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).

Cationic Covalent Organic Nanosheets for Rapid and Selective Capture of Perrhenate: An Analogue of Radioactive Pertechnetate from Aqueous Solution

Capture of radioactive TcO₄^- from nuclear wastes is extremely desirable for waste disposal and environmental restoration. Here, we report the synthesis of hydrolytically stable cationic covalent organic nanosheets (iCON) for efficient uptake of ReO₄^-, a nonradioactive surrogate of TcO₄^-. The iCON combines cationic guanidine-based knots with hydroxyl anchored neutral edge units and chloride ions loosely bonded in the pores, rendering extremely fast exchange kinetics toward ReO₄^- with high uptake capacity of 437 mg g^-1 and prominent distribution coefficient of 5.0 × 10⁵. The removal efficiency remains stable over a pH range of 3-12 and allows selective capture of ReO₄^- in the presence of excessive competing anions such as NO₃^-, CO₃^2-, PO₄^3- and SO₄^2- with good removal efficiency for ReO₄^- in a simulated Hanford LAW Melter Recycle Stream. Anion exchange between the ReO₄^- in solution and the chloride ion in iCON plays dominant role in the adsorption of ReO₄^-. The iCON shows promise for effective removal of radioactive ⁹⁹Tc from nuclear waste.