Aim: To identify risk factors of actual appendiceal perforation when computed tomography (CT) scans suggest nonperforated appendicitis and accordingly determine surgical priority.
Methods: We collected database of 1362 patients who underwent an appendectomy for acute appendicitis between 2006 and 2013. A single radiologist selected 1236 patients whose CT scans were suggestive of nonperforated appendicitis. Patients were divided into 2 groups: actual nonperforation group and actual perforation group according to intraoperative and pathologic features. Comparison of the 2 groups were made using binary logistic regression.
Results: Of 1236 patients, 90 (7.3%) were found to have actual appendiceal perforation. Four risk factors related with actual appendiceal perforation were identified: body temperature≥37.6 °C (HR=1.912, 95%CI: 1.161-3.149; P=0.011), out-of-hospital symptom duration≥72 h (HR=2.454, 95%CI: 1.292-4.662; P=0.006), age≥35 years (HR=3.358, 95%CI: 1.968-5.728; P<0.001), and appendiceal diameter on CT scan≥8 mm (HR=4.294, 95%CI: 1.034-17.832; P=0.045). Actual appendiceal perforation group showed longer operation time, later initiation of diet, longer use of parenteral narcotics, longer hospital stay, and higher incidence of postoperative complications (P<0.05).
Conclusion: We proposed here new criteria to select patients with adverse clinical outcomes after appendectomy among the patients with radiologically nonperforated appendicitis. Surgical appendectomy outcomes could be improved by determining the surgical priority according to our criteria.
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http://dx.doi.org/10.3748/wjg.v21.i7.2131 | DOI Listing |
J Pediatr Surg
January 2025
McGill University Faculty of Medicine and Health Sciences, Canada; Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Qc, Canada.
Purpose: This study evaluates the effectiveness of machine learning (ML) algorithms for improving the preoperative diagnosis of acute appendicitis in children, focusing on the accurate prediction of the severity of disease.
Methods: An anonymized clinical and operative dataset was retrieved from the medical records of children undergoing emergency appendectomy between 2014 and 2021. We developed an ML pipeline that pre-processed the dataset and developed algorithms to predict 5 appendicitis grades (1 - non-perforated, 2 - localized perforation, 3 - abscess, 4 - generalized peritonitis, and 5 - generalized peritonitis with abscess).
Cureus
December 2024
Pediatrics, Rutgers Robert Wood Johnson Medical School, New Brunswick, USA.
Background: Acute appendicitis is one of the most common causes of an acute abdomen among pediatric patients. The diagnosis of appendicitis is challenging due to the nonspecific presentation. Diagnosis is based on historical, physical, and serologic information as well as right lower quadrant ultrasound (RLQ US).
View Article and Find Full Text PDFJSLS
January 2025
Department of Surgery, University Clinical Center, Tuzla, Bosnia and Herzegovina. (Dr. Delibegovic).
Background And Objectives: Securing the base of the appendix is the most critical part of laparoscopic appendectomy in children. Determining the average values of the appendix, will facilitate the creation of suitable instruments, and will also have an impact on research in imaging studies.
Methods: One hundred and eight patients with the acute appendicitis were randomized into 2 groups: group I: children aged 2-10 years old, group II: children >10 years of age.
Diagnostics (Basel)
December 2024
1st Department of Pediatric Surgery, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Background: This specific study evaluates the accuracy of two ratios, Neutrophil-to-Lymphocyte (N/L) and Platelet-to-Lymphocyte (P/L), as inflammatory markers on differentiating simple and complicated appendicitis preoperatively.
Methods: The medical records of 341 children, up to 16 years old, with suspected acute appendicitis (AA) who underwent appendectomy, laparoscopic or open, between January 2020 and December 2022, in our department, were retrospectively reviewed. Routine blood exams and the demographic details were obtained.
Int J Surg Case Rep
January 2025
Department of Surgery, Aga Khan University Hospital, Stadium Road, Karachi 74800, Pakistan. Electronic address:
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