Related Experiment Video
Updated: Jun 21, 2025
Technique and Patient Selection Criteria of Right Anterior Mini-Thoracotomy for Minimal Access Aortic Valve Replacement
Published on: March 26, 2018
Impact of D-dimer on in-hospital mortality following aortic dissection: A systematic review and meta-analysis
Sashwath Srikanth1, Shabnam Abrishami2, Lakshmi Subramanian1
1Department of Medicine, ECU Health Medical Center, Greenville, NC 27834, United States.
Background:
The utility of D-dimer (DD) as a biomarker for acute aortic dissection (AD) is recognized. Yet, its predictive value for in-hospital mortality remains uncertain and subject to conflicting evidence.
Aim:
To conduct a meta-analysis of AD-related in-hospital mortality (ADIM) with elevated DD levels.
Methods:
We searched PubMed, Scopus, Embase, and Google Scholar for AD and ADIM literature through May 2022. Heterogeneity was assessed using I 2 statistics and effect size (hazard or odds ratio) analysis with random-effects models. Sample size, study type, and patients' mean age were used for subgroup analysis. The significance threshold was P < 0.05.
Results:
Thirteen studies (3628 patients) were included in our study. The pooled prevalence of ADIM was 20% (95%CI: 15%-25%). Despite comparable demographic characteristics and comorbidities, elevated DD values were associated with higher ADIM risk (unadjusted effect size: 1.94, 95%CI: 1.34-2.8; adjusted effect size: 1.12, 95%CI: 1.05-1.19, P < 0.01). Studies involving patients with a mean age of < 60 years exhibited an increased mortality risk (effect size: 1.43, 95%CI: 1.23-1.67, P < 0.01), whereas no significant difference was observed in studies with a mean age > 60 years. Prospective and larger sample size studies (n > 250) demonstrated a heightened likelihood of ADIM associated with elevated DD levels (effect size: 2.57, 95%CI: 1.30-5.08, P < 0.01 vs effect size: 1.05, 95%CI: 1.00-1.11, P = 0.05, respectively).
Conclusion:
Our meta-analysis shows elevated DD increases in-hospital mortality risk in AD patients, highlighting the need for larger, prospective studies to improve risk prediction models.
