Background: Total thoracoscopic segmentectomy (TTS) is a technically challenging procedure in children but results in more parenchyma preservation, better pain control, better cosmetic results, and a shorter hospital stay. However, definitive data describing the learning curve of TTS has yet to be obtained. Here, we review the safety and efficiency of our initial experiences with pediatric TTS and evaluate our learning curve.
Methods: This was a retrospective study of all pediatric patients undergoing TTS between December 2016 and January 2020. Pediatric patients who underwent TTS were included, while those undergoing lobectomy or wedge resection were excluded.
Results: One hundred and twelve patients were retrospectively analyzed to evaluate the learning curve and were divided chronologically into three phases, the ascending phase (A), plateau phase (B) and descending phase (C), through cumulative summation (CUSUM) of the operative time (OT). Phases A, B, and C comprised 28, 51, and 33 cases, respectively. OT decreased significantly from phases A to B (p < 0.001) and from phase B to C (p = 0.076). No significant differences were observed in the demographic factors among the three phases. The conversion rate was zero, and the complication rate was 0.9%. Differences in technical parameters, such as length of stay and chest tube duration, were statistically insignificant between phases A and B or B and C. There were no mortalities.
Conclusion: CUSUM indicates that the learning curve of at least 79 cases is required for TTS in our institute. We emphasize that the learning curve should be cautiously interpreted because many factors in different institutions may influence the exact parabola and actual learning curve.
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http://dx.doi.org/10.1007/s00464-023-09987-8 | 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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