Clinical Phenotypes of Patients Hospitalized for an Asthma Exacerbation: Prognostic Implications

Clinical Characteristics and Outcomes of the Phenotypes of COPD-Bronchiectasis Association

INTRODUCTION

Although COPD may frequently co-exist with bronchiectasis [COPD-bronchiectasis associated (CBA)], little is known regarding the clinical heterogeneity. We aimed to identify the phenotypes and compare the clinical characteristics and prognosis of CBA.

METHODS

We conducted a retrospective cohort study involving 2928 bronchiectasis patients, 5158 COPD patients, and 1219 patients with CBA hospitalized between July 2017 and December 2020. We phenotyped CBA with a two-step clustering approach and validated in an independent retrospective cohort with decision-tree algorithms.

RESULTS

Compared with patients with COPD or bronchiectasis alone, patients with CBA had significantly longer disease duration, greater lung function impairment, and increased use of intravenous antibiotics during hospitalization. We identified five clusters of CBA. Cluster 1 (N=120, CBA-MS) had predominantly moderate-severe bronchiectasis, Cluster 2 (N=108, CBA-FH) was characterized by frequent hospitalization within the previous year, Cluster 3 (N=163, CBA-BI) had bacterial infection, Cluster 4 (N=143, CBA-NB) had infrequent hospitalization but no bacterial infection, and Cluster 5 (N=113, CBA-NHB) had no hospitalization or bacterial infection in the past year. The decision-tree model predicted the cluster assignment in the validation cohort with 91.8% accuracy. CBA-MS, CBA-BI, and CBA-FH exhibited higher risks of hospital re-admission and intensive care unit admission compared with CBA-NHB during follow-up (all P<0.05). Of the five clusters, CBA-FH conferred the worst clinical prognosis.

CONCLUSION

Bronchiectasis severity, recent hospitalizations and sputum culture findings are three defining variables accounting for most heterogeneity of CBA, the characterization of which will help refine personalized clinical management.

Clinical Subtypes of Neutrophilic Asthma: A Cluster Analysis From Australasian Severe Asthma Network

BACKGROUND

Clinical heterogeneity may exist within asthma subtypes defined by inflammatory markers. However, the heterogeneity of neutrophilic asthma (NA) remains largely unexplored.

OBJECTIVE

To explore potential clusters and the stability of NA.

METHODS

Participants with NA from the Australasian Severe Asthma Network underwent a multidimensional assessment. They were then asked to participate in a 12-month longitudinal cohort study. We explored potential clusters using a hierarchical cluster analysis and validated the differential future risk of asthma exacerbations in the identified clusters. A decision tree analysis was developed to predict cluster assignments. Finally, the stability of prespecified clusters was examined within 1 month.

RESULTS

Three clusters were identified in 149 patients with NA. Cluster 1 (n = 99; 66.4%) was characterized by female-predominant nonsmokers with well-controlled NA, cluster 2 (n = 16; 10.7%) by individuals with comorbid anxiety/depressive symptoms with poorly controlled NA, and cluster 3 by older male smokers with late-onset NA. Cluster 2 had a greater proportion of participants with severe exacerbations (P = .005), hospitalization (P = .010), and unscheduled visits (P = .013) and a higher number of emergency room visits (P = .039) than that of the other two clusters. The decision tree assigned 92.6% of participants correctly. Most participants (87.5%; n = 7) in cluster 2 had a stable NA phenotype, whereas participants of clusters 1 and 3 had variable phenotypes.

CONCLUSIONS

We identified three clinical clusters of NA, in which cluster 2 represents an uncontrolled and stable NA subtype with an elevated risk of exacerbations. These findings have clinical implications for the management of NA.

The effects of the COVID-19 pandemic on PICU admissions for severe asthma exacerbations: A single-center experience

BACKGROUND

The incidence of severe asthma exacerbations (SAE) requiring a pediatric intensive care unit (PICU) admission during the coronavirus disease 2019 (COVID-19) pandemic (and its association with public restrictions) is largely unknown. We examined the trend of SAE requiring PICU admission before, during, and after COVID-19 restrictions in Amsterdam, the Netherlands, and its relationship with features such as environmental triggers and changes in COVID-19 restriction measures.

