In International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE : The objective of this study was to develop a scoring system based on clinical and imaging features to distinguish complicated appendicitis (CA) from uncomplicated appendicitis (UCA) during pregnancy.
METHOD : This was a retrospective case-control study. Patients diagnosed with acute appendicitis during pregnancy were included, and they were divided into CA group and UCA group based on the intraoperative findings and the biopsy results. Multivariate logistic regression and machine learning were employed to establish predictive mode.
RESULTS : 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 indexes, including symptom duration time>24h, fever, heart rate≥98 bpm, monocyte count≥0.72*10^9/L, lymphocyte count≤1*10^9/L and direct bilirubin≥4.75 umol/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%CI 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.
CONCLUSION : 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.
Li Ping, Zhang Zhuo, Weng Shanshan, Nie Hu
2023-Feb-10
Acute Appendicitis, Complicated Appendicitis, Decision Tree, Predictive Model, Pregnancy
