A Nomogram for Predicting Severe Exacerbations in Stable COPD Patients
Authors Chen X, Wang Q, Hu Y, Zhang L, Xiong W, Xu Y, Yu J, Wang Y
Received 10 October 2019
Accepted for publication 18 December 2019
Published 18 February 2020 Volume 2020:15 Pages 379—388
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 3
Editor who approved publication: Prof. Dr. Chunxue Bai
Xueying Chen,1 Qi Wang,1 Yinan Hu,1 Lei Zhang,1 Weining Xiong,1 Yongjian Xu,1 Jun Yu,2 Yi Wang1
1Department of Respiratory and Critical Care Medicine, Key Laboratory of Pulmonary Diseases of Health Ministry, Key Cite of National Clinical Research Center for Respiratory Disease, Wuhan Clinical Medical Research Center for Chronic Airway Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, People’s Republic of China; 2Department of Thoracic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, People’s Republic of China
Correspondence: Yi Wang
Department of Respiratory and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hongshan District, Wuhan, Hubei Province, People’s Republic of China
Department of Thoracic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hongshan District, Wuhan, Hubei Province, People’s Republic of China
Objective: To develop a practicable nomogram aimed at predicting the risk of severe exacerbations in COPD patients at three and five years.
Methods: COPD patients with prospective follow-up data were extracted from Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) obtained from National Heart, Lung and Blood Institute (NHLBI) Biologic Specimen and Data Repository Information Coordinating Center. We comprehensively considered the demographic characteristics, clinical data and inflammation marker of disease severity. Cox proportional hazard regression was performed to identify the best combination of predictors on the basis of the smallest Akaike Information Criterion. A nomogram was developed and evaluated on discrimination, calibration, and clinical efficacy by the concordance index (C-index), calibration plot and decision curve analysis, respectively. Internal validation of the nomogram was assessed by the calibration plot with 1000 bootstrapped resamples.
Results: Among 1711 COPD patients, 523 (30.6%) suffered from at least one severe exacerbation during follow-up. After stepwise regression analysis, six variables were determined including BMI, severe exacerbations in the prior year, comorbidity index, post-bronchodilator FEV1% predicted, and white blood cells. Nomogram to estimate patients’ likelihood of severe exacerbations at three and five years was established. The C-index of the nomogram was 0.74 (95%CI: 0.71– 0.76), outperforming ADO, BODE and DOSE risk score. Besides, the calibration plot of three and five years showed great agreement between nomogram predicted possibility and actual risk. Decision curve analysis indicated that implementation of the nomogram in clinical practice would be beneficial and better than aforementioned risk scores.
Conclusion: Our new nomogram was a useful tool to assess the probability of severe exacerbations at three and five years for COPD patients and could facilitate clinicians in stratifying patients and providing optimal therapies.
Keywords: severe exacerbations, COPD, nomogram, prediction model
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