METHODS

In this single-center, retrospective cohort study, all PICU admissions of children aged ≥2 years for severe asthma at the Amsterdam UMC between 2018 and 2022 were included. The concentrations of ambient fine particulate matter (PM2.5 ) and pollen were obtained from official monitoring stations.

RESULTS

Between January 2018 and December 2022, 228 children were admitted to the PICU of the Amsterdam UMC for SAE. While we observed a decrease in admissions during periods of more stringent restriction, there was an increase in the PICU admission rate for SAE in some periods following the lifting of restrictions. In particular, following the COVID-19 restrictions in 2021, we observed a peak incidence of admissions from August to November, which was higher than any other peak during the indicated years. No association with air pollution or pollen was observed.

CONCLUSION

We hypothesize that an increase in clinically diagnosed viral infections after lockdown periods was the reason for the altered incidence of SAE at the PICU in late 2021, rather than air pollution and pollen concentrations.

Development of a nomogram to estimate the risk of community-acquired pneumonia in adults with acute asthma exacerbations

OBJECTIVE

The aim of this study is to investigate the clinical characteristics of acute asthma exacerbations (AEs) with community-acquired pneumonia (CAP) in adults and establish a CAP prediction model for hospitalized patients with AEs.

METHODS

We retrospectively collected clinical data from 308 patients admitted to Beijing Luhe Hospital, Capital Medical University, for AEs from December 2017 to August 2021. The patients were divided into CAP and non-CAP groups based on whether they had CAP. We used the Lasso regression technique and multivariate logistic regression analysis to select optimal predictors. We then developed a predictive nomogram based on the optimal predictors. The bootstrap method was used for internal validation. We used the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) to assess the nomogram's discrimination, accuracy, and clinical practicability.

RESULTS

The prevalence of CAP was 21% (65/308) among 308 patients hospitalized for AEs. Independent predictors of CAP in patients hospitalized with an AE (P < 0.05) were C-reactive protein > 10 mg/L, fibrinogen > 4 g/L, leukocytes > 10 × 109 /L, fever, use of systemic corticosteroids before admission, and early-onset asthma. The AUC of the nomogram was 0.813 (95% CI: 0.753-0.872). The concordance index of internal validation was 0.794. The calibration curve was satisfactorily consistent with the diagonal line. The DCA indicated that the nomogram provided a higher clinical net benefit when the threshold probability of patients was 3% to 89%.

CONCLUSIONS

The nomogram performed well in predicting the risk of CAP in hospitalized patients with AEs, thereby providing rapid guidance for clinical decision-making.

Cluster analysis of patients with chronic rhinosinusitis and asthma after endoscopic sinus surgery

BACKGROUND

Patients with chronic rhinosinusitis with nasal polyps and asthma (CRSwAS) are highly heterogenous in severity and prognosis. The clinical phenotypes and inflammatory endotypes of CRSwAS and their association with outcomes of endoscopic sinus surgery (ESS) have not been fully studied yet.

OBJECTIVE

We aimed to find out the clinical phenotypes of CRSwAS and explore their relationship with ESS outcomes using cluster analysis.

METHODS

We recruited 103 consecutive adult patients with CRSwAS who had undergone ESS and been followed up for more than 1 year. For cluster analysis, we collected the data from 63 variables pertaining to demographic characteristics, preoperative disease status, surgical techniques, postoperative medical treatment, and outcomes. Eosinophilic CRS was defined as greater than or equal to 10 eosinophils/high-power field, and sinus computed tomography was evaluated by Lund-Mackay sinus computed tomography score (LM score).

RESULTS

We screened 92 eligible patients and 13 preoperative variables for balanced iterative reducing and clustering using hierarchies cluster analysis. Patients with CRSwAS were divided into 4 clusters with distinct ESS outcomes: (1) cluster 1, characterized by aspirin-exacerbated respiratory disease, eosinophilic CRS, high preoperative LM score, moderate-to-severe asthma, and uncontrolled CRS after ESS; (2) cluster 2, characterized as having female dominance (66.67%), non-aspirin-exacerbated respiratory disease, eosinophilic CRS, high preoperative LM score, moderate-to-severe asthma, and uncontrolled CRS after ESS; (3) cluster 3, characterized as having female dominance (95.83%), noneosinophilic CRS, low preoperative LM score, moderate asthma, and controlled CRS after ESS; and (4) cluster 4, characterized as men-only, smoker, noneosinophilic CRS, low preoperative LM score, mild asthma, and controlled CRS after ESS.

