Background: Recent trials and metanalysis have demonstrated the favorable results of laparoscopic cholecystectomy (LC) and laparoscopic common bile duct exploration (LCBDE) for the treatment of cholecysto-choledocholithiasis. The aim of this study was to evaluate the LC + LCBDE learning curve including transcystic and transductal approaches and its effect on the outcomes.
Methods: We identified all unselected patients who underwent LC + LCBDE by a single surgeon between May 2017 and July 2021. Pre-, intra-, and postoperative data were analyzed using the cumulative sum (CUSUM) analysis to evaluate the learning curve.
Results: A total of 110 patients were included. Total postoperative complications rate was 12.7%, including bile leakage in six (5.5%) patients. Mean length of hospital stay was 2.7 (1-14) days. No patient had conversion to open surgery. The CUSUM graph divided the learning curve into three distinct phases: (1) Learning (1-38), (2) Competence (39-61) and (3) Proficiency (62-110). There was a significant increase in the transcystic approach rate with each phase (44.7% vs 73.9% vs 98%; P < .001). A significant decrease in the operative time (150.9 vs 117.6 vs 99.9 min; P < .001) and complication rate (21.1% vs 21.7% vs 2%; P = .01) were observed across the three phases.
Conclusion: Our data suggest that the learning curve for complete competence in LC + LCBDE is approximately 60 cases, provided that proper training is available. The initial learning phase can be carried out safely and efficiently with acceptable results.
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http://dx.doi.org/10.1002/jhbp.1228 | DOI Listing |
Updates Surg
January 2025
Department of Gastrointestinal Surgery, The First People's Hospital of Foshan, No. 81 Lingnan Avenue North, Foshan, China.
The surgical risk is higher for obese patients undergoing laparoscopic left hemicolectomy. To enhance the surgical safety and efficacy for obese patients, we have innovatively integrated the advantages of various surgical approaches to modify a pancreas-guided C-shaped surgical procedure. The safety and quality were assessed through a retrospective analysis.
View Article and Find Full Text PDFPediatr Cardiol
January 2025
Department of Infectious Disease, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, No. 1678 Dongfang Road, Pudong New Area, Shanghai, 200127, China.
Kawasaki disease (KD) is a febrile vasculitis disorder, with coronary artery lesions (CALs) being the most severe complication. Early detection of CALs is challenging due to limitations in echocardiographic equipment (UCG). This study aimed to develop and validate an artificial intelligence algorithm to distinguish CALs in KD patients and support diagnostic decision-making at admission.
View Article and Find Full Text PDFBMC Surg
January 2025
Department of Cardiothoracic Surgery, Fifth Affiliated Hospital of Sun Yat-Sen University, No.52 East Meihua Road, Zhuhai, Guangdong Province, 519000, China.
Background: Laparoscopic-assisted single-port mediastinoscopic esophagectomy is a safe and effective emerging minimally invasive esophagectomy, but little has been reported about the learning curve for this technology. The goal of the study was to determine the number of procedures to achieve different levels of proficiency on the learning curve.
Methods: This study retrospectively analyzed data from consecutive surgeries performed by the same surgeon at the same center from 2016 to 2021.
BMC Psychiatry
January 2025
Department of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China.
Background: The neurasthenia-depression controversy has lasted for several decades. It is challenging to solve the argument by symptoms alone for syndrome-based disease classification. Our aim was to identify objective electroencephalography (EEG) measures that can differentiate neurasthenia from major depressive disorder (MDD).
View Article and Find Full Text PDFNPJ Syst Biol Appl
January 2025
Department of Bioscience & Bioengineering, Indian Institute of Technology, Jodhpur, Rajasthan, India.
Classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) poses significant challenges for cytopathologists, often necessitating clinical tests and biopsies that delay treatment initiation. To address this, we developed a machine learning-based approach utilizing resected lung-tissue microbiome of AC and SCC patients for subtype classification. Differentially enriched taxa were identified using LEfSe, revealing ten potential microbial markers.
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