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Diagnostic Performance of Total Platelet Count, Platelet-to-Lymphocyte Ratio, and Monocyte-to-Lymphocyte Ratio for Overall and Complicated Pediatric Acute Appendicitis: A Systematic Review and Meta-Analysis.

Javier Arredondo MonteroBlanca Paola Pérez RiverosNerea Martín-Calvo
Published in: Surgical infections (2023)
Background: The aim of this study was to analyze the diagnostic performance of total platelet count (PC), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) in pediatric acute appendicitis (PAA). Methods: We conducted a systematic review of the literature in the main databases of medical bibliography. Two independent reviewers selected the articles and extracted relevant data. Methodological quality was assessed using the QUADAS2 index. A synthesis of the results, a standardization of the metrics, and four random effect meta-analyses were performed. Results: Thirteen studies including data from 4,373 participants (2,767 patients with confirmed diagnosis of PAA and 1,606 controls) were included. Five studies compared PC, and the meta-analysis including three of them showed a non-significant mean difference of -34.47 platelets/1 × 10 9 /L (95% confidence interval [CI], -88.10 to 19.16). Seven publications compared PLR and the meta-analysis of those studies showed significant mean differences between patients with PAA and controls (dif: 49.84; 95% CI, 25.82-73.85) as well as between patients with complicated and uncomplicated PAA (dif: 49.42; 95% CI, 25.47-73.37). Four studies compared MLR and the meta-analysis including all of them showed a non-significant mean difference of -1.30 (95% CI, -3.52 to 0.92). Conclusions: Although existing evidence is heterogeneous and limited, PLR appears to be a promising molecule for the diagnosis of PAA and for the discrimination between complicated and uncomplicated PAA. Our results do not support the use of PC or MLR as biomarkers in PAA.
Keyphrases
  • case control
  • peripheral blood
  • meta analyses
  • systematic review
  • big data
  • electronic health record
  • healthcare
  • endothelial cells
  • artificial intelligence
  • immune response
  • quality improvement