CONCLUSION

CRSwAS has distinct clusters, each corresponding to unique clinical and inflammatory characteristics and ESS outcomes.

Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis

INTRODUCTION

The definition of asthma phenotypes has not been fully established, neither there are cluster studies showing homogeneous results to solidly establish clear phenotypes. The purpose of this study was to develop a classification algorithm based on unsupervised cluster analysis, identifying clusters that represent clinically relevant asthma phenotypes that may share asthma-related outcomes.

METHODS

We performed a multicentre prospective cohort study, including adult patients with asthma (N=512) from the MEGA study (Mechanisms underlying the Genesis and evolution of Asthma). A standardised clinical history was completed for each patient. Cluster analysis was performed using the kernel k-groups algorithm.

RESULTS

Four clusters were identified. Cluster 1 (31.5% of subjects) includes adult-onset atopic patients with better lung function, lower BMI, good asthma control, low ICS dose, and few exacerbations. Cluster 2 (23.6%) is made of adolescent-onset atopic asthma patients with normal lung function, but low adherence to treatment (59% well-controlled) and smokers (48%). Cluster 3 (17.1%) includes adult-onset patients, mostly severe non-atopic, with overweight, the worse lung function and asthma control, and receiving combination of treatments. Cluster 4 (26.7%) consists of the elderly-onset patients, mostly female, atopic (64%), with high BMI and normal lung function, prevalence of smokers and comorbidities.

CONCLUSION

We defined four phenotypes of asthma using unsupervised cluster analysis. These clusters are clinically relevant and differ from each other as regards FEV1, age of onset, age, BMI, atopy, asthma severity, exacerbations, control, social class, smoking and nasal polyps.

Body Composition-Specific Asthma Phenotypes: Clinical Implications

Background: Previous studies have indicated the limitations of body mass index for defining disease phenotypes. The description of asthma phenotypes based on body composition (BC) has not been largely reported. Objective: To identify and characterize phenotypes based on BC parameters in patients with asthma. Methods: A study with two prospective observational cohorts analyzing adult patients with stable asthma (n = 541 for training and n = 179 for validation) was conducted. A body composition analysis was performed for the included patients. A cluster analysis was conducted by applying a 2-step process with stepwise discriminant analysis. Logistic regression models were used to evaluate the association between identified phenotypes and asthma exacerbations (AEs). The same algorithm for cluster analysis in the independent validation set was used to perform an external validation. Results: Three clusters had significantly different characteristics associated with asthma outcomes. An external validation identified the similarity of the participants in training and the validation set. In the training set, cluster Training (T) 1 (29.4%) was “patients with undernutrition”, cluster T2 (18.9%) was “intermediate level of nutrition with psychological dysfunction”, and cluster T3 (51.8%) was “patients with good nutrition”. Cluster T3 had a decreased risk of moderate-to-severe and severe AEs in the following year compared with the other two clusters. The most important BC-specific factors contributing to being accurately assigned to one of these three clusters were skeletal muscle mass and visceral fat area. Conclusion: We defined three distinct clusters of asthma patients, which had distinct clinical features and asthma outcomes. Our data reinforced the importance of evaluating BC to determining nutritional status in clinical practice.

Obese, non-eosinophilic asthma: frequent exacerbators in a real-world setting

OBJECTIVE

In the UK, asthma deaths are at their highest level this century. Increased recognition of at-risk patients is needed. This study phenotyped frequent asthma exacerbators and used machine learning to predict frequent exacerbators.

METHODS

Patients admitted to a district general hospital with an asthma exacerbation between 1st March 2018 and 1st March 2020 were included. Patients were organized into two groups: "Infrequent Exacerbators" (1 admission in the previous 12 months) and "Frequent Exacerbators" (≥2 admissions in the previous 12 months). Patient data were retrospectively collected from hospital and primary care records. Machine learning models were used to predict frequent exacerbators.

