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Peer-reviewedCC BYFrontiers in cardiovascular medicine

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

  • Zeqi Bi1,2,3
  • Yingliang Zhao1,2,3
  • Chong Sheng2,3
  • Yuanxi Luo1
  • Yi Jiang1,2,3
  • Dongjin Wang1,2,3
  • Yunxing Xue2,3
  1. 1Department of Cardiovascular Surgery, Nanjing Drum Tower Hospital, Peking Union Medical College Graduate School, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, China.
  2. 2Department of Cardiovascular Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
  3. 3Institute of Cardiothoracic Vascular Disease, Nanjing University, Nanjing, China.
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Abstract

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 P < 0.001). TrajeC was associated with the highest mortality risk. Five clinical variables, namely age, preoperative creatinine, total transfusion volume, end-stage renal disease, and operative duration, were ultimately incorporated into the final logistic regression model. The model achieved an AUC of 0.73 (95% CI: 0.69-0.78) in the training cohort and 0.69 (95% CI: 0.61-0.76) in the validation cohort, demonstrating moderate, stable discriminative performance and no apparent overfitting.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.

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Cite

BibTeXRIS
BibTeX
@article{bi2026postoperative,
  title = {{Postoperative platelet dynamic trajectories in patients with type A aortic dissection and development of a predictive model for thrombocytopenia.}},
  author = {Zeqi Bi and Yingliang Zhao and Chong Sheng and Yuanxi Luo and Yi Jiang and Dongjin Wang and Yunxing Xue},
  journal = {Frontiers in cardiovascular medicine},
  year = {2026},
  month = oct,
  doi = {10.3389/fcvm.2026.1948231},
  url = {https://europepmc.org/article/MED/42909009},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Postoperative platelet dynamic trajectories in patients with type A aortic dissection and development of a predictive model for thrombocytopenia.
AU  - Zeqi Bi
AU  - Yingliang Zhao
AU  - Chong Sheng
AU  - Yuanxi Luo
AU  - Yi Jiang
AU  - Dongjin Wang
AU  - Yunxing Xue
PY  - 2026
DA  - 2026/10/07
JO  - Frontiers in cardiovascular medicine
DO  - 10.3389/fcvm.2026.1948231
UR  - https://europepmc.org/article/MED/42909009
AB  - 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 P < 0.001). TrajeC was associated with the highest mortality risk. Five clinical variables, namely age, preoperative creatinine, total transfusion volume, end-stage renal disease, and operative duration, were ultimately incorporated into the final logistic regression model. The model achieved an AUC of 0.73 (95% CI: 0.69-0.78) in the training cohort and 0.69 (95% CI: 0.61-0.76) in the validation cohort, demonstrating moderate, stable discriminative performance and no apparent overfitting.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.
SN  - 2297-055X
ER  -