Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning
BackgroundPatients with diabetes face an increased risk of postoperative pulmonary infection (PPI). However, precise predictive models specific to this patient group are lacking.ObjectiveTo develop and validate a machine learning model for predicting PPI risk in patients with diabetes.MethodsThis re...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2024.1501854/full |
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| _version_ | 1850138607067594752 |
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| author | Chunxiu Zhao Bingbing Xiang Jie Zhang Pingliang Yang Qiaoli Liu Shun Wang |
| author_facet | Chunxiu Zhao Bingbing Xiang Jie Zhang Pingliang Yang Qiaoli Liu Shun Wang |
| author_sort | Chunxiu Zhao |
| collection | DOAJ |
| description | BackgroundPatients with diabetes face an increased risk of postoperative pulmonary infection (PPI). However, precise predictive models specific to this patient group are lacking.ObjectiveTo develop and validate a machine learning model for predicting PPI risk in patients with diabetes.MethodsThis retrospective study enrolled 1,269 patients with diabetes who underwent elective non-cardiac, non-neurological surgeries at our institution from January 2020 to December 2023. Predictive models were constructed using nine different machine learning algorithms. Feature selection was conducted using Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. Model performance was assessed via the Area Under the Curve (AUC), precision, accuracy, specificity and F1-score.ResultsThe Ada Boost classifier (ADA) model exhibited the best performance with an AUC of 0.901, Accuracy of 0.91, Precision of 0.82, specificity of 0.98, PPV of 0.82, and NPV of 0.82. LASSO feature selection identified six optimal predictive factors: postoperative transfer to the ICU, Age, American Society of Anesthesiologists (ASA) physical status score, chronic obstructive pulmonary disease (COPD) status, surgical department, and duration of surgery.ConclusionOur study developed a robust predictive model using six clinical features, offering a valuable tool for clinical decision-making and personalized prevention strategies for PPI in patients with diabetes. |
| format | Article |
| id | doaj-art-cebcaffb6d834bad8f19a94cea383ebb |
| institution | OA Journals |
| issn | 1664-042X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj-art-cebcaffb6d834bad8f19a94cea383ebb2025-08-20T02:30:32ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2024-12-011510.3389/fphys.2024.15018541501854Predicting postoperative pulmonary infection risk in patients with diabetes using machine learningChunxiu Zhao0Bingbing Xiang1Jie Zhang2Pingliang Yang3Qiaoli Liu4Shun Wang5Department of Critical Care Medicine, Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Chengdu, Sichuan, ChinaDepartment of Anesthesiology, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Anesthesiology, Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, ChinaDepartment of Anesthesiology, Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, ChinaDepartment of Anesthesiology, Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, ChinaDepartment of Anesthesiology, Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, ChinaBackgroundPatients with diabetes face an increased risk of postoperative pulmonary infection (PPI). However, precise predictive models specific to this patient group are lacking.ObjectiveTo develop and validate a machine learning model for predicting PPI risk in patients with diabetes.MethodsThis retrospective study enrolled 1,269 patients with diabetes who underwent elective non-cardiac, non-neurological surgeries at our institution from January 2020 to December 2023. Predictive models were constructed using nine different machine learning algorithms. Feature selection was conducted using Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. Model performance was assessed via the Area Under the Curve (AUC), precision, accuracy, specificity and F1-score.ResultsThe Ada Boost classifier (ADA) model exhibited the best performance with an AUC of 0.901, Accuracy of 0.91, Precision of 0.82, specificity of 0.98, PPV of 0.82, and NPV of 0.82. LASSO feature selection identified six optimal predictive factors: postoperative transfer to the ICU, Age, American Society of Anesthesiologists (ASA) physical status score, chronic obstructive pulmonary disease (COPD) status, surgical department, and duration of surgery.ConclusionOur study developed a robust predictive model using six clinical features, offering a valuable tool for clinical decision-making and personalized prevention strategies for PPI in patients with diabetes.https://www.frontiersin.org/articles/10.3389/fphys.2024.1501854/fulldiabetes mellituspostoperative pulmonary infectionmachine learningrisk predictionAda Boost classifier |
| spellingShingle | Chunxiu Zhao Bingbing Xiang Jie Zhang Pingliang Yang Qiaoli Liu Shun Wang Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning Frontiers in Physiology diabetes mellitus postoperative pulmonary infection machine learning risk prediction Ada Boost classifier |
| title | Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning |
| title_full | Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning |
| title_fullStr | Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning |
| title_full_unstemmed | Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning |
| title_short | Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning |
| title_sort | predicting postoperative pulmonary infection risk in patients with diabetes using machine learning |
| topic | diabetes mellitus postoperative pulmonary infection machine learning risk prediction Ada Boost classifier |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2024.1501854/full |
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