Postoperative platelet dynamic trajectories in patients with type A aortic dissection and development of a predictive model for thrombocytopenia.

Background: Thrombocytopenia is common after cardiac surgery and is associated with adverse postoperative outcomes. However, platelet counts are typically assessed at single time points, and longitudinal platelet trajectories after acute type A aortic dissection (ATAAD) surgery remain poorly characterized. This study used trajectory analysis to identify perioperative platelet count trajectories in patients undergoing ATAAD surgery. Methods: We retrospectively included 773 patients who underwent surgery for Stanford type A aortic dissection in this study. Platelet count data were collected over time within the first seven days after surgery. Trajectory patterns of platelet counts were determined using growth mixture modeling (GMM). The Kaplan-Meier method and log-rank test were applied to compare intergroup survival differences, and Cox regression analysis was performed to estimate mortality risks and stratify patient phenotypes. With trajectory phenotype as the outcome, least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by the construction of a logistic regression predictive model. Results: GMM identified three distinct nonlinear postoperative platelet trajectories, designated as TrajeA, TrajeB, and TrajeC. Kaplan-Meier survival analysis demonstrated significant differences in both early (postoperative days 7-30) and long-term survival among trajectory groups (log-rank Conclusions: A persistently low postoperative platelet trajectory is associated with elevated short- and long-term mortality in patients undergoing acute type A aortic dissection surgery.