EVALUATING THE PREOPERATIVE NEUTROPHIL TO LYMPHOCYTE RATIO FOR PREDICTING POSTOPERATIVE INFECTIOUS COMPLICATIONS IN CHILDREN WITH ACUTE APPENDICITIS.

J Peop Univ Med Health Sci. 2025:15(2), 117-125. http://doi.org/10.46536/jpumhs/2025/15.02.631

Abstract

INTRODUCTION: Appendicitis is among the primary causes of acute abdomen in children, with appendectomy being the standard treatment. However, postoperative complications, occurring in 5 28% of cases, present significant challenges. OBJECTIVES: This study evaluates the prognostic
value of preoperative neutrophil-to-lymphocyte ratio (NLR) for postoperative infectious
complications in children with acute appendicitis. METHODS: Current prospective cross-sectional research was carried out at Avicenna Medical College & Avicenna Hospital, Lahore, involving 57 children (aged 5-12) who underwent emergency appendectomy from April 2023 to April 2024.
Preoperative medical data, including NLR, were collected. Postoperative complications were tracked for one month. Statistical analysis was done using SPSS-26, with logistic regression analysis and the receiver operating characteristic (ROC) curve analysis was applied to identify predictors. RESULTS: Of the 57 children, 12 (21.1%) developed postoperative complications, primarily superficial surgical site infections and prolonged paralytic ileus. The mean NLR was significantly higher in the complication group (12.6 ± 7.97) compared to the non-complication group (5.01 ± 3.45; p < 0.001). Logistic regression identified NLR as an independent predictor (OR: 1.24; 95% CI: 1.002-1.58), with an ROC AUC of 0.85, indicating strong predictive value. The optimal NLR cutoff was 9.4, with 78.6% sensitivity and 94.3% specificity. CONCLUSION: Elevated preoperative NLR is a significant predictor of postoperative infectious complications in pediatric acute appendicitis. Implementing NLR assessment may help identifying high-risk patients at early stage, optimizing postoperative monitoring and management to reduce morbidity.
Postoperative Complications, Predictive Biomarker