Influencia del dióxido de nitrógeno ambientalen las consultas a urgencias por asma en un entorno con baja contaminación: análisis de series temporales y casos cruzados / Impact of atmospheric nitrogen dioxide on emergency department visits for asthma in a low air pollution area: a time series and case-crossover study

Objetivo: Analizar la asociación entre los niveles ambientales de dióxido de nitrógeno (NO2) y el número de consultas a urgencias por un episodio de agudización de asma bronquial en la población adulta de un entorno urbano con bajos niveles de contaminación. Método: Estudio ecológico retrospectivo de series temporales. Se consideraron las visitas por asma de pacientes mayores de 14 años que acudieron a un servicio de urgencias de forma consecutiva entre 2010 y 2018 (3.287 días). La asociación entre la concentración media de NO2 y el número diario de visitas a urgencias por asma se estudió mediante un modelo lineal generalizado con regresión de Poisson. Se evaluó el impacto en el riesgo individual mediante un análisis de casos cruzados. Se ajustó por las variables confusoras meteorológicas, se corrigió la estacionalidad mediante análisis de tendencias y se evaluaron tres lags temporales (0, 1 y 3 días). Resultados: Se analizaron 2.527 urgencias por asma correspondientes a 1.588 pacientes (edad media 51 ± 21 años, 70% mujeres). Hubo una asociación positiva significativa (riesgo relativo: RR = 1,056, IC 95%: 1,006-1,108; p < 0,05) entre la concentración de NO2 y un mayor riesgo de consulta a urgencias por asma a los 3 días. Un incremento de 10 μgr/m3 de NO2 explicó el 5,3% de las consultas (fracción atribuible: FA = 5,30, IC 95%: 0,60-9,75; p < 0,05). Conclusiones: El incremento de los niveles ambientales de NO2 se asocia con un mayor número de urgencias hospitalarias por exacerbación de asma en adultos en un entorno con baja contaminación. (AU)

Objectives: To analyze the association between atmospheric levels of nitrogen dioxide (NO2) and the number of visits by adults to an emergency department (ED) for exacerbated asthma in an urban area with low levels of air pollution. Material and methods: Retrospective ecological time-series study. We quantified ED visits for asthma by consecutive patients over the age of 14 years between 2010 and 2018 (3287 days). The association between the mean atmospheric concentration of NO2 and the number of daily visits to the ED for asthma was analyzed with generalized linear regression analysis (Poisson modeling). The impact of exposure on individual risk was assessed by crossover analysis of case periods. We adjusted for confounding meteorologic variables, potential variability due to seasonal changes was corrected by trend analysis, and 3 time lags were assessed (0, 1, and 3 days). Results: We analyzed 2527 asthma emergencies in 1588 patients (70% female) with a mean (SD) age of 51 (21) years. A significant positive association (relative risk, 1.056, 95% CI, 1.006-1.108; P .05) between atmospheric NO2 concentration and greater risk of visiting an ED within 3 days was detected. An increase of 10 µg/m3 of NO2 accounted for 5.3% of the visits (attributable fraction, 5.30, 95% CI, 0.60-9.75; P .05). Conclusion: In an urban area with low pollution levels, an elevation in atmospheric NO2 is associated with more hospital ED visits for asthma attacks in adults. (AU)

Impact of atmospheric nitrogen dioxide on emergency department visits for asthma in a low air pollution area: a time series and case-crossover study. / Influencia del dióxido de nitrógeno ambiental en las consultas a urgencias por asma en un entorno con baja contaminación: análisis de series temporales y casos cruzados.

