Enfermedad pulmonar obstructiva crónica y salud respiratoria / Chronic obstructive pulmonary disease and respiratory health

La enfermedad pulmonar obstructiva crónica (EPOC) es una enfermedad que cursa con manifestaciones multisistémicas y agudizaciones, y que conlleva una importante carga de morbilidad, mortalidad y costes sanitarios. Distintas medidas terapéuticas y de prevención juegan un papel importante en mejorar el pronóstico y la salud respiratoria de estos pacientes. Realizar actividad física de forma generalizada, llevar a cabo unos hábitos dietéticos y nutricionales adecuados, abandonar el consumo de tabaco y alcanzar un estado de inmunización óptimo son varias de las acciones de salud recomendadas. Estas también enlentecerán el proceso de envejecimiento. Además, se recomienda realizar políticas de salud pública para reducir la contaminación del aire y el cambio climático. Por último, debemos prestar especial atención a las características del sueño de estos pacientes y llevar a cabo un abordaje terapéutico que incluya una mejor calidad del sueño. (AU)

Chronic obstructive pulmonary disease (COPD) is a disease with multisystemic manifestations and exacerbations that lead to a significant burden of morbidity, mortality, and health care costs. Various therapeutic and preventive measures play an important role in improving the prognosis and respiratory health of the patients. General physical activity, proper dietary and nutritional habits, smoking cessation, and achieving an optimal immunization status are some of the recommended health actions. They also slow down the aging process. In addition, public health policies are recommended to reduce air pollution and climate change. Finally, we should pay special attention to the sleep characteristics of these patients and carry out a therapeutic approach that includes better sleep quality. (AU)

Machine learning for the development of diagnostic models of decompensated heart failure or exacerbation of chronic obstructive pulmonary disease.

Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are two chronic diseases with the greatest adverse impact on the general population, and early detection of their decompensation is an important objective. However, very few diagnostic models have achieved adequate diagnostic performance. The aim of this trial was to develop diagnostic models of decompensated heart failure or COPD exacerbation with machine learning techniques based on physiological parameters. A total of 135 patients hospitalized for decompensated heart failure and/or COPD exacerbation were recruited. Each patient underwent three evaluations: one in the decompensated phase (during hospital admission) and two more consecutively in the compensated phase (at home, 30 days after discharge). In each evaluation, heart rate (HR) and oxygen saturation (Ox) were recorded continuously (with a pulse oximeter) during a period of walking for 6 min, followed by a recovery period of 4 min. To develop the diagnostic models, predictive characteristics related to HR and Ox were initially selected through classification algorithms. Potential predictors included age, sex and baseline disease (heart failure or COPD). Next, diagnostic classification models (compensated vs. decompensated phase) were developed through different machine learning techniques. The diagnostic performance of the developed models was evaluated according to sensitivity (S), specificity (E) and accuracy (A). Data from 22 patients with decompensated heart failure, 25 with COPD exacerbation and 13 with both decompensated pathologies were included in the analyses. Of the 96 characteristics of HR and Ox initially evaluated, 19 were selected. Age, sex and baseline disease did not provide greater discriminative power to the models. The techniques with S and E values above 80% were the logistic regression (S: 80.83%; E: 86.25%; A: 83.61%) and support vector machine (S: 81.67%; E: 85%; A: 82.78%) techniques. The diagnostic models developed achieved good diagnostic performance for decompensated HF or COPD exacerbation. To our knowledge, this study is the first to report diagnostic models of decompensation potentially applicable to both COPD and HF patients. However, these results are preliminary and warrant further investigation to be confirmed.

Unexpected Paratracheal Opacity

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Tosferina en el adulto: el enemigo visible / Whooping Cough: The Visible Enemy

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[Translated article] Whooping Cough: The Visible Enemy / Tosferina en el adulto: el enemigo visible

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Immunosenescence, Immune Fitness and Vaccination Schedule in the Adult Respiratory Patient.

Immunosenescence is the gradual deterioration of the immune system caused by advancing age. It is associated with a reduced ability to respond to infections and develop long-term immune memory. It plays a key role in the development of respiratory diseases that are more common in older people, such as asthma, COPD, diffuse interstitial disease and respiratory infections in the elderly. We call immune fitness the establishment of lifestyle habits that can improve our immune capacity. We now know that good eating habits, good social relationships, not smoking, limiting alcohol consumption, exercising, controlling stress levels and establishing a proper vaccination programme can slow down the process of immunosenescence. Influenza and pneumococcal vaccines (PCV13 and PPSV23 conjugate) are well established in the adult vaccination schedule. The new pneumococcal vaccines PCV15 and PCV20 will help to extend protection against pneumococcal disease in adults. The vaccine against COVID-19 is currently the most useful tool to prevent the disease and reduce its pathogenicity. COPD patients and others with respiratory diseases may benefit from prevention of herpes zoster and Bordetella pertussis through vaccination. Respiratory syncytial virus (RSV) vaccine may be another vaccine to be added to the schedule, pending the results of its studies.

