Abstract
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.
Keywords: ARDS, bioelectrical impedance analysis, body fat percentage, COVID-19, obesity
1. Introduction
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for a significant number of deaths and health impairments worldwide [1]. The illness is characterized mostly by respiratory clinical manifestations ranging from mild to severe clinical forms [2]. However, in 10–20% of patients, the disease is complicated by respiratory failure, acute respiratory distress syndrome (ARDS), and multisystem organ failure [3,4].
Despite continuous research efforts, the exact mechanisms of clinical deterioration are not fully elucidated. In a practical manner, it would be beneficial to single out conditions and clinical characteristics associated with ARDS development in order to construct prediction models and promptly single out patients at risk of clinical deterioration. According to the available literature, many parameters are associated with COVID-19 ARDS, such as older age, male sex, certain comorbidities, smoking, impaired gas exchange, elevated biomarkers of inflammation, and impaired coagulation [5,6,7,8,9,10,11]. However, the exact selection of predictors and their impact on ARDS development in hospitalized COVID-19 patients are not uniform across the literature, mostly due to the significant heterogeneity of cohort characteristics and methodological approach.
Obesity, according to body mass index (BMI), is associated with disease severity and the need for ICU treatment (OR 1.2–5.5) [12,13,14,15]. However, BMI is a basic anthropometric measurement that neglects body composition and the presence of adipose tissue. Given that most of the pathophysiological mechanisms through which obesity possibly affects the COVID-19 course are the effect of adipose tissue, assessing body composition solely using the BMI could be suboptimal [16,17,18].
Therefore, we aimed to single out admission predictors of ARDS in hospitalized COVID-19 patients, based on sociodemographic and medical history data, as well as blood gas and laboratory analysis on admission. Additionally, we aimed to examine the impact of obesity on COVID-19 ARDS, as well as compare BMI with anthropometric measurements given by BIA method, such as total body and visceral fat. To the best of the authors’ knowledge, this is the first registry regarding BIA measurements and COVID-19 outcomes.
2. Materials and Methods
2.1. Study Population
An observational, prospective cohort study included 407 consecutive COVID-19 patients hospitalized at the University Clinical Center Kragujevac between September 2021 and March 2022. The study was granted approval by the University Clinical Center Kragujevac (Serbia) Ethical committee.
Our COVID-19 center consisted of standard, semi-intensive, and intensive care units focused on severe to critically ill patients [19]. Patients were followed during the hospitalization, and ARDS development was observed as a primary endpoint. The ARDS is an acute onset clinical syndrome characterized by diffuse, bilateral alveolar damage, inflammation, and edema, resulting in respiratory failure [20]. ARDS was defined using the Berlin criteria [21]: (1) timing within 1 week of clinical insult or new/worsening respiratory symptoms; (2) bilateral opacities seen on chest imaging, not fully explained by effusions, lobar/lung collapse, or nodules; (3) respiratory failure not fully explained by cardiac failure/fluid overload; (4) oxygenation impairment, defined as the ratio of partial pressure of arterial oxygen (PaO2) to the fraction of inspired oxygen (FiO2) below 300 mm Hg.
Inclusion and exclusion criteria are presented in Supplementary Table S1. We note that, during the data collection period, 1240 patients were hospitalized in our COVID-19 center; however, 825 patients were excluded according to exclusion criteria. Since there was an insufficient number of underweight patients (total of eight patients according to either BMI or BF% measurement), those patients were additionally excluded from further analysis.
2.2. Data Collection
Sociodemographic and medical history data were obtained using patients’ electronic medical records (Health Informational System version 2, ComTrade, Kragujevac, Serbia). Arterial and peripheral venous blood for further analysis was routinely sampled at the time of admission.
Within 48 h of hospital admission, patients were measured on the TANITA BC-543 device (Tanita Corporation, Tokyo, Japan). According to the manufacturer’s instructions, patients were measured in the morning, barefoot, in light clothing, before the first meal. In order to minimize the effect of acute infection and fever, patients were measured in an afebrile state.
Anthropometric parameters of interest were the following:
-
(I)
BMI, calculated using the formula BMI [kg/m2] = BM [kg]/BH2 [m2], where BM is the body mass expressed in kg (with 0.1 kg precision), and BH is body height expressed in m (with 0.01 m precision). According to BMI values, patients were categorized as (I) underweight < 18.5 kg/m2, (II) normal weight 18.6–24.9 kg/m2, (III) overweight 25–29.9 kg/m2, (IV) class 1 obesity 30–34.9 kg/m2, (V) class 2 obesity 35–39.9 kg/m2, or (VI) class 3 obesity > 40 kg/m2 [22].
-
(II)
Body fat percentage (BF%), expressed as a percentage of the total mass (with 0.1% precision). According to BF% values, regarding age and sex, patients were categorized as (I) low BF%, (II) normal BF%, (III) high BF%, or (IV) very high BF% (age- and sex-adjusted cutoff values are presented in Table 1) [23].
