Predictors of Long-term Mortality after Hospitalization for Severe COPD Exacerbation

Analyzing Clinical Parameters and Bacterial Profiles to Uncover the COPD Exacerbations: A Focus on Intensive Care Unit Challenges

Background and Objectives: Chronic obstructive pulmonary disease (COPD) poses a significant healthcare challenge worldwide, frequently leading to exacerbations necessitating intensive care unit admissions for potentially life-threatening complications. We aimed to investigate correlations between laboratory parameters, bacteriological agents, ventilation mode, and survival rates among COPD patients admitted to the ICU. Materials and Methods: Data were collected from the Pulmonology Department of Mures Clinical County Hospital, Romania, from 1 January 2022 to 30 October 2023. Eighty-four COPD patients required ICU transfer, except for concurrent SARS-CoV-2 infections. Results: Ventilation modes exhibited a significant correlation with specific bacteriological agents, orotracheal intubation being more prevalent in infections with Acinetobacter baumanii, Staphylococcus aureus, and Streptococcus pneumoniae (p < 0.001). Negative cultures were predominantly found in patients managed with non-invasive ventilation. Laboratory parameters revealed an association between elevated white blood cell counts and bacteriological superinfection, particularly with Escherichia coli (p < 0.001). Different bacteriological agents had different survival rates. Patients infected with Acinetobacter baumanii exhibited the highest mortality rate, while those with Staphylococcus aureus had the lowest (p < 0.01). Conclusions: The importance of prompt evaluation of laboratory parameters and bacteriological findings is underscored by these findings, particularly in ICU settings where ventilation and bacteriological profiles influence patient outcomes. The identification of elevated WBC counts is a marker of bacterial superinfection. The association between specific bacterial agents and ventilation modes highlights the importance of tailored treatment based on microbial profiles. These insights can be applied to refine treatment protocols and enhance survival rates in severe COPD exacerbations that require ICU management.

Analysis of the correlation between BMI and respiratory tract microbiota in acute exacerbation of COPD

Objective

To investigate the distribution differences in the respiratory tract microbiota of AECOPD patients in different BMI groups and explore its guiding value for treatment.

Methods

Sputum samples of thirty-eight AECOPD patients were collected. The patients were divided into low, normal and high BMI group. The sputum microbiota was sequenced by 16S rRNA detection technology, and the distribution of sputum microbiota was compared. Rarefaction curve, α-diversity, principal coordinate analysis (PCoA) and measurement of sputum microbiota abundance in each group were performed and analyzed by bioinformatics methods.

Results

1. The rarefaction curve in each BMI group reached a plateau. No significant differences were observed in the OTU total number or α-diversity index of microbiota in each group. PCoA showed significant differences in the distance matrix of sputum microbiota between the three groups, which was calculated by the Binary Jaccard and the Bray Curtis algorithm. 2. At the phylum level, most of the microbiota were Proteobacteria, Bacteroidetes Firmicutes, Actinobacteria, and Fusobacteria. At the genus level, most were Streptococcus, Prevotella, Haemophilus, Neisseria and Bacteroides. 3. At the phylum level, the abundance of Proteobacteria in the low group was significantly higher than that in normal and high BMI groups, the abundances of Firmicutes in the low and normal groups were significantly lower than that in high BMI groups. At the genus level, the abundance of Haemophilus in the low group was significantly higher than that in high BMI group, and the abundances of Streptococcus in the low and normal BMI groups were significantly lower than that in the high BMI group.

Conclusions

1. The sputum microbiota of AECOPD patients in different BMI groups covered almost all microbiota, and BMI had no significant association with total number of respiratory tract microbiota or α-diversity in AECOPD patients. However, there was a significant difference in the PCoA between different BMI groups. 2. The microbiota structure of AECOPD patients differed in different BMI groups. Gram-negative bacteria (G-) in the respiratory tract of patients predominated in the low BMI group, while gram-positive bacteria (G+) predominated in the high BMI group.

