Litcius/Paper detail

The Diagnostic Value of the Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio for Deep Venous Thrombosis: A Systematic Review and Meta-Analysis

Chenming Hu, Bin Zhao, Qianling Ye, Jun Zou, Xiang Li, Huaping Wu

2023Clinical and Applied Thrombosis/Hemostasis10 citationsDOIOpen Access PDF

Abstract

The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are emerging tools that can be used in the diagnosis of deep venous thrombosis (DVT). This study aims to evaluate the diagnostic value of NLR and PLR for patients with DVT. Our meta-analysis included 11 eligible studies and extracted relevant diagnostic indicators. Of these studies, 4 focused on the NLR, 1 on the PLR, while 6 evaluated both. For the 10 studies on NLR, the pooled sensitivity, specificity, positive-likelihood ratio, and negative-likelihood ratio were 74%, 66%, 2.16, and 0.4, respectively. The estimated diagnostic odds ratio (DOR) was 5.3, and the area under the curve (AUC) of the summary receiver operating characteristic (SROC) curves was 0.74. For the 7 studies on the PLR, the pooled sensitivity, specificity, positive-likelihood ratio, and negative-likelihood ratio were 0.65, 0.77, 2.89, and 0.45, respectively. The estimated DOR was 6.64, and the SROC-AUC was 0.79. Our findings showed that the NLR and PLR exhibit moderate diagnostic accuracy and may be helpful biomarkers for the diagnosis of DVT. Future prospective, well-designed studies with large sample sizes will be required to provide additional evidence to establish cutoff values and clinical value of these indicators.

Topics & Concepts

Likelihood ratios in diagnostic testingDiagnostic odds ratioReceiver operating characteristicMedicineMeta-analysisVenous thrombosisNeutrophil to lymphocyte ratioOdds ratioInternal medicineLymphocyteArea under the curveThrombosisCutoffGastroenterologyPhysicsQuantum mechanicsVenous Thromboembolism Diagnosis and ManagementInflammatory Biomarkers in Disease PrognosisBlood Coagulation and Thrombosis Mechanisms