Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management
Abstract Background Post-induction hypotension (PIH) increases surgical complications including myocardial injury, acute kidney injury, delirium, stroke, prolonged hospitalization, and endangerment of the patient's life. Machine learning is an effective tool to analyze large amounts of data and...
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BMC
2025-02-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-02930-y |
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| author | Ming Chen Dingyu Zhang |
| author_facet | Ming Chen Dingyu Zhang |
| author_sort | Ming Chen |
| collection | DOAJ |
| description | Abstract Background Post-induction hypotension (PIH) increases surgical complications including myocardial injury, acute kidney injury, delirium, stroke, prolonged hospitalization, and endangerment of the patient's life. Machine learning is an effective tool to analyze large amounts of data and identify perioperative complication factors. This study aims to identify risk factors for PIH and develop predictive models to support anesthesia management. Methods A dataset of 5406 patients was analyzed using machine learning methods. Logistic regression, random forest, XGBoost, and neural network models were compared. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis (DCA). Results The logistic regression model achieved an AUROC of 0.74 (95% CI: 0.71–0.77), outperforming the random forest (AUROC: 0.71), XGBoost (AUROC: 0.72), and neural network (AUROC: 0.72) models. In terms of calibration, logistic regression demonstrated superior performance, as reflected by Brier Scores and calibration curves, followed by XGBoost, random forest, and neural network. Decision curve analysis indicated that the logistic regression model provided the greatest clinical utility among all models. Baseline blood pressure, age, sex, type of surgery, platelet count, and certain anesthesia-inducing drugs were identified as important features. Conclusions This study provides a valuable tool for personalized preoperative risk assessment and customized anesthesia management, allowing for early intervention and improved patient outcomes. Integration of machine learning models into electronic medical record systems can facilitate real-time risk assessment and prediction. |
| format | Article |
| id | doaj-art-42f4882a03374c3fad2e09a6b21ed8da |
| institution | DOAJ |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-42f4882a03374c3fad2e09a6b21ed8da2025-08-20T03:10:55ZengBMCBMC Medical Informatics and Decision Making1472-69472025-02-012511910.1186/s12911-025-02930-yMachine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia managementMing Chen0Dingyu Zhang1Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyAbstract Background Post-induction hypotension (PIH) increases surgical complications including myocardial injury, acute kidney injury, delirium, stroke, prolonged hospitalization, and endangerment of the patient's life. Machine learning is an effective tool to analyze large amounts of data and identify perioperative complication factors. This study aims to identify risk factors for PIH and develop predictive models to support anesthesia management. Methods A dataset of 5406 patients was analyzed using machine learning methods. Logistic regression, random forest, XGBoost, and neural network models were compared. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis (DCA). Results The logistic regression model achieved an AUROC of 0.74 (95% CI: 0.71–0.77), outperforming the random forest (AUROC: 0.71), XGBoost (AUROC: 0.72), and neural network (AUROC: 0.72) models. In terms of calibration, logistic regression demonstrated superior performance, as reflected by Brier Scores and calibration curves, followed by XGBoost, random forest, and neural network. Decision curve analysis indicated that the logistic regression model provided the greatest clinical utility among all models. Baseline blood pressure, age, sex, type of surgery, platelet count, and certain anesthesia-inducing drugs were identified as important features. Conclusions This study provides a valuable tool for personalized preoperative risk assessment and customized anesthesia management, allowing for early intervention and improved patient outcomes. Integration of machine learning models into electronic medical record systems can facilitate real-time risk assessment and prediction.https://doi.org/10.1186/s12911-025-02930-yPost-induction hypotensionMachine learningLogistic regressionRisk factorsPersonalized medicine |
| spellingShingle | Ming Chen Dingyu Zhang Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management BMC Medical Informatics and Decision Making Post-induction hypotension Machine learning Logistic regression Risk factors Personalized medicine |
| title | Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management |
| title_full | Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management |
| title_fullStr | Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management |
| title_full_unstemmed | Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management |
| title_short | Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management |
| title_sort | machine learning based prediction of post induction hypotension identifying risk factors and enhancing anesthesia management |
| topic | Post-induction hypotension Machine learning Logistic regression Risk factors Personalized medicine |
| url | https://doi.org/10.1186/s12911-025-02930-y |
| work_keys_str_mv | AT mingchen machinelearningbasedpredictionofpostinductionhypotensionidentifyingriskfactorsandenhancinganesthesiamanagement AT dingyuzhang machinelearningbasedpredictionofpostinductionhypotensionidentifyingriskfactorsandenhancinganesthesiamanagement |