A clinical prediction model for complicated appendicitis in children younger than five years of age

Wei Feng1, Xu-Feng Zhao1, Miao-Miao Li2

  • 1Graduate school, Tianjin Medical University, Tianjin, 300070, China.

BMC Pediatrics
|August 27, 2020
PubMed

Insights

A new prediction model using age, white blood cell count, and symptom duration effectively differentiates complicated appendicitis in young children. This tool offers a promising approach for diagnosing acute appendicitis in this vulnerable age group.

Area of Science:

  • Pediatric Surgery
  • Medical Diagnostics
  • Clinical Research

Background:

  • Diagnosing complicated appendicitis in children under five remains challenging due to a lack of specific methods.
  • Acute appendicitis in young children requires accurate differentiation between simple and complicated forms for appropriate management.

Purpose of the Study:

  • To identify independent factors associated with complicated appendicitis in children younger than five years.
  • To develop and validate a prediction model for distinguishing simple from complicated appendicitis in this age group.

Main Methods:

  • A retrospective analysis of 382 children under five with acute appendicitis was performed.
  • Independent factors were identified using univariate and multivariate analyses.
  • A prediction model was developed and validated on a separate cohort of 156 patients, comparing its performance against the ALVARADO score and Pediatric Appendicitis Score (PAS).

Main Results:

  • Complicated appendicitis was observed in 63.9% of the patients.
  • Age, white blood cell count, and symptom duration were identified as independent predictors.
  • The developed model demonstrated high discrimination (AUC: 0.830) and outperformed PAS and the ALVARADO score.

Conclusions:

  • Age, white blood cell count, and symptom duration are key predictors for complicated appendicitis in young children.
  • The novel prediction model shows promise in differentiating simple and complicated appendicitis, aiding clinical decision-making.
Abstract