-
(III)
Visceral fat (VF) level, according to which patients were categorized as (I) normal (1–9), (II) high (10–14), or (III) very high (≥15) [24].
Table 1.
Age- and sex-adjusted cutoff values for BF% categories.
| Sex | Age (Years) |
BF% Categories | |||
|---|---|---|---|---|---|
| Low | Normal | High (Overweight) |
Very High (Obesity) |
||
| Female | 20–39 | <21% | 21–32.9% | 33–39.5% | >39.5% |
| 40–59 | <23% | 23–33.9% | 34–40% | >40% | |
| ≥60 | 24% | 24–35.9% | 36–41.5% | >41.5% | |
| Male | 20–39 | <7% | 7–19.9% | 20–25% | >25% |
| 40–59 | <10.5% | 10.5–21.9% | 22–27.5% | >27.5% | |
| ≥60 | <12% | 12–24.9% | 25–30% | >30% | |
Abbreviations: BF%—Body fat percentage. BF% and VF levels were assessed using BIA analysis, by measuring the tissue’s impedance to a low current electrical impulse.
2.3. Statistical Analysis
Statistical analysis was performed using the SPSS statistical package (version 25.0, IBM corporation, Armonk, NY, USA). Differences in quantitative data were tested using the Mann–Whitney U test. If applicable, continuous data were further transformed into a binary variable. When dividing continuous variables into categories, an accepted reference line or cutoff values given by ROC analysis (Supplementary Table S2) were used, depending on clinical applicability. After identifying the variables associated with primary outcome, uni- and multivariable binary logistic regression was performed. The strength of the relationship between examined variables and outcome was expressed as the odds ratio (OR) with 95% confidence interval (95% CI) for univariate, and as the adjusted odds ratio (aOR) with 95% CI for multivariate analysis. A p-value < 0.05 was considered significant.
3. Results
Our cohort consisted of 407 adult COVID-19 patients hospitalized at the University Clinical Center Kragujevac (Serbia), whose characteristics are presented in Table 2. During the hospitalization, 98.5% of patients required some form of oxygen support, 51.6% of patients required noninvasive/invasive ventilation, and 35.1% of patients developed ARDS, with a mortality rate of 17.4%. Younger age and female sex were associated with ARDS development, while patients with and without ARDS did not significantly differ in terms of comorbidities. In addition, patients who developed ARDS had a shorter period between disease onset and the need for inpatient treatment, more frequently required oxygen support upon admission, had a longer hospital stay, and had a higher mortality rate.
Table 2.
Demographic and medical history data, with regard to ARDS development.
| Cohort Characteristics | Frequency (Number of Cases) or Median Value (with IQR) |
p-Value | |||
|---|---|---|---|---|---|
| Cohort | No ARDS | ARDS | |||
| Age [years] | 68.0 (IQR 17.0) | 68.5 (IQR 16.0) | 66.0 (IQR 19.0) | 0.006 * | |
| Sex | Male | 62.9% (n = 256) | 68.9% (n = 182) | 51.7% (n = 74) | 0.001 * |
| Female | 37.1% (n = 151) | 31.1% (n = 82) | 48.3% (n = 69) | ||
| COMORBIDITIES | |||||
| Arterial hypertension | 68.1% (n = 277) | 70.5% (n = 186) | 63.6% (n = 91) | 0.182 | |
| Diabetes mellitus | 26.5% (n = 108) | 25.4% (n = 67) | 28.7% (n = 41) | 0.482 | |
| Chronic kidney disease | 14.7% (n = 60) | 14.8% (n = 39) | 14.7% (n = 21) | 1.000 | |
| Neurological condition 1 | 3.4% (n = 14) | 3.8% (n = 10) | 2.8% (n = 4) | 0.778 | |
| Previous myocardial infarction | 3.9% (n = 16) | 3.8% (n = 10) | 4.2% (n = 6) | 0.797 | |
| Malignancy | 6.1% (n = 25) | 7.2% (n = 19) | 4.2% (n = 6) | 0.283 | |
| Obstructive lung disease 2 | 3.2% (n = 13) | 3.4% (n = 9) | 2.8% (n = 4) | 1.000 | |
| Charlson Comorbidity Index | 3.0 (IQR 2.0) | 3.0 (IQR 2.0) | 3.0 (IQR 3.0) | 0.052 | |
| DISEASE COURSE AND OUTCOME | |||||
| Time from disease onset to hospital admission [days] | 6.0 (IQR 6.0) | 7.0 (IQR 7.0) | 6.0 (IQR 5.0) | 0.003 * | |
| ARDS development | 35.1% (n = 143) | / | / | / | |
| Mortality | 17.4% (n = 71) | 9% (n = 24) | 44.1% (n = 63) | <0.001 * | |
| Hospital stay [days] | 17.0 (IQR 11.0) | 14.5 (IQR 9.75) | 21.0 (IQR 14.0) | <0.001 * | |
| Oxygen support requirement on admission | 92.9% (n = 378) | 90.5% (n = 239) | 97.2% (n = 139) | 0.014 * | |
Abbreviations: ARDS—acute respiratory distress syndrome; IQR—interquartile range. * Statistical significance level at <0.05. 1 Neurological condition: the presence of history of stroke, brain tumor or malformation, vascular disease, dementia of any etiology, etc. 2 Obstructive lung disease: the presence of either chronic obstructive lung disease or bronchial asthma.
