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

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.

Figure 1

The flow chart of study population.

Figure 2

Predictors’ selection using the LASSO regression method. (a) LASSO coefficient profiles of the 30 potential variables. A coefficient profile plot was produced against the log (λ) sequence. (b) A 10-fold cross-validation was used in the LASSO regression. Binomial deviance curve was plotted versus log (λ) and dotted vertical lines were drawn based on 1 standard error criteria. The included variables were: age, sex, smoking status, hospitalization for AECOPD in the previous year, hypertension, diabetes mellitus, bronchiectasis, interstitial lung disease, ischemic heart diseases, cerebrovascular disease, chronic kidney disease, chronic hepatic insufficiency, high WBC, low eosinophils, high neutrophil percent, high neutrophil counts, low lymphocyte percent, low lymphocyte counts, low platelets, low RDW, high NLR, high PLR, low TC, low LDL-C, high CRP, low albumin, low pre-albumin, low PNI, in-hospital SCS treatment, in-hospital antibiotics treatment.

Figure 3

The nomogram for predicting corticosteroid treatment failure in patients with AECOPD with a smoking history. The naive C-Statistic: 0.644; optimism-corrected C-Statistic: 0.631.

Figure 4

The nomogram for predicting corticosteroid treatment failure in patients with AECOPD without smoking history. The naïve C-Statistic: 0.647; optimism-corrected C-Statistic 0.635.

Figure 5

The decision curve analysis of nomogram to predict corticosteroid treatment failure.