%0 Journal Article
%T Impact of D-dimer on in-hospital mortality following aortic dissection: A systematic review and meta-analysis.
%A Srikanth S
%A Abrishami S
%A Subramanian L
%A Mahadevaiah A
%A Vyas A
%A Jain A
%A Nathaniel S
%A Gnanaguruparan S
%A Desai R
%J World J Cardiol
%V 16
%N 6
%D 2024 Jun 26
%M 38993588
暂无%R 10.4330/wjc.v16.i6.355
%X <B>BACKGROUND: </B>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.<BR><B>OBJECTIVE: </B>To conduct a meta-analysis of AD-related in-hospital mortality (ADIM) with elevated DD levels.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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).<BR><B>CONCLUSIONS: </B>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.