Identifying Novel Clusters of Patients With Prolonged Mechanical Ventilation Using Trajectories of Rapid Shallow Breathing Index
ObjectivePatients with prolonged mechanical ventilation (PMV) are comprised of a heterogeneous population, creating great challenges for clinical management and study design. The study aimed to identify subclusters of PMV patients based on trajectories of rapid shallow breathing index (RSBI), and to...
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| Format: | Article |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2022.880896/full |
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| author | Tsung-Ming Yang Tsung-Ming Yang Tsung-Ming Yang Lin Chen Lin Chen Chieh-Mo Lin Chieh-Mo Lin Chieh-Mo Lin Hui-Ling Lin Hui-Ling Lin Tien-Pei Fang Tien-Pei Fang Huiqing Ge Huabo Cai Yucai Hong Zhongheng Zhang |
| author_facet | Tsung-Ming Yang Tsung-Ming Yang Tsung-Ming Yang Lin Chen Lin Chen Chieh-Mo Lin Chieh-Mo Lin Chieh-Mo Lin Hui-Ling Lin Hui-Ling Lin Tien-Pei Fang Tien-Pei Fang Huiqing Ge Huabo Cai Yucai Hong Zhongheng Zhang |
| author_sort | Tsung-Ming Yang |
| collection | DOAJ |
| description | ObjectivePatients with prolonged mechanical ventilation (PMV) are comprised of a heterogeneous population, creating great challenges for clinical management and study design. The study aimed to identify subclusters of PMV patients based on trajectories of rapid shallow breathing index (RSBI), and to develop a machine learning model to predict the cluster membership based on baseline variables.MethodsThis was a retrospective cohort study conducted in respiratory care center (RCC) at a tertiary academic medical center. The RCC referral criteria were patients with mechanical ventilation for at least 21 days with stable hemodynamic and oxygenation status. Patients admitted to the RCC from April 2009 to December 2020 were screened. Two-step clustering through linear regression modeling and k-means was employed to find clusters of the trajectories of RSBI. The number of clusters was chosen by statistical metrics and domain expertise. A gradient boosting machine (GBM) was trained, exploiting variables on RCC admission, to predict cluster membership.ResultsA total of 1371 subjects were included in the study. Four clusters were identified: cluster A showed persistently high RSBI; cluster B was characterized by a constant low RSBI over time; Cluster C was characterized by increasing RSBI; and cluster D showed a declining RSBI. Cluster A showed the highest mortality rate (72%), followed by cluster D (63%), C (62%) and B (61%; p = 0.005 for comparison between 4 clusters). GBM was able to predict cluster membership with an accuracy of > 0.95 in ten-fold cross validation. Highly ranked variables for the prediction of clusters included thyroid-stimulating hormone (TSH), cortisol, platelet, free thyroxine (T4) and serum magnesium.ConclusionsPatients with PMV are composed of a heterogeneous population that can be classified into four clusters by using trajectories of RSBI. These clusters can be easily predicted with baseline clinical variables. |
| first_indexed | 2024-12-12T11:56:22Z |
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| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-12-12T11:56:22Z |
| publishDate | 2022-07-01 |
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| series | Frontiers in Medicine |
| spelling | doaj.art-09c32c35ff504143966d0e6f05a132e72022-12-22T00:25:11ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2022-07-01910.3389/fmed.2022.880896880896Identifying Novel Clusters of Patients With Prolonged Mechanical Ventilation Using Trajectories of Rapid Shallow Breathing IndexTsung-Ming Yang0Tsung-Ming Yang1Tsung-Ming Yang2Lin Chen3Lin Chen4Chieh-Mo Lin5Chieh-Mo Lin6Chieh-Mo Lin7Hui-Ling Lin8Hui-Ling Lin9Tien-Pei Fang10Tien-Pei Fang11Huiqing Ge12Huabo Cai13Yucai Hong14Zhongheng Zhang15Division of Pulmonary and Critical Care Medicine, Chiayi Chang Gung Memorial Hospital, Chiayi, TaiwanSchool of Traditional Chinese Medicine, Chang Gung University, Taoyuan, TaiwanDepartment of Respiratory Care, Chang Gung University of Science and Technology, Chiayi, TaiwanDepartment of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, ChinaKey Laboratory of Emergency and Trauma, Ministry of Education, College of Emergency and Trauma, Hainan Medical University, Haikou, ChinaDivision of Pulmonary and Critical Care Medicine, Chiayi Chang Gung Memorial Hospital, Chiayi, TaiwanCollege of Medicine, Graduate