The Association between Bronchiectasis and Chronic Obstructive Pulmonary Disease: Data from the European Bronchiectasis Registry (EMBARC).

Rationale: COPD and bronchiectasis are commonly reported together. Studies report varying impacts of co-diagnosis on outcomes, which may be related to different definitions of disease used across studies. Objectives: To investigate the prevalence of chronic obstructive pulmonary disease (COPD) associated with bronchiectasis and its relationship with clinical outcomes. We further investigated the impact of implementing the standardized ROSE criteria (radiological bronchiectasis [R], obstruction [FEV1/FVC ratio <0.7; O], symptoms [S], and exposure [⩾10 pack-years of smoking; E]), an objective definition of the association of bronchiectasis with COPD. Methods: Analysis of the EMBARC (European Bronchiectasis Registry), a prospective observational study of patients with computed tomography-confirmed bronchiectasis from 28 countries. The ROSE criteria were used to objectively define the association of bronchiectasis with COPD. Key outcomes during a maximum of 5 years of follow-up were exacerbations, hospitalization, and mortality. Measurements and Main Results: A total of 16,730 patients with bronchiectasis were included; 4,336 had a clinician-assigned codiagnosis of COPD, and these patients had more exacerbations, worse quality of life, and higher severity scores. We observed marked overdiagnosis of COPD: 22.2% of patients with a diagnosis of COPD did not have airflow obstruction and 31.9% did not have a history of ⩾10 pack-years of smoking. Therefore, 2,157 patients (55.4%) met the ROSE criteria for COPD. Compared with patients without COPD, patients who met the ROSE criteria had increased risks of exacerbations and exacerbations resulting in hospitalization during follow-up (incidence rate ratio, 1.25; 95% confidence interval, 1.15-1.35; vs. incidence rate ratio, 1.69; 95% confidence interval, 1.51-1.90, respectively). Conclusions: The label of COPD is often applied to patients with bronchiectasis who do not have objective evidence of airflow obstruction or a smoking history. Patients with a clinical label of COPD have worse clinical outcomes.

Bronchiectasis and asthma: Data from the European Bronchiectasis Registry (EMBARC).

BACKGROUND: Asthma is commonly reported in patients with a diagnosis of bronchiectasis. OBJECTIVE: The aim of this study was to evaluate whether patients with bronchiectasis and asthma (BE+A) had a different clinical phenotype and different outcomes compared with patients with bronchiectasis without concomitant asthma. METHODS: A prospective observational pan-European registry (European Multicentre Bronchiectasis Audit and Research Collaboration) enrolled patients across 28 countries. Adult patients with computed tomography-confirmed bronchiectasis were reviewed at baseline and annual follow-up visits using an electronic case report form. Asthma was diagnosed by the local investigator. Follow-up data were used to explore differences in exacerbation frequency between groups using a negative binomial regression model. Survival analysis used Cox proportional hazards regression. RESULTS: Of 16,963 patients with bronchiectasis included for analysis, 5,267 (31.0%) had investigator-reported asthma. Patients with BE+A were younger, were more likely to be female and never smokers, and had a higher body mass index than patients with bronchiectasis without asthma. BE+A was associated with a higher prevalence of rhinosinusitis and nasal polyps as well as eosinophilia and Aspergillus sensitization. BE+A had similar microbiology but significantly lower severity of disease using the bronchiectasis severity index. Patients with BE+A were at increased risk of exacerbation after adjustment for disease severity and multiple confounders. Inhaled corticosteroid (ICS) use was associated with reduced mortality in patients with BE+A (adjusted hazard ratio 0.78, 95% CI 0.63-0.95) and reduced risk of hospitalization (rate ratio 0.67, 95% CI 0.67-0.86) compared with control subjects without asthma and not receiving ICSs. CONCLUSIONS: BE+A was common and was associated with an increased risk of exacerbations and improved outcomes with ICS use. Unexpectedly we identified significantly lower mortality in patients with BE+A.

Objective sputum colour assessment and clinical outcomes in bronchiectasis: data from the European Bronchiectasis Registry (EMBARC).

