Anthropometric Measurements and Admission Parameters as Predictors of Acute Respiratory Distress Syndrome in Hospitalized COVID-19 Patients.

Aim: We aimed to single out admission predictors of acute respiratory distress syndrome (ARDS) in hospitalized COVID-19 patients and investigate the role of bioelectrical impedance (BIA) measurements in ARDS development. Method: An observational, prospective cohort study was conducted on 407 consecutive COVID-19 patients hospitalized at the University Clinical Center Kragujevac between September 2021 and March 2022. Patients were followed during the hospitalization, and ARDS was observed as a primary endpoint. Body composition was assessed using the BMI, body fat percentage (BF%), and visceral fat (VF) via BIA. Within 24 h of admission, patients were sampled for blood gas and laboratory analysis. Results: Patients with BMI above 30 kg/m2, very high BF%, and/or very high VF levels were at a significantly higher risk of developing ARDS compared to nonobese patients (OR: 4.568, 8.892, and 2.448, respectively). In addition, after performing multiple regression analysis, six admission predictors of ARDS were singled out: (1) very high BF (aOR 8.059), (2) SaO2 < 87.5 (aOR 5.120), (3) IL-6 > 59.75 (aOR 4.089), (4) low lymphocyte count (aOR 2.880), (5) female sex (aOR 2.290), and (6) age < 68.5 (aOR 1.976). Conclusion: Obesity is an important risk factor for the clinical deterioration of hospitalized COVID-19 patients. BF%, assessed through BIA measuring, was the strongest independent predictor of ARDS in hospitalized COVID-19 patients.

The functional consequences in severe and critical covid-19 survivors - A pilot study.

PURPOSE: To perform pulmonary function tests (PFT) in severe COVID-19 survivors one and five months after hospital discharge in order to assess the lung function, as well to identify clinical characteristics and PFT parameters associated with worse cardiopulmonary exercise testing (CPET). MATERIAL AND METHODS: A prospective study included 75 patients with severe form of COVID-19. PFT was conducted one and five months after hospital discharge, in addition to CPET in a second assessment. Patients with a previous history of chronic respiratory diseases were excluded from our study. RESULTS: One month after hospital discharge, all examined patients had diffusion lung capacity for carbon-monoxide(DLco%) below the 80% of predicted values (in mean 58%), with 40% of patients having a restrictive pattern (total lung capacity(TLC) < 80%). In a repeated assessment after five months, pathological DLco% persisted in 40% of patients, while all other PFT parameters were normal. CPET showed reduced maximum oxygen consumption during exercise testing (VO2peak%) values in 80% of patients (in mean 69%), and exercise ventilatory inefficiency in 60%. Patients with VO2peak < 60% had significantly lower values of examined PFT parameters, both one and five months after hospital discharge. Patients with VO2peak% ≥ 60% had a significantly higher increase after the second assessment for Forced expiratory volume in 1st second (FEV1%), Forced expiratory volume in 1st second and forced vital capacity ratio (FEV1/FVC), DLco% and Diffusion lung capacity for carbon monoxide corrected for alveolar volume (DLco/VA). CONCLUSION: Significant functional abnormalities, according to PFT and CPET, was present both one and five months in severe COVID-19 survivors, thus emphasizing the importance of a comprehensive follow-up including both resting and dynamic functional assessment in these patients.

Admission Predictors of Mortality in Hospitalized COVID-19 Patients-A Serbian Cohort Study.

BACKGROUND: Early prediction of COVID-19 patients' mortality risk may be beneficial in adequate triage and risk assessment. Therefore, we aimed to single out the independent morality predictors of hospitalized COVID-19 patients among parameters available on hospital admission. METHODS: An observational, retrospective-prospective cohort study was conducted on 703 consecutive COVID-19 patients hospitalized in the University Clinical Center Kragujevac between September and December 2021. Patients were followed during the hospitalization, and in-hospital mortality was observed as a primary end-point. Within 24 h of admission, patients were sampled for blood gas and laboratory analysis, including complete blood cell count, inflammation biomarkers and other biochemistry, coagulation parameters, and cardiac biomarkers. Socio-demographic and medical history data were obtained using patients' medical records. RESULTS: The overall prevalence of mortality was 28.4% (n = 199). After performing multiple regression analysis on 20 parameters, according to the initial univariate analysis, only four independent variables gave statistically significant contributions to the model: SaO2 < 88.5 % (aOR 3.075), IL-6 > 74.6 pg/mL (aOR 2.389), LDH > 804.5 U/L (aOR 2.069) and age > 69.5 years (aOR 1.786). The C-index of the predicted probability calculated using this multivariate logistic model was 0.740 (p < 0.001). CONCLUSIONS: Parameters available on hospital admission can be beneficial in predicting COVID-19 mortality.

The Role of Bioelectrical Impedance Analysis in Predicting COVID-19 Outcome.

Background: Published data regarding the impact of obesity on COVID-19 outcomes are inconsistent. However, in most studies, body composition was assessed using body mass index (BMI) alone, thus neglecting the presence and distribution of adipose tissue. Therefore, we aimed to investigate the impact of body and visceral fat on COVID-19 outcomes. Methods: Observational, prospective cohort study included 216 consecutive COVID-19 patients hospitalized at University Clinical Center Kragujevac (Serbia) from October to December 2021. Body composition was assessed using the BMI, body fat percentage (%BF), and visceral fat (VF) via bioelectrical impedance analysis (BIA). In addition to anthropometric measurements, variables in the research were socio-demographic and medical history data, as well as admission inflammatory biomarkers. Primary end-points were fatal outcomes and intensive care unit (ICU) admission. Results: The overall prevalence of obesity was 39.3% according to BMI and 50.9% according to % BF, while 38.4% of patients had very high VF levels. After adjusting odds ratio values for cofounding variables and obesity-related conditions, all three anthropometric parameters were significant predictors of primary end-points. However, we note that % BF and VF, compared to BMI, were stronger predictors of both mortality (aOR 3.353, aOR 3.05, and aOR 2.387, respectively) and ICU admission [adjusted odds ratio (aOR) 7.141, aOR 3.424, and aOR 3.133, respectively]. Conclusion: Obesity is linked with COVID-19 mortality and ICU admission, with BIA measurements being stronger predictors of outcome compared to BMI use alone.