Abstract
BackgroundD-dimer, a coagulation-related indicator, has recently been used as a tool for the diagnosis of periprosthetic joint infection (PJI), but its reliability is uncertain. The purpose of this systematic review and meta-analysis was to explore the accuracy of D-dimer in the diagnosis of PJI after joint arthroplasty.MethodsWe systematically searched the MEDLINE, EMBASE, and Cochrane databases for relevant literature about D-dimer in the diagnosis of PJI. QUADAS-2 was used to assess the risk of bias and clinical applicability of each included study. We used the bivariate meta-analysis framework to pool the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the SROC curve (AUC). Univariate meta-regression and subgroup analyses were performed to explore the sources of heterogeneity.ResultsWe included 8 eligible studies. The pooled diagnostic sensitivity and specificity were 0.82 (95% CI, 0.70–0.89) and 0.70 (95% CI, 0.55–0.82), respectively. The pooled PLR, NLR, and DOR were 2.7 (95% CI, 1.7–4.4), 0.26 (95% CI, 0.15–0.46), and 10 (95% CI, 4–25), respectively. The AUC was 0.83 (95% CI, 0.8–0.86). Serum D-dimer might have higher diagnostic accuracy than plasma D-dimer for PJI (pooled sensitivity: 0.88 vs 0.67; pooled specificity: 0.76 vs 0.61).ConclusionsD-dimer has limited performance for the diagnosis of PJI.
Highlights
Periprosthetic joint infection (PJI) is a rare and devastating complication that affects 0.7–2.4% of patients after hip or knee arthroplasty [1,2,3]
D-dimer is a specific degradation product of fibrin monomer that is crosslinked by activating factor XIII and hydrolyzed by fibrinolytic enzyme [8]
It is a specific marker of the fibrinolysis process and mainly reflects the function of fibrinolysis [8]
Summary
We systematically searched the MEDLINE, EMBASE, and Cochrane databases for relevant literature about D-dimer in the diagnosis of PJI. QUADAS-2 was used to assess the risk of bias and clinical applicability of each included study. We used the bivariate meta-analysis framework to pool the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the SROC curve (AUC). Univariate meta-regression and subgroup analyses were performed to explore the sources of heterogeneity
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