Evolution and long­term respiratory sequelae after severe COVID-19 pneumonia: nitric oxide diffusion measurement value.

INTRODUCTION: There are no published studies assessing the evolution of combined determination of the lung diffusing capacity for both nitric oxide and carbon monoxide (DLNO and DLCO) 12 months after the discharge of patients with COVID-19 pneumonia. METHODS: Prospective cohort study which included patients who were assessed both 3 and 12 months after an episode of SARS-CoV-2 pneumonia. Their clinical status, health condition, lung function testings (LFTs) results (spirometry, DLNO-DLCO analysis, and six-minute walk test), and chest X-ray/computed tomography scan images were compared. RESULTS: 194 patients, age 62 years (P25-75, 51.5-71), 59% men, completed the study. 17% required admission to the intensive care unit. An improvement in the patients' exercise tolerance, the extent of the areas of ground-glass opacity, and the LFTs between 3 and 12 months following their hospital discharge were found, but without a decrease in their degree of dyspnea or their self-perceived health condition. DLNO was the most significantly altered parameter at 12 months (19.3%). The improvement in DLNO-DLCO mainly occurred at the expense of the recovery of alveolar units and their vascular component, with the membrane factor only improving in patients with more severe infections. CONCLUSIONS: The combined measurement of DLNO-DLCO is the most sensitive LFT for the detection of the long-term sequelae of COVID-19 pneumonia and it explain better their pathophysiology.

Comparison of COVID-19 and non-COVID-19 papers.

OBJECTIVE: The need to generate evidence related to COVID-19, the acceleration of publication and peer-review process and the competition between journals may have influenced the quality of COVID-19 papers. Our objective was to compare the characteristics of COVID-19 papers against those of non-COVID-19 papers and identify the variables in which they differ. METHOD: We conducted a journal-matched case-control study. Cases were COVID-19 papers and controls were non-COVID-19 papers published between March 2020 and January 2021. Journals belonging to five different Journal Citations Reports categories were selected. Within each selected journal, a COVID-19 paper (where there was one) and another non-COVID-19 paper were selected. Conditional logistic regression models were fitted. RESULTS: We included 81 COVID-19 and 143 non-COVID-19 papers. Descriptive observational studies and analytical observational studies had, respectively, a 55-fold (odds ratio [OR]: 55.12; 95% confidence interval [95%CI]: 7.41-409.84) and 19-fold (OR: 19.28; 95%CI: 3.09-120.31) higher likelihood of being COVID-19 papers, respectively, and also a higher probability of having a smaller sample size (OR: 7.15; 95%CI: 2.33-21.94). COVID-19 papers had a higher probability of being cited since their publication (OR: 4.97; 95%CI: 1.63-15.10). CONCLUSIONS: The characteristics of COVID-19 papers differed from those of non-COVID-19 papers published in the first months of the pandemic. In order to ensure the publication of good scientific evidence the quality of COVID-19-papers should be preserved.

COVID-19 Variability Within European Countries Sourced From ECDC Data. Is Variability Explained by Specific Country Policies?

Background: Europe has had a large variability in COVID-19 incidence between and within countries, particularly after June 2020. We aim to assess the variability between European countries and regions located in a given country. Methods: We used ECDC information including countries having 7 regions or more. The metric used to assess the regional variability within a country was the intercuartilic range in a weekly basis for 32 weeks between June 29th 2020 and February 1st 2021. We also calculated each country's overall variability across the 32 weeks using the distances from the regional curves of the 14-day incidence rates to the corresponding national curve, using the L2 metric for functional data. We afterwards standardised this metric to a scale from 0 to 100 points. We repeated the calculations excluding island regions. Results: The variability between and within countries was large. Slovenia, Spain and Portugal have the greatest variability. Spain and Slovenia held also the top three places for the greatest number of weeks (Spain for 19 weeks and Slovenia for 10) with the highest variability. For variability among the incidence curves across the 32-week period, Slovenia, Portugal and Spain ranked first in functional variability, when all the regions were analysed but also when the island regions were excluded. Conclusions: These differences might be due to how countries tackled the epidemiological situation. The persistent variability in COVID-19 incidence between regions of a given country suggests that governmental action may have an important role in applying epidemiological control measures.