OBJECTIVES: To analyze the association between atmospheric levels of nitrogen dioxide (NO2) and the number of visits by adults to an emergency department (ED) for exacerbated asthma in an urban area with low levels of air pollution. MATERIAL AND METHODS: Retrospective ecological time-series study. We quantified ED visits for asthma by consecutive patients over the age of 14 years between 2010 and 2018 (3287 days). The association between the mean atmospheric concentration of NO2 and the number of daily visits to the ED for asthma was analyzed with generalized linear regression analysis (Poisson modeling). The impact of exposure on individual risk was assessed by crossover analysis of case periods. We adjusted for confounding meteorologic variables, potential variability due to seasonal changes was corrected by trend analysis, and 3 time lags were assessed (0, 1, and 3 days). RESULTS: We analyzed 2527 asthma emergencies in 1588 patients (70% female) with a mean (SD) age of 51 (21) years. A significant positive association (relative risk, 1.056, 95% CI, 1.006-1.108; P .05) between atmospheric NO2 concentration and greater risk of visiting an ED within 3 days was detected. An increase of 10 µg/m3 of NO2 accounted for 5.3% of the visits (attributable fraction, 5.30, 95% CI, 0.60-9.75; P .05). CONCLUSION: In an urban area with low pollution levels, an elevation in atmospheric NO2 is associated with more hospital ED visits for asthma attacks in adults.

OBJETIVO: Analizar la asociación entre los niveles ambientales de dióxido de nitrógeno (NO2) y el número de consultas a urgencias por un episodio de agudización de asma bronquial en la población adulta de un entorno urbano con bajos niveles de contaminación. METODO: Estudio ecológico retrospectivo de series temporales. Se consideraron las visitas por asma de pacientes mayores de 14 años que acudieron a un servicio de urgencias de forma consecutiva entre 2010 y 2018 (3.287 días). La asociación entre la concentración media de NO2 y el número diario de visitas a urgencias por asma se estudió mediante un modelo lineal generalizado con regresión de Poisson. Se evaluó el impacto en el riesgo individual mediante un análisis de casos cruzados. Se ajustó por las variables confusoras meteorológicas, se corrigió la estacionalidad mediante análisis de tendencias y se evaluaron tres lags temporales (0, 1 y 3 días). RESULTADOS: Se analizaron 2.527 urgencias por asma correspondientes a 1.588 pacientes (edad media 51 ± 21 años, 70% mujeres). Hubo una asociación positiva significativa (riesgo relativo: RR = 1,056, IC 95%: 1,006-1,108; p 0,05) entre la concentración de NO2 y un mayor riesgo de consulta a urgencias por asma a los 3 días. Un incremento de 10 µgr/m3 de NO2 explicó el 5,3% de las consultas (fracción atribuible: FA = 5,30, IC 95%: 0,60-9,75; p 0,05). CONCLUSIONES: El incremento de los niveles ambientales de NO2 se asocia con un mayor número de urgencias hospitalarias por exacerbación de asma en adultos en un entorno con baja contaminación.

Methylprednisolone in adults hospitalized with COVID-19 pneumonia : An open-label randomized trial (GLUCOCOVID).

PURPOSE: To determine whether a 6-day course of methylprednisolone (MP) improves outcome in patients with severe SARS-CoV­2 (Corona Virus Disease 2019 [COVID-19]). METHODS: The study was a multicentric open-label trial of COVID-19 patients who were aged ≥ 18 years, receiving oxygen without mechanical ventilation, and with evidence of systemic inflammatory response who were assigned to standard of care (SOC) or SOC plus intravenous MP (40 mg bid for 3 days followed by 20 mg bid for 3 days). The primary outcome was a composite of death, admission to the intensive care unit, or requirement for noninvasive ventilation. Both intention-to-treat (ITT) and per protocol (PP) analyses were performed. RESULTS: A total of 91 patients were screened, and 64 were randomized (mean age70 ± 12 years). In the ITT analysis, 14 of 29 patients (48%) in the SOC group and 14 of 35 (40%) in the MP group suffered the composite endpoint (40% versus 20% in patients under 72 years and 67% versus 48% in those over 72 years; p = 0.25). In the PP analysis, patients on MP had a significantly lower risk of experiencing the composite endpoint (age-adjusted risk ratio 0.42; 95% confidence interval, CI 0.20-0.89; p = 0.043). CONCLUSION: The planned sample size was not achieved, and our results should therefore be interpreted with caution. The use of MP had no significant effect on the primary endpoint in ITT analysis; however, the PP analysis showed a beneficial effect due to MP, which consistent with other published trials support the use of glucocorticoids in severe cases of COVID-19.

Assessment of Nocturnal Autonomic Cardiac Imbalance in Positional Obstructive Sleep Apnea. A Multiscale Nonlinear Approach.