Development and validation of a prognostic nomogram among patients with acute exacerbation of chronic obstructive pulmonary disease in intensive care unit

Background

Acute exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD) contributes significantly to mortality among patients with COPD in Intensive care unit (ICU). This study aimed to develop a nomogram to predict 30-day mortality among AECOPD patients in ICU.

Methods

In this retrospective cohort study, we extracted AECOPD patients from Medical Information Mart for Intensive Care III (MIMIC-III) database. Multivariate logistic regression based on Akaike information criterion (AIC) was used to establish the nomogram. Internal validation was performed by a bootstrap resampling approach with 1000 replications. The discrimination and calibration of the nomogram were evaluated by Harrell’s concordance index (C-index) and Hosmer–Lemeshow (HL) goodness-of-fit test. Decision curve analysis (DCA) was performed to evaluate its clinical application.

Results

A total of 494 patients were finally included in the study with a mean age of 70.8 years old. 417 (84.4%) patients were in the survivor group and 77 (15.6%) patients were in the non-survivor group. Multivariate logistic regression analysis based on AIC included age, pO₂, neutrophil-to-lymphocyte ratio (NLR), prognostic nutritional index (PNI), invasive mechanical ventilation and vasopressor use to construct the nomogram. The adjusted C-index was 0.745 (0.712, 0.778) with good calibration (HL test, P = 0.147). The Kaplan–Meier survival curves revealed a significantly lower survival probability in the high-risk group than that in the low-risk group (P < 0.001). DCA showed that nomogram was clinically useful.

Conclusion

The nomogram developed in this study could help clinicians to stratify AECOPD patients and provide appropriate care in clinical setting.

Prognostic Role of Chronic Obstructive Pulmonary Disease and Asthma Physiology Score for in-Hospital and 1-year Mortality in Patients with Acute Exacerbations of COPD

Background and Objectives: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) often lead to high mortality. Chronic obstructive pulmonary disease and asthma physiology score (CAPS) is a simple clinical severity score. The aim of this study was to explore whether CAPS could be an effective predictor for in-hospital and 1-year mortality in AECOPD patients. Methods. We used CAPS to grade all patients and record their clinical characteristics. The receiver operator characteristic (ROC) curve was used to determine the cut-off of CAPS that discriminated survivors and non-survivors. Univariate and multivariate logistic regression analyses and Cox regression analyses were used to identify the risk factors for in-hospital and 1-year mortality, respectively. Results. 240 patients were enrolled in our study; 18 patients died during hospitalization and 29 patients died during the 1-year follow-up. Compared with in-hospital survivors, those who died were older (80.83 ± 6.06 vs. 76.94 ± 8.30 years old, P = 0.019) and had a higher percentage of congestive heart failure (61.1% vs. 14.4%, P < 0.001), higher CAPS levels (31.11 ± 10.05 vs. 16.49 ± 7.11 points, P < 0.001), and a lower BMI (19.48 ± 3.26 vs. 21.50 ± 3.86, P = 0.032). The area under the ROC curve of CAPS for in-hospital death was 0.91 (95% CI: 0.85–0.96) with a sensitivity of 0.889 and a specificity of 0.802 for a cut-off point of 21 points. CAPS ≥21 points was an independent risk factor for in-hospital mortality after adjustment for relative risk (RR) (RR = 13.28, 95% CI: 1.97–89.53, P = 0.008). Univariate Cox regression analysis showed that a CAPS ≥21 points (HR = 4.07, 95% CI: 1.97–8.44) was a risk factor for 1-year mortality. However, multivariate Cox regression analysis showed that CAPS (HR = 2.24, 95% CI: 0.90–5.53) was not associated with 1-year mortality. Conclusion: A CAPS ≥21 points was a strong and independent risk factor for in-hospital mortality in AECOPD patients and CAPS had no impact on the 1-year mortality in patients with acute exacerbations of COPD after discharge.