BACKGROUND: A validated 4-point sputum colour chart can be used to objectively evaluate the levels of airway inflammation in bronchiectasis patients. In the European Bronchiectasis Registry (EMBARC), we tested whether sputum colour would be associated with disease severity and clinical outcomes. METHODS: We used a prospective, observational registry of adults with bronchiectasis conducted in 31 countries. Patients who did not produce spontaneous sputum were excluded from the analysis. The Murray sputum colour chart was used at baseline and at follow-up visits. Key outcomes were frequency of exacerbations, hospitalisations for severe exacerbations and mortality during up to 5-year follow-up. RESULTS: 13 484 patients were included in the analysis. More purulent sputum was associated with lower forced expiratory volume in 1 s (FEV1), worse quality of life, greater bacterial infection and a higher bronchiectasis severity index. Sputum colour was strongly associated with the risk of future exacerbations during follow-up. Compared to patients with mucoid sputum (reference group), patients with mucopurulent sputum experienced significantly more exacerbations (incident rate ratio (IRR) 1.29, 95% CI 1.22-1.38; p<0.0001), while the rates were even higher for patients with purulent (IRR 1.55, 95% CI 1.44-1.67; p<0.0001) and severely purulent sputum (IRR 1.91, 95% CI 1.52-2.39; p<0.0001). Hospitalisations for severe exacerbations were also associated with increasing sputum colour with rate ratios, compared to patients with mucoid sputum, of 1.41 (95% CI 1.29-1.56; p<0.0001), 1.98 (95% CI 1.77-2.21; p<0.0001) and 3.05 (95% CI 2.25-4.14; p<0.0001) for mucopurulent, purulent and severely purulent sputum, respectively. Mortality was significantly increased with increasing sputum purulence, hazard ratio 1.12 (95% CI 1.01-1.24; p=0.027), for each increment in sputum purulence. CONCLUSION: Sputum colour is a simple marker of disease severity and future risk of exacerbations, severe exacerbations and mortality in patients with bronchiectasis.

Airway clearance management in people with bronchiectasis: data from the European Bronchiectasis Registry (EMBARC).

BACKGROUND: International guidelines recommend airway clearance management as one of the important pillars of bronchiectasis treatment. However, the extent to which airway clearance is used for people with bronchiectasis in Europe is unclear. The aim of the study was to identify the use of airway clearance management in patients with bronchiectasis across different countries and factors influencing airway clearance use. METHODS: This was a prospective observational study using data from the European Multicentre Bronchiectasis Audit and Research Collaboration (EMBARC) Registry between January 2015 and April 2022. Prespecified options for airway clearance management were recorded, including airway clearance techniques, devices and use of mucoactive drugs. RESULTS: 16 723 people with bronchiectasis from 28 countries were included in the study. The mean age was 67 years (interquartile range 57-74 years, range 18-100 years) and 61% were female. 72% of the participants reported daily sputum expectoration and 52% (95% CI 51-53%) of all participants reported using regular airway clearance management. Active cycle of breathing technique was used by 28% of the participants and airway clearance devices by 16% of participants. The frequency of airway clearance management and techniques used varied significantly between different countries. Participants who used airway clearance management had greater disease severity and worse symptoms, including a higher daily sputum volume, compared to those who did not use it regularly. Mucoactive drugs were also more likely to be used in participants with more severe disease. Access to specialist respiratory physiotherapy was low throughout Europe, but particularly low in Eastern Europe. CONCLUSIONS: Only a half of people with bronchiectasis in Europe use airway clearance management. Use of and access to devices, mucoactive drugs and specialist chest physiotherapy appears to be limited in many European countries.

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort.

BACKGROUND: Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster. METHODS: Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3. RESULTS: Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3. CONCLUSIONS: During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis.

Treatment Failure and Clinical Stability in Severe Community-Acquired Pneumonia.

Treatment failure and clinical stability are important outcomes in community-acquired pneumonia (CAP). It is essential to know the causes and risk factors for treatment failure and delay in reaching clinical stability in CAP. The study of both as well as the associated underlying mechanisms and host response are key to improving outcomes in pneumonia.

Cost-sensitive ordinal classification methods to predict SARS-CoV-2 pneumonia severity.

