Diagnostic Accuracy of Raja Isteri Pengiran Anak Saleha (RIPASA) and Modified Alvarado Scoring System (MASS) in Acute Appendicitis

Abstract

Background: The exact diagnosis of acute appendicitis still remains a clinical problem despite the advances in imaging techniques. Clinical scoring systems such as the Modified Alvarado Scoring System (MASS) and Raja Isteri Pengiran Anak Saleha (RIPASA) score have been developed to aid in diagnosis and to decrease unnecessary appendectomies.

Purpose: This study was conducted to compare the diagnostic performance of RIPASA and MASS in patients with suspected acute appendicitis.

Methods: This prospective study was conducted over one year from April 2024 to March 2025. Ninety patients aged >10 years presenting with right iliac fossa pain and undergoing emergency appendectomy were included. All patients were evaluated using both RIPASA and MASS scoring systems. Histopathological examination (HPE) of the appendectomy specimen served as the gold standard for diagnosis.

Results: Mean age was 40.7 ± 11.7 years. Histopathological examination confirmed acute appendicitis in 67 (74.4%) patients. The mean RIPASA and MASS scores were 10.22±1.45 and 7.77±1.03, respectively. Both scoring systems showed significant association with histopathological findings. ROC analysis demonstrated superior performance of RIPASA with an AUC of 0.770 compared to MASS with an AUC of 0.701. At the optimal cut-off values, RIPASA showed higher sensitivity (77.6% vs. 73.1%), specificity (73.9% vs. 60.9%), and diagnostic accuracy (77.0% vs. 70.1%) than MASS.

Conclusion: Both scoring systems were helpful to diagnose acute appendicitis. However, in this present study, the diagnostic performance of RIPASA was slightly better than MASS.

  • Page Number : 74-81

  • Published Date : 2026-09-10

  • Keywords
    Acute Appendicitis, Raja isteri pengiran anak saleha, Modified alvarado scoring system, Diagnostic accuracy, Histopathology

  • DOI Number
    10.15415/jmrh.2026.122007

  • Authors
    Jeevanjot Kaur, Deepak Kumar, Samir Anand, Priyanjana Krishna, and Sushant Badgal

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