Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study

Abstract

Yan Yin,1 Jinfu Xu,2 Shaoxi Cai,3 Yahong Chen,4 Yan Chen,5 Manxiang Li,6 Zhiqiang Zhang,7 Jian Kang1 1Department of Pulmonary and Critical Care Medicine, The First Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China; 2Department of Pulmonary and Critical Care Medicine, Shanghai Pulmonary Hospital, Institute of Respiratory Medicine, School of Medicine, Tongji University, Shanghai, People’s Republic of China; 3Department of Pulmonary and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China; 4Department of Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, People’s Republic of China; 5Department of Pulmonary and Critical Care Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People’s Republic of China; 6Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 7Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, People’s Republic of ChinaCorrespondence: Jian Kang, Department of Pulmonary and Critical Care Medicine, The First Hospital of China Medical University, 155 Nanjing Street (North), Heping District, Shenyang, 110001, Liaoning, People’s Republic of China, Tel [phone], Fax [phone], Email [email protected]: There is an unmet clinical need for an accurate and objective diagnostic tool for early detection of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). DETECT (NCT03556475) was a multicenter, observational, cross-sectional study aiming to develop and validate multivariable prediction models for AECOPD occurrence and severity in patients with chronic obstructive pulmonary disease (COPD) in China.Patients and Methods: Patients aged ≥ 40 years with moderate/severe COPD, AECOPD, or no COPD were consecutively enrolled between April 22, 2020, and January 18, 2021, across seven study sites in China. Multivariable prediction models were constructed to identify AECOPD occurrence (primary outcome) and AECOPD severity (secondary outcome). Candidate variables were selected using a stepwise procedure, and the bootstrap method was used for internal model validation.Results: Among 299 patients enrolled, 246 were included in the final analysis, of whom 30.1%, 40.7%, and 29.3% had COPD, AECOPD, or no COPD, respectively. Mean age was 64.1 years. Variables significantly associated with AECOPD occurrence (P< 0.05) and severity (P< 0.05) in the final models included COPD disease-related characteristics, as well as signs and symptoms. Based on cut-off values of 0.374 and 0.405 for primary and secondary models, respectively, the performance of the primary model constructed to identify AECOPD occurrence (AUC: 0.86; sensitivity: 0.84; specificity: 0.77), and of the secondary model for AECOPD severity (AUC: 0.81; sensitivity: 0.90; specificity: 0.73) indicated high diagnostic accuracy and clinical applicability.Conclusion: By leveraging easy-to-collect patient and disease data, we developed identification tools that can be used for timely detection of AECOPD and its severity. These tools may help physicians diagnose AECOPD in a timely manner, before further disease progression and possible hospitalizations.Keywords: chronic obstructive pulmonary disease, acute exacerbation, prediction model, diagnosis

Keywords

• chronic obstructive pulmonary disease
• acute exacerbation
• prediction model
• diagnosis