Abstract
BACKGROUND: Perioperative stroke is a serious and potentially fatal complication following non-cardiac surgery. Thus, it is important to identify the risk factors and develop an effective prognostic model to predict the incidence of perioperative stroke following non-cardiac surgery. METHODS AND FINDINGS: We identified potential risk factors and built a model to predict the incidence of perioperative stroke using logistic regression derived from hospital registry data of adult patients that underwent non-cardiac surgery from 2008 to 2019 at The First Medical Center of Chinese PLA General Hospital. Our model was then validated using the records of two additional hospitals to demonstrate its clinical applicability. In our hospital cohorts, 223,415 patients undergoing non-cardiac surgery were included in this study with 525 (0.23%) patients experiencing a perioperative stroke. Thirty-three indicators including several intraoperative variables had been identified as potential risk factors. After multi-variate analysis and stepwise elimination (P < 0.05), 13 variables including age, American Society of Anesthesiologists (ASA) classification, hypertension, previous stroke, valvular heart disease, preoperative steroid hormones, preoperative β-blockers, preoperative mean arterial pressure, preoperative fibrinogen to albumin ratio, preoperative fasting plasma glucose, emergency surgery, surgery type and surgery length were screened as independent risk factors and incorporated to construct the final prediction model. Areas under the curve were 0.893 (95% confidence interval (CI) [0.879, 0.908]; P < 0.001) and 0.878 (95% CI [0.848, 0.909]; P < 0.001) in the development and internal validation cohorts. In the external validation cohorts derived from two other independent hospitals, the areas under the curve were 0.897 and 0.895. In addition, our model outperformed currently available prediction tools in discriminative power and positive net benefits. To increase the accessibility of our predictive model to doctors and patients evaluating perioperative stroke, we published an online prognostic software platform, 301 Perioperative Stroke Risk Calculator (301PSRC). The main limitations of this study included that we excluded surgical patients with an operation duration of less than one hour and that the construction and external validation of our model were from three independent retrospective databases without validation from prospective databases and non-Chinese databases. CONCLUSIONS: In this work, we identified 13 independent risk factors for perioperative stroke and constructed an effective prediction model with well-supported external validation in Chinese patients undergoing non-cardiac surgery. The model may provide potential intervention targets and help to screen high-risk patients for perioperative stroke prevention.