The prevalence of obesity was 37.9% according to BMI and 49.6% according to BF%, while 38.6% of patients had an excessive level of VF. (Figure 1) Furthermore, patients who developed ARDS more frequently had BMI above 30 kg/m2 (p < 0.001), as well as very high body fat percentage (p < 0.001) and visceral fat level (p < 0.001).
Figure 1.
Percentage distribution of body composition categories for (a) BMI, (b) %BF, and (c) VF level, with regard to ARDS development. Abbreviations: ARDS—acute respiratory distress syndrome; BMI—body mass index; VF—visceral fat; %BF—body fat percentage. * Statistical significance at <0.05.
The admission blood gas and laboratory analysis is presented in Table 3. Patients with ARDS, compared to those without, had significant differences in blood cell count (lower lymphocyte and higher granulocyte count), more impaired gas exchange (lower values of oxygen saturation (SaO2) and partial oxygen pressure (PaO2)), and more increased inflammatory biomarkers (including C-reactive protein (CRP), lactate dehydrogenase (LDH), and interleukin-6 (IL-6)).
Table 3.
Admission blood gas and laboratory analysis, with regard to ARDS development.
| Blood Gas and Laboratory Analysis | Median Values (IQR) | p-Value | ||
|---|---|---|---|---|
| Cohort | No ARDS | ARDS | ||
| PaO2 [kPa] | 6.9 (IQR 1.4) | 14.5 (IQR 9.75) | 6.4 (IQR 1.2) | <0.001 * |
| SaO2 [%] | 88 (IQR 6) | 89 (IQR 5) | 86 (IQR 5) | <0.001 * |
| WBC [109/L] | 8.2 (IQR 4.8) | 8.2 (IQR 4.5) | 8.3 (IQR 6.6) | 0.751 |
| Granulocyte count [109/L] | 6.9 (IQR 4.9) | 6.5 (IQR 4.85) | 7.4 (IQR 4.1) | 0.710 |
| Granulocytes [%] | 84.2 (IQR 11.7) | 83 (IQR 12.2) | 85.9 (IQR 10.8) | 0.001 * |
| Lymphocyte count [109/L] | 0.7 (IQR 0.5) | 0.73 (IQR 0.57) | 0.69 (IQR 0.43) | 0.009 * |
| Lymphocytes [%] | 9.1 (IQR 8.5) | 9.5 (IQR 8.6) | 8.5 (IQR 6.4) | 0.036 * |
| RBC [1012/L] | 4.5 (IQR 0.7) | 4.48 (IQR 0.81) | 4.47 (IQR 0.69) | 0.968 |
| HGB [g/L] | 134 (IQR 21) | 134.5 (IQR 21.75) | 133 (IQR 21) | 0.081 |
| PLT [109/L] | 201 (IQR 113) | 203.5 (IQR 108) | 199 (IQR 115) | 0.771 |
| INR | 1.08 (IQR 0.18) | 1.08 (IQR 0.16) | 1.07 (IQR 0.19) | 0.079 |
| aPTT [s] | 31.4 (IQR 6.85) | 31.5 (IQR 7) | 33.3 (IQR 6.3) | 0.917 |
| Fibrinogen [g/L] | 6.5 (IQR 2.15) | 6.26 (IQR 2.06) | 6.22 (IQR 2.09) | 0.485 |
| D-dimer [ug/mL] | 0.93 (IQR 0.97) | 0.97 (IQR 1.25) | 0.85 (IQR 0.76) | 0.159 |
| Albumin [g/L] | 36 (IQR 4) | 36 (IQR 4) | 36 (IQR 5) | 0.337 |
| AST [IU/L] | 42 (IQR 35) | 41 (IQR 31) | 43 (IQR 41) | 0.271 |
| ALT [IU/L] | 36 (IQR 36) | 36 (IQR 38.25) | 41 (IQR 40) | 0.830 |
| GGT [IU/L] | 41 (IQR 65) | 42 (IQR 65) | 41 (IQR 64) | 0.819 |
| BUN [mmol/L] | 7.8 (IQR 5.3) | 8.4 (IQR 5.5) | 7.1 (IQR 4.2) | 0.054 |
| Creatinine [mmol/L] | 92 (IQR 41) | 91 (IQR 42) | 94 (IQR 40) | 0.216 |
| LDH [U/L] | 773 (IQR 382) | 702 (IQR 388) | 890 (IQR 242) | <0.001 * |
| Ferritin [ug/L] | 838 (IQR 745) | 815 (IQR 792) | 877 (IQR 746) | 0.335 |
| CK [U/L] | 107(IQR 166) | 91.5 (IQR 152.75) | 156 (IQR 179) | 0.001 * |
| CKMB [U/L] | 18 (IQR 10) | 18 (IQR 9) | 18 (IQR 11) | 0.386 |
| CRP [mg/L] | 99 (IQR 96.1) | 96.6 (IQR 92.5) | 108.7 (IQR 99.8) | 0.009 * |
| PCT [ng/mL] | 0.11 (IQR 0.18) | 0.1 (IQR 0.17) | 0.114 (IQR 0.18) | 0.413 |
| cTnI [ng/mL] | 0.0038 (IQR 0.014) | 0.00145 (IQR 0.0145) | 0.006 (IQR 0.0127) | 0.637 |
| pro-BNP [pg/mL] | 559 (IQR 880) | 604 (IQR 959) | 389 (IQR 895) | 0.066 |
| IL-6 [pg/mL] | 58.7 (IQR 97) | 47.4 (IQR 89.15) | 89.8 (IQR 119.3) | <0.001 * |
