Persistent Pneumonitis Post-COVID 19: description and risk factors

Background: Some patients present persistent ground glass opacities (GGO) and/or consolidations after an acute episode of SARS-CoV-2 pneumonia (COVID19). Risk factors for persistent pneumonitis (PPN) and potential response to corticosteroids remain unclear. Objective(s): To evaluate the clinical characteristics of patients with PPN, as well as to detect possible risk factors and the role of corticosteroids. Method(s): We conducted a prospective, controlled, multicenter analysis of patients hospitalized because of COVID19 with (n=152) or without (n=140) PPN. PPN was defined by the persistence of pulmonary opacities in a chest CT scan >14 days after admission. Characteristics of participants were obtained from their medical records. A CT score was used to quantify parenchymal abnormalities when PPN was suspected. Result(s): Compared to controls, patients with PPN were older and suffered more comorbidities, also D-dimer and Creactive protein levels were higher. The most frequent features observed in CT scans were GGO (97%), consolidation (95%), bronchial dilatation (93%) and reticular pattern (92%) with a CT score of 16.12+/-4.26. Multivariate logistic regression identified age and C-reactive protein levels on admission as independent risk factors for PPN. No significant differences were observed in thoracic CT scan one-month after discharge in patients treated with higher corticosteroids doses (>50 mg/day after discharge) compared to lower doses. Conclusion(s): Age and raised C-reactive protein levels on admission are significant risk factors of PPN after COVID19. Treatment with high doses of corticosteroids does not seem to add benefit.

Deep-learning characterization and quantification of COVID-19 pneumonia lesions from chest CT images

A relevant percentage of COVID-19 patients present bilateral pneumonia. Disease progression and healing is characterized by the presence of different parenchymal lesion patterns. Artificial intelligence algorithms have been developed to identify and assess the related lesions and properly segment affected lungs, however very little attention has been paid to automatic lesion subtyping. In this work we present artificial intelligence algorithms based on CNN to automatically identify and quantify COVID-19 pneumonia patterns. A Dense-efficient CNN architecture is presented to automatically segment the different lesion subtypes. The proposed technique has been independently tested in a multicentric cohort of 100 patients, showing Dice coefficients of 0.988±0.01 for ground glass opacities, 0.948±0.05 for consolidations, and 0.999±0.0003 for healthy tissue with respect to radiologist's reference segmentations, and high correlations with respect to radiologist severity visual scores. © 2022 SPIE.

COVID-19 and lung involvement

In late December 2019, a novel coronavirus emerged and had a rapid and worldwide spread, resulting in an ongoing pandemic. This virus, designated SARS-CoV-2, causes a respiratory disease named COVID-19 which can range in severity, depending not only on the viral infection but also conditioned by the immune system and the host's response. COVID-19 is often associated with aggressive and uncontrolled inflammation that may lead to acute respiratory distress syndrome (ARDS), multiorgan damage and failure, and death. In this chapter, we review the general characteristics of SARS-CoV-2 infection, its interaction with target cells and the resulting immune response, as well as current and potential therapeutic interventions. © 2022 Elsevier B.V.

Long Covid-19: Proposed Primary Care Clinical Guidelines for Diagnosis and Disease Management

Long COVID-19 may be defined as patients who, four weeks after the diagnosis of SARS-Cov-2 infection, continue to have signs and symptoms not explainable by other causes. The estimated frequency is around 10% and signs and symptoms may last for months. The main long-term manifestations observed in other coronaviruses (Severe Acute Respiratory Syndrome (SARS), Middle East respiratory syndrome (MERS)) are very similar to and have clear clinical parallels with SARS-CoV-2: mainly respiratory, musculoskeletal, and neuropsychiatric. The growing number of patients worldwide will have an impact on health systems. Therefore, the main objective of these clinical practice guidelines is to identify patients with signs and symptoms of long COVID-19 in primary care through a protocolized diagnostic process that studies possible etiologies and establishes an accurate differential diagnosis. The guidelines have been developed pragmatically by compiling the few studies published so far on long COVID-19, editorials and expert opinions, press releases, and the authors' clinical experience. Patients with long COVID-19 should be managed using structured primary care visits based on the time from diagnosis of SARS-CoV-2 infection. Based on the current limited evidence, disease management of long COVID-19 signs and symptoms will require a holistic, longitudinal follow up in primary care, multidisciplinary rehabilitation services, and the empowerment of affected patient groups.