Key result
A convolutional neural network fails to outperform simple MAP rules or trend models for predicting hypotension.
Why the study?
Machine learning algorithms for intraoperative hypotension prediction may not outperform simple mean arterial pressure thresholds, and performance may depend heavily on the chosen hypotension definition.
Does an open-source convolutional neural network improve intraoperative hypotension prediction compared to simple hemodynamic rules in noncardiac surgery cases?
Observational (n=500)
Does an open-source convolutional neural network improve intraoperative hypotension prediction compared to simple hemodynamic rules in noncardiac surgery cases?
Complex neural networks offer no predictive advantage over simple hemodynamic rules for predicting absolute intraoperative hypotension, highlighting the importance of aligning models with clinically relevant definitions.
MAP thresholds match CNN performance for IOH prediction; leaves open whether complex models add clinical value beyond simple rules.
Importance. Intraoperative hypotension (IOH) is associated with postoperative morbidity and mortality. Machine learning algorithms such as the Hypotension Prediction Index are deployed clinically but may not outperform simple mean arterial pressure (MAP) thresholds, and the choice of IOH definition may shape performance as much as the choice of model. Objective. To compare an open-source convolutional neural network (CNN) trained on MAP time series against two rule-based comparators (a MAP threshold rule and a three-feature trend-based logistic regression) across four IOH definitions using deployment-relevant metrics. Design, Setting, and Participants. Diagnostic study using the publicly available VitalDB database; 500 noncardiac surgery cases yielded 209,213 prediction windows. Main Outcomes and Measures. Discrimination, calibration, net benefit, alert burden, and lead time across four definitions (MAP <65 mm Hg for ≥1 or ≥5 minutes, <55 mm Hg for ≥1 minute, and ≥20% decrease from baseline). Results. For the three absolute definitions, all models performed comparably (AUROC 0.71–0.75, overlapping 95% confidence intervals [CIs]), with similar net benefit, alert burden (44–48 alerts/h), and lead time (~7.5 minutes). For the relative-decrease definition, the trend model incorporating baseline (AUROC 0.78; 95% CI, 0.74–0.82) outperformed the CNN (0.54; 95% CI, 0.51–0.58) and the MAP threshold (0.49; 95% CI, 0.44–0.53), with nonoverlapping CIs. Conclusions and Relevance. A neural network offered no advantage over a one-line MAP rule for absolute-threshold hypotension. For a percentage-drop definition, only the model incorporating patient baseline performed well. Aligning model and metric with the clinically relevant definition mattered more than model complexity.
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Shannon et al. (2026) conducted an observational in Intraoperative hypotension (n=500). Convolutional neural network (CNN) trained on MAP time series vs. MAP threshold rule and a three-feature trend-based logistic regression was evaluated on Discrimination (AUROC) for predicting intraoperative hypotension (AUROC 0.54 (CNN) vs 0.78 (trend model) for relative-decrease, 95% CI 0.51-0.58). A convolutional neural network offered no advantage over simple MAP rules for absolute hypotension, and was outperformed by a trend model for relative-decrease definitions (AUROC 0.54 vs 0.78).
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