - Leonid O Petrov
- Department of Radiation and Surgical Treatment of Abdominal Diseases, A. Tsyb Medical Radiological Center, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
| | - Sergei A Ivanov
- Department of Administration, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
| | - Pavel V Sokolov
- Department of Operation Unit, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
| | - Vladimir S Trifanov
- Department of Abdominal Oncology, P. Herzen Moscow Oncological Institute, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
| | - Nikolai A Grishin
- Department of Abdominal Oncology, P. Herzen Moscow Oncological Institute, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
| | - Ruslan I Moshurov
- Department of Abdominal Oncology, P. Herzen Moscow Oncological Institute, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
| | - Peter V Shegai
- Center for Innovative Radiological and Regenerative Technologies, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
| | - Andrei D Kaprin
- Department of Administration, FSBI “National Medical Research Radiological Center” of the Ministry of Health of the Russian Federation, Obninsk 249036, Kaluzhskaya Oblast, Russia
- Department of Urology and Operative Nephrology with Course of Oncology, Medical Faculty, Medical Institute, Peoples’ Friendship University of Russia, Moscow 117198, Moskva, Russia
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Alhulaili ZM, Pleijhuis RG, Hoogwater FJH, Nijkamp MW, Klaase JM. Risk stratification of postoperative pancreatic fistula and other complications following pancreatoduodenectomy. How far are we? A scoping review. Langenbecks Arch Surg 2025; 410:62. [PMID: 39915344 PMCID: PMC11802655 DOI: 10.1007/s00423-024-03581-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 12/16/2024] [Indexed: 02/09/2025]
Abstract
PURPOSE Pancreatoduodenectomy (PD) is a challenging procedure which is associated with high morbidity rates. This study was performed to make an overview of risk factors included in risk stratification methods both logistic regression models and models based on artificial intelligence algorithms to predict postoperative pancreatic fistula (POPF) and other complications following PD and to provide insight in the extent to which these tools were validated. METHODS Five databases were searched to identify relevant studies. Calculators, equations, nomograms, and artificial intelligence models that addressed POPF and other complications were included. Only PD resections were considered eligible. There was no exclusion of the minimally invasive techniques reporting PD resections. All other pancreatic resections were excluded. RESULTS 90 studies were included. Thirty-five studies were related to POPF, thirty-five studies were related to other complications following PD and twenty studies were related to artificial intelligence predication models after PD. Among the identified risk factors, the most used factors for POPF risk stratification were the main pancreatic duct diameter (MPD) (80%) followed by pancreatic texture (51%), whereas for other complications the most used factors were age (34%) and ASA score (29.4%). Only 26% of the evaluated risk stratification tools for POPF and other complications were externally validated. This percentage was even lower for the risk models using artificial intelligence which was 20%. CONCLUSION The MPD was the most used factor when stratifying the risk of POPF followed by pancreatic texture. Age and ASA score were the most used factors for the stratification of other complications. Insight in clinically relevant risk factors could help surgeons in adapting their surgical strategy and shared decision-making. This study revealed that the focus of research still lies on developing new risk models rather than model validation, hampering clinical implementation of these tools for decision support.
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Affiliation(s)
- Zahraa M Alhulaili
- Department of Hepato-Pancreato- Biliary Surgery and Liver Transplantation University Medical Center Groningen, University of Groningen, 30001 9700 RB, Groningen, Netherlands
| | - Rick G Pleijhuis
- Department of Internal Medicine University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Frederik J H Hoogwater
- Department of Hepato-Pancreato- Biliary Surgery and Liver Transplantation University Medical Center Groningen, University of Groningen, 30001 9700 RB, Groningen, Netherlands
| | - Maarten W Nijkamp
- Department of Hepato-Pancreato- Biliary Surgery and Liver Transplantation University Medical Center Groningen, University of Groningen, 30001 9700 RB, Groningen, Netherlands
| | - Joost M Klaase
- Department of Hepato-Pancreato- Biliary Surgery and Liver Transplantation University Medical Center Groningen, University of Groningen, 30001 9700 RB, Groningen, Netherlands.
