Fibrinogen: A Feasible Biomarker in Identifying the Severity and Acute Exacerbation of Chronic Obstructive Pulmonary Disease

A predictive model for frequent exacerbator phenotype of acute exacerbations of chronic obstructive pulmonary disease

Background

The frequent exacerbator phenotype of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is characterized by experiencing at least two exacerbations per year, leading to a significant economic burden on healthcare systems worldwide. Although several biomarkers have been shown to be effective in assessing AECOPD severity in recent years, there is a lack of studies on markers to predict the frequent exacerbator phenotype of AECOPD. The current study aimed to develop a new predictive model for the frequent exacerbator phenotype of AECOPD based on rapid, inexpensive, and easily obtained routine markers.

Methods

This was a single-center, retrospective study that enrolled a total of 2,236 AECOPD patients. The participants were divided into two groups based on the frequency of exacerbations: infrequent group (n=1,827) and frequent group (n=409). They underwent a complete blood count, as well as blood biochemistry, blood lipid and coagulation testing, and general characteristics were also recorded. Univariate analysis and binary multivariate logistic regression analyses were used to explore independent risk factors for the frequent exacerbator phenotype of AECOPD, which could be used as components of a new predictive model. The receiver operator characteristic (ROC) curve was used to assess the predictive value of the new model, which consisted of all significant risk factors predicting the primary outcome. The nomogram risk prediction model was established using R software.

Results

Age, gender, length of stay (LOS), neutrophils, monocytes, eosinophils, direct bilirubin (DBil), gamma-glutamyl transferase (GGT), and the glucose-to-lymphocyte ratio (GLR) were independent risk factors for the frequent exacerbator phenotype of AECOPD. The area under the curve (AUC) of the new predictive model was 0.681 [95% confidence interval (CI): 0.653-0.708], and the sensitivity was 63.6% (95% CI: 58.9-68.2%) and the specificity was 65.0% (95% CI: 60.3-69.6%).

Conclusions

A new predictive model based on demographic characteristics and blood parameters can be used to predict the frequency of acute exacerbations in the management of chronic obstructive pulmonary disease (COPD).

Association of Serum Albumin and Copeptin with Early Clinical Deterioration and Instability in Community-Acquired Pneumonia

Background: There is a paucity of data on biomarkers for the early deterioration and clinical instability of patients in community-acquired pneumonia (CAP), as treatment failure occurs in the first seven days in 90% of patients. Aim: To evaluate serum albumin and copeptin with CURB-65, PSI scoring and ATS/IDSA minor criteria for the prediction of early mortality or ICU-admission (7 days) and clinical instability after 72 h. Methods: In 100 consecutive hospitalized adult CAP patients, PSI-scores, CURB-65 scores, ATS/IDSA 2007 minor criteria, copeptin and albumin on admission were evaluated. Univariate and multivariate Cox regression analysis was performed to assess independent risk factors for early combined mortality or ICU admission. Predictive powers of albumin and copeptin were tested with ROC curves and ICU-free survival probability was tested using Kaplan−Meier analysis. Results: Albumin was lower and copeptin higher in patients with short-term adverse outcomes (p < 0.05). Cox regression analysis showed that albumin [HR (95% CI): 0.41 (0.18−0.94, p = 0.034)] and copeptin [HR (95% CI): 1.94 (1.03−3.67, p = 0.042)] were independent risk factors for early combined mortality or ICU admission (7 days). The Kaplan−Meier analysis observed that high copeptin (>27.12 ng/mL) and low albumin levels (<2.85 g/dL) had a lower (p < 0.001) survival probability. The diagnostic accuracy of albumin was better than copeptin. The inclusion of albumin and copeptin into ATS/IDSA minor criteria significantly improved their predictive power. Conclusions: Both biomarkers serum albumin and copeptin can predict early deterioration and clinical instability in hospitalized CAP patients and increase the prognostic power of the traditional clinical scoring systems.