Deep learning models for the prediction of intraoperative hypotension.

Abstract Background Intraoperative hypotension is associated with a risk of postoperative organ dysfunction. In this study, we aimed to present deep learning algorithms for real-time predictions 5, 10, and 15 min before a hypotensive event. Methods In this retrospective observational study, deep learning algorithms were developed and validated using biosignal waveforms acquired from patient monitoring of noncardiac surgery. The classification model was a binary classifier of a hypotensive event (MAP Results In total, 3301 patients were included. For invasive models, the multichannel model with an arterial pressure waveform, electrocardiography, photoplethysmography, and capnography showed greater AUROC than the arterial-pressure-only models (AUROC15-min, 0.897 [95% confidence interval {CI}: 0.894–0.900] vs 0.891 [95% CI: 0.888–0.894]) and lesser MAE (MAE15-min, 7.76 mm Hg [95% CI: 7.64–7.87 mm Hg] vs 8.12 mm Hg [95% CI: 8.02–8.21 mm Hg]). For the noninvasive models, the multichannel model showed greater AUROCs than that of the photoplethysmography-only models (AUROC15-min, 0.762 [95% CI: 0.756–0.767] vs 0.694 [95% CI: 0.686–0.702]) and lesser MAEs (MAE15-min, 11.68 mm Hg [95% CI: 11.57–11.80 mm Hg] vs 12.67 [95% CI: 12.56–12.79 mm Hg]). Conclusions Deep learning models can predict hypotensive events based on biosignals acquired using invasive and noninvasive patient monitoring. In addition, the model shows better performance when using combined rather than single signals.