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3
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Huang L, Jiang B, Lai J, Wu D, Chen J, Tian Y, Chen S. Efficacy of the two-parts wrapping technique in reducing postoperative complications in laparoscopic pancreaticoduodenectomy. Surg Endosc 2024:10.1007/s00464-024-11028-x. [PMID: 39009728 DOI: 10.1007/s00464-024-11028-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 06/30/2024] [Indexed: 07/17/2024]
Abstract
BACKGROUND The advancement of laparoscopic technology has broadened the application of laparoscopic pancreaticoduodenectomy (LPD) for treating pancreatic head and ampullary tumors. Despite its benefits, postoperative pancreatic fistula (POPF) and postpancreatectomy hemorrhage (PPH) remain significant complications. Ligamentum teres hepatis wrapping around the gastroduodenal artery (GDA) stump show limitations in reducing POPF and PPH. METHODS This study retrospectively analyzed patients undergoing LPD from January 2016 to October 2023, We compared the effectiveness of the two-parts wrapping (the ligamentum teres hepatis wrapping of the gastroduodenal artery stump and the omentum flap wrapping of the pancreatojejunal anastomosis) and ligamentum teres hepatis wrapping around the gastroduodenal artery (GDA) in reducing postoperative pancreatic fistula (POPF) and postpancreatectomy hemorrhage (PPH), using propensity score matching for the analysis. RESULTS A total of 172 patients were analyzed, showing that the two-parts wrapping group significantly reduced the rates of overall and severe complications, POPF, and PPH compared to ligamentum teres hepatis wrapping around the GDA group. Specifically, the study found lower rates of grade B/C POPF and no instances of PPH in the two-parts wrapping group, alongside shorter postoperative hospital stays and drainage removal times. These benefits were particularly notable in patients with soft pancreatic textures and pancreatic duct diameters of < 3 mm. CONCLUSION The two-parts wrapping technique significantly reduce the risks of POPF and PPH in LPD, offering a promising approach for patients with soft pancreas and pancreatic duct diameter of < 3 mm.
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Affiliation(s)
- Long Huang
- Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, China
- Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, 350001, China
| | - Binhua Jiang
- Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, China
- Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, 350001, China
| | - Jianlin Lai
- Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, China
- Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, 350001, China
| | - Dihang Wu
- Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, China
- Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, 350001, China
| | - Junjie Chen
- Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, China
- Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, 350001, China
| | - Yifeng Tian
- Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, China.
- Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, 350001, China.
| | - Shi Chen
- Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, China.
- Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, 350001, China.
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Hu H, Liu G, Yang Y. Letter to Editor Regarding Article "A Machine Learning Approach to Predict Postoperative Pancreatic Fistula After Pancreaticoduodenectomy Using Only Preoperatively Known Data". Ann Surg Oncol 2024; 31:4709-4710. [PMID: 38557910 DOI: 10.1245/s10434-024-15237-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Affiliation(s)
- Haiyang Hu
- Department of Cardiology and Critical Care Medicine, Affiliated Hospital of Jining Medical College, Jining, China
| | - Guoshuai Liu
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yanfei Yang
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
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Ashraf Ganjouei A, Wang J, Alseidi A, Adam MA. Reply to: Letter to Editor Regarding Article "A Machine Learning Approach to Predict Postoperative Pancreatic Fistula after Pancreaticoduodenectomy Using Only Preoperatively Known Data" by Yang, Yanfei et al. Ann Surg Oncol 2024; 31:4711-4712. [PMID: 38568375 DOI: 10.1245/s10434-024-15240-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 06/14/2024]
Affiliation(s)
| | - Jane Wang
- Department of Surgery, University of California, San Francisco, CA, USA
| | - Adnan Alseidi
- Department of Surgery, Division of Surgical Oncology, University of California, San Francisco, CA, USA
| | - Mohamed A Adam
- Department of Surgery, Division of Surgical Oncology, University of California, San Francisco, CA, USA.
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Brydges G, Uppal A, Gottumukkala V. Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians. Curr Oncol 2024; 31:2727-2747. [PMID: 38785488 PMCID: PMC11120613 DOI: 10.3390/curroncol31050207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/07/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024] Open
Abstract
This narrative review explores the utilization of machine learning (ML) and artificial intelligence (AI) models to enhance perioperative cancer care. ML and AI models offer significant potential to improve perioperative cancer care by predicting outcomes and supporting clinical decision-making. Tailored for perioperative professionals including anesthesiologists, surgeons, critical care physicians, nurse anesthetists, and perioperative nurses, this review provides a comprehensive framework for the integration of ML and AI models to enhance patient care delivery throughout the perioperative continuum.
