Machine learning models for coagulation dysfunction risk in inpatients administered β-lactam antibiotics
The β-Lactam antibiotics represent a widely used class of antibiotics, yet the latent and often overlooked risk of coagulation dysfunction associated with their use underscores the need for proactive assessment. Machine learning methodologies can offer valuable insights into evaluating the risk of c...
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| Format: | Article |
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2024.1503713/full |
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| author | Yuqing Hua Yuqing Hua Na Li Jiahui Lao Zhaoyang Chen Shiyu Ma Xiao Li |
| author_facet | Yuqing Hua Yuqing Hua Na Li Jiahui Lao Zhaoyang Chen Shiyu Ma Xiao Li |
| author_sort | Yuqing Hua |
| collection | DOAJ |
| description | The β-Lactam antibiotics represent a widely used class of antibiotics, yet the latent and often overlooked risk of coagulation dysfunction associated with their use underscores the need for proactive assessment. Machine learning methodologies can offer valuable insights into evaluating the risk of coagulation dysfunction associated with β-lactam antibiotics. This study aims to identify the risk factors associated with coagulation dysfunction related to β-lactam antibiotics and to develop machine learning models for estimating the risk of coagulation dysfunction with real-world data. A retrospective study was performed using machine learning modeling analysis on electronic health record data, employing five distinct machine learning methods. The study focused on adult inpatients discharged from 1 January 2018, to 31 December 2021, at the First Affiliated Hospital of Shandong First Medical University. The models were developed for estimating the risk of coagulation dysfunction associated with various β-lactam antibiotics based on electronic health record feasibility. The dataset was divided into training and test sets to assess model performance using metrics such as total accuracy and area under the curve. The study encompassed risk-factor analysis and machine learning model development for coagulation dysfunction in inpatients administered different β-lactam antibiotics. A total of 45,179 participants were included in the study. The incidence of coagulation disorders related to cefazolin sodium, cefoperazone/sulbactam sodium, cefminol sodium, amoxicillin/sulbactam sodium, and piperacillin/tazobactam sodium was 2.4%, 5.4%, 1.5%, 5.5%, and 4.8%, respectively. Machine learning models for estimating coagulation dysfunction associated with each β-lactam antibiotic underwent validation with 5-fold cross-validation and test sets. On the test set, the optimal models for cefazolin sodium, cefoperazone/sulbactam sodium, cefminol sodium, amoxicillin/sulbactam sodium, and piperacillin/tazobactam sodium yielded AUC values of 0.798, 0.768, 0.919, 0.783, and 0.867, respectively. The study findings suggest that machine learning classifiers can serve as valuable tools for identifying patients at risk of coagulation dysfunction associated with β-lactam antibiotics and intervening based on high-risk predictions. Enhanced access to administrative and clinical data could further enhance the predictive performance of machine learning models, thereby expanding pharmacovigilance efforts. |
| format | Article |
| id | doaj-art-54b3dd9ae67a45dd92bae71142a84ba1 |
| institution | Kabale University |
| issn | 1663-9812 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Pharmacology |
| spelling | doaj-art-54b3dd9ae67a45dd92bae71142a84ba12024-11-26T04:26:21ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122024-11-011510.3389/fphar.2024.15037131503713Machine learning models for coagulation dysfunction risk in inpatients administered β-lactam antibioticsYuqing Hua0Yuqing Hua1Na Li2Jiahui Lao3Zhaoyang Chen4Shiyu Ma5Xiao Li6Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaDepartment of Clinical Pharmacy, Affiliated Hospital of Jining Medical University, Jining, ChinaShandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaCenter for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaShandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaRuijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaShandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, ChinaThe β-Lactam antibiotics represent a widely used class of antibiotics, yet the latent and often overlooked risk of coagulation dysfunction associated with their use underscores the need for proactive assessment. Machine learning methodologies can offer valuable insights into evaluating the risk of coagulation dysfunction associated with β-lactam antibiotics. This study aims to identify the risk factors associated with coagulation dysfunction related to β-lactam antibiotics and to develop machine learning models for estimating the risk of coagulation dysfunction with real-world data. A retrospective study was performed using machine learning modeling analysis on electronic health record data, employing five distinct machine learning methods. The study focused on adult inpatients discharged from 1 January 2018, to 31 December 2021, at the First Affiliated Hospital of Shandong First Medical University. The models were developed for estimating the risk of coagulation dysfunction associated with various β-lactam antibiotics based on electronic health record feasibility. The dataset was divided into training and test sets to assess model performance using metrics such as total accuracy and area under the curve. The study encompassed risk-factor analysis and machine learning model development for coagulation dysfunction in inpatients administered different β-lactam antibiotics. A total of 45,179 participants were included in the study. The incidence of coagulation disorders related to cefazolin sodium, cefoperazone/sulbactam sodium, cefminol sodium, amoxicillin/sulbactam sodium, and piperacillin/tazobactam sodium was 2.4%, 5.4%, 1.5%, 5.5%, and 4.8%, respectively. Machine learning models for estimating coagulation dysfunction associated with each β-lactam antibiotic underwent validation with 5-fold cross-validation and test sets. On the test set, the optimal models for cefazolin sodium, cefoperazone/sulbactam sodium, cefminol sodium, amoxicillin/sulbactam sodium, and piperacillin/tazobactam sodium yielded AUC values of 0.798, 0.768, 0.919, 0.783, and 0.867, respectively. The study findings suggest that machine learning classifiers can serve as valuable tools for identifying patients at risk of coagulation dysfunction associated with β-lactam antibiotics and intervening based on high-risk predictions. Enhanced access to administrative and clinical data could further enhance the predictive performance of machine learning models, thereby expanding pharmacovigilance efforts.https://www.frontiersin.org/articles/10.3389/fphar.2024.1503713/fullβ-lactam antibioticscoagulation disordersrisk factorsmachine learningpharmacovigilance |
| spellingShingle | Yuqing Hua Yuqing Hua Na Li Jiahui Lao Zhaoyang Chen Shiyu Ma Xiao Li Machine learning models for coagulation dysfunction risk in inpatients administered β-lactam antibiotics Frontiers in Pharmacology β-lactam antibiotics coagulation disorders risk factors machine learning pharmacovigilance |
| title | Machine learning models for coagulation dysfunction risk in inpatients administered β-lactam antibiotics |
| title_full | Machine learning models for coagulation dysfunction risk in inpatients administered β-lactam antibiotics |
| title_fullStr | Machine learning models for coagulation dysfunction risk in inpatients administered β-lactam antibiotics |
| title_full_unstemmed | Machine learning models for coagulation dysfunction risk in inpatients administered β-lactam antibiotics |
| title_short | Machine learning models for coagulation dysfunction risk in inpatients administered β-lactam antibiotics |
| title_sort | machine learning models for coagulation dysfunction risk in inpatients administered β lactam antibiotics |
| topic | β-lactam antibiotics coagulation disorders risk factors machine learning pharmacovigilance |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2024.1503713/full |
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