Abbreviations: ALT—alanine transaminase; aPTT—activated partial thromboplastin clotting time; AST—aspartate transaminase; BUN—blood urea nitrogen; CK—creatine kinase; CKMB—muscle–brain form of creatine kinase; CRP—C-reactive protein; D—D-dimer; GGT—gamma-glutamyl transferase; Gran—granulocyte; Hgb—hemoglobin; hsTnI—high-sensitivity troponin I; IL-6—interleukin 6; INR—international normalized ratio; LDH—lactate dehydrogenase; Lym—lymphocytes; NT pro-BNP—N-terminal pro-brain natriuretic peptide; PaO2—partial pressure of oxygen; PCT—procalcitonin; PLT—platelets; RBC—red blood cells; SaO2—oxygen saturation of blood; WBC—white blood cells. * Statistical significance level at <0.05.
After performing the univariate analysis, 11 variables were selected for the final binary logistic regression model. (Table 4) Six variables gave statistically significant contributions to the model: (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 years (aOR 1.976). The score was statistically significant (c11 = 178.54; p < 0.001), with a sensitivity of 76%, specificity of 87.7%, and C-index of 0.885 (p < 0.001) (Figure 2).
Table 4.
Crude and adjusted OR for variables available upon hospital admission with regard to predicting ARDS development of hospitalized COVID-19 patients.
| Variable | Frequency of ARDS | Crude OR | Adjusted OR | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | p-Value | OR (95% CI) | p-Value | |||
| PaO2 [kPa] | ≥6.85 | 20.5% | 1 | / | Excluded for multicollinearity ** | |
| <6.85 | 52.4% | 4.282 (2.770–6.619) | <0.001 * | |||
| SaO2 [kPa] | ≥87.5 | 18.9% | 1 | / | 1 | / |
| <87.5 | 56.9% | 5.670 (3.635–8.844) | <0.001 * | 5.120 (2.758–9.505) | <0.001 * | |
| Lymphocyte count [109/L] | ≥1.20 | 14.9% | 1 | / | 1 | / |
| <1.20 | 39.1% | 3.662 (1.807–7.422) | <0.001 * | 2.880 (1.218–6.809) | 0.016 * | |
| LDH [U/L] | ≤793.5 | 24.8% | 1 | / | 1 | / |
| >793.5 | 49.1% | 2.934 (1.898–4.538) | <0.001 * | 1.078 (0.580–2.002) | 0.812 | |
| CK [U/L] | ≤171 | 29.8% | 1 | / | 1 | / |
| >171 | 45.0% | 1.927 (1.257–2.955) | 0.003 * | 1.911 (0.978–3.733) | 0.058 | |
| CRP [mg/L] | ≤108.5 | 30.6% | 1 | / | 1 | / |
| >108.5 | 40.9% | 1.572 (1.039–2.376) | 0.032 * | 1.096 (0.594–2.024) | 0.768 | |
| IL-6 [pg/mL] | ≤59.75 | 21.6% | 1 | / | 1 | / |
| >59.75 | 49.7% | 3.586 (2.327–5.525) | <0.001 * | 4.089 (2.136–7.826) | <0.001 * | |
| Age [years] | ≥68.5 | 29.8% | 1 | / | 1 | / |
| <68.5 | 39.7% | 1.554 (1.027–2.349) | 0.037 * | 1.976 (1.038–3.762) | 0.038 * | |
| Sex | Male | 28.9% | 1 | / | 1 | / |
| Female | 45.7% | 2.070 (1.361–3.147) | 0.001 * | 2.290 (1.158–4.529) | 0.017 * | |
| Need for oxygen therapy upon admission | No | 13.8% | 1 | / | Excluded for multicollinearity ** | |
| Yes | 36.8% | 3.635 (1.239–10.661) | 0.019 * | |||
| BMI | <30 | 22.1% | 1 | / | Excluded for multicollinearity ** | |
| ≥30 | 56.5% | 4.568 (2.955–7.060) | <0.001 * | |||
| BF% | Normal/High | 13.2% | 1 | / | 1 | / |
| Very high | 57.4% | 8.892 (5.439–14.538) | <0.001 * | 8.059 (3.990–16.276) | <0.001 * | |
| VF | Normal/High | 27.2% | 1 | / | 1 | / |
| Very high | 47.8% | 2.448 (1.610–3.722) | <0.001 * | 1.159 (0.566–2.372) | 0.686 | |
| Time from disease onset [days] | / | / | 0.923 (0.877–0.970) | 0.002 * | 0.941 (0.867–1.020) | 0.140 |
Abbreviations: aOR—adjusted OR; ARDS—acute respiratory distress syndrome; BMI—body mass index; BF%—body fat percentage; CI—confidence interval; CK—creatine kinase; CRP—C-reactive protein; VF—visceral fat; IL-6—interleukin 6; LDH—lactate dehydrogenase; OR—odds ratio; PaO2—partial pressure of oxygen; SaO2—oxygen saturation of the blood. * Statistical significance level at <0.5. ** Variables excluded due to the multicollinearity principle of multiple logistic regression.