Validity of Lung Ultrasound: Is an Image Worth More Than a Thousand Sounds?

INTRODUCTION: There is debate as to whether lung-ultrasound (LUS) can replace lung-auscultation (LA) in the assessment of respiratory diseases. METHODOLOGY: The diagnostic validity, safety, and reliability of LA and LUS were analyzed in patients admitted in a pulmonary ward due to decompensated obstructive airway diseases, decompensated interstitial diseases, and pulmonary infections, in a prospective study. Standard formulas were used to calculate the diagnostic sensitivity, specificity, and accuracy. The interobserver agreement with respect to the LA and LUS findings was evaluated based on the Kappa coefficient (ᴋ). RESULTS: A total of 115 patients were studied. LUS was more sensitive than the LA in evaluating pulmonary infections (93.59% vs. 77.02%; p = 0.001) and more specifically in the case of decompensated obstructive airway diseases (95.6% vs. 19.10%; p = 0.001). The diagnostic accuracy of LUS was also greater in the case of pulmonary infections (75.65% vs. 60.90%; p = 0.02). The sensitivity and specificity of the combination of LA and LUS was 95.95%, 50% in pulmonary infections, 76.19%, 100% in case of decompensated obstructive airway diseases, and (100%, 88.54%) in case of interstitial diseases. (ᴋ) was 0.71 for an A-pattern, 0.73 for pathological B-lines, 0.94 for condensations, 0.89 for pleural-effusion, 0.63 for wheezes, 0.38 for rhonchi, 0.68 for fine crackles, 0.18 for coarse crackles, and 0.29 for a normal LA. CONCLUSIONS: There is a greater interobserver agreement in the interpretation of LUS-findings compared to that of LA-noises, their combined use improves diagnostic performance in all diseases examined.

Alterations in Respiratory Function Test Three Months after Hospitalisation for COVID-19 Pneumonia: Value of Determining Nitric Oxide Diffusion.

Three to four months after hospitalisation for COVID-19 pneumonia, the most frequently described alteration in respiratory function tests (RFTs) is decreased carbon monoxide transfer capacity (DLCO). METHODS: This is a prospective cohort study that included patients hospitalised because of SARS-CoV-2 pneumonia, three months after their discharge. A clinical evaluation, analytical parameters, chest X-ray, six-minute walk test, spirometry and DLCO-DLNO analysis were performed. Demographic variables, comorbidities, and variables related to the severity of the admission were recorded. RESULTS: Two hundred patients completed the study; 59.5% men, age 62 years, 15.5% admitted to the intensive care unit. The most frequent functional alteration, in 27% of patients, was in the DLCO-DLNO combination. This alteration was associated with age, male sex, degree of dyspnoea, poorer perception of health, and limited ability for physical effort. These patients also presented higher levels of D-Dimer and more residual radiological alterations. In 42% of the patients with diffusion alterations, only reduced DLNO was presented, along with lower D-Dimer levels and less capillary volume involvement. The severity of the process was associated with the reduction in DLCO-DLNO. CONCLUSIONS: The most sensitive RFT for the detection of the sequelae of COVID-19 pneumonia was the combined measurement of DLCO-DLNO and this factor was related to patient health status and their capacity for physical exertion. In 40% of these cases, there was only a reduction in DLNO, a finding that may indicate less pulmonary vascular involvement.

[A Proposed Epidemiologic Risk Threshold for SARS-CoV-2 for Assisting Healthcare Decision-Making]. / Sistema de ayuda a la toma de decisiones sanitarias. Propuesta de umbrales de riesgo epidemiológico ante SARS-CoV-2.

