Comprehensive Nomograms Using Routine Biomarkers Beyond Eosinophil Levels: Enhancing Predictability of Corticosteroid Treatment Outcomes in AECOPD

Lin Feng; Jiachen Li; Zhenbei Qian; Chenglong Li; Darui Gao; Yongqian Wang; Wuxiang Xie; Yutong Cai; Zhaohui Tong; Lirong Liang

doi:10.2147/JIR.S450447

Comprehensive Nomograms Using Routine Biomarkers Beyond Eosinophil Levels: Enhancing Predictability of Corticosteroid Treatment Outcomes in AECOPD

J Inflamm Res. 2024 Mar 8:17:1511-1526. doi: 10.2147/JIR.S450447. eCollection 2024.

Authors

Lin Feng¹, Jiachen Li¹, Zhenbei Qian², Chenglong Li^{3

4}, Darui Gao^{3

4}, Yongqian Wang^{3

4}, Wuxiang Xie^{3

4}, Yutong Cai⁵, Zhaohui Tong², Lirong Liang¹

Affiliations

¹ Department of Clinical Epidemiology, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China.
² Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People's Republic of China.
³ Heart and Vascular Health Research Center, Peking University Clinical Research Institute, Peking University First Hospital, Beijing, People's Republic of China.
⁴ Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, People's Republic of China.
⁵ Centre for Environmental Health and Sustainability, Department of Health Sciences, University of Leicester, Leicester, UK.

Abstract

Purpose: Patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) exhibit heterogeneous responses to corticosteroid treatment. We aimed to determine whether combining eosinophil levels with other routine clinical indicators can enhance the predictability of corticosteroid treatment outcomes and to come up with a scoring system.

Patients and methods: Consecutive patients admitted with AECOPD receiving corticosteroid treatment between July 2013 and March 2022 at Beijing Chao-Yang Hospital were retrospectively analyzed. Data on patients' demographics, smoking status, hospitalization for AECOPD in the previous year, comorbidities, blood laboratory tests, in-hospital treatment and clinical outcomes were collected. Least absolute shrinkage and selection operator (LASSO) regression and backward logistic regression were used for predictor selection, and predictive nomograms were developed. The discrimination and calibration of the nomograms were assessed using the area under the receiver operating curve (AUC) and calibration plots. Internal validation was performed using the 500-bootstrap method, and clinical utility was evaluated using decision curve analysis (DCA).

Results: Among the 3254 patients included, 804 (24.7%) had treatment failure. A nomogram of eosinophils, platelets, C-reactive protein (CRP), low density lipoprotein cholesterol, prognostic nutritional index (PNI), hospitalization for AECOPD in the previous year, ischemic heart diseases and chronic hepatic disease was developed to predict treatment failure for patients with a smoking history. For patients without a smoking history, a nomogram of CRP, PNI, ischemic heart diseases and chronic hepatic disease was developed. Although the AUCs of these two nomograms were only 0.644 and 0.647 respectively, they were significantly superior to predictions based solely on blood eosinophil levels.

Conclusion: We developed easy-to-use comprehensive nomograms utilizing readily available clinical biomarkers related to inflammation, nutrition and immunity, offering modestly enhanced predictive value for treatment outcomes in corticosteroid-treated patients with AECOPD. Further investigations into novel biomarkers and additional patient data are imperative to optimize the predictive performance.

Keywords: chronic obstructive pulmonary disease; glucocorticoids; least absolute shrinkage and selection operator; prediction model.

Grants and funding

This work was supported by grants from the Beijing Municipal Science & Technology Commission (grant no. Z201100005520029), Beijing Municipal Administration of Hospitals Incubating Program (grant no. PX2020014), Reform and Development Program of Beijing Institute of Respiratory Medicine (Ggyfz202424) and Beijing Key Specialists in Major Epidemic Prevention and Control. The funder had no role in the study design, data collection, data analysis and interpretation, writing of the report, or the decision to submit the article for publication.