Medical Journal of The Islamic Republic of Iran (MJIRI)
Iran University of Medical Sciences
Wed, May 8, 2024
[Archive]
Volume 36, Issue 1 (1-2022)
Med J Islam Repub Iran 2022 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Agha Majidi M, Arab M, Ghodssi-Ghassemabadi R, Nouri B, Ghavami B, Sheibani K. Torsion among Women with Acute Lower Abdominal Pain: A Retrospective Cross-Sectional Study. Med J Islam Repub Iran 2022; 36 (1) :1126-1130
URL: http://mjiri.iums.ac.ir/article-1-8045-en.html
URL: http://mjiri.iums.ac.ir/article-1-8045-en.html
Mona Agha Majidi 
, Maliheh Arab , Robabeh Ghodssi-Ghassemabadi , Behnaz Nouri , Behnaz Ghavami , Kourosh Sheibani
, Maliheh Arab , Robabeh Ghodssi-Ghassemabadi , Behnaz Nouri , Behnaz Ghavami , Kourosh Sheibani
Imam Hossein Medical Center, Tehran, Iran , drmarab@sbmu.ac.ir
Abstract: (442 Views)
Background: Lower abdominal or pelvic pain is a common complaint among women and one of the most challenging findings to evaluate. We performed the present study to construct a new algorithm for predicting the chance of ovarian torsion among women with acute lower abdominal pain.
Methods: This diagnostic retrospective cross-sectional study was performed on all female individuals who were referred to Imam Hossein Medical Center, Tehran, Iran, with the chief complaint of acute lower abdominal pain, and underwent laparotomy between 2010 and 2016. Clinical and paraclinical findings were evaluated to construct a predictive model for ovarian torsion. The variables were compared in 2 groups. The first group included individuals with a final diagnosis of ovarian torsion and the second group included those individuals with any diagnosis other than ovarian torsion. All data were compared between these 2 groups using SPSS software Version 21 to find the related findings with a predictive value for ovarian torsion.
Results: A total of 372 participants were evaluated, of whom 116 participants (31.2%) had ovarian torsion (case group) and 256 participants had other diagnoses for their lower abdominal pain (control group). Nausea and vomiting (p < 0.001), tenderness (p < 0.001), the size of ovarian mass (p = 0.004), and the percentage of polymorphonuclear (p < 0.001) showed significant relationships with ovarian torsion as the final diagnosis. Multiple logistic regression models were constructed to predict the factors affecting ovarian torsion, and a scoring system was designed to predict ovarian torsion, with a sensitivity of 77.59% (68.9%- 84.8%) and specificity of 74.61% (68.8% 79.8%).
Conclusion: The proposed model is suitable for predicting ovarian torsion and its necessary information is readily available from individuals’ history, examination findings, laboratory results, and an ultrasound exam.
Methods: This diagnostic retrospective cross-sectional study was performed on all female individuals who were referred to Imam Hossein Medical Center, Tehran, Iran, with the chief complaint of acute lower abdominal pain, and underwent laparotomy between 2010 and 2016. Clinical and paraclinical findings were evaluated to construct a predictive model for ovarian torsion. The variables were compared in 2 groups. The first group included individuals with a final diagnosis of ovarian torsion and the second group included those individuals with any diagnosis other than ovarian torsion. All data were compared between these 2 groups using SPSS software Version 21 to find the related findings with a predictive value for ovarian torsion.
Results: A total of 372 participants were evaluated, of whom 116 participants (31.2%) had ovarian torsion (case group) and 256 participants had other diagnoses for their lower abdominal pain (control group). Nausea and vomiting (p < 0.001), tenderness (p < 0.001), the size of ovarian mass (p = 0.004), and the percentage of polymorphonuclear (p < 0.001) showed significant relationships with ovarian torsion as the final diagnosis. Multiple logistic regression models were constructed to predict the factors affecting ovarian torsion, and a scoring system was designed to predict ovarian torsion, with a sensitivity of 77.59% (68.9%- 84.8%) and specificity of 74.61% (68.8% 79.8%).
Conclusion: The proposed model is suitable for predicting ovarian torsion and its necessary information is readily available from individuals’ history, examination findings, laboratory results, and an ultrasound exam.
Type of Study: Original Research |
Subject:
Gynecology & Obstetrics
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
