Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study
IntroductionIntrinsically, chronic obstructive pulmonary disease (COPD) is a highly heterogonous disease. Several sex differences in COPD, such as risk factors and prevalence, were identified. However, sex differences in clinical features of acute exacerbation chronic obstructive pulmonary disease (...
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2023.1105854/full |
| _version_ | 1827978467649519616 |
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| author | Zhihong Chen Jiajia Wang Hanchao Wang Yu Yao Huojin Deng Junnan Peng Xinglong Li Zhongruo Wang Xingru Chen Wei Xiong Qin Wang Tao Zhu |
| author_facet | Zhihong Chen Jiajia Wang Hanchao Wang Yu Yao Huojin Deng Junnan Peng Xinglong Li Zhongruo Wang Xingru Chen Wei Xiong Qin Wang Tao Zhu |
| author_sort | Zhihong Chen |
| collection | DOAJ |
| description | IntroductionIntrinsically, chronic obstructive pulmonary disease (COPD) is a highly heterogonous disease. Several sex differences in COPD, such as risk factors and prevalence, were identified. However, sex differences in clinical features of acute exacerbation chronic obstructive pulmonary disease (AECOPD) were not well explored. Machine learning showed a promising role in medical practice, including diagnosis prediction and classification. Then, sex differences in clinical manifestations of AECOPD were explored by machine learning approaches in this study.MethodsIn this cross-sectional study, 278 male patients and 81 female patients hospitalized with AECOPD were included. Baseline characteristics, clinical symptoms, and laboratory parameters were analyzed. The K-prototype algorithm was used to explore the degree of sex differences. Binary logistic regression, random forest, and XGBoost models were performed to identify sex-associated clinical manifestations in AECOPD. Nomogram and its associated curves were established to visualize and validate binary logistic regression.ResultsThe predictive accuracy of sex was 83.930% using the k-prototype algorithm. Binary logistic regression revealed that eight variables were independently associated with sex in AECOPD, which was visualized by using a nomogram. The AUC of the ROC curve was 0.945. The DCA curve showed that the nomogram had more clinical benefits, with thresholds from 0.02 to 0.99. The top 15 sex-associated important variables were identified by random forest and XGBoost, respectively. Subsequently, seven clinical features, including smoking, biomass fuel exposure, GOLD stages, PaO2, serum potassium, serum calcium, and blood urea nitrogen (BUN), were concurrently identified by three models. However, CAD was not identified by machine learning models.ConclusionsOverall, our results support that the clinical features differ markedly by sex in AECOPD. Male patients presented worse lung function and oxygenation, less biomass fuel exposure, more smoking, renal dysfunction, and hyperkalemia than female patients with AECOPD. Furthermore, our results also suggest that machine learning is a promising and powerful tool in clinical decision-making. |
| first_indexed | 2024-04-09T21:22:24Z |
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| id | doaj.art-1f40c64b1b274782b8e3f3b192530050 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-04-09T21:22:24Z |
| publishDate | 2023-03-01 |
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| series | Frontiers in Medicine |
| spelling | doaj.art-1f40c64b1b274782b8e3f3b1925300502023-03-28T04:34:37ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2023-03-011010.3389/fmed.2023.11058541105854Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional studyZhihong Chen0Jiajia Wang1Hanchao Wang2Yu Yao3Huojin Deng4Junnan Peng5Xinglong Li6Zhongruo Wang7Xingru Chen8Wei Xiong9Qin Wang10Tao Zhu11Respiratory Medicine and Critical Care Medicine, Zhongshan Hospital of Fudan University, Shanghai, ChinaRheumatology Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaRespiratory Medicine and Critical Care Medicine, and Preclinical Research Center, Suining Central Hospital, Suining, ChinaRespiratory Medicine and Critical Care Medicine, and Preclinical Research Center, Suining Central Hospital, Suining, ChinaRespiratory Medicine and Critical Care Medicine, ZhuJiang Hospital of Southern Medical University, Guangzhou, ChinaRespiratory Medicine and Critical Care Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaRespiratory Medicine and Critical Care Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Mathematics, University of California, Davis, CA, United StatesRespiratory Medicine and Critical Care Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaRespiratory Medicine and Critical Care Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaRespiratory Medicine and Critical Care Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaRespiratory Medicine and Critical Care Medicine, and Preclinical Research Center, Suining Central Hospital, Suining, ChinaIntroductionIntrinsically, chronic obstructive pulmonary disease (COPD) is a highly heterogonous disease. Several sex differences in COPD, such as risk factors and prevalence, were identified. However, sex differences in clinical features of acute exacerbation chronic obstructive pulmonary disease (AECOPD) were not well explored. Machine learning showed a promising role in medical practice, including diagnosis prediction and classification. Then, sex differences in clinical manifestations of AECOPD were explored by machine learning approaches in this study.MethodsIn this cross-sectional study, 278 male patients and 81 female patients hospitalized with AECOPD were included. Baseline characteristics, clinical symptoms, and laboratory parameters were analyzed. The K-prototype algorithm was used to explore the degree of sex differences. Binary logistic regression, random forest, and XGBoost models were performed to identify sex-associated clinical manifestations in AECOPD. Nomogram and its associated curves were established to visualize and validate binary logistic regression.ResultsThe predictive accuracy of sex was 83.930% using the k-prototype algorithm. Binary logistic regression revealed that eight variables were independently associated with sex in AECOPD, which was visualized by using a nomogram. The AUC of the ROC curve was 0.945. The DCA curve showed that the nomogram had more clinical benefits, with thresholds from 0.02 to 0.99. The top 15 sex-associated important variables were identified by random forest and XGBoost, respectively. Subsequently, seven clinical features, including smoking, biomass fuel exposure, GOLD stages, PaO2, serum potassium, serum calcium, and blood urea nitrogen (BUN), were concurrently identified by three models. However, CAD was not identified by machine learning models.ConclusionsOverall, our results support that the clinical features differ markedly by sex in AECOPD. Male patients presented worse lung function and oxygenation, less biomass fuel exposure, more smoking, renal dysfunction, and hyperkalemia than female patients with AECOPD. Furthermore, our results also suggest that machine learning is a promising and powerful tool in clinical decision-making.https://www.frontiersin.org/articles/10.3389/fmed.2023.1105854/fullacute exacerbation of chronic obstructive pulmonary diseasesexbinary logistic regressionnomogrammachine learningK-prototypes algorithm |
| spellingShingle | Zhihong Chen Jiajia Wang Hanchao Wang Yu Yao Huojin Deng Junnan Peng Xinglong Li Zhongruo Wang Xingru Chen Wei Xiong Qin Wang Tao Zhu Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study Frontiers in Medicine acute exacerbation of chronic obstructive pulmonary disease sex binary logistic regression nomogram machine learning K-prototypes algorithm |
| title | Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study |
| title_full | Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study |
| title_fullStr | Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study |
| title_full_unstemmed | Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study |
| title_short | Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study |
| title_sort | machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease a multicenter cross sectional study |
| topic | acute exacerbation of chronic obstructive pulmonary disease sex binary logistic regression nomogram machine learning K-prototypes algorithm |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2023.1105854/full |
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