Efficacy of CRP in combination with D-dimer in predicting adverse postoperative outcomes of patients with acute Stanford type A aortic dissection

Purpose: The present study evaluated the efficacy of C-reactive protein (CRP) and D-dimer and the combination of them as prognostic indicators for patients with acute type A aortic dissection (ATAAD).

Methods: This is a retrospective cohort study. From January 2019 to December 2021, patients with ATAAD admitted to the emergency medicine center of our hospital within 24 h after symptoms (chest pain, back pain, abdominal pain and so on) onset were enrolled in our study. Serum concentration of CRP and D-dimer were measured during hospitalization. Logistic regression was used to evaluate the association between these two biomarkers and in-hospital adverse outcomes (IAO) by adjusting confounding factors. Predictive efficacy was assessed by area under the curve (AUC) of receiver operating characteristic curve.

Results: A total of 199 patients with ATAAD were finally enrolled. They were categorized as Non-IAO group (n = 146) and IAO group (n = 53) according to postoperative outcomes. After controlling for potentially confounding variables, we found categorized variables that admission CRP > 54.28 mg/L, admission D-dimer > 8.45 mg/L and peak D-dimer > 24.89 mg/L were independent predictors of in-hospital adverse outcomes. Multiple Logistic regression analysis revealed that the odd ratios were 2.9 for admission D-dimer > 8.45 [95% Confidence Interval (CI) 1.11-7.5, p = 0.03], 4.9 for admission CRP > 54.28 (95% CI 1.6-14.9, p = 0.005) and 5.7 for peak D-dimer > 24.89 (95% CI 2.49-13, p < 0.001). The predictive accuracy of the combination of three categorized variables (AUC: 0.867, 95% CI 0.813-0.921, p < 0.001) was superior to that of any other one alone.

Conclusion: Admission D-dimer > 8.45 mg/L, peak D-dimer > 24.89 mg/L and admission CRP > 54.28 mg/L are independent predictors of in-hospital adverse outcomes in patients with ATAAD. Combination of these three markers will improve the predictive efficacy.