ArticleWorld journal of clinical cases2021
Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery.
Article in World journal of clinical cases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 3 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Prediction models for post-induction hypotension in patients undergoing general anesthesia: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled 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
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Review
- Hypovolemia assessment with inferior vena cava collapsibility index in cardiac surgery patients: a cross-sectional study.Journal of cardiothoracic surgery · 2026Article
- An interpretable machine learning tool for predicting perioperative cardiac events in patients scheduled for hip fracture surgery: insights from the multicenter LUSHIP study.Journal of anesthesia, analgesia and critical care · 2025Article
- Developing a machine learning-based prediction model for postinduction hypotension.Journal of clinical monitoring and computing · 2025Article
- Risk factors for intraoperative hypotension during cardiac surgery.Indian journal of thoracic and cardiovascular surgery · 2025Article
- Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management.BMC medical informatics and decision making · 2025Article
- Carotid blood flow changes following a simulated end-inspiratory occlusion maneuver measured by ultrasound can predict hypotension after the induction of general anesthesia: an observational study.BMC anesthesiology · 2024Observational
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Authors and funding
5 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundHypotension after the induction of anesthesia is known to be associated with various adverse events. The involvement of a series of factors makes the prediction of hypotension during anesthesia quite challenging.
aimTo explore the ability and effectiveness of a random forest (RF) model in the prediction of post-induction hypotension (PIH) in patients undergoing cardiac surgery.
methodsPatient information was obtained from the electronic health records of the Second Affiliated Hospital of Hainan Medical University. The study included patients, ≥ 18 years of age, who underwent cardiac surgery from December 2007 to January 2018. An RF algorithm, which is a supervised machine learning technique, was employed to predict PIH. Model performance was assessed by the area under the curve (AUC) of the receiver operating characteristic. Mean decrease in the Gini index was used to rank various features based on their importance.
resultsOf the 3030 patients included in the study, 1578 (52.1%) experienced hypotension after the induction of anesthesia. The RF model performed effectively, with an AUC of 0.843 (0.808-0.877) and identified mean blood pressure as the most important predictor of PIH after anesthesia. Age and body mass index also had a significant impact.
conclusionThe generated RF model had high discrimination ability for the identification of individuals at high risk for a hypotensive event during cardiac surgery. The study results highlighted that machine learning tools confer unique advantages for the prediction of adverse post-anesthesia events.
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