SynthesisFrontiers in medicine2026
Prediction models for post-induction hypotension in patients undergoing general anesthesia: a systematic review and meta-analysis.
Synthesis in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Background: Post-induction hypotension (PIH) is a frequent perioperative complication during general anesthesia and adversely affects patient outcomes. Although machine-learning techniques have been widely applied to develop PIH prediction models, the methodological quality and predictive performance of existing models lack systematic evaluation. Objective: To systematically review the predictive performance, methodological quality, and common predictors of PIH prediction models in patients undergoing general anesthesia, and to provide evidence for clinical practice and future model development. Methods: PubMed, Embase, Web of Science, Cochrane Library, CNKI, and Wanfang Data were searched from inception to April 2026 for studies developing or validating PIH prediction models. 2 reviewers independently screened studies, extracted data, and assessed risk of bias using the Prediction model Risk of Bias Assessment Tool (PROBAST). A random-effects meta-analysis was performed for the area under the receiver operating characteristic curve (AUC). Pooled odds ratios (ORs) for predictors appearing in at least 3 studies were calculated. Results: 17 studies (50 prediction models, 40,864 patients) published between 2011 and 2026 were included. The AUC/C-statistics reported by the modeling groups ranged from 0.68 to 0.95, and those reported by the validation groups ranged from 0.654 to 0.893, only 1 model was validated on an external institutional dataset (AUC 0.654). The pooled AUC of the 17 optimal models was 0.81 (95% CI 0.77-0.85). Age (OR 1.031, 95% CI 1.016-1.046) and propofol dose (OR 1.357, 95% CI 1.046-1.667) were significant risk predictors. PROBAST assessment rated 15 studies (88.2%) as having high overall risk of bias and 2 studies (11.8%) as low risk; most concerns arose from the statistical analysis domain. Conclusion: Current PIH prediction models show good discriminative ability, but most have a high risk of bias and lack external validation. Future research should standardize the definition of PIH, improve predictor selection, and conduct multicenter external validation to facilitate clinical translation. Systematic review registration: CRD420261372822.
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