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Affiliation(s)
- Garry Brydges
- Division of Anesthesiology, Critical Care & Pain Medicine, The University of Texas at MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Abhineet Uppal
- Department of Colon & Rectal Surgery, The University of Texas at MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Vijaya Gottumukkala
- Department of Anesthesiology & Perioperative Medicine, The University of Texas at MD Anderson Cancer Center, 1400-Unit 409, Holcombe Blvd, Houston, TX 77030, USA
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Muaddi H, Salehinejad H, Thiels CA. Persistent challenges in pancreatic surgery: Postoperative pancreatic fistula prediction in the machine learning era-Response to: Machine learning versus logistic regression for the prediction of complications after pancreaticoduodenectomy. Surgery 2024; 175:1466-1467. [PMID: 38040594 DOI: 10.1016/j.surg.2023.10.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 10/24/2023] [Indexed: 12/03/2023]
Affiliation(s)
- Hala Muaddi
- Division of Hepatobiliary and Pancreas Surgery, Mayo Clinic, Rochester, MN. https://twitter.com/HalaMuaddi
| | - Hojjat Salehinejad
- Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN; Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN. https://twitter.com/SalehinejadH
| | - Cornelius A Thiels
- Division of Hepatobiliary and Pancreas Surgery, Mayo Clinic, Rochester, MN.
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Yang F, Windsor JA, Fu DL. Optimizing prediction models for pancreatic fistula after pancreatectomy: Current status and future perspectives. World J Gastroenterol 2024; 30:1329-1345. [PMID: 38596504 PMCID: PMC11000089 DOI: 10.3748/wjg.v30.i10.1329] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/15/2024] [Accepted: 02/25/2024] [Indexed: 03/14/2024] Open
Abstract
Postoperative pancreatic fistula (POPF) is a frequent complication after pancreatectomy, leading to increased morbidity and mortality. Optimizing prediction models for POPF has emerged as a critical focus in surgical research. Although over sixty models following pancreaticoduodenectomy, predominantly reliant on a variety of clinical, surgical, and radiological parameters, have been documented, their predictive accuracy remains suboptimal in external validation and across diverse populations. As models after distal pancreatectomy continue to be progressively reported, their external validation is eagerly anticipated. Conversely, POPF prediction after central pancreatectomy is in its nascent stage, warranting urgent need for further development and validation. The potential of machine learning and big data analytics offers promising prospects for enhancing the accuracy of prediction models by incorporating an extensive array of variables and optimizing algorithm performance. Moreover, there is potential for the development of personalized prediction models based on patient- or pancreas-specific factors and postoperative serum or drain fluid biomarkers to improve accuracy in identifying individuals at risk of POPF. In the future, prospective multicenter studies and the integration of novel imaging technologies, such as artificial intelligence-based radiomics, may further refine predictive models. Addressing these issues is anticipated to revolutionize risk stratification, clinical decision-making, and postoperative management in patients undergoing pancreatectomy.
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Affiliation(s)
- Feng Yang
- Department of Pancreatic Surgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - John A Windsor
- Surgical and Translational Research Centre, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1142, New Zealand
| | - De-Liang Fu
- Department of Pancreatic Surgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
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Lee W, Park HJ, Lee HJ, Song KB, Hwang DW, Lee JH, Lim K, Ko Y, Kim HJ, Kim KW, Kim SC. Deep learning-based prediction of post-pancreaticoduodenectomy pancreatic fistula. Sci Rep 2024; 14:5089. [PMID: 38429308 PMCID: PMC10907568 DOI: 10.1038/s41598-024-51777-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 01/09/2024] [Indexed: 03/03/2024] Open
Abstract
Postoperative pancreatic fistula is a life-threatening complication with an unmet need for accurate prediction. This study was aimed to develop preoperative artificial intelligence-based prediction models. Patients who underwent pancreaticoduodenectomy were enrolled and stratified into model development and validation sets by surgery between 2016 and 2017 or in 2018, respectively. Machine learning models based on clinical and body composition data, and deep learning models based on computed tomographic data, were developed, combined by ensemble voting, and final models were selected comparison with earlier model. Among the 1333 participants (training, n = 881; test, n = 452), postoperative pancreatic fistula occurred in 421 (47.8%) and 134 (31.8%) and clinically relevant postoperative pancreatic fistula occurred in 59 (6.7%) and 27 (6.0%) participants in the training and test datasets, respectively. In the test dataset, the area under the receiver operating curve [AUC (95% confidence interval)] of the selected preoperative model for predicting all and clinically relevant postoperative pancreatic fistula was 0.75 (0.71-0.80) and 0.68 (0.58-0.78). The ensemble model showed better predictive performance than the individual ML and DL models.