RESULTS

200 patients admitted for asthma exacerbations were randomly selected (73% female; mean age 47.8 years). Peripheral eosinophilia was uncommon in either group (21% vs 19%). More frequent exacerbators were being treated with high-dose ICS than infrequent exacerbators (46.5% vs 23.2%; P < 0.001), and frequent exacerbators used more SABA inhalers (10.9 vs 7.40; P = 0.01) in the year preceding the current admission. BMI was raised in both groups (34.2 vs 30.9). Logistic regression was the most accurate machine learning model for predicting frequent exacerbators (AUC = 0.80).

CONCLUSIONS

Patients admitted for asthma are predominately female, obese and non-eosinophilic. Patients who require multiple admissions per year have poorer asthma control at baseline. Machine learning algorithms can predict frequent exacerbators using clinical data available in primary care. Instead of simply increasing the dose of corticosteroids, multidisciplinary management targeting Th2-low inflammation should be considered for these patients.

Eosinophils and Chronic Respiratory Diseases in Hospitalized COVID-19 Patients

BACKGROUND

Studies on the role of eosinophils in coronavirus disease 2019 (COVID-19) are scarce, though available findings suggest a possible association with disease severity. Our study analyzes the relationship between eosinophils and COVID-19, with a focus on disease severity and patients with underlying chronic respiratory diseases.

METHODS

We performed a retrospective analysis of 3018 subjects attended at two public hospitals in Madrid (Spain) with PCR-confirmed SARS-CoV-2 infection from January 31 to April 17, 2020. Patients with eosinophil counts less than 0.02×109/L were considered to have eosinopenia. Individuals with chronic respiratory diseases (n=384) were classified according to their particular underlying condition, i.e., asthma, chronic pulmonary obstructive disease, or obstructive sleep apnea.

RESULTS

Of the 3018 patients enrolled, 479 were excluded because of lack of information at the time of admission. Of 2539 subjects assessed, 1396 patients presented an eosinophil count performed on admission, revealing eosinopenia in 376 cases (26.93%). Eosinopenia on admission was associated with a higher risk of intensive care unit (ICU) or respiratory intensive care unit (RICU) admission (OR:2.21; 95%CI:1.42-3.45; p<0.001) but no increased risk of mortality (p>0.05).

CONCLUSIONS

Eosinopenia on admission conferred a higher risk of severe disease (requiring ICU/RICU care), but was not associated with increased mortality. In patients with chronic respiratory diseases who develop COVID-19, age seems to be the main risk factor for progression to severe disease or death.

Total IgE Variability Is Associated with Future Asthma Exacerbations: A 1-Year Prospective Cohort Study

BACKGROUND

Few prospective studies have investigated the relationship between IgE variability and risk for asthma exacerbations (AEs).

OBJECTIVE

To explore the relationship between IgE variability and AEs.

METHODS

Recruited patients with stable asthma underwent two serum total IgE tests within a month (at screening [baseline IgE] and at 1 month) to obtain the coefficient of variation (CV) of base 10 log-transformed IgE. Patients with IgE CV were divided into IgE CV-high and IgE CV-low cohorts based on the CV median and were observed within 12 months, during which the association between IgE variability and AEs was explored using a negative binomial regression model.

RESULTS

The IgE CV levels obtained from 340 patients classified patients into two groups (n = 170 for the IgE CV-high and IgE CV-low groups, respectively) based on the serum total IgE CV median of 2.12% (quartiles 1 and 3: 0.98% and 3.91%, respectively). The IgE CV-high patients exhibited worse asthma control and lung function and more marked airway inflammation, and received more intensive medication use compared with IgE CV-low patients. The IgE CV-high patients exhibited increased rates of moderate-to-severe (adjusted rate ratio = 2.88; 95% confidence interval, 1.65-5.03; P < .001) and severe (adjusted rate ratio = 2.16; 95% confidence interval, 1.08-4.32; P = .029) AEs during the follow-up year compared with IgE CV-low patients. Furthermore, sputum IL-6 partially mediated the associations between IgE CV with moderate-to-severe and severe AEs.

CONCLUSIONS

Variability in total serum IgE levels is an easily obtained and practical measure for predicting AEs. Future studies are needed to investigate whether IgE variability can be used to guide precision medicine in asthma.