INTRODUCTION: The SARS-CoV-2 pandemic is the most important health challenge observed in 100 years, and since its emergence has generated the highest excess of non-war-related deaths in the western world. Since this disease is highly contagious and 33% of cases are asymptomatic, it is crucial to develop methods to predict its course. We developed a predictive model for Covid-19 infection in Spanish provinces. METHODS: We applied main components analysis to epidemiological data for Spanish provinces obtained from the National Centre of Epidemiology, based on the epidemiological curve between 24 February and 8 June 2020. Using this method, we classified provinces according to their epidemiological progress (worst, intermediate, and good). RESULTS: We identified 2 components that explained 99% of variability in the 52 epidemiological curves. The first component can be interpreted as the crude incidence rate trend and the second component as the speed of increase or decrease in the incidence rate during the period analysed. We identified 10 provinces in the group with the worst progress and 17 in the intermediate group. The threshold values for the 7-day incidence rate for an alert 1 (intermediate) were 134 cases/100,000 inhabitants, and 167 for alert 2 (high), respectively, showing a high discriminative power between provinces. CONCLUSIONS: These alert levels might be useful for deciding which measures may affect population mobility and other public health decisions when considering community transmission of SARS-CoV-2 in a given geographical area. This information would also facilitate intercomparison between healthcare areas and Autonomous Communities.

Use of conventional chest imaging and artificial intelligence in covid-19 infection. A review of the literature

ABSTRACT The coronavirus disease caused by SARS-Cov-2 is a pandemic with millions of confirmed cases around the world and a high death toll. Currently, the real-time polymerase chain reaction (RT-PCR) is the standard diagnostic method for determining COVID-19 infection. Various failures in the detection of the disease by means of laboratory samples have raised certain doubts about the characterization of the infection and the spread of contacts.In clinical practice, chest radiography (RT) and chest computed tomography (CT) are extremely helpful and have been widely used in the detection and diagnosis of COVID-19. RT is the most common and widely available diagnostic imaging technique, however, its reading by less qualified personnel, in many cases with work overload, causes a high number of errors to be committed. Chest CT can be used for triage, diagnosis, assessment of severity, progression, and response to treatment. Currently, artificial intelligence (AI) algorithms have shown promise in image classification, showing that they can reduce diagnostic errors by at least matching the diagnostic performance of radiologists.This review shows how AI applied to thoracic radiology speeds up and improves diagnosis, allowing to optimize the workflow of radiologists. It can provide an objective evaluation and achieve a reduction in subjectivity and variability. AI can also help to optimize the resources and increase the efficiency in the management of COVID-19 infection. RESUMEN La enfermedad causada por el coronavirus SARS-Cov-2 es una pandemia con millones de casos confirmados en todo el mundo y un alto número de fallecimientos. Actualmente, la reacción en cadena de la polimerasa en tiempo real (RT-PCR) es el método de diagnóstico estándar para determinar la infección por COVID-19. Diversos fracasos en la detección de la enfermedad por medio de muestras de laboratorio han planteado ciertas dudas sobre la caracterización de la infección y la propagación a los contactos.En la práctica clínica, la radiografía de tórax (RT) y la tomografía computarizada de tórax (TC) son extremadamente útiles y se han utilizado extensamente en la detección y diagnóstico de la COVID-19. La RT es la técnica de diagnóstico por imagen más común y la que está más ampliamente disponible, sin embargo, su lectura por personal menos cualificado, en muchos casos con sobrecarga de trabajo, hace que se cometa un gran número de errores. La tomografía computarizada del tórax se puede utilizar para el triaje, el diagnóstico, la evaluación de la gravedad, la progresión y la respuesta al tratamiento. Actualmente, los algoritmos de inteligencia artificial (IA) han resultado prometedores en la clasificación de imágenes, mostrando que pueden reducir los errores de diagnóstico, como mínimo igualando el rendimiento diagnóstico de los radiólogos.Esta revisión muestra cómo la IA aplicada a la radiología torácica acelera y mejora el diagnóstico, lo que permite optimizar el flujo de trabajo de los radiólogos. Puede proporcionar una evaluación objetiva y lograr una reducción de la subjetividad y la variabilidad. La IA también puede ayudar a optimizar los recursos y aumentar la eficiencia en la gestión de la infección por COVID-19.