Figure 2.
Receiver operator characteristics (ROC) curve of multiple regression analysis model in predicting ARDS. Legend: Blue line—ROC curve, red line—diagonal reference line.
4. Discussion
This study aimed to examine the impact of obesity on ARDS development and the likelihood of variables available upon admission to predict ARDS in hospitalized COVID-19 patients. Patients who developed ARDS had a shorter time from disease onset to hospital admission, and, with each day that passed from disease onset to hospital admission, the risk of ARDS development decreased by 7.7% (OR 0.923) [25].
The majority of patients had disturbed nutritional status according to all three anthropometric measurements (BMI, BF%, and VF). Body composition disturbances in hospitalized COVID-19 patients are not unexpected. Firstly, the high prevalence of obesity and obesity-related comorbidities has reached pandemic proportions [22], with consequences also noticeable in Serbia. According to research from 2006, 55.7% of the adult population in Serbia was overweight or obese [26]. Secondly, several studies have demonstrated that obesity is a significant risk factor for hospital admission [12,13,14,15]; therefore, it is somewhat expected to have a high prevalence of obesity among hospitalized COVID-19 patients. Furthermore, patients with BMI above 30 kg/m2, very high BF%, and/or very high VF levels were at significantly higher risk of developing ARDS compared to nonobese patients (OR: 4.568, 8.892, and 2.448, respectively), which is in accordance with most of the literature data [12,13,14,18,27,28,29,30,31,32]. Obesity augments the risk of critical illness and mortality in SARS-CoV-2 infection through multiple mechanisms, including chronic inflammation, immune dysregulation, respiratory compromise, impaired pulmonary function, and endocrine and endothelial dysfunction [16,17,33]. However, the majority of mechanisms through which obesity can initiate the clinical deterioration of COVID-19 patients, especially in terms of inflammation and immune response dysregulation, are the effect of the adipose tissue. In obese patients, adipose tissue is responsible for chronic low-grade inflammation, characterized by increased production of cytokines and acute-phase reactants, as well as the macrophage accumulation, resulting in both innate and adaptive immune response impairment. Additionally, chronic inflammation in obese patients could potentially enhance the inflammatory response to COVID-19, resulting in a hyperinflammatory state and disease progression [16,17,34]. This could potentially explain why BF% was a stronger predictor of ARDS development compared to BMI, and why a very high BF% was the strongest independent predictor of ARDS (aOR 8.059) in a multiple regression analysis.
Interestingly, the VF level did not maintain statistical significance in a final regression model. This result could partially be explained by the inadequacy of BIA visceral fat assessment to differentiate adipose tissue between visceral and abdominal compartments [35].
In addition to obesity, the most common comorbidities in our cohort were arterial hypertension, diabetes mellitus, and chronic kidney disease. Despite literature data linking several comorbidities with clinical deterioration and worse outcomes in COVID-19 patients, no comorbidity showed a significant impact on ARDS development in our research [36]. Such differences could partially be explained by the characteristics of the comorbidities in the examined cohort, such as their prevalence, severity, duration, medication burden, and presence of disease-related complications. Therefore, the presence of well-controlled, uncomplicated comorbidities in our cohort could be a possible explanation for the absent impact of the most frequent comorbidities on the ARDS development. Additionally, patients initially hospitalized or requiring hospital treatment for non-COVID-19 pathology, with terminal stage of malignant disease, and with end-stage of renal disease were excluded from our study.
