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Source: Long COVID-Lungs  Feb 10, 2022  1 year, 9 months, 3 weeks, 13 hours, 58 minutes ago

Austrian Study Warns Many Post COVID-19 Individuals Sustain Persistent Systemic Inflammation That Causes Structural And Functional Lung Abnormality!

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Austrian Study Warns Many Post COVID-19 Individuals Sustain Persistent Systemic Inflammation That Causes Structural And Functional Lung Abnormality!
Source: Long COVID-Lungs  Feb 10, 2022  1 year, 9 months, 3 weeks, 13 hours, 58 minutes ago
Long COVID-Lungs: Austrian researchers from the Medical University of Innsbruck, the Karl Landsteiner Institute, University Hospital Innsbruck and St. Vinzenz Hospital have in a new study found that many post COVID-19 individuals sustain continuous systemic inflammation that causes structural and functional lung abnormality!

The study team characterized the kinetics of respiratory and symptom recovery following COVID-19.
The team conducted a longitudinal, multi-center observational study in ambulatory and hospitalized COVID-19 patients recruited in early 2020 (n = 145). Pulmonary computed tomography (CT) and lung function (LF) readouts, symptom prevalence, clinical and laboratory parameters were collected during acute COVID-19 and at 60-, 100- and 180-days follow-up visits. Recovery kinetics and risk factors were investigated by logistic regression. Classification of clinical features and participants was accomplished by unsupervised and semi-supervised multi-parameter clustering and machine learning.
The study findings shockingly showed that at the six-month follow-up, 49% of participants reported persistent symptoms.
The frequency of structural lung CT abnormalities ranged from 18% in the mild outpatient cases to 76% in the ICU convalescents. Prevalence of impaired LF ranged from 14% in the mild outpatient cases to 50% in the ICU survivors.
It was also found that incomplete radiological lung recovery was associated with increased anti-S1/S2 antibody titer, IL-6 and CRP levels at the early follow-up.
The Long COVID-Lungs study findings demonstrate that the risk of perturbed pulmonary recovery could be robustly estimated at early follow-up by clustering and machine learning classifiers employing solely non-CT and non-LF parameters.
The study findings conclude that the severity of acute COVID-19 and protracted systemic inflammation is strongly linked to persistent structural and functional lung abnormality. Automated screening of multi-parameter health record data may assist at the prediction of incomplete pulmonary recovery and optimize COVID-19 follow-up management.
The study findings were published in the peer reviewed journal: eLife.
The study findings most importantly revealed that protracted inflammation following COVID-19 is strongly linked to long-term changes in lung structure and function.
The study findings suggest that monitoring individuals for markers of inflammation after infection with the SARS-CoV-2 virus could help identify those at risk of long-term lung problems and optimize follow-up care.
Despite the fact that a vast majority of COVID-19 patients display mild disease, a significant proportion reports lingering or recurring clinical symptoms and full recovery can take several months to years.
Co-first author, Dr Thomas Sonnweber, a lung specialist at the Medical University of Innsbruck told Thailand Medi cal News, "Symptoms lasting beyond 12 weeks are found in as many as 10 percent of COVID-19 patients and robust, resource-saving tools assessing people's individual risk of lung complications are urgently needed. We analyzed the frequency of lung structure and function changes and persistent symptoms in patients six months after a COVID-19 diagnosis, to investigate whether there are clinical hallmarks that can predict their risk of developing long COVID."
The study team evaluated the recovery of 145 primarily hospitalized patients diagnosed with COVID-19 between March and June 2020 who took part in the Austrian clinical study called "Development of Interstitial Lung Disease in COVID-19 (CovILD)."
The team retrospectively assessed patient characteristics during their acute COVID-19 infection and then performed follow-up investigations at 60, 100 and 180 days. At each visit, they assessed symptoms and physical performance using a questionnaire, and conducted lung function tests, blood tests and a chest scan.
It was found that almost half (49 percent) of patients had persistent complaints six months after diagnosis, with the most common complaints being impaired physical performance (34.7 percent of patients), sleep disorders (27.1 percent) and breathlessness on exertion (22.8 percent).
Though the frequency of these symptoms declined as time passed, they were slower to resolve towards the end of the convalescence period, at the 100-day and 180-day follow-up visits.
Shocking, it was found that six months after diagnosis, a third of patients (33.6 percent) had impaired lung function and almost half of patients (48.5 percent) had chest scans showing structural lung abnormalities, with one in five patients (19.4 percent) having moderate-to-severe lung alterations.
In order to to identify risk factors associated with these long-term problems, the study team used machine learning algorithms to look for patterns of clinical features in the patients who had long COVID symptoms.
The team found that risk factors linked to severe and critical COVID-19 infection ie namely being male, having long-term conditions such as high blood pressure, and high anti-SARS-CoV-2 antibody levels were also linked to long-term symptom persistence.
Besides these factors, elevated markers of inflammation, both body-wide and within blood vessels were also associated with long-term lung abnormalities.
The study team then tested if algorithms using these risk factors could predict COVID outcomes in a different group of patients. They found that although the inflammation markers predicted who would develop lung structure abnormalities, they could not accurately predict who would develop lung function problems or other symptoms such as breathlessness.
Concerningly, this suggests that even if patients have detectable changes to their lungs 60 days after diagnosis, this may not manifest as symptoms or changes in lung function yet, but could still lead to problems later.
The study team said that the algorithms need to be validated in larger groups of patients with COVID-19 before they can be reliably used to predict long-term COVID-19 outcomes.
The study team have published their findings as an open-source risk assessment tool for other researchers to use.
Co-senior author, Dr Judith Löffler-Ragg, a lung specialist at the Medical University of Innsbruck added, "In our study group of patients, we found a high frequency of structural and functional lung abnormalities and persistent symptoms six months after a COVID-19 diagnosis, and a recovery trajectory that slowed after three months. Our risk models revealed a set of clinical measurements linked to lengthened recovery, independent to the severity of infection, which include known inflammatory markers. We hope that these could be used to identify those at risk of persistent lung problems and optimize their care to prevent long-term disability."
The study team also advocates that all individuals that have been exposed to the SARS-CoV-2 coronavirus, irrespective if they were asymptomatic or symptomatic or only had mild symptoms, should constantly got for regular health screenings and check for biomarkers for systemic inflammation and monitor their lung health as the a previous SARS-CoV-2 infection can affect the body in a variety of ways without people even knowing till it is too late.
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