Efficacy of CRP In Combination With D-Dimer In Predicting Adverse Postoperative Outcomes of Patients With Acute Stanford Type A Aortic Dissection

Abstract

Objectives: The present study aimed to evaluate the efficacy of 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 2017 to December 2020, all consecutive patients with ATAAD admitted to the emergency medicine center of our hospital within 24 hours 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 on admission. The univariate and multivariate logistic regression analyses were used to identify these predictors of adverse postoperative outcomes by adjusting other thrombotic or inflammatory biomarkers. Predictive efficacy was defined by area under the curve (AUC) of receiver operating characteristic curve (ROC).Results: A total of 213 patients with ATAAD were finally enrolled. They were categorized as group A (n=160) and group B (n=53) according to postoperative outcomes. Compared with group A, CRP levels of group B were higher (22.40(10.00, 32.51) vs. 11.06(6.21, 22.88), p<0.01)and so were D-dimer levels (12.55(7.74, 30.92) vs 6.21(2.75, 9.45), p<0.01). After controlling for potentially relevant confounding variables, we found admission CRP and D-dimer were independent predictors of adverse postoperative outcomes (odds ratio, 1.163; 95% CI, 1.066 to 1.269; p = 0.001; odds ratio, 1.227; 95% CI, 1.096 to 1.374; p<0.001, respectively). The predictive accuracy of the combination of CRP and D-dimer (AUC：0.849) was superior to that of CRP or D-dimer alone (AUC：0.669，0.763, respectively).Conclusion: CRP and D-dimer on admission are independent predictors of adverse postoperative outcomes in patients with ATAAD, which are easy to get without paying more either for the individual or the medical system. Combination of these two markers will improve the predictive efficacy.