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Affiliation(s)
- Woohyung Lee
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Asan Medical Center, Brain Korea21 Project, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Hack-Jin Lee
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Asan Medical Center, Brain Korea21 Project, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
- R&D Team, DoAI Inc., Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Ki Byung Song
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Asan Medical Center, Brain Korea21 Project, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Dae Wook Hwang
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Asan Medical Center, Brain Korea21 Project, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Jae Hoon Lee
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Asan Medical Center, Brain Korea21 Project, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Kyongmook Lim
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Asan Medical Center, Brain Korea21 Project, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
- R&D Team, DoAI Inc., Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Yousun Ko
- Department of Convergence Medicine and Radiology, Research Institute of Radiology and Institute of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyoung Jung Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Kyung Won Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
| | - Song Cheol Kim
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Asan Medical Center, Brain Korea21 Project, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
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Wang JG, Lei K, You K, Xu J, Liu ZJ. Wrapping pancreaticojejunostomy using the ligamentum teres hepatis during laparoscopic pancreaticoduodenectomy: a propensity score matching analysis. World J Surg Oncol 2023; 21:356. [PMID: 37978553 PMCID: PMC10656888 DOI: 10.1186/s12957-023-03255-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 11/13/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND AND OBJECTIVE It is controversial whether wrapping around the pancreaticojejunostomy (PJ) could reduce the rate of postoperative pancreatic fistula (POPF), especially in laparoscopic pancreaticoduodenectomy (LPD). This study aims to summarize our single-center initial experience in wrapping around PJ using the ligamentum teres hepatis (LTH) and demonstrate the feasibility and safety of this method. METHODS Patients who underwent LPD applying the procedure of wrapping around the PJ were identified. The cohort was compared to the cohort with standard non-wrapping PJ. A 1:1 propensity score matching (PSM) was performed to compare the early postoperative outcomes of the two cohorts. Risk factors for POPF were determined by using univariate and multivariate logistic regression analysis. RESULTS Overall, 143 patients were analyzed (LPD without wrapping (n = 91) and LPD with wrapping (n = 52)). After 1:1 PSM, 48 patients in each cohort were selected for further analysis. Bile leakage, DGE, intra-abdominal infection, postoperative hospital stays, harvested lymph nodes, and R0 resection were comparable between the two cohorts. However, the wrapping cohort was associated with significantly less POPF B (1 vs 18, P = 0.003), POPF C (0 vs 8, P = 0.043), and Clavien-Dindo classification level III-V (5 vs 26, P = 0.010). No patients died due to the clinically relevant POPF in the two cohorts. No patients who underwent the LTH wrapping procedure developed complications directly related to the wrapping procedure. After PSM, whether wrapping was an independent risk factor for POPF (OR = 0.202; 95%CI:0.080-0.513; P = 0.001). CONCLUSIONS Wrapping the LTH around the PJ technique for LPD was safe, efficient, and reproducible with favorable perioperative outcomes in selected patients. However, further validations using high-quality RCTs are still required to confirm the findings of this study.
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Affiliation(s)
- Jia-Guo Wang
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, 400010, China
| | - Kai Lei
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, 400010, China
| | - Ke You
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, 400010, China
| | - Jie Xu
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, 400010, China
| | - Zuo-Jin Liu
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, 400010, China.
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Ashraf Ganjouei A, Wang JJ, Romero-Hernandez F, Alseidi A, Adam MA. ASO Author Reflections: Machine Learning-Based Preoperative Prediction of Pancreatic Fistula after Pancreaticoduodenectomy. Ann Surg Oncol 2023; 30:7764-7765. [PMID: 37610488 DOI: 10.1245/s10434-023-14152-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 07/30/2023] [Indexed: 08/24/2023]
Affiliation(s)
| | - Jaeyun Jane Wang
- Department of Surgery, University of California, San Francisco, CA, USA
| | | | - Adnan Alseidi
- Division of Surgical Oncology, Department of Surgery, University of California, San Francisco, CA, USA
| | - Mohamed A Adam
- Division of Surgical Oncology, Department of Surgery, University of California, San Francisco, CA, USA.
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