Intensive Care Unit, West China Hospital of Sichuan University, Chengdu city, China.
Department of Emergency Medicine, West China Hospital of Sichuan University, Chengdu city, China.
Int J Gynaecol Obstet. 2023 Aug;162(2):744-751. doi: 10.1002/ijgo.14719. Epub 2023 Feb 27.
To develop a scoring system based on clinical and imaging features to distinguish complicated appendicitis (CA) from uncomplicated appendicitis (UCA) during pregnancy.
This was a retrospective case-control study. Patients diagnosed with acute appendicitis during pregnancy were included, and they were divided into a CA group and a UCA group based on the intraoperative findings and the biopsy results. Multivariate logistic regression and machine learning were employed to establish a predictive model.
A total of 342 patients were included in this study. Among them, 141 (41.23%) patients were diagnosed with CA. The predictive model contained six indices, including symptom duration time more than 24 h, fever, heart rate at least 98 beats/minute, monocyte count at least 0.72 × 10 /L, lymphocyte count at least 1 × 10 /L and direct bilirubin at least 4.75 μmol/L. The total score was 31 points, and a score of more than 15.5 points predicted the development of CA during pregnancy with area under the curve (AUC) of 0.80 (95% confidence interval 0.75-0.84) and specificity of 0.84. A decision flow chart for distinguishing CA from UCA during pregnancy was developed by Decision Tree with an AUC of 0.78.
The models combining clinical findings and laboratory tests, developed by two methods, can distinguish CA from UCA in pregnancy in a convenient and visualized way.
The research has been registered in Chinese Clinical Trial Registry on January 7, 2022 with registration ID ChiCTR2200055339.
建立一种基于临床和影像学特征的评分系统，以区分妊娠期复杂阑尾炎（CA）与单纯性阑尾炎（UCA）。
这是一项回顾性病例对照研究。纳入妊娠期诊断为急性阑尾炎的患者，并根据术中发现和活检结果将其分为 CA 组和 UCA 组。采用多变量逻辑回归和机器学习建立预测模型。
本研究共纳入 342 例患者，其中 141 例（41.23%）患者诊断为 CA。预测模型包含 6 个指标，包括症状持续时间超过 24 小时、发热、心率至少 98 次/分钟、单核细胞计数至少 0.72×10 /L、淋巴细胞计数至少 1×10 /L 和直接胆红素至少 4.75μmol/L。总分为 31 分，得分超过 15.5 分预测妊娠期发生 CA 的曲线下面积（AUC）为 0.80（95%置信区间 0.75-0.84），特异性为 0.84。通过决策树建立了一种用于区分妊娠期 CA 和 UCA 的决策流程图，AUC 为 0.78。
这两种方法建立的结合临床发现和实验室检查的模型，可以方便直观地区分妊娠期 CA 和 UCA。
本研究已于 2022 年 1 月 7 日在中国临床试验注册中心注册，注册号 ChiCTR2200055339。