La inmunosenescencia es el deterioro gradual del sistema inmune provocado por el avance de la edad. Se asocia a una menor capacidad para responder a las infecciones y desarrollar memoria inmune a largo plazo. Es parte fundamental en el desarrollo de las enfermedades respiratorias más frecuentes en edades avanzadas, como el asma, la EPOC, la patología intersticial difusa y las infecciones respiratorias del anciano.Llamamos fitness inmunológico al establecimiento de unos hábitos de vida que puedan mejorar nuestra capacidad inmunitaria. Actualmente sabemos que tener buenos hábitos alimentarios, buenas relaciones sociales, no fumar, limitar el consumo de alcohol, hacer ejercicio, controlar los niveles de estrés y establecer un correcto programa de vacunación permiten ralentizar el proceso de inmunosenescencia.Las vacunas de la gripe y las antineumocócicas (la conjugada PCV13 y la PPSV23) están bien establecidas en el calendario vacunal del adulto. Las nuevas vacunas antineumocócicas PCV15 y PCV20 van a servir para ampliar la protección contra la enfermedad neumocócica en el adulto. La vacuna contra la COVID-19 es, en el momento actual, la herramienta más útil para prevenir la enfermedad y disminuir su patogenicidad. Los pacientes con EPOC y otros con enfermedades respiratorias podrían beneficiarse de la prevención del herpes zóster y Bordetella pertussis mediante la vacunación. La vacuna contra el virus respiratorio sincitial (VRS) puede ser otra de las siguientes que formen parte de este calendario, en espera de los resultados de sus estudios.

Decrease in Readmissions after Hospitalisation for COPD Exacerbation through a Home Care Model.

Objective: To decrease readmissions at 30 and 90 days post-discharge from a hospital admission for chronic obstructive pulmonary disease exacerbation (COPDE) through the home care model of the Ambulatory Chronic Respiratory Care Unit (ACRCU), increase patient survival at one year, and validate our readmission risk scale (RRS). Materials and methods: This was an observational study, with a prospective data collection and a retrospective data analysis. A total of 491 patients with a spirometry diagnosis of chronic obstructive pulmonary disease (COPD) requiring hospitalisation for an exacerbation were included in the study. Subjects recruited within the first year (204 cases) received conventional care (CC). In the following year a home care (HC) programme was implemented and of those recruited that year (287) 104 were included in the ACRCU, administered by a specialised nurse. Results: In the group of patients included in the home care model of the Ambulatory Chronic Respiratory Care Unit (ACRCU) a lower number of readmissions was observed at 30 and 90 days after discharge (30.5% vs. 50%, p = 0.012 and 47.7% vs. 65.2%, p = 0.031, respectively) and a greater one-year survival (85.3% vs. 59.1%, p < 0.001). The validation of our RRS revealed that the tool's capacity to predict readmissions at both 30 and 90 days was not high (AUC = 0.69 and AUC = 0.66, respectively). Conclusions: The inclusion of exacerbator or fragile COPD patients in the ACRCU could achieve a decrease in readmissions and an increase in survival. The number of episodes of exacerbation within the 12 months prior to the hospital admission is the variable that best predicts the risk of readmission.

Objetivo: Disminuir los reingresos a los 30 y 90 días tras el alta por un ingreso hospitalario por exacerbación de enfermedad pulmonar obstructiva crónica (EPOC) a través del modelo de atención domiciliaria de la Unidad de Cuidados Crónicos Respiratorios Ambulatorios (UCCRA), aumentar la supervivencia al año y validar nuestra escala de riesgo de reingreso (ERR). Material y métodos: Estudio observacional con recogida prospectiva de datos. Se incluyó en el estudio a un total de 491 pacientes con diagnóstico espirométrico de enfermedad pulmonar obstructiva crónica que requirieron hospitalización por una agudización. Los sujetos reclutados dentro del primer año (204 casos) recibieron atención convencional (AC). Al año siguiente se implementó un programa de atención domiciliaria (AD) y de los pacientes reclutados ese año (287), 104 fueron incluidos en la UCCRA con seguimiento de una enfermera especializada. Resultados: En el grupo de pacientes incluidos en el modelo de atención domiciliaria de la UCCRA se observó un menor número de reingresos a los 30 y 90 días tras el alta (30,5% vs 50%, p = 0,012 y 47,7% vs. 65,2%, p = 0,031, respectivamente) y una mayor supervivencia al año (85,3% vs. 59,1%, p < 0,001). La validación de nuestra ERR reveló que la capacidad de la misma para predecir reingresos tanto a los 30 como a los 90 días no era alta (AUC = 0,69 y AUC = 0,66, respectivamente). Conclusiones: La inclusión de pacientes con EPOC agudizadores o frágiles en la UCCRA podría conseguir una disminución de los reingresos y una aumento de la supervivencia. El número de agudizaciones en los 12 meses previos al ingreso hospitalario es la variable que mejor predice el riesgo de reingreso.

Elevated levels of arginase activity are related to inflammation in patients with COPD exacerbation.

