Research Paper Volume 16, Issue 15 pp 11577—11590

Synergistic insights: the integrated role of CT/CTP and clinical parameters in hemorrhagic transformation prediction

Jianwen Jia1, *, , Zeping Jin1, *, , Jing Dong2, , Jumei Huang1, , Yang Wang1, *, , Yunpeng Liu1, *, ,

  • 1 Department of Neurosurgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
  • 2 Department of Medical Engineering, Tsinghua University Yuquan Hospital, Beijing, People’s Republic of China
* Equal contribution

Received: February 15, 2024       Accepted: July 9, 2024       Published: August 9, 2024      

https://doi.org/10.18632/aging.206026
How to Cite

Copyright: © 2024 Jia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Acute ischemic stroke presents significant challenges in healthcare, notably due to the risk and poor prognosis associated with hemorrhagic transformation (HT). Currently, there is a notable gap in the early clinical stage for a valid and reliable predictive model for HT.

Methods: This single-center retrospective study analyzed data from 224 patients with acute ischemic stroke due to large vessel occlusion. We collected comprehensive clinical data, CT, and CTP parameters. A predictive model for HT was developed, incorporating clinical indicators alongside imaging data, and its efficacy was evaluated using decision curve analysis and calibration curves. In addition, we have also built a free browser-based online calculator based on this model for HT prediction.

Results: The study identified atrial fibrillation and hypertension as significant risk factors for HT. Patients with HT showed more extensive initial ischemic damage and a smaller ischemic penumbra. Our novel predictive model, integrating clinical indicators with CT and CTP parameters, demonstrated superior predictive value compared to models based solely on clinical indicators.

Conclusions: The research highlighted the intricate interplay of clinical and imaging parameters in HT post-thrombectomy. It established a multifaceted predictive model, enhancing the understanding and management of acute ischemic stroke. Future studies should focus on validating this model in broader cohorts, further investigating the causal relationships, and exploring the nuanced effects of these parameters on patient outcomes post-stroke.

Abbreviations

AIS: Acute ischemic stroke; HT: hemorrhagic transformation; No HT: non-hemorrhagic transformation; CTP: Computed Tomography perfusion; CT: Computed Tomography; DPT: door to puncture time; mTICI: modified Thrombolysis in Cerebral Infarction; DSA: digital subtraction angiography; mRS 90: 90-day modified Rankin Scale; CBF: cerebral blood flow; CBV: cerebral blood volume; MTT: mean transit time; TTP: time to peak; Tmax: time-to-maximum; ECASS: the European Cooperative Acute Stroke Study; ROC: Receiver Operating Characteristic; DCA: Decision Curve Analysis; ASPECTS: the Alberta Stroke Program Early CT Score.