Risk Factors for Length of Hospital Stay in Acute Exacerbation Chronic Obstructive Pulmonary Disease: A Multicenter Cross-Sectional Study

Clinical Characteristics of Overweight Patients With Acute Exacerbation Chronic Obstructive Pulmonary Disease (AECOPD)

INTRODUCTION

Low body weight in patients with COPD is associated with a poor prognosis and more comorbidities. However, the impact of increased body weight in patients with COPD remains controversial. The aim of this study was to explore the clinical features of overweight patients with AECOPD.

METHODS

In this multicenter cross-sectional study, a total of 647 AECOPD patients were recruited. Finally, 269 normal weight and 162 overweight patients were included. Baseline characteristics and clinical and laboratory data were collected. The least absolute shrinkage and selection operator (LASSO) regression was performed to determine potential features, which were substituted into binary logistic regression to reveal overweight-associated clinical features. The nomogram and its associated curves were established to visualize and verify the logistic regression model.

RESULTS

Six potential overweight-associated variables were selected by LASSO regression. Subsequently, a binary logistic regression model identified that the rates of type 2 diabetes (T2DM) and hypertension and levels of lymphocytes (LYM)%, and alanine aminotransferase (ALT) were independent variables of overweight in AECOPD patients. The C-index and AUC of the ROC curve of the nomogram were 0.671 and 0.666, respectively. The DCA curve revealed that the nomogram had more clinical benefits if the threshold was at a range of 0.22~0.78.

CONCLUSIONS

Collectively, we revealed that T2DM and hypertension were more common, and LYM% and ALT were higher in AECOPD patients with overweight than those with normal weight. The result suggests that AECOPD patients with overweight are at risk for additional comorbidities, potentially leading to worse outcomes.

A Systematic Review of the Pulmonary Microbiome in Patients with Acute Exacerbation COPD Requiring ICU Admission

Background: Chronic obstructive pulmonary disease (COPD) is a major health concern. Acute exacerbations (AECOPD) may require intensive care unit (ICU) admission and mechanical ventilation. Acute infections and chronic colonization of the respiratory system are known to precipitate AECOPD. Detailed knowledge of the respiratory microbiome could lead to effective treatment and prevention of exacerbations. Objective: The aim of this review is to summarize the available evidence on the respiratory microbiome of patients with a severe AECOPD requiring mechanical ventilation and intensive care admission. Methods: A systematic literature search was conducted to identify the published papers until January 2023. The collected data were then subjected to qualitative analysis. After the first analysis, a secondary focused review of the most recent publications studying the relationship between microbiome and mortality in AECOPD was performed. Results: Out of 120 screened articles six articles were included in this review. Potentially pathogenic microorganisms (PPMs) were identified in 30% to 72% of the patients with community-acquired bacteria, gram-negative enteric bacilli, Stenotrophomonas and Pseudomonas being the most frequently isolated. During hospitalization, 21% of patients experienced colonization by PPMs. Adequate antimicrobial therapy resulted in the eradication of 77% of the identified PPMs. However, 24% of the bacteria displayed multi-drug resistance leading to prolonged or failure of eradication. Conclusion: PPMs are prevalent in a significant proportion of patients experiencing an AECOPD. The most identified PPMs include community-acquired pathogens and gram-negative enteric bacilli. Notably, no differences in mortality or duration of ventilation were observed between patients with and without isolated PPMs. However, the included studies did not investigate the virome of the patients, which may influence the microbiome and the outcome of infection. Therefore, further research is essential to comprehensively investigate the complete microbial and viral composition of the lower respiratory system in COPD patients admitted to the ICU.

Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study

Introduction

Intrinsically, chronic obstructive pulmonary disease (COPD) is a highly heterogonous disease. Several sex differences in COPD, such as risk factors and prevalence, were identified. However, sex differences in clinical features of acute exacerbation chronic obstructive pulmonary disease (AECOPD) were not well explored. Machine learning showed a promising role in medical practice, including diagnosis prediction and classification. Then, sex differences in clinical manifestations of AECOPD were explored by machine learning approaches in this study.

Methods

In this cross-sectional study, 278 male patients and 81 female patients hospitalized with AECOPD were included. Baseline characteristics, clinical symptoms, and laboratory parameters were analyzed. The K-prototype algorithm was used to explore the degree of sex differences. Binary logistic regression, random forest, and XGBoost models were performed to identify sex-associated clinical manifestations in AECOPD. Nomogram and its associated curves were established to visualize and validate binary logistic regression.

