Development and external validation of temporal fusion transformer models for continuous intraoperative blood pressure forecasting

Abstract

During surgery, intraoperative hypotension is associated with postoperative morbidity and should therefore be avoided. Predicting the occurrence of hypotension in advance may allow timely interventions to prevent hypotension. Previous prediction models mostly use high-resolution waveform data, which is often not available. We utilised a novel temporal fusion transformer (TFT) algorithm to predict intraoperative blood pressure trajectories 7min in advance. We trained the model with low-resolution data (sampled every 15s) from 73,009 patients who were undergoing general anaesthesia for non-cardiothoracic surgery between January 1, 2017, and December 30, 2020, at the General Hospital of Vienna, Austria. The data set contained information on patient demographics, vital signs, medication, and ventilation. The model was evaluated using an internal (n=8113) and external test set (n=5065) obtained from the openly accessible Vital Signs Database. In the internal test set, the mean absolute error for predicting mean arterial blood pressure was 0.376 standard deviations-or 4mmHg-and 0.622 standard deviations-or 7mmHg-in the external test set. We also adapted the TFT model to binarily predict the occurrence of hypotension as defined by mean arterial blood pressure < 65mmHg in the next one, three, five, and 7min. Here, model discrimination was excellent, with a mean area under the receiver operating characteristic curve (AUROC) of 0.933 in the internal test set and 0.919 in the external test set. Our TFT model is capable of accurately forecasting intraoperative arterial blood pressure using only low-resolution data showing a low prediction error. When used for binary prediction of hypotension, we obtained excellent performance. No external funding.