Positional obstructive sleep apnea (POSA) is a major phenotype of sleep apnea. Supine-predominant positional patients are frequently characterized by milder symptoms and less comorbidity due to a lower age, body mass index, and overall apnea-hypopnea index. However, the bradycardia-tachycardia pattern during apneic events is known to be more severe in the supine position, which could affect the cardiac regulation of positional patients. This study aims at characterizing nocturnal heart rate modulation in the presence of POSA in order to assess potential differences between positional and non-positional patients. Patients showing clinical symptoms of suffering from a sleep-related breathing disorder performed unsupervised portable polysomnography (PSG) and simultaneous nocturnal pulse oximetry (NPO) at home. Positional patients were identified according to the Amsterdam POSA classification (APOC) criteria. Pulse rate variability (PRV) recordings from the NPO readings were used to assess overnight cardiac modulation. Conventional cardiac indexes in the time and frequency domains were computed. Additionally, multiscale entropy (MSE) was used to investigate the nonlinear dynamics of the PRV recordings in POSA and non-POSA patients. A total of 129 patients (median age 56.0, interquartile range (IQR) 44.8-63.0 years, median body mass index (BMI) 27.7, IQR 26.0-31.3 kg/m2) were classified as POSA (37 APOC I, 77 APOC II, and 15 APOC III), while 104 subjects (median age 57.5, IQR 49.0-67.0 years, median BMI 29.8, IQR 26.6-34.7 kg/m2) comprised the non-POSA group. Overnight PRV recordings from positional patients showed significantly higher disorderliness than non-positional subjects in the smallest biological scales of the MSE profile (τ = 1: 0.25, IQR 0.20-0.31 vs. 0.22, IQR 0.18-0.27, p < 0.01) (τ = 2: 0.41, IQR 0.34-0.48 vs. 0.37, IQR 0.29-0.42, p < 0.01). According to our findings, nocturnal heart rate regulation is severely affected in POSA patients, suggesting increased cardiac imbalance due to predominant positional apneas.

Estudio de la adherencia al tratamiento con presión continua positiva en la vía aérea en pacientes con síndrome de apnea obstructiva del sueño en el confinamiento impuesto durante la pandemia de COVID-19 / Study of the Adherence to continuous positive airway pressure Treatment in Patients with Obstructive Sleep Apnea Syndrome in the Confinement During the COVID-19 Pandemic

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A machine learning-based test for adult sleep apnoea screening at home using oximetry and airflow.

The most appropriate physiological signals to develop simplified as well as accurate screening tests for obstructive sleep apnoea (OSA) remain unknown. This study aimed at assessing whether joint analysis of at-home oximetry and airflow recordings by means of machine-learning algorithms leads to a significant diagnostic performance increase compared to single-channel approaches. Consecutive patients showing moderate-to-high clinical suspicion of OSA were involved. The apnoea-hypopnoea index (AHI) from unsupervised polysomnography was the gold standard. Oximetry and airflow from at-home polysomnography were parameterised by means of 38 time, frequency, and non-linear variables. Complementarity between both signals was exhaustively inspected via automated feature selection. Regression support vector machines were used to estimate the AHI from single-channel and dual-channel approaches. A total of 239 patients successfully completed at-home polysomnography. The optimum joint model reached 0.93 (95%CI 0.90-0.95) intra-class correlation coefficient between estimated and actual AHI. Overall performance of the dual-channel approach (kappa: 0.71; 4-class accuracy: 81.3%) significantly outperformed individual oximetry (kappa: 0.61; 4-class accuracy: 75.0%) and airflow (kappa: 0.42; 4-class accuracy: 61.5%). According to our findings, oximetry alone was able to reach notably high accuracy, particularly to confirm severe cases of the disease. Nevertheless, oximetry and airflow showed high complementarity leading to a remarkable performance increase compared to single-channel approaches. Consequently, their joint analysis via machine learning enables accurate abbreviated screening of OSA at home.

Electromyography-Based Respiratory Onset Detection in COPD Patients on Non-Invasive Mechanical Ventilation.

