Diagnostic Accuracy of C-reactive Protein, Procalcitonin, White Blood Cell Count, and Neutrophil-Lymphocyte Ratio in the Early Detection of Post-surgical Infections: A Systematic Review

C反应蛋白、降钙素原、白细胞计数和中性粒细胞/淋巴细胞比值在术后感染早期检测中的诊断准确性:系统评价

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Abstract

Early detection of post-surgical infections is crucial for improving patient outcomes and reducing healthcare burdens. This systematic review evaluates the diagnostic accuracy of C-reactive protein (CRP), procalcitonin (PCT), white blood cell count (WBC), and neutrophil-lymphocyte ratio (NLR) in identifying early post-surgical infections across various surgical specialties. A comprehensive search was conducted in PubMed, MEDLINE, Embase, and Cochrane Library following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, yielding eight high-quality studies, including meta-analyses, randomized controlled trials, and cohort studies. The findings indicate that CRP is the most extensively studied biomarker, with postoperative day (POD) 3-7 levels demonstrating moderate to high predictive value in abdominal, colorectal, spinal, and pancreatic surgeries. PCT was effective in guiding the management of adhesion-related small bowel obstruction, while NLR showed moderate diagnostic performance in orthopedic infections. Sensitivity and specificity varied across biomarkers and surgical types, with CRP showing the highest accuracy in spinal surgery (100% sensitivity and 96.8% specificity). Quality assessment using AMSTAR 2, ROB 2, QUADAS-2, and NOS tools revealed a moderate risk of bias in most studies due to heterogeneity in methodologies and biomarker cutoffs. The results support the integration of biomarker-based infection monitoring into perioperative protocols to optimize patient management, facilitate early discharge, and reduce unnecessary antibiotic use. Future research should focus on large-scale multicenter trials to establish standardized biomarker thresholds and explore the potential of combining multiple biomarkers with artificial intelligence-driven predictive models.

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