A Nomogram for Predicting Severe Exacerbations in Stable COPD Patients

Overview

Journal Int J Chron Obstruct Pulmon Dis

Publisher Dove Medical Press

Specialty Pulmonary Medicine

Date 2020 Feb 29

PMID 32110006

Citations 10

Authors

Xueying Chen

Qi Wang

Yinan Hu

Lei Zhang

Weining Xiong

Yongjian Xu

Jun Yu

Yi Wang

Affiliations

Soon will be listed here.

Abstract

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 FEV% 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.

Citing Articles

Comparison of BODE and ADO Indices in Predicting COPD-Related Medical Costs.

Li C, Chang H, Tseng C, Tsai Y, Liu J, Tsai M Medicina (Kaunas). 2023; 59(3).

PMID: 36984578 PMC: 10057417. DOI: 10.3390/medicina59030577.

Development of a model for predicting the severity of chronic obstructive pulmonary disease.

Gu Y, Chen L, Qiu R, Wang S, Chen P Front Med (Lausanne). 2023; 9:1073536.

PMID: 36590951 PMC: 9800610. DOI: 10.3389/fmed.2022.1073536.

Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study.

Zeng S, Arjomandi M, Luo G JMIR Med Inform. 2022; 10(2):e33043.

PMID: 35212634 PMC: 8917430. DOI: 10.2196/33043.

Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study.

Zeng S, Arjomandi M, Tong Y, Liao Z, Luo G J Med Internet Res. 2022; 24(1):e28953.

PMID: 34989686 PMC: 8778560. DOI: 10.2196/28953.

CONQUEST Quality Standards: For the Collaboration on Quality Improvement Initiative for Achieving Excellence in Standards of COPD Care.

Pullen R, Miravitlles M, Sharma A, Singh D, Martinez F, Hurst J Int J Chron Obstruct Pulmon Dis. 2021; 16:2301-2322.

PMID: 34413639 PMC: 8370848. DOI: 10.2147/COPD.S313498.

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