OBJECTIVE: To study the suitability of costsensitive ordinal artificial intelligence-machine learning (AIML) strategies in the prognosis of SARS-CoV-2 pneumonia severity. MATERIALS & METHODS: Observational, retrospective, longitudinal, cohort study in 4 hospitals in Spain. Information regarding demographic and clinical status was supplemented by socioeconomic data and air pollution exposures. We proposed AI-ML algorithms for ordinal classification via ordinal decomposition and for cost-sensitive learning via resampling techniques. For performance-based model selection, we defined a custom score including per-class sensitivities and asymmetric misprognosis costs. 260 distinct AI-ML models were evaluated via 10 repetitions of 5×5 nested cross-validation with hyperparameter tuning. Model selection was followed by the calibration of predicted probabilities. Final overall performance was compared against five well-established clinical severity scores and against a 'standard' (non-cost sensitive, non-ordinal) AI-ML baseline. In our best model, we also evaluated its explainability with respect to each of the input variables. RESULTS: The study enrolled n = 1548 patients: 712 experienced low, 238 medium, and 598 high clinical severity. d = 131 variables were collected, becoming d ' = 148 features after categorical encoding. Model selection resulted in our best-performing AI-ML pipeline having: a) no imputation of missing data, b) no feature selection (i.e. using the full set of d ' features), c) 'Ordered Partitions' ordinal decomposition, d) cost-based reimbalance, and e) a Histogram-based Gradient Boosting classifier. This best model (calibrated) obtained a median accuracy of 68.1% [67.3%, 68.8%] (95% confidence interval), a balanced accuracy of 57.0% [55.6%, 57.9%], and an overall area under the curve (AUC) 0.802 [0.795, 0.808]. In our dataset, it outperformed all five clinical severity scores and the 'standard' AI-ML baseline. DISCUSSION & CONCLUSION: We conducted an exhaustive exploration of AI-ML methods designed for both ordinal and cost-sensitive classification, motivated by a real-world application domain (clinical severity prognosis) in which these topics arise naturally. Our model with the best classification performance exploited successfully the ordering information of ground truth classes, coping with imbalance and asymmetric costs. However, these ordinal and cost-sensitive aspects are seldom explored in the literature.

Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality.

BACKGROUND: There exists consistent empirical evidence in the literature pointing out ample heterogeneity in terms of the clinical evolution of patients with COVID-19. The identification of specific phenotypes underlying in the population might contribute towards a better understanding and characterization of the different courses of the disease. The aim of this study was to identify distinct clinical phenotypes among hospitalized patients with SARS-CoV-2 pneumonia using machine learning clustering, and to study their association with subsequent clinical outcomes as severity and mortality. METHODS: Multicentric observational, prospective, longitudinal, cohort study conducted in four hospitals in Spain. We included adult patients admitted for in-hospital stay due to SARS-CoV-2 pneumonia. We collected a broad spectrum of variables to describe exhaustively each case: patient demographics, comorbidities, symptoms, physiological status, baseline examinations (blood analytics, arterial gas test), etc. For the development and internal validation of the clustering/phenotype models, the dataset was split into training and test sets (50% each). We proposed a sequence of machine learning stages: feature scaling, missing data imputation, reduction of data dimensionality via Kernel Principal Component Analysis (KPCA), and clustering with the k-means algorithm. The optimal cluster model parameters -including k, the number of phenotypes- were chosen automatically, by maximizing the average Silhouette score across the training set. RESULTS: We enrolled 1548 patients, each of them characterized by 92 clinical attributes (d=109 features after variable encoding). Our clustering algorithm identified k=3 distinct phenotypes and 18 strongly informative variables: Phenotype A (788 cases [50.9% prevalence] - age ∼ 57, Charlson comorbidity ∼ 1, pneumonia CURB-65 score ∼ 0 to 1, respiratory rate at admission ∼ 18 min-1, FiO2 ∼ 21%, C-reactive protein CRP ∼ 49.5 mg/dL [median within cluster]); phenotype B (620 cases [40.0%] - age ∼ 75, Charlson ∼ 5, CURB-65 ∼ 1 to 2, respiration ∼ 20 min-1, FiO2 ∼ 21%, CRP ∼ 101.5 mg/dL); and phenotype C (140 cases [9.0%] - age ∼ 71, Charlson ∼ 4, CURB-65 ∼ 0 to 2, respiration ∼ 30 min-1, FiO2 ∼ 38%, CRP ∼ 152.3 mg/dL). Hypothesis testing provided solid statistical evidence supporting an interaction between phenotype and each clinical outcome: severity and mortality. By computing their corresponding odds ratios, a clear trend was found for higher frequencies of unfavourable evolution in phenotype C with respect to B, as well as more unfavourable in phenotype B than in A. CONCLUSION: A compound unsupervised clustering technique (including a fully-automated optimization of its internal parameters) revealed the existence of three distinct groups of patients - phenotypes. In turn, these showed strong associations with the clinical severity in the progression of pneumonia, and with mortality.

K-Means Clustering Identifies Diverse Clinical Phenotypes in COVID-19 Patients: Implications for Mortality Risks and Remdesivir Impact.

