A new decision making model for diagnosing acute appendicitis among non-pregnant women and its comparison with alvarado scoring system

• Published in: Medical journal of the Islamic Republic of Iran Vol. 35; p. 93
• Main Authors: Arab, Maliheh, Nemati Honar, Behzad, Ghavami, Behnaz, Ghodssi-Ghassemabadi, Robabeh, Aghaei, Mahsa, Yousefi, Nasrin, Sheibani, Kourosh
• Format: Journal Article
• Language: English
• Published: Iran Iran University of Medical Sciences 2021
• Subjects: Original; Decision Making; Model; Prognosis; Appendicitis
• ISSN: 1016-1430; 2251-6840
• DOI: 10.47176/mjiri.35.93

Although acute appendicitis is a common problem, it remains a difficult diagnosis to establish, particularly among females of reproductive age. The present study was conducted to devise a new decision making model for diagnosing acute appendicitis in non-pregnant women. The present study was a retrospective study consisting of women who had undergone an appendectomy between 2007 and 2015 at the emergency department of Imam Hossein Medical Center, Tehran, Iran. The inclusion criteria were being a female, presenting with abdominal pain, being a suspected case of acute appendicitis, and undergoing an emergency appendectomy. A classification and regression tree (CART) analysis was performed to partition exam and laboratory data obtained from these patients into homogeneous groups in order to develop a prediction rule for appendicitis diagnosis. The study population included 433 non pregnant women who underwent emergency operations with a preliminary diagnosis of acute appendicis. Out of these patients, 295 patients (68.1%) were appendicitis positive based on the pathology exam results, while 138 patients had a normal appendix, indicating a negative appendectomy rate of 31.8%. The final devised CART model included hemoglobin level, PMN count, age, and history of abdominal incision and yielded a sensitivity of 82.7% and specificity of 55.8%, which were better than Alvarado prediction results for the Asian population. We have devised a simple and cost effective prediction model for predicting the outcome among non-pregnant women undergoing emergency appendectomy operation with good sensitivity and specificity compared to the Alvarado model.