Among demographic characteristics, the female sex was shown to be an independent predictor of ARDS (aOR 2.290). Although the male sex is commonly linked with a higher risk of ICU admission and mortality in hospitalized COVID-19 patients [37,38], some explanations could include different cohort characteristics, more present high-risk behavior related to COVID-19, higher frequency of pre-existing comorbidities, differences in innate immune response, and different activity and expression of ACE 2 [39,40]. In addition, literature data advocate older age as ARDS and mortality risk factors in hospitalized COVID-19 patients [5,10,41]. However, our study singled out age below 68 years to be an independent predictor of ARDS (aOR 1.976). A possible explanation for such a result could be that older patients had a significantly higher mortality rate compared to those younger than 68 years (24.5% and 11.4%, respectively; p = 0.001). Thus, those patients experienced lethal outcome in greater numbers before ICU admittance and ARDS development/diagnosis.
With regard to gas exchange upon admission, patients who developed ARDS during the course of inpatient treatment more frequently required oxygen support upon admission and had more prominent hypoxemia, assessed as lower values of PaO2 and SaO2. Additionally, low SaO2 (≤87%) was the second strongest predictor of ARDS in the multiple regression analysis model. Such findings are of significant practical importance, since both PaO2 and SaO2 are affordable, widely used, and feasible indicators of hypoxemia’s severity. Pronounced hypoxemia upon admission has been confirmed to be a significant risk factor for COVID-19 progression and lethal outcome [5,7,42]. The combined effects of capillary damage associated with COVID-19 affect tissue oxygenation in key organs. A vicious cycle ensues, as inflammation and hypoxia cause capillary function to deteriorate, which accelerates hypoxia-induced inflammation, oxidative stress, tissue damage, and ARDS [43].
With regard to laboratory analysis upon admission, patients with and without ARDS differed in several parameters. Firstly, patients who developed ARDS had significantly higher values of inflammatory biomarkers upon admission, including IL-6, CRP, and LDH. Furthermore, IL-6 > 59.75 pg/mL upon admission was an independent predictor of ARDS with an aOR of 4.089. A pronounced inflammatory response is considered to be one of the keystones of COVID-19 progression, and elevated inflammatory biomarkers are one of the most commonly advocated predictors of ICU admission and death [10,44,45]. Secondly, patients significantly differed in blood cell count. Clinical observation of lymphopenia has been apparent since the start of the COVID-19 pandemic and may be associated with worsening disease [46]. Our results are in accordance with the literature in that a low lymphocyte count in our cohort was singled out as an independent predictor of ARDS development (aOR 2.880).
Lastly, we singled out independent predictors of ARDS development and provided an ARDS risk assessment score relying on the parameters available upon hospital admission: y = 0.007 + (very high BF%) × 8.059 + (SaO2 < 87.5%) × 5.120 + (IL6 > 59.75 pg/mL) × 4.089 + (low lymphocyte count) × 2.880 + (female sex) × 2.290 + (age < 68.5 years) × 1.976. The model had a C-index of 0.885 (p < 0.001), indicating a strong predictive model. Due to its high specificity (87.7%) and negative predictive value (86.6%), this model could have clinical importance as a “rule-out” tool in helping physicians define patients with a high risk of developing ARDS. Selecting predictors of worse outcomes and understanding the mechanisms responsible for the clinical deterioration of hospitalized COVID-19 patients has been the focus of scientific research for the past 2 years. More than 30 different parameters can be linked with ARDS development in COVID-19, and several risk assessment scores can be found [5,6,7,8,9,10,11,47,48,49]. However, the selection of predictors, as well as their cutoff and aOR values, significantly varies across the literature. Additionally, proposed predictive models differ in terms of accuracy (C-index according to ROC analysis), sensitivity, and specificity, which can greatly influence their potential utilization. These variations can be explained by differences in the statistical and methodological approach, patient characteristics, availability of examined parameters, health system organization, SARS-CoV-2 variant predominance, etc. Therefore, no specific model should be generally accepted irrespective of geographical, sociodemographic, patient structure, hospital equipment, and other important specifics. Instead, such research tends to extend the data to understand the mechanisms of COVID-19 progression and to point out that, in different settings, physicians can select patients with a higher risk of disease progression and death. In addition, to the authors’ knowledge, this is the first registry to use admission BIA measurements in an ARDS prediction model. Despite their limitations, available and affordable BIA measurements gave the strongest independent predictor of ARDS development.
Our study had certain limitations. Firstly, active infection can potentially affect the BIA measurement, especially in the febrile state [50]. This limitation could, therefore, have been responsible for misinterpreting the body composition, in terms of a higher percentage of body fat. In order to minimize this effect, patients were measured within the first 48 h upon admission, in the absence of fever. Secondly, although BIA measurements provide satisfactory insight into total body fat and fat-free mass, this method has difficulties in distinguishing visceral from abdominal fat, for which computed tomography and magnetic resonance imaging remain the gold standard [35,51]. This could have been responsible for underestimating the individual impact of VF levels in our cohort. Lastly, the number of patients included may be insufficient for the generalization of the results. Therefore, we encourage fellow physicians and researchers to continue investigating BIA measurements in predicting COVID-19 outcome.
