Deep learning models for the prediction of intraoperative hypotension

Solam Lee, Hyung Chul Lee, Yu Seong Chu, Seung Woo Song, Gyo Jin Ahn, Hunju Lee, Sejung Yang, Sang Baek Koh

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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 <65 mm Hg) or a non-hypotensive event by analysing biosignal waveforms. The regression model was developed to directly estimate the MAP. The primary outcome was area under the receiver operating characteristic (AUROC) curve and the mean absolute error (MAE). 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.

Original languageEnglish
Pages (from-to)808-817
Number of pages10
JournalBritish Journal of Anaesthesia
Volume126
Issue number4
DOIs
Publication statusPublished - 2021 Apr

Bibliographical note

Funding Information:
MD–PhD/Medical Scientist Training Program through the Korea Health Industry Development Institute , funded by the Ministry of Health and Welfare , Republic of Korea.

Publisher Copyright:
© 2021 British Journal of Anaesthesia

All Science Journal Classification (ASJC) codes

  • Anesthesiology and Pain Medicine

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