Muscle manifestations and CK levels in COVID infection: results of a large cohort of patients inside a Pandemic COVID-19 Area (preprint)

Objective: to investigate both muscular manifestations and CK levels of a large cohort of patients with COVID-19 infection and to determine whether hyperckemia is associated with morbidity and mortality.Methods: Data of 615 patients discharged from ASST Ovest Milanese (Milan, Lombardy, Italy) with final diagnosis of COVID-19 infection were retrospectively extracted from electronical medical records from 21 February to 1 May 2020. Patients were descriptively analyzed with respect to the following variables: sex, age, muscular manifestations (including myalgia/arthralgia and fatigue), respiratory involvement (SARS pneumonia or respiratory failure) and history of falls. Association between patients’ characteristics and CK levels was investigated. In addition, the proportion of patients who died following access to the ER was calculated. Finally, the effect of CK levels and other patients’ features on mortality was estimated using a logistic regression model.Results: 176 (28.6%) patients had raised serum CK levels. 88 (14.3%) had muscular manifestations, of which 81 (13.2%) complained fatigue and 17 (2.8%) had myalgia and/or arthralgia. CK levels were significantly associated with respiratory involvement and fatal outcome.Conclusions: Our study provides preliminary evidence that hyperckemia is a predictor of respiratory involvement and fatal outcome in patients with COVID-19 infection. For patients with muscle damage symptoms, screening for COVID-19 infection is recommended together with the dosage of CK level.

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: a population-based cohort study from Italy (preprint)

BackgroundThe novel coronavirus (SARS-CoV-2) pandemic spread rapidly worldwide increasing exponentially in Italy. To date, there is lack of studies describing clinical characteristics of the population most at risk of infection. Hence, we aimed to identify clinical predictors of SARS-CoV-2 infection risk and to develop and validate a score predicting SARS-CoV-2 infection risk comparing it with unspecific surrogates. MethodsRetrospective case/control study using administrative health-related database was carried out in Southern Italy (Campania region) among beneficiaries of Regional Health Service aged over than 30 years. For each subject with Covid-19 confirmed diagnosis (case), up to five controls were randomly matched for gender, age and municipality of residence. Odds ratios and 90% confidence intervals for associations between candidate predictors and risk of infection were estimated by means of conditional logistic regression. SARS-CoV-2 Infection Score (SIS), was developed by generating a total aggregate score obtained from assignment of a weight at each selected covariate using coefficients estimated from the model. Finally, the score was categorized by assigning increasing values from 1 to 4. SIS was validated by comparison with specific and unspecific predictors of SARS-CoV-2 infection. ResultsSubjects suffering from diabetes, anaemias, Parkinsons disease, mental disorders, cardiovascular and inflammatory bowel and kidney diseases showed increased risk of SARS-CoV-2 infection. Similar estimates were recorded for men and women and younger and older than 65 years. Fifteen conditions significantly contributed to the SIS. As SIS value increases, risk progressively increases, being odds of SARS-CoV-2 infection among people with the highest SIS value (SIS=4), 1.74 times higher than those unaffected by any SIS contributing conditions (SIS=1). ConclusionThis study identified conditions and diseases making individuals more vulnerable to SARS-CoV-2 infection. Our results are a decision-maker support tool for identifying population most at risk allowing adoption of preventive measures to minimize a potential new relapse damage.