ArticleJMIR medical informatics2021
Short-Term Event Prediction in the Operating Room (STEP-OP) of Five-Minute Intraoperative Hypotension Using Hybrid Deep Learning: Retrospective Observational Study and Model Development.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 8 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Registry Construction of Intraoperative Vital Signs and Clinical Information for Retrospective Cohort Study in Surgical Patients
Development of an Artificial Intelligence Algorithm to Predict Hypotension Risk After Induction in Cesarean Sections With Spinal Anesthesia
Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Clinical performance of machine-learning algorithms to predict intraoperative hypotension: a meta-analysis.BMC surgery · 2025Pooled it
- Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis.Journal of translational medicine · 2024Pooled it
- Early prediction of intraoperative hypotension: development and validation of the HypoBridCast hybrid deep learning model.BMC anesthesiology · 2026Article
- Towards reliable prediction of intraoperative hypotension: a cross-center evaluation of deep learning-based and MAP-derived methods.Journal of clinical monitoring and computing · 2026Article
- Machine Learning Methods for the Prediction of Intraoperative Hypotension with Biosignal Waveforms.Medicina (Kaunas, Lithuania) · 2025Observational
- Multitask Attention-Based Neural Network for Intraoperative Hypotension Prediction.Bioengineering (Basel, Switzerland) · 2023Article
- On the Horizon: Specific Applications of Automation and Artificial Intelligence in Anesthesiology.Current anesthesiology reports · 2023Article
- Effective hemodynamic monitoring.Critical care (London, England) · 2022Review
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIntraoperative hypotension has an adverse impact on postoperative outcomes. However, it is difficult to predict and treat intraoperative hypotension in advance according to individual clinical parameters.
objectiveThe aim of this study was to develop a prediction model to forecast 5-minute intraoperative hypotension based on the weighted average ensemble of individual neural networks, utilizing the biosignals recorded during noncardiac surgery.
methodsIn this retrospective observational study, arterial waveforms were recorded during noncardiac operations performed between August 2016 and December 2019, at Seoul National University Hospital, Seoul, South Korea. We analyzed the arterial waveforms from the big data in the VitalDB repository of electronic health records. We defined 2s hypotension as the moving average of arterial pressure under 65 mmHg for 2 seconds, and intraoperative hypotensive events were defined when the 2s hypotension lasted for at least 60 seconds. We developed an artificial intelligence-enabled process, named short-term event prediction in the operating room (STEP-OP), for predicting short-term intraoperative hypotension.
resultsThe study was performed on 18,813 subjects undergoing noncardiac surgeries. Deep-learning algorithms (convolutional neural network [CNN] and recurrent neural network [RNN]) using raw waveforms as input showed greater area under the precision-recall curve (AUPRC) scores (0.698, 95% CI 0.690-0.705 and 0.706, 95% CI 0.698-0.715, respectively) than that of the logistic regression algorithm (0.673, 95% CI 0.665-0.682). STEP-OP performed better and had greater AUPRC values than those of the RNN and CNN algorithms (0.716, 95% CI 0.708-0.723).
conclusionsWe developed STEP-OP as a weighted average of deep-learning models. STEP-OP predicts intraoperative hypotension more accurately than the CNN, RNN, and logistic regression models.
trial registrationClinicalTrials.gov NCT02914444; https://clinicaltrials.gov/ct2/show/NCT02914444.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.