INTRODUCTION: The impact of remdesivir on mortality in patients with COVID-19 is still controversial. We aimed to identify clinical phenotype clusters of COVID-19 hospitalized patients with highest benefit from remdesivir use and validate these findings in an external cohort. METHODS: We included consecutive patients hospitalized between February 2020 and February 2021 for COVID-19. The derivation cohort comprised subjects admitted to Hospital Clinic of Barcelona. The validation cohort included patients from Hospital Universitari Mutua de Terrassa (Terrassa) and Hospital Universitari La Fe (Valencia), all tertiary centers in Spain. We employed K-means clustering to group patients according to reverse transcription polymerase chain reaction (rRT-PCR) cycle threshold (Ct) values and lymphocyte counts at diagnosis, and pre-test symptom duration. The impact of remdesivir on 60-day mortality in each cluster was assessed. RESULTS: A total of 1160 patients (median age 66, interquartile range (IQR) 55-78) were included. We identified five clusters, with mortality rates ranging from 0 to 36.7%. Highest mortality rate was observed in the cluster including patients with shorter pre-test symptom duration, lower lymphocyte counts, and lower Ct values at diagnosis. The absence of remdesivir administration was associated with worse outcome in the high-mortality cluster (10.5% vs. 36.7%; p < 0.001), comprising subjects with higher viral loads. These results were validated in an external multicenter cohort of 981 patients. CONCLUSIONS: Patients with COVID-19 exhibit varying mortality rates across different clinical phenotypes. K-means clustering aids in identifying patients who derive the greatest mortality benefit from remdesivir use.

Basic host response parameters to classify mortality risk in COVID-19 and community-acquired pneumonia.

Improved phenotyping in pneumonia is necessary to strengthen risk assessment. Via a feasible and multidimensional approach with basic parameters, we aimed to evaluate the effect of host response at admission on severity stratification in COVID-19 and community-acquired pneumonia (CAP). Three COVID-19 and one CAP multicenter cohorts including hospitalized patients were recruited. Three easily available variables reflecting different pathophysiologic mechanisms-immune, inflammation, and respiratory-were selected (absolute lymphocyte count [ALC], C-reactive protein [CRP] and, SpO2/FiO2). In-hospital mortality and intensive care unit (ICU) admission were analyzed as outcomes. A multivariable, penalized maximum likelihood logistic regression was performed with ALC (< 724 lymphocytes/mm3), CRP (> 60 mg/L), and, SpO2/FiO2 (< 450). A total of 1452, 1222 and 462 patients were included in the three COVID-19 and 1292 in the CAP cohort for the analysis. Mortality ranged between 4 and 32% (0 to 3 abnormal biomarkers) and 0-9% in SARS-CoV-2 pneumonia and CAP, respectively. In the first COVID-19 cohort, adjusted for age and sex, we observed an increased odds ratio for in-hospital mortality in COVID-19 with elevated biomarkers altered (OR 1.8, 3, and 6.3 with 1, 2, and 3 abnormal biomarkers, respectively). The model had an AUROC of 0.83. Comparable findings were found for ICU admission, with an AUROC of 0.76. These results were confirmed in the other COVID-19 cohorts Similar OR trends were reported in the CAP cohort; however, results were not statistically significant. Assessing the host response via accessible biomarkers is a simple and rapidly applicable approach for pneumonia.

[SEPAR Recommendations for COVID-19 Vaccination in Patients With Respiratory Diseases]. / Recomendaciones SEPAR sobre la vacuna COVID-19 en las enfermedades respiratorias.

The Spanish Society of Pneumonology and Thoracic Surgery (SEPAR) has elaborated this document of recommendations for COVID-19 vaccination in patients with respiratory diseases aimed to help healthcare personnel make decisions about how to act in case of COVID-19 vaccination in these patients.The recommendations have been developed by a group of experts in this field after reviewing the materials published up to March 7, 2021, the information provided by different scientific societies, drug agencies and the strategies of the governmental bodies up to this date.We can conclude that COVID-19 vaccines are not only safe and effective, but also prior in vulnerable patients with chronic respiratory diseases. In addition, an active involvement of healthcare professionals, who manage these diseases, in the vaccination strategy is the key to achieve good adherence and high vaccination coverage.

