Deep learning algorithms for predicting intraoperative hypotension: A systematic review and meta-analysis.

BACKGROUND: Intraoperative hypotension (IOH) is associated with myocardial injury, acute kidney injury, perioperative stroke, and 30-day mortality. Deep learning (DL) refers to a family of machine-learning methods that learn multiple levels of data representation through nonlinear transformations. In perioperative monitoring, DL models can learn patterns from time-varying physiological signals and combine them with clinical variables to estimate impending IOH risk. DL algorithms applied to continuous physiological waveforms therefore represent a rapidly expanding paradigm for early IOH prediction, but their pooled diagnostic accuracy has not been synthesised within a unified diagnostic-accuracy framework. OBJECTIVE: To quantify the pooled diagnostic accuracy of DL-based IOH prediction models and to identify factors that modify their performance. METHODS: PubMed, Embase, Web of Science, and the Cochrane Library were searched through March 2026. Methodological quality was appraised with PROBAST+AI and certainty of evidence with GRADE. A bivariate random-effects model generated pooled sensitivity, specificity, and SROC-AUC; heterogeneity was quantified by τ2(Se), τ2(Sp), and ρ. Subgroup analyses (prediction window, DL architecture, modality, IOH reference, analysis unit) and random-effects meta-regression were performed. RESULTS: Twelve studies were included; nine contributed 22 validation datasets. Pooled sensitivity was 0.78 (95% CI 0.73-0.81), specificity 0.88 (0.82-0.92), and SROC-AUC 0.87 (0.83-0.90); diagnostic odds ratio 24.7 (16.1-37.9), positive likelihood ratio 6.31, negative likelihood ratio 0.26. Heterogeneity was τ2(Se) = 0.25, τ2(Sp) = 1.04, ρ = -0.28. The 5-min window achieved the highest performance (sensitivity 0.81; specificity 0.91). Meta-regression identified DL architecture as the only significant moderator of specificity (P = 0.02), with hybrid CNN-RNN exceeding pure CNN (β = 1.77, 95% CI 0.45-3.09). Deeks' test was non-significant (P = 0.06). GRADE certainty was Low. CONCLUSIONS: DL models for IOH prediction achieve moderate diagnostic accuracy, with hybrid CNN-RNN architectures and 5-min prediction windows showing the most favourable performance. Universal absence of formal calibration assessment, scarce external validation, and geographic concentration constrain immediate clinical translation. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD420261377604 Reporting guideline: PRISMA-DTA.