Institute of Clinical Medical Sciences, Chang Gung University, Taoyuan, TaiwanDepartment of Nursing, Chang Gung University of Science and Technology, Chiayi, TaiwanDepartment of Respiratory Therapy, Chang Gung University, Taoyuan, TaiwanDepartment of Respiratory Therapy, Chiayi Chang Gung Memorial Hospital, Chiayi, TaiwanDepartment of Respiratory Care, Chang Gung University of Science and Technology, Chiayi, TaiwanDepartment of Respiratory Therapy, Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan0Department of Respiratory Care, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China1Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China1Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China1Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaObjectivePatients with prolonged mechanical ventilation (PMV) are comprised of a heterogeneous population, creating great challenges for clinical management and study design. The study aimed to identify subclusters of PMV patients based on trajectories of rapid shallow breathing index (RSBI), and to develop a machine learning model to predict the cluster membership based on baseline variables.MethodsThis was a retrospective cohort study conducted in respiratory care center (RCC) at a tertiary academic medical center. The RCC referral criteria were patients with mechanical ventilation for at least 21 days with stable hemodynamic and oxygenation status. Patients admitted to the RCC from April 2009 to December 2020 were screened. Two-step clustering through linear regression modeling and k-means was employed to find clusters of the trajectories of RSBI. The number of clusters was chosen by statistical metrics and domain expertise. A gradient boosting machine (GBM) was trained, exploiting variables on RCC admission, to predict cluster membership.ResultsA total of 1371 subjects were included in the study. Four clusters were identified: cluster A showed persistently high RSBI; cluster B was characterized by a constant low RSBI over time; Cluster C was characterized by increasing RSBI; and cluster D showed a declining RSBI. Cluster A showed the highest mortality rate (72%), followed by cluster D (63%), C (62%) and B (61%; p = 0.005 for comparison between 4 clusters). GBM was able to predict cluster membership with an accuracy of > 0.95 in ten-fold cross validation. Highly ranked variables for the prediction of clusters included thyroid-stimulating hormone (TSH), cortisol, platelet, free thyroxine (T4) and serum magnesium.ConclusionsPatients with PMV are composed of a heterogeneous population that can be classified into four clusters by using trajectories of RSBI. These clusters can be easily predicted with baseline clinical variables.https://www.frontiersin.org/articles/10.3389/fmed.2022.880896/fullprolonged mechanical ventilationrapid shallow breathing indexgradient boosting machinemortalityICU |
| spellingShingle | Tsung-Ming Yang Tsung-Ming Yang Tsung-Ming Yang Lin Chen Lin Chen Chieh-Mo Lin Chieh-Mo Lin Chieh-Mo Lin Hui-Ling Lin Hui-Ling Lin Tien-Pei Fang Tien-Pei Fang Huiqing Ge Huabo Cai Yucai Hong Zhongheng Zhang Identifying Novel Clusters of Patients With Prolonged Mechanical Ventilation Using Trajectories of Rapid Shallow Breathing Index Frontiers in Medicine prolonged mechanical ventilation rapid shallow breathing index gradient boosting machine mortality ICU |
| title | Identifying Novel Clusters of Patients With Prolonged Mechanical Ventilation Using Trajectories of Rapid Shallow Breathing Index |
| title_full | Identifying Novel Clusters of Patients With Prolonged Mechanical Ventilation Using Trajectories of Rapid Shallow Breathing Index |
| title_fullStr | Identifying Novel Clusters of Patients With Prolonged Mechanical Ventilation Using Trajectories of Rapid Shallow Breathing Index |
| title_full_unstemmed | Identifying Novel Clusters of Patients With Prolonged Mechanical Ventilation Using Trajectories of Rapid Shallow Breathing Index |
| title_short | Identifying Novel Clusters of Patients With Prolonged Mechanical Ventilation Using Trajectories of Rapid Shallow Breathing Index |
| title_sort | identifying novel clusters of patients with prolonged mechanical ventilation using trajectories of rapid shallow breathing index |
| topic | prolonged mechanical ventilation rapid shallow breathing index gradient boosting machine mortality ICU |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2022.880896/full |
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