Transformer-based deep learning model for real-time prediction of intraoperative hypotension using dynamic time-series vital signs: A retrospective study

by Shouqiang Zhu, Weihai Shi, Haichuan Qian, Xiaoqiang Tong, Rongliang Hu, Jinhua Bo, Xiaoping Gu Background The clinical importance of transient intraoperative hypotension (IOH) remains debated, and existing models often rely on high-resolution waveform data that are not routinely available. Methods and findings We developed a Transformer-based deep learning model to predict IOH in real time using continuous vital sign time-series data.

The model was trained on 319,699 surgical cases from a tertiary hospital in China (2013–2023) and externally validated using an independent dataset from South Korea. Model interpretability was explored through a real-time alert simulation using 10 representative surgical cases from the internal validation cohort, comparing predicted IOH risk trajectories with measured mean arterial pressure (MAP).

To assess clinical relevance, a nested cohort study evaluated the association between IOH burden (cumulative MAP ≤65/60/55 mmHg in mmHg·min) and postoperative acute kidney injury (AKI) and acute kidney disease (AKD). The Transformer model achieved strong prediction performance at 5-, 10-, and 15-min horizons (AUCs 0.904, 0.892, 0.882; recall ≥88.3%).