5. Conclusions
Obesity is an important risk factor for the clinical deterioration of hospitalized COVID-19 patients. Because of the insight into the total body and visceral fat, BIA measurements could be a useful and affordable tool in selecting COVID-19 patients with a high risk of developing ARDS.
Acknowledgments
The authors would like to thank all physicians, nurses, and other team members of the University Clinical Center Kragujevac (Serbia) COVID-19 center for dedicated work over the past 3 years. The authors would like to thank Nela Đonović and Dragan Vasiljević for academic help in implementing and interpreting BIA measurements in clinical practice.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines11041199/s1, Table S1: Flowchart of patient enrolment; Table S2: Threshold values for continuous variables, according to the ROC analysis or laboratory reference lines.
Author Contributions
Conceptualization, Đ.S. and M.P. (Marina Petrović); methodology, N.Z. and I.Č.; validation, V.Z. and M.P. (Marina Petrović); formal analysis, V.S.; investigation, I.S., M.P. (Marija Popović), T.N., M.M. and M.P. (Mina Poskurica); resources, V.Z. and M.P. (Marina Petrović); writing—original draft preparation, T.N., M.M., M.P. (Mina Poskurica) and M.M.; writing—review and editing, N.Ć. and Đ.S.; supervision, V.Z. and I.Č.; project administration, D.T., A.D. and M.P. (Mina Poskurica). All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of University Clinical Center Kragujevac, Serbia (protocol number: 01/21/397; date of approval: 19 August 2021).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The data presented in this study are available on request from the corresponding author.
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This research received no external funding.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
References
- 1.Rahman S., Montero M.T.V., Rowe K., Kirton R., Kunik F., Jr. Epidemiology, pathogenesis, clinical presentations, diagnosis and treatment of COVID-19: A review of current evidence. Expert Rev. Clin. Pharmacol. 2021;14:601–621. doi: 10.1080/17512433.2021.1902303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Knoll R., Schultze J.L., Schulte-Schrepping J. Monocytes and Macrophages in COVID-19. Front. Immunol. 2021;12:720109. doi: 10.3389/fimmu.2021.720109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., Zhang L., Fan G., Xu J., Gu X., et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497–506. doi: 10.1016/S0140-6736(20)30183-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Zhou F., Yu T., Du R., Fan G., Liu Y., Liu Z., Xiang J., Wang Y., Song B., Gu X., et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet. 2020;395:1054–1062. doi: 10.1016/S0140-6736(20)30566-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Gallo Marin B., Aghagoli G., Lavine K., Yang L., Siff E.J., Chiang S.S., Salazar-Mather T.P., Dumenco L., Savaria M.C., Aung S.N., et al. Predictors of COVID-19 severity: A literature review. Rev. Med. Virol. 2021;31:1–10. doi: 10.1002/rmv.2146. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Masvekar R.R., Kosa P., Jin K., Dobbs K., Stack M.A., Castagnoli R., Quaresima V., Su H.C., Imberti L., Notarangelo L.D., et al. Prognostic value of serum/plasma neurofilament light chain for COVID-19-associated mortality. Ann. Clin. Transl. Neurol. 2022;9:622–632. doi: 10.1002/acn3.51542. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Petrilli C.M., Jones S.A., Yang J., Rajagopalan H., O’Donnell L., Chernyak Y., Tobin K.A., Cerfolio R.J., Francois F., Horwitz L.I. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: Prospective cohort study. BMJ. 2020;369:m1966. doi: 10.1136/bmj.m1966. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Xu W., Sun N.N., Gao H.N., Chen Z.Y., Yang Y., Ju B., Tang L.L. Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning. Sci. Rep. 2021;11:2933. doi: 10.1038/s41598-021-82492-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Gujski M., Jankowski M., Rabczenko D., Gorynski P., Juszczyk G. The Prevalence of Acute Respiratory Distress Syndrome (ARDS) and Outcomes in Hospitalized Patients with COVID-19-A Study Based on Data from the Polish National Hospital Register. Viruses. 2022;14:76. doi: 10.3390/v14010076. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Mesas A.E., Cavero-Redondo I., Alvarez-Bueno C., Sarria Cabrera M.A., Maffei de Andrade S., Sequi-Dominguez I., Martinez-Vizcaino V. Predictors of in-hospital COVID-19 mortality: A comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions. PLoS ONE. 2020;15:e0241742. doi: 10.1371/journal.pone.0241742. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Statsenko Y., Al Zahmi F., Habuza T., Gorkom K.N., Zaki N. Prediction of COVID-19 severity using laboratory findings on admission: Informative values, thresholds, ML model performance. BMJ Open. 2021;11:e044500. doi: 10.1136/bmjopen-2020-044500. