Background: Iatrogenic colorectal perforation is a rare event with a relevant mortality and the need for surgical therapy in around ¾ of cases.
Methods: In this retrospective multicentric cohort study iatrogenic colorectal perforations from 2004 to 2021 were analyzed. Primary outcome parameters were incidence and clinical success of 1st line endoscopic treatment. Comparative analysis of interventional and non-interventional perforations was performed and predictors for clinical success of endoscopic therapy were identified.
Results: From 103,570 colonoscopies 213 (0.2%) iatrogenic perforations were identified. 68.4% were interventional (80 during polypectomy/EMR, 54 during ESD and 11 for other reasons) and 31.6% non-interventional perforations (39 by the tip, 19 by the shaft, 7 by inversion, two by biopsy and one by distension). Incidence of 1st line endoscopic therapy was 61.0% and clinical success 81.5%. Other non-surgical therapies were conducted in 8.9% with clinical success in 94.7% of cases. In interventional perforations both incidence and clinical success of 1st line endoscopic therapy were significantly higher compared to non-interventional perforations [71.7% vs. 38.2% (p < 0.01) resp. 86.5% vs. 61.5% (p < 0.01)]. Mortality was 2.3% and significantly lower in the group of interventional perforations (0.7% vs. 5.9%, p = 0.037). Multivariable analysis revealed perforation size < 5 mm as only independent predictor for clinical success of 1st line endoscopic treatment [OR 14.85 (1.57-140.69), p = 0.019].
Conclusions: Endoscopic therapy is treatment of choice in the majority of iatrogenic colorectal perforations. In case of interventional perforations it is highly effective but only a minority of non-interventional perforations are good candidates for endoscopic treatment.
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http://dx.doi.org/10.1007/s00464-023-09920-z | DOI Listing |
J Cancer Res Ther
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Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Shandong, China.
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January 2025
Department of Cardiovascular & Thoracic Surgery, Sandra Atlas Bass Heart Hospital at North Shore University Hospital, Northwell Health, 300 Community Drive, 1 DSU, Manhasset, NY, 11030, USA.
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Recent Findings: Safe and effective mobilization of patients with cardiogenic shock receiving tMCS can be accomplished.
Clin Oral Investig
January 2025
Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand.
Objectives: The aim of this systematic review was to assess the effect of DM (Type 1 and Type 2 Diabetes) and hyperglycaemia on the physical and mechanical properties of dentine which is critical for successful endodontic treatment.
Method: An electronic search of the following databases: PubMed, MEDLINE, Web of Science and the grey literature was performed up until July 2024. In vitro and in vivo studies on the effect of DM or hyperglycaemia on the mechanical and physical properties of dentine were included.
Neurosurg Rev
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Department of Neurointervention, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China.
To evaluate the safety and efficacy of staged coiling followed by flow diverter (FD) in the treatment of ruptured intracranial aneurysms(RIAs). A retrospective analysis was conducted on 20 patients with RIAs treated with staged coiling followed by FD at a single center, between April 2015 and September 2024. Patient demographics, aneurysm characteristics, clinical and imaging outcomes were reviewed.
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Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.
This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.
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