INTRODUCTION: Within the pathogenesis of the chronic obstructive pulmonary disease (COPD) there are interactions between different inflammatory mediators that are enhanced during an exacerbation. Arginase is present in bronchial epithelial cells, endothelial, fibroblasts and alveolar macrophages, which make it a probable key enzyme in the regulation of inflammation and remodelling. We aimed to find a potential relationship between arginase activity, inflammatory mediators in COPD patients in stable phase and during exacerbations. METHODS: We performed a prospective, observational study of cases and controls, with 4 study groups (healthy controls, stable COPD, COPD during an exacerbation and COPD 3 months after exacerbation). We measured arginase, inflammation markers (IL-6, IL-8, TNF-∝, IFN-γ and C reactive protein), and mediators of immunity: neutrophils, monocytes, total TCD3 + lymphocytes (CD3ζ), CD4 + T cells, CD8 + T cells, NK cells. RESULTS: A total of 49 subjects were recruited, average age of 69.73 years (59.18% male). Arginase activity is elevated during an exacerbation of COPD, and this rise is related to an increase in IL-6 production. The levels of IL-6 and IL-8 remained elevated in patients with COPD at 3 months after hospital exacerbation. We did not find a clear relationship between arginase activity, immunity or with the degree of obstruction in COPD patients. CONCLUSIONS: Arginase activity is elevated during an exacerbation of COPD, and it could be related to an increase in the production of IL-6. Levels of IL-6, IL-8, and arginase activity remain elevated in patients with COPD at 3 months after hospital exacerbation. Arginase activity could contribute to the development of COPD.

Archivos de Bronconeumología: Continue to growth, everyone’s goal / Archivos de Bronconeumologia: seguir creciendo, un objetivo de todos

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First and Second Waves of Coronavirus Disease 2019 in Madrid, Spain: Clinical Characteristics and Hematological Risk Factors Associated With Critical/Fatal Illness.

OBJECTIVES: This study aims to determine similarities and differences in clinical characteristics between the patients from two waves of severe acute respiratory syndrome coronavirus-2 infection at the time of hospital admission, as well as to identify risk biomarkers of coronavirus disease 2019 severity. DESIGN: Retrospective observational study. SETTING: A single tertiary-care center in Madrid. PATIENTS: Coronavirus disease 2019 adult patients admitted to hospital from March 4, 2020, to March 25, 2020 (first infection wave), and during July 18, 2020, and August 20, 2020 (second infection wave). INTERVENTIONS: Treatment with a hospital-approved drug cocktail during hospitalization. MEASUREMENTS AND MAIN RESULTS: Demographic, clinical, and laboratory data were compared between the patients with moderate and critical/fatal illness across both infection waves. The median age of patients with critical/fatal coronavirus disease 2019 was 67.5 years (interquartile range, 56.75-78.25 yr; 64.5% male) in the first wave and 59.0 years (interquartile range, 48.25-80.50 yr; 70.8% male) in the second wave. Hypertension and dyslipidemia were major comorbidities in both waves. Body mass index over 25 and presence of bilateral pneumonia were common findings. Univariate logistic regression analyses revealed an association of a number of blood parameters with the subsequent illness progression and severity in both waves. However, some remarkable differences were detected between both waves that prevented an accurate extrapolation of prediction models from the first wave into the second wave. Interleukin-6 and d-dimer concentrations at the time of hospital admission were remarkably higher in patients who developed a critical/fatal condition only during the first wave (p < 0.001), although both parameters significantly increased with disease worsening in follow-up studies from both waves. Multivariate analyses from wave 1 rendered a predictive signature for critical/fatal illness upon hospital admission that comprised six blood biomarkers: neutrophil-to-lymphocyte ratio (≥ 5; odds ratio, 2.684 [95% CI, 1.143-6.308]), C-reactive protein (≥ 15.2 mg/dL; odds ratio, 2.412 [95% CI, 1.006-5.786]), lactate dehydrogenase (≥ 411.96 U/L; odds ratio, 2.875 [95% CI, 1.229-6.726]), interleukin-6 (≥ 78.8 pg/mL; odds ratio, 5.737 [95% CI, 2.432-13.535]), urea (≥ 40 mg/dL; odds ratio, 1.701 [95% CI, 0.737-3.928]), and d-dimer (≥ 713 ng/mL; odds ratio, 1.903 [95% CI, 0.832-4.356]). The predictive accuracy of the signature was 84% and the area under the receiver operating characteristic curve was 0.886. When the signature was validated with data from wave 2, the accuracy was 81% and the area under the receiver operating characteristic curve value was 0.874, albeit most biomarkers lost their independent significance. Follow-up studies reassured the importance of monitoring the biomarkers included in the signature, since dramatic increases in the levels of such biomarkers occurred in critical/fatal patients over disease progression. CONCLUSIONS: Most parameters analyzed behaved similarly in the two waves of coronavirus disease 2019. However, univariate logistic regression conducted in both waves revealed differences in some parameters associated with poor prognosis in wave 1 that were not found in wave 2, which may reflect a different disease stage of patients on arrival to hospital. The six-biomarker predictive signature reported here constitutes a helpful tool to classify patient's prognosis on arrival to hospital.