Inflammatory phenotyping by latent class analysis and machine learning-based prediction of postoperative complications in pediatric appendicitis: a retrospective cohort study

Background Acute appendicitis exhibits heterogeneous inflammatory responses that conventional single-marker assessments fail to capture. This study identified distinct inflammatory phenotypes using latent class analysis (LCA) and developed machine learning models to predict postoperative complications in pediatric appendicitis. Methods This retrospective cohort study included 402 pediatric patients who underwent laparoscopic appendectomy. LCA was performed using nine inflammatory and clinical in