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.