Agha Majidi Mona, Arab Maliheh, Ghodssi-Ghassemabadi Robabeh, Nouri Behnaz, Ghavami Behnaz, Sheibani Kourosh
Imam Hossein Medical Center, Shahid Beheshti University of Medical Sciences,Tehran, Iran.
Imam Hossein Medical Center, Tehran, Iran.
Med J Islam Repub Iran. 2022 Dec 3;36:147. doi: 10.47176/mjiri.36.147. eCollection 2022.
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. 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. 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 ( < 0.001), tenderness ( < 0.001), the size of ovarian mass ( = 0.004), and the percentage of polymorphonuclear ( < 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%). 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.
下腹部或盆腔疼痛是女性常见的主诉之一,也是最难评估的症状之一。我们开展本研究以构建一种新算法,用于预测急性下腹部疼痛女性发生卵巢扭转的可能性。这项诊断性回顾性横断面研究针对所有因急性下腹部疼痛为主诉转诊至伊朗德黑兰伊玛目侯赛因医疗中心,并在2010年至2016年间接受剖腹手术的女性个体进行。对临床和辅助检查结果进行评估,以构建卵巢扭转的预测模型。将变量在两组中进行比较。第一组包括最终诊断为卵巢扭转的个体,第二组包括除卵巢扭转外有其他任何诊断的个体。使用SPSS 21版软件对这两组之间的所有数据进行比较,以找出对卵巢扭转具有预测价值的相关结果。总共评估了372名参与者,其中116名参与者(31.2%)发生了卵巢扭转(病例组),256名参与者下腹部疼痛有其他诊断(对照组)。恶心和呕吐(<0.001)、压痛(<0.001)、卵巢肿块大小(=0.004)以及多形核细胞百分比(<0.001)与最终诊断为卵巢扭转显示出显著相关性。构建了多个逻辑回归模型以预测影响卵巢扭转的因素,并设计了一个评分系统来预测卵巢扭转,其灵敏度为77.59%(68.9%-84.8%),特异度为74.61%(68.8%-79.8%)。所提出的模型适用于预测卵巢扭转,其必要信息可从个体病史、检查结果、实验室结果和超声检查中轻松获得。