Establishment of predictive models for acute complicated appendicitis during pregnancy-A retrospective case-control study.

ABSTRACT

OBJECTIVE: 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 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. 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 indices, including symptom duration time more than 24 h, fever, heart rate at least 98 beats/minute, monocyte count at least 0.72 × 109 /L, lymphocyte count at least 1 × 109 /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. 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. TRIAL REGISTRATION The research has been registered in Chinese Clinical Trial Registry on January 7, 2022 with registration ID ChiCTR2200055339.