To optimize long-term nocturnal non-invasive ventilation in patients with chronic obstructive pulmonary disease, surface diaphragm electromyography (EMGdi) might be helpful to detect patient-ventilator asynchrony. However, visual analysis is labor-intensive and EMGdi is heavily corrupted by electrocardiographic (ECG) activity. Therefore, we developed an automatic method to detect inspiratory onset from EMGdi envelope using fixed sample entropy (fSE) and a dynamic threshold based on kernel density estimation (KDE). Moreover, we combined fSE with adaptive filtering techniques to reduce ECG interference and improve onset detection. The performance of EMGdi envelopes extracted by applying fSE and fSE with adaptive filtering was compared to the root mean square (RMS)-based envelope provided by the EMG acquisition device. Automatic onset detection accuracy, using these three envelopes, was evaluated through the root mean square error (RMSE) between the automatic and mean visual onsets (made by two observers). The fSE-based method provided lower RMSE, which was reduced from 298 ms to 264 ms when combined with adaptive filtering, compared to 301 ms provided by the RMS-based method. The RMSE was negatively correlated with the proposed EMGdi quality indices. Following further validation, fSE with KDE, combined with adaptive filtering when dealing with low quality EMGdi, indicates promise for detecting the neural onset of respiratory drive.

Symbolic dynamics to enhance diagnostic ability of portable oximetry from the phone oximeter in the detection of paediatric sleep apnoea.

OBJECTIVE: This study is aimed at assessing symbolic dynamics as a reliable technique to characterise complex fluctuations of portable oximetry in the context of automated detection of childhood obstructive sleep apnoea-hypopnoea syndrome (OSAHS). APPROACH: Nocturnal oximetry signals from 142 children with suspected OSAHS were acquired using the Phone Oximeter: a portable device that integrates a pulse oximeter with a smartphone. An apnoea-hypopnoea index (AHI) ≥5 events/h from simultaneous in-lab polysomnography was used to confirm moderate-to-severe childhood OSAHS. Symbolic dynamics was used to parameterise non-linear changes in the overnight oximetry profile. Conventional indices, anthropometric measures, and time-domain linear statistics were also considered. Forward stepwise logistic regression was used to obtain an optimum feature subset. Logistic regression (LR) was used to identify children with moderate-to-severe OSAHS. MAIN RESULTS: The histogram of 3-symbol words from symbolic dynamics showed significant differences (p <0.01) between children with AHI <5 events/h and moderate-to-severe patients (AHI ≥5 events/h). Words representing increasing oximetry values after apnoeic events (re-saturations) showed relevant diagnostic information. Regarding the performance of individual characterization approaches, the LR model composed of features from symbolic dynamics alone reached a maximum performance of 78.4% accuracy (65.2% sensitivity; 86.8% specificity) and 0.83 area under the ROC curve (AUC). The classification performance improved combining all features. The optimum model from feature selection achieved 83.3% accuracy (73.5% sensitivity; 89.5% specificity) and 0.89 AUC, significantly (p-value <0.01) outperforming the other models. SIGNIFICANCE: Symbolic dynamics provides complementary information to conventional oximetry analysis enabling reliable detection of moderate-to-severe paediatric OSAHS from portable oximetry.

Oximetry use in obstructive sleep apnea.

INTRODUCTION: Overnight oximetry has been proposed as an accessible, simple, and reliable technique for obstructive sleep apnea syndrome (OSAS) diagnosis. From visual inspection to advanced signal processing, several studies have demonstrated the usefulness of oximetry as a screening tool. However, there is still controversy regarding the general application of oximetry as a single screening methodology for OSAS. Areas covered: Currently, high-resolution portable devices combined with pattern recognition-based applications are able to achieve high performance in the detection of this disease. In this review, recent studies involving automated analysis of oximetry by means of advanced signal processing and machine learning algorithms are analyzed. Advantages and limitations are highlighted and novel research lines aimed at improving the screening ability of oximetry are proposed. Expert commentary: Oximetry is a cost-effective tool for OSAS screening in patients showing high pretest probability for the disease. Nevertheless, exhaustive analyses are still needed to further assess unattended oximetry monitoring as a single diagnostic test for sleep apnea, particularly in the pediatric population and in populations with significant comorbidities. In the following years, communication technologies and big data analyses will overcome current limitations of simplified sleep testing approaches, changing the detection and management of OSAS.

