Clinical significance of plasma D-dimer in ovarian cancer: A meta-analysis

BACKGROUND: D-dimer has been widely used for the diagnosis and prognosis of ovarian cancer, but there is still controversy on its prediction value of ovarian cancer. OBJECTIVES: To explore the clinical significance of plasma D-dimer level on ovarian cancer systematically. METHODS: Using PubMed, Cochrane Library, and Web of Science libraries, all the relevant studies for the diagnostic and prognostic value of plasma D-dimer for ovarian cancer and the relationship between elevated D-dimer level and venous thromboembolism (VTE) risk of ovarian cancer were searched till May 30, 2016. Standardized mean difference (SMD), odds ratio (OR), hazard ratio (HR), and 95% confidence interval (CI) were appropriately pooled. RESULTS: A total of 15 eligible studies involving a total of 1437 cancer patients were included. No significant association was found between high D-dimer level and overall survival of patients with ovarian cancer (HR 1.32, 95% CI: 0.90–1.95, P = .044). However, subgroup analysis indicated that the sample sizes could explain the heterogeneity between studies. And elevated D-dimer could predict increased risk of mortality when the sample sizes were >100 (HR 1.800, 95% CI: 1.283–2.523, P = .845). Besides, plasma D-dimer level was significantly higher in malignant ovarian cancer patients compared with benign controls (SMD 0.774, 95% CI: 0.597–0.951, P = .39), higher in advanced ovarian cancer patients (International Federation of Gynecology and Obstetrics [FIGO] classification III and IV) than in early stage ovarian cancer patients (FIGO classification I and II, SMD 0.611, 95% CI: 0.373–0.849, P = .442). And high D-dimer level indicated high VTE risk (OR 4.068, 95% CI: 2.423–6.829, P = .629) of ovarian cancer patients. CONCLUSION: The plasma D-dimer level in ovarian cancer patients can predict the changes that correlated with disease progression and the VTE risk. But its predictive value for the prognosis of ovarian cancer was significantly dependent on the sample sizes. More well-designed studies with large sample sizes are needed to validate and update the findings of present study.