Prediction for Perioperative Stroke Using Intraoperative Parameters

• Published in: Journal of the American Heart Association Vol. 13; no. 16; p. e032216
• Main Authors: Oh, Mi‐Young, Jung, Young Mi, Kim, Won‐Pyo, Lee, Hyung‐Chul, Kim, Tae Kyong, Ko, Sang‐Bae, Lim, Jaehyun, Lee, Seung Mi
• Format: Journal Article
• Language: English
• Published: Wiley 20.08.2024
• Subjects: intraoperative physiological parameter; machine learning; perioperative stroke
• ISSN: 2047-9980; 2047-9980
• DOI: 10.1161/JAHA.123.032216

Background Perioperative stroke is a severe complication following surgery. To identify patients at risk for perioperative stroke, several prediction models based on the preoperative factors were suggested. Prediction models often focus on preoperative patient characteristics to assess stroke risk. However, most existing models primarily base their predictions on the patient's baseline characteristics before surgery. We aimed to develop a machine‐learning model incorporating both pre‐ and intraoperative variables to predict perioperative stroke. Methods and Results This study included patients who underwent noncardiac surgery at 2 hospitals with the data of 15 752 patients from Seoul National University Hospital used for development and temporal internal validation, and the data of 449 patients from Boramae Medical Center used for external validation. Perioperative stroke was defined as a newly developed ischemic lesion on diffusion‐weighted imaging within 30 days of surgery. We developed a prediction model composed of pre‐ and intraoperative factors (integrated model) and compared it with a model consisting of preoperative features alone (preoperative model). Perioperative stroke developed in 109 (0.69%) patients in the Seoul National University Hospital group and 11 patients (2.45%) in the Boramae Medical Center group. The integrated model demonstrated superior predictive performance with area under the curve values of 0.824 (95% CI, 0.762–0.880) versus 0.584 (95% CI, 0.499–0.667; P <0.001) in the internal validation; and 0.716 (95% CI, 0.560–0.859) versus 0.505 (95% CI, 0.343–0.654; P =0.018) in the external validation, compared to the preoperative model. Conclusions We suggest that incorporating intraoperative factors into perioperative stroke prediction models can improve their accuracy.