Identifying Novel Clusters of Patients With Prolonged Mechanical Ventilation Using Trajectories of Rapid Shallow Breathing Index

Tsung-Ming Yang; Lin Chen; Chieh-Mo Lin; Hui-Ling Lin; Tien-Pei Fang; Huiqing Ge; Huabo Cai; Yucai Hong; Zhongheng Zhang

doi:10.3389/fmed.2022.880896

Identifying Novel Clusters of Patients With Prolonged Mechanical Ventilation Using Trajectories of Rapid Shallow Breathing Index

Front Med (Lausanne). 2022 Jul 4:9:880896. doi: 10.3389/fmed.2022.880896. eCollection 2022.

Authors

Tsung-Ming Yang^{1

2

3}, Lin Chen^{4

5}, Chieh-Mo Lin^{1

6

7}, Hui-Ling Lin^{8

9}, Tien-Pei Fang^{3

9}, Huiqing Ge¹⁰, Huabo Cai¹¹, Yucai Hong¹¹, Zhongheng Zhang¹¹

Affiliations

¹ Division of Pulmonary and Critical Care Medicine, Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan.
² School of Traditional Chinese Medicine, Chang Gung University, Taoyuan, Taiwan.
³ Department of Respiratory Care, Chang Gung University of Science and Technology, Chiayi, Taiwan.
⁴ Department of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.
⁵ Key Laboratory of Emergency and Trauma, Ministry of Education, College of Emergency and Trauma, Hainan Medical University, Haikou, China.
⁶ College of Medicine, Graduate Institute of Clinical Medical Sciences, Chang Gung University, Taoyuan, Taiwan.
⁷ Department of Nursing, Chang Gung University of Science and Technology, Chiayi, Taiwan.
⁸ Department of Respiratory Therapy, Chang Gung University, Taoyuan, Taiwan.
⁹ Department of Respiratory Therapy, Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan.
¹⁰ Department of Respiratory Care, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
¹¹ Key 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, China.

Abstract

Objective: Patients 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.

Methods: This 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.

Results: A 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.

Conclusions: Patients 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.

Keywords: ICU; gradient boosting machine; mortality; prolonged mechanical ventilation; rapid shallow breathing index.