Linfoma pleural asociado a empiema crónico / Pleural Lymphoma Associated With Chronic Empyema

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Assessment of oximetry-based statistical classifiers as simplified screening tools in the management of childhood obstructive sleep apnea.

PURPOSE: A variety of statistical models based on overnight oximetry has been proposed to simplify the detection of children with suspected obstructive sleep apnea syndrome (OSAS). Despite the usefulness reported, additional thorough comparative analyses are required. This study was aimed at assessing common binary classification models from oximetry for the detection of childhood OSAS. METHODS: Overnight oximetry recordings from 176 children referred for clinical suspicion of OSAS were acquired during in-lab polysomnography. Several training and test datasets were randomly composed by means of bootstrapping for model optimization and independent validation. For every child, blood oxygen saturation (SpO2) was parameterized by means of 17 features. Fast correlation-based filter (FCBF) was applied to search for the optimum features. The discriminatory power of three statistical pattern recognition algorithms was assessed: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and logistic regression (LR). The performance of each automated model was evaluated for the three common diagnostic polysomnographic cutoffs in pediatric OSAS: 1, 3, and 5 events/h. RESULTS: Best screening performances emerged using the 1 event/h cutoff for mild-to-severe childhood OSAS. LR achieved 84.3% accuracy (95% CI 76.8-91.5%) and 0.89 AUC (95% CI 0.83-0.94), while QDA reached 96.5% PPV (95% CI 90.3-100%) and 0.91 AUC (95% CI 0.85-0.96%). Moreover, LR and QDA reached diagnostic accuracies of 82.7% (95% CI 75.0-89.6%) and 82.1% (95% CI 73.8-89.5%) for a cutoff of 5 events/h, respectively. CONCLUSIONS: Automated analysis of overnight oximetry may be used to develop reliable as well as accurate screening tools for childhood OSAS.

Assessment of automated analysis of portable oximetry as a screening test for moderate-to-severe sleep apnea in patients with chronic obstructive pulmonary disease.

BACKGROUND: The coexistence of obstructive sleep apnea syndrome (OSAS) and chronic obstructive pulmonary disease (COPD) leads to increased morbidity and mortality. The development of home-based screening tests is essential to expedite diagnosis. Nevertheless, there is still very limited evidence on the effectiveness of portable monitoring to diagnose OSAS in patients with pulmonary comorbidities. OBJECTIVE: To assess the influence of suffering from COPD in the performance of an oximetry-based screening test for moderate-to-severe OSAS, both in the hospital and at home. METHODS: A total of 407 patients showing moderate-to-high clinical suspicion of OSAS were involved in the study. All subjects underwent (i) supervised portable oximetry simultaneously to in-hospital polysomnography (PSG) and (ii) unsupervised portable oximetry at home. A regression-based multilayer perceptron (MLP) artificial neural network (ANN) was trained to estimate the apnea-hypopnea index (AHI) from portable oximetry recordings. Two independent validation datasets were analyzed: COPD versus non-COPD. RESULTS: The portable oximetry-based MLP ANN reached similar intra-class correlation coefficient (ICC) values between the estimated AHI and the actual AHI for the non-COPD and the COPD groups either in the hospital (non-COPD: 0.937, 0.909-0.956 CI95%; COPD: 0.936, 0.899-0.960 CI95%) and at home (non-COPD: 0.731, 0.631-0.808 CI95%; COPD: 0.788, 0.678-0.864 CI95%). Regarding the area under the receiver operating characteristics curve (AUC), no statistically significant differences (p >0.01) between COPD and non-COPD groups were found in both settings, particularly for severe OSAS (AHI ≥30 events/h): 0.97 (0.92-0.99 CI95%) non-COPD vs. 0.98 (0.92-1.0 CI95%) COPD in the hospital, and 0.87 (0.79-0.92 CI95%) non-COPD vs. 0.86 (0.75-0.93 CI95%) COPD at home. CONCLUSION: The agreement and the diagnostic performance of the estimated AHI from automated analysis of portable oximetry were similar regardless of the presence of COPD both in-lab and at-home. Particularly, portable oximetry could be used as an abbreviated screening test for moderate-to-severe OSAS in patients with COPD.