Validation of IDSA/ATS Guidelines for ICU Admission in Adults Over 80 Years Old With Community-Acquired Pneumonia

Introduction: The 2007 IDSA/ATS guidelines for community-acquired pneumonia (CAP) recommended intensive care unit (ICU) admission for adults meeting severe CAP criteria. We aimed to validate the accuracy of IDSA/ATS criteria in patients≥80 years old (very elderly patients, VEP) with CAP. Methods: Prospective cohort study of VEP with CAP admitted to three Spanish hospitals between 1996 and 2019. We compared patients who did and did not require ICU admission. We also assessed factors independently associated with ICU admission, as well as the accuracy of severe CAP criteria for ICU admission and mortality. Major criteria include septic shock and invasive mechanical ventilation while minor criteria encompass other variables related to hemodynamics and respiratory insufficiency as well as level of consciousness, renal function, blood parameters indicative of sepsis and body temperature. Results: Of the 2006 VEP with CAP, 519 (26%) met severe CAP criteria, while 204 (10%) required ICU admission. Concordance between severe CAP criteria and the decision to admit the patient to the ICU occurred in 1591 (79%) cases (k coefficient, 0.33), with a sensitivity of 75% and specificity of 80% in predicting ICU admission. All patients with invasive mechanical ventilation received care in ICUs, while 45 (44%) patients with septic shock—previously stabilized in the emergency room—did not. Thirty-day mortality of ICU-admitted patients with septic shock was lower than that of patients in wards (30% vs. 60%, p=0.013). In contrast, patients with severe CAP and only minor criteria had similar mortality. Conclusions: IDSA/ATS criteria for severe CAP predict ICU admission in VEP moderately well. While patients with septic shock and invasive mechanical ventilation warrant ICU admission, severe CAP without major severity criteria in VEP may be acceptably manageable in wards. (AU)

Immune treatment in COVID-19

Current immune treatment directed to avoid viral replication relies mainly in convalescent plasma and monoclonalantibodies (mAbs). No clinical benefit for convalescent plasmahas been reported in a meta-analysis and systematic reviewcompared to standard of care. MAbs are recombinant proteinscapable to bind with SARS-CoV-2 preventing its entrance into cells. Several mAbs have shown reduction in viral load and/or progression of the disease such as casirivimab-imdevimab,bamlanivimab-etesevimab and sotrovimab. After the apparition of Omicron variant, it has been reported that sotrovimabretained its activity whereas the other two combinations exhibited loss of neutralizing activity. Several aspects as the target population, timing and doses, serological patient statusand evolution of variants still require attention, monitorizationand further studies for knowledge gaps (AU).

Evaluación de secuelas respiratorias en los pacientes con COVID-19, dónde estamos y hacia dónde vamos. Estudios CIBERESUCICOVID y RECOVID para comparar pacientes ingresados en UCI vs. sala convencional / Evaluation of Respiratory Sequelae in Patients With COVID-19, Where we are and Where we are Going. CIBERESUCICOVID and RECOVID Studies to Compare Patients Admitted to ICU vs Conventional Ward

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CIBERESUCICOVID: un proyecto estratégico para una mejor comprensión y manejo clínico de la COVID-19 en pacientes críticos / CIBERESUCICOVID: A strategic project for a better understanding and clinical management of COVID-19 in critical patients

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Tres claves para seleccionar el antibiótico oral adecuado en las infecciones respiratorias / Three keys to the appropriate choice of oral antibiotic treatment in the respiratory tract infections

La exacerbación de la enfermedad pulmonar obstructiva crónica y la neumonía adquirida en la comunidad son las infecciones del tracto respiratorio inferior más frecuentes en la práctica clínica diaria. La selección del antibiótico es un componente crucial en su tratamiento y, en la mayoría de las ocasiones, se realiza de forma empírica. Las sociedades científicas elaboran recomendaciones terapéuticas basadas en la evidencia científica y/o recomendaciones de expertos que son de gran ayuda para los clínicos. Los betalactámicos, las fluoroquinolonas y los macrólidos son los fármacos más utilizados por vía oral. Desde un punto de vista práctico, existen tres claves para la adecuada elección del tratamiento antibiótico oral, que son la efectividad, la seguridad y el impacto ecológico en la microbiota del paciente, incluyendo el desarrollo de resistencias, que van a ser valoradas en profundidad en esta revisión

Exacerbation of chronic obstructive pulmonary disease and community-acquired pneumonia are the most frequent infections of the lower respiratory tract in daily clinical practice. Antibiotic selection is a crucial component in its treatment and, in most cases, it is performed empirically. Scientific societies make therapeutic recommendations based on scientific evidence and / or expert recommendations that are of great help to clinicians. Beta-lactams, fluoroquinolones and macrolides are the most commonly used drugs for oral administration. From a practical point of view, there are three keys to the appropriate choice of oral antibiotic treatment, which are the effectiveness, safety and the ecological impact on the patient's microbiota, including the development of resistance, which will be assessed in depth in this review

Bronquiectasias y microorganismos multirresistentes: ¿el nicho ideal? / Bronchiectasis and Multidrug-resistant Microorganisms: The Ideal Niche?

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