ArticleBMC surgery2025
Development of a new predictive clinico-biological score for acute appendicitis in the pediatric population.
Article in BMC surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Predicting Complicated Appendicitis: What Can Machine Learning Add?Diagnostics (Basel, Switzerland) · 2026Article
- Letter to the editor regarding the article "Development of a new predictive clinico-biological score for acute appendicitis in the pediatric population".BMC surgery · 2026Article
- Microbiological spectrum and antimicrobial resistance patterns in pediatric acute appendicitis: implications for evidence-based empirical therapy.Frontiers in pediatrics · 2026Article
- The CHANSE score: a novel clinical tool incorporating the heel drop test for the diagnosis of pediatric appendicitis.Frontiers in pediatrics · 2025Article
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14 authors.
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No grant is acknowledged in the PubMed record.
Abstract
introductionAcute appendicitis (AA) is the most common surgical emergency in pediatric patients, yet its diagnosis remains challenging due to variable clinical presentations and the overlap with other causes of abdominal pain. Predictive scores can enhance diagnostic accuracy, reduce unnecessary surgeries, and optimize resource utilization, especially in resource-limited settings. This study aimed to identify key predictive factors for acute appendicitis in children and to develop a combined clinical and biological scoring system to enhance diagnostic accuracy for this condition. PATIENTS AND
methodsWe prospectively enrolled children aged 2 to 14 years presenting with abdominal pain to the pediatric surgery department at Fattouma Bourguiba University Hospital over a one-year period. Clinical, biological, and sonographic data were analyzed to identify key predictive factors and derive a predictive scoring model for acute appendicitis in this population. Model performance was assessed using the area under the curve (AUC) on the same dataset used for model derivation.
resultsAmong 420 children presenting with abdominal pain, 91 (21.7%) were diagnosed with AA. The median age of the children was 7 years, with a male-to-female ratio of 1.48. Binary logistic regression identified the following predictive factors: right iliac fossa tenderness, right iliac fossa guarding, positive right single-leg hop, and neutrophil percentage ≥ 75%. The predictive model achieved an AUC of 0.901 (95% CI [0.85–0.93]) with a significance of P < 0.0001. The optimal cut-off for the model was 5, yielding a sensitivity of 61.04%, specificity of 95.38%, positive predictive value (PPV) of 76.4%, and negative predictive value (NPV) of 90.5%. Stratified management strategies based on the score were proposed to guide clinical decision-making effectively.
conclusionThe developed predictive score offers a simple, practical, and highly specific tool for diagnosing AA in pediatric emergencies. Its integration of functional signs, such as the right-sided single-leg hop test, enhances clinical utility, making it particularly valuable in resource-constrained settings. Further validations are warranted to confirm its generalizability and reliability. CLINICAL TRIAL NUMBER: Not applicable. LEVEL OF EVIDENCE: II
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