Results

The predictive accuracy of sex was 83.930% using the k-prototype algorithm. Binary logistic regression revealed that eight variables were independently associated with sex in AECOPD, which was visualized by using a nomogram. The AUC of the ROC curve was 0.945. The DCA curve showed that the nomogram had more clinical benefits, with thresholds from 0.02 to 0.99. The top 15 sex-associated important variables were identified by random forest and XGBoost, respectively. Subsequently, seven clinical features, including smoking, biomass fuel exposure, GOLD stages, PaO2, serum potassium, serum calcium, and blood urea nitrogen (BUN), were concurrently identified by three models. However, CAD was not identified by machine learning models.

Conclusions

Overall, our results support that the clinical features differ markedly by sex in AECOPD. Male patients presented worse lung function and oxygenation, less biomass fuel exposure, more smoking, renal dysfunction, and hyperkalemia than female patients with AECOPD. Furthermore, our results also suggest that machine learning is a promising and powerful tool in clinical decision-making.

Risk factors for hyponatremia in acute exacerbation chronic obstructive pulmonary disease (AECOPD): a multicenter cross-sectional study

BACKGROUND

Hyponatremia is an independent predictor of poor prognosis, including increased mortality and readmission, in COPD patients. Identifying modifiable etiologies of hyponatremia may help reduce adverse events in patients with AECOPD. Therefore, the aim of this study was to explore the risk factors and underlying etiologies of hyponatremia in AECOPD patients.

METHODS

A total of 586 AECOPD patients were enrolled in this multicenter cross-sectional study. Finally, 323 had normonatremia, and 90 had hyponatremia. Demographics, underlying diseases, comorbidities, symptoms, and laboratory data were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to select potential risk factors, which were substituted into binary logistic regression to identify independent risk factors. Nomogram was built to visualize and validate binary logistics regression model.

RESULTS

Nine potential hyponatremia-associated variables were selected by LASSO regression. Subsequently, a binary logistic regression model identified that smoking status, rate of community-acquired pneumonia (CAP), anion gap (AG), erythrocyte sedimentation rate (ESR), and serum magnesium (Mg²⁺) were independent variables of hyponatremia in AECOPD patients. The AUC of ROC curve of nomogram was 0.756. The DCA curve revealed that the nomogram could yielded more clinical benefits if the threshold was between 10% and 52%.

CONCLUSIONS

Collectively, our results showed that smoking status, CAP, AG, ESR, and serum Mg²⁺ were independently associated with hyponatremia in AECOPD patients. Then, these findings indicate that pneumonia, metabolic acidosis, and hypomagnesemia were the underlying etiologies of hyponatremia in AECOPD patients. However, their internal connections need further exploration.

Clinical significance of the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in acute exacerbations of COPD: present and future

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are a leading cause of hospitalisation and death in COPD patients. In addition to the identification of better strategies to prevent AECOPD, there is an intense focus on discovering novel markers of disease severity that enhance risk stratification on hospital admission for the targeted institution of aggressiveversussupportive treatments. In the quest for such biomarkers, an increasing body of evidence suggests that specific indexes derived from routine complete blood counts,i.e.the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR), can significantly predict adverse outcomes in AECOPD. This narrative review discusses the current evidence regarding the association between the NLR and the PLR on admission and several clinical end-points (need for invasive ventilation, noninvasive mechanical ventilation failure, admission to an intensive care unit, pulmonary hypertension, length of hospitalisation, and mortality) in AECOPD. Future research directions and potential clinical applications of these haematological indexes in this patient group are also discussed.

A Comprehensive Systematic Review and Meta-Analysis of the Association between the Neutrophil-to-Lymphocyte Ratio and Adverse Outcomes in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

The neutrophil-to-lymphocyte ratio (NLR) predicts adverse outcomes in stable chronic obstructive pulmonary disease (COPD); however, its prognostic role in acute exacerbations (AECOPD) is less clear. We conducted a systematic review and meta-analysis of the association between the NLR on admission and adverse outcomes (mortality, need for mechanical ventilation, transfer to the intensive care unit, length of stay, pulmonary hypertension, or their combination) in AECOPD by searching PubMed, Web of Science, and Scopus from inception to April 2022. Risk of bias and certainty of evidence were assessed using the Joanna Briggs Institute Critical Appraisal Checklist and the Grades of Recommendation, Assessment, Development, and Evaluation, respectively. In 15 studies (n = 10,038 patients), the NLR was significantly associated with the risk of adverse outcomes (odds ratio = 1.054, 95% CI 1.016 to 1.093, p = 0.005; low certainty of evidence; standard mean difference = 0.82, 95% CI 0.57 to 1.06, p < 0.001; high certainty of evidence). Pooled sensitivity, specificity, and area under the curve were 0.71 (95% CI 0.64 to 0.77), 0.73 (95% CI 0.65 to 0.80), and 0.78 (95% CI 0.74 to 0.81), respectively. In our study, the NLR on admission was significantly associated with adverse outcomes in AECOPD patients, suggesting the potential utility of this biomarker for early risk stratification and management in this group.