SynthesisBMC surgery2025
Clinical performance of machine-learning algorithms to predict intraoperative hypotension: a meta-analysis.
Synthesis in BMC surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for post-induction hypotension in patients undergoing general anesthesia: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimIntraoperative hypotension is one of the most common surgical adverse effects, often associated with postoperative organ damage and complications. The use of non-invasive methods to predict hypotension during surgery could significantly reduce these complications. Recently developed machine learning algorithms show promise for improving the prediction of hypotension and enhancing detection during surgical procedures. Our meta-analysis aimed to evaluate the clinical performance of machine learning algorithms for predicting intraoperative hypotension.
methodA systematic search of PubMed, Scopus, Web of Science, and Google Scholar was conducted to retrieve articles published up to August 2024. Two independent researchers screened observational accuracy studies and also identified randomized controlled trials (RCTs) of AI-guided intraoperative management. Observational studies reporting diagnostic/prognostic accuracy metrics were included in the quantitative synthesis. After evaluating the quality of the studies, data extraction and analysis were accomplished.
resultsFive observational studies, including a total of 42,509 participants, were included in this study. Four studies were conducted in South Korea, and one in Taiwan. The machine learning algorithms applied in these studies for electrocardiography, basal systolic, and diastolic blood pressure. These studies assessed model performance based on sensitivity, specificity, and the area under the ROC curve (AUROC). The overall effect size, represented in a forest plot, showed specificity of 0.73 (95% CI: 0.67-0.79), sensitivity of 0.75 (95% CI: 0.71-0.79), and an AUROC of 0.83 (95% CI: 0.77-0.89). In addition, we identified RCTs evaluating AI/HPI-guided intraoperative management, due to heterogeneous endpoints.
conclusionMachine learning algorithms demonstrate acceptable performance and advantages in predicting intraoperative hypotension during surgical procedures. The application of these algorithms could be beneficial in future surgeries to reduce complications.
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Registered trials
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.