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Te R, Zhu B, Ma H, Zhang X, Chen S, Huang Y, Qi G. Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety. BMC Anesthesiol 2024; 24:136. [PMID: 38594630 PMCID: PMC11003123 DOI: 10.1186/s12871-024-02523-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 04/03/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Adequate preoperative evaluation of the post-intubation hemodynamic instability (PIHI) is crucial for accurate risk assessment and efficient anesthesia management. However, the incorporation of this evaluation within a predictive framework have been insufficiently addressed and executed. This study aims to developed a machine learning approach for preoperatively and precisely predicting the PIHI index values. METHODS In this retrospective study, the valid features were collected from 23,305 adult surgical patients at Peking Union Medical College Hospital between 2012 and 2020. Three hemodynamic response sequences including systolic pressure, diastolic pressure and heart rate, were utilized to design the post-intubation hemodynamic instability (PIHI) index by computing the integrated coefficient of variation (ICV) values. Different types of machine learning models were constructed to predict the ICV values, leveraging preoperative patient information and initiatory drug infusion. The models were trained and cross-validated based on balanced data using the SMOTETomek technique, and their performance was evaluated according to the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and R-squared index (R2). RESULTS The ICV values were proved to be consistent with the anesthetists' ratings with Spearman correlation coefficient of 0.877 (P < 0.001), affirming its capability to effectively capture the PIHI variations. The extra tree regression model outperformed the other models in predicting the ICV values with the smallest MAE (0.0512, 95% CI: 0.0511-0.0513), RMSE (0.0792, 95% CI: 0.0790-0.0794), and MAPE (0.2086, 95% CI: 0.2077-0.2095) and the largest R2 (0.9047, 95% CI: 0.9043-0.9052). It was found that the features of age and preoperative hemodynamic status were the most important features for accurately predicting the ICV values. CONCLUSIONS Our results demonstrate the potential of the machine learning approach in predicting PIHI index values, thereby preoperatively informing anesthetists the possible anesthetic risk and enabling the implementation of individualized and precise anesthesia interventions.
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Affiliation(s)
- Rigele Te
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Bo Zhu
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
| | - Haobo Ma
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Isreal Deaconess Medical Center, Boston, MA, 02215, USA
| | - Xiuhua Zhang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Shaohui Chen
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Yuguang Huang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Geqi Qi
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing, 100044, China
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De Rosa S, Bignami E, Bellini V, Battaglini D. The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management. Anesth Analg 2024:00000539-990000000-00808. [PMID: 38557728 DOI: 10.1213/ane.0000000000006969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imaging data providing high qualitative assessments. Several machine-learning and deep-learning models using imaging techniques have been recently developed and validated to predict difficult airways. Despite advances in AI modeling. In this review article, we describe the advantages of using AI models. We explore how these methods could impact clinical practice. Finally, we discuss predictive modeling for difficult laryngoscopy using machine-learning and the future approach with intelligent intubation devices.
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Affiliation(s)
- Silvia De Rosa
- From the Centre for Medical Sciences - CISMed, University of Trento, Trento, Italy
- Anesthesia and Intensive Care, Santa Chiara Regional Hospital, APSS Trento, Trento, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Denise Battaglini
- Anesthesia and Intensive Care, IRCCS Ospedale Policlinico San Martino, Genova, Italy
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Zhou CM, Wang Y, Xue Q, Yang JJ, Zhu Y. Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms. Front Public Health 2022; 10:937471. [PMID: 36033770 PMCID: PMC9399522 DOI: 10.3389/fpubh.2022.937471] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/12/2022] [Indexed: 01/25/2023] Open
Abstract
Background In this paper, we examine whether machine learning and deep learning can be used to predict difficult airway intubation in patients undergoing thyroid surgery. Methods We used 10 machine learning and deep learning algorithms to establish a corresponding model through a training group, and then verify the results in a test group. We used R for the statistical analysis and constructed the machine learning prediction model in Python. Results The top 5 weighting factors for difficult airways identified by the average algorithm in machine learning were age, sex, weight, height, and BMI. In the training group, the AUC values and accuracy and the Gradient Boosting precision were 0.932, 0.929, and 100%, respectively. As for the modeled effects of predicting difficult airways in test groups, among the models constructed by the 10 algorithms, the three algorithms with the highest AUC values were Gradient Boosting, CNN, and LGBM, with values of 0.848, 0.836, and 0.812, respectively; In addition, among the algorithms, Gradient Boosting had the highest accuracy with a value of 0.913; Additionally, among the algorithms, the Gradient Boosting algorithm had the highest precision with a value of 100%. Conclusion According to our results, Gradient Boosting performed best overall, with an AUC >0.8, an accuracy >90%, and a precision of 100%. Besides, the top 5 weighting factors identified by the average algorithm in machine learning for difficult airways were age, sex, weight, height, and BMI.
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Affiliation(s)
- Cheng-Mao Zhou
- Department of Anaesthesiology, Central People's Hospital of Zhanjiang, Zhanjiang, China,Department of Anesthesiology, Pain and Perioperative Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China,Anesthesia and Big Data Research Group, Central People's Hospital of Zhanjiang, Zhanjiang, China,*Correspondence: Cheng-Mao Zhou
| | - Ying Wang
- Department of Anesthesiology, Pain and Perioperative Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qiong Xue
- Department of Anesthesiology, Pain and Perioperative Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-Jun Yang
- Department of Anesthesiology, Pain and Perioperative Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China,Jian-Jun Yang
| | - Yu Zhu
- Department of Anaesthesiology, Central People's Hospital of Zhanjiang, Zhanjiang, China,Anesthesia and Big Data Research Group, Central People's Hospital of Zhanjiang, Zhanjiang, China,Yu Zhu
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Bellini V, Valente M, Turetti M, Del Rio P, Saturno F, Maffezzoni M, Bignami E. Current Applications of Artificial Intelligence in Bariatric Surgery. Obes Surg 2022; 32:2717-2733. [PMID: 35616768 PMCID: PMC9273529 DOI: 10.1007/s11695-022-06100-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 11/27/2022]
Abstract
The application of artificial intelligence technologies is growing in several fields of healthcare settings. The aim of this article is to review the current applications of artificial intelligence in bariatric surgery. We performed a review of the literature on Scopus, PubMed and Cochrane databases, screening all relevant studies published until September 2021, and finally including 36 articles. The use of machine learning algorithms in bariatric surgery is explored in all steps of the clinical pathway, from presurgical risk-assessment and intraoperative management to complications and outcomes prediction. The models showed remarkable results helping physicians in the decision-making process, thus improving the quality of care, and contributing to precision medicine. Several legal and ethical hurdles should be overcome before these methods can be used in common practice.
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Marina Valente
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Melania Turetti
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Paolo Del Rio
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Francesco Saturno
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Massimo Maffezzoni
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126, Parma, Italy
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Tsai YCM, Russotto V, Parotto M. Predicting the Difficult Airway: How Useful Are Preoperative Airway Tests? CURRENT ANESTHESIOLOGY REPORTS 2022. [DOI: 10.1007/s40140-022-00525-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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