Malperfusion in Acute Type A Aortic Dissection: Development of a Predictive Diagnostic Model.

Abstract

Objective: To investigate the clinical predictors of malperfusion in patients with acute type A aortic dissection (ATAAD) and to construct a diagnostic model to identify high-risk individuals.

Methods: A retrospective analysis of 553 ATAAD patients from Tongji Hospital divided into malperfusion and non-malperfusion groups was conducted. Logistic regression was used to identify independent predictors of the outcome. Model performance via the Hosmer-Lemeshow test, decision curve analysis (DCA), the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and predictive values.

Results: Malperfusion was observed in 28.4% of ATAAD patients. Significant predictors included elevated lactate dehydrogenase (LDH) (OR: 1.0019, 95% CI: 1.0002-1.0036, P = 0.027), alanine aminotransferase (ALT) (OR: 0.9936, 95% CI: 0.987-1.000, P = 0.046) and estimated glomerular filtration rate (eGFR) (OR: 0.9877, 95% CI: 0.977-0.998, P = 0.021), suggesting roles for tissue ischemia and impaired renal or hepatic function. Other variables, such as D-dimer, uric acid, creatinine, and NT-proBNP, showed trends toward significance but did not reach the 0.05 threshold. The model demonstrated good calibration (Hosmer-Lemeshow P = 0.318), moderate discriminatory power (AUC = 0.725), high specificity (93.62%), and low sensitivity (26.75%).

Conclusion: The model based on routine biochemical markers provides a practical approach for the early identification of malperfusion in ATAAD patients. It shows strong specificity and clinical utility, although its limited sensitivity highlights the need for further refinement. Future improvements should focus on incorporating additional clinical or imaging data to increase diagnostic accuracy.