Finding the Right Biological: Eosinophil Subset Differences in Asthma and Chronic Obstructive Pulmonary Disease

Freeman, Christine M.; Curtis, Jeffrey L.; Hastie, Annette T.

doi:10.1164/rccm.202305-0811ed

Cited by 2 publications

(1 citation statement)

References 24 publications

(38 reference statements)

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“… 61 Recent studies have discerned between inflammatory and resident eosinophil subtypes, indicating that the inflammatory eosinophils predominantly contribute to COPD associated with type-2 inflammation. 62 , 63 Therefore, inflammatory eosinophils might offer a more accurate guide than total eosinophils for corticosteroid treatment decisions. Furthermore, factors such as corticosteroid treatment prior to admission, presenting symptoms and signs, and blood gas examination results might also have potential implications for personalized corticosteroid treatments.…”

Section: Discussionmentioning

confidence: 99%

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

Feng,

Li,

Qian

et al. 2024

JIR

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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.

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Section: Discussionmentioning

confidence: 99%