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Popkin B.M., Du S., Green W.D., Beck M.A., Algaith T., Herbst C.H., Alsukait R.F., Alluhidan M., Alazemi N., Shekar M. Individuals with obesity and COVID-19: A global perspective on the epidemiology and biological relationships. Obes. Rev. 2020;21:e13128. doi: 10.1111/obr.13128. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Pranata R., Lim M.A., Yonas E., Vania R., Lukito A.A., Siswanto B.B., Meyer M. Body mass index and outcome in patients with COVID-19: A dose-response meta-analysis. Diabetes Metab. 2021;47:101178. doi: 10.1016/j.diabet.2020.07.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Chu Y., Yang J., Shi J., Zhang P., Wang X. Obesity is associated with increased severity of disease in COVID-19 pneumonia: A systematic review and meta-analysis. Eur. J. Med. Res. 2020;25:64. doi: 10.1186/s40001-020-00464-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Yang J., Hu J., Zhu C. Obesity aggravates COVID-19: A systematic review and meta-analysis. J. Med. Virol. 2021;93:257–261. doi: 10.1002/jmv.26237. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Sattar N., McInnes I.B., McMurray J.J.V. Obesity Is a Risk Factor for Severe COVID-19 Infection: Multiple Potential Mechanisms. Circulation. 2020;142:4–6. doi: 10.1161/CIRCULATIONAHA.120.047659. [DOI] [PubMed] [Google Scholar]
- 17.Kwok S., Adam S., Ho J.H., Iqbal Z., Turkington P., Razvi S., Le Roux C.W., Soran H., Syed A.A. Obesity: A critical risk factor in the COVID-19 pandemic. Clin. Obes. 2020;10:e12403. doi: 10.1111/cob.12403. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Watanabe M., Caruso D., Tuccinardi D., Risi R., Zerunian M., Polici M., Pucciarelli F., Tarallo M., Strigari L., Manfrini S., et al. Visceral fat shows the strongest association with the need of intensive care in patients with COVID-19. Metabolism. 2020;111:154319. doi: 10.1016/j.metabol.2020.154319. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.World Health Organization . COVID-19 Clinical Management: Living Guidance. World Health Organization; Geneva, Switzerland: 2021. [Google Scholar]
- 20.Battaglini D., Fazzini B., Silva P.L., Cruz F.F., Ball L., Robba C., Rocco P.R.M., Pelosi P. Challenges in ARDS Definition, Management, and Identification of Effective Personalized Therapies. J. Clin. Med. 2023;12:1381. doi: 10.3390/jcm12041381. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Force A.D.T., Ranieri V.M., Rubenfeld G.D., Thompson B.T., Ferguson N.D., Caldwell E., Fan E., Camporota L., Slutsky A.S. Acute respiratory distress syndrome: The Berlin Definition. JAMA. 2012;307:2526–2533. doi: 10.1001/jama.2012.5669. [DOI] [PubMed] [Google Scholar]
- 22.Obesity: Preventing and Managing the Global Epidemic . World Health Organization Technical Report Series. Volume 894. World Health Organization; Geneva, Switzerland: 2000. Report of a WHO Consultation; p. 252. [PubMed] [Google Scholar]
- 23.Gallagher D., Heymsfield S.B., Heo M., Jebb S.A., Murgatroyd P.R., Sakamoto Y. Healthy percentage body fat ranges: An approach for developing guidelines based on body mass index. Am. J. Clin. Nutr. 2000;72:694–701. doi: 10.1093/ajcn/72.3.694. [DOI] [PubMed] [Google Scholar]
- 24.TANITA-Medical Product Guide. TANITA; Arlington Heights, IL, USA: 2021. [Google Scholar]
- 25.Dreher M., Kersten A., Bickenbach J., Balfanz P., Hartmann B., Cornelissen C., Daher A., Stohr R., Kleines M., Lemmen S.W., et al. The Characteristics of 50 Hospitalized COVID-19 Patients with and Without ARDS. Dtsch. Arztebl. Int. 2020;117:271–278. doi: 10.3238/arztebl.2020.0271. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Grujic V., Dragnic N., Radic I., Harhaji S., Susnjevic S. Overweight and obesity among adults in Serbia: Results from the National Health Survey. Eat. Weight Disord. 2010;15:e34–e42. doi: 10.1007/BF03325278. [DOI] [PubMed] [Google Scholar]
- 27.Fernandez Crespo S., Perez-Matute P., Iniguez Martinez M., Fernandez-Villa T., Dominguez-Garrido E., Oteo J.A., Marcos-Delgado A., Flores C., Riancho J.A., Rojas-Martinez A., et al. Severity of COVID-19 attributable to obesity according to BMI and CUN-BAE. Semergen. 2022;48:101840. doi: 10.1016/j.semerg.2022.101840. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Ogata H., Mori M., Jingushi Y., Matsuzaki H., Katahira K., Ishimatsu A., Enokizu-Ogawa A., Taguchi K., Moriwaki A., Yoshida M. Impact of visceral fat on the prognosis of coronavirus disease 2019: An observational cohort study. BMC Infect. Dis. 2021;21:1240. doi: 10.1186/s12879-021-06958-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Zhang X., Lewis A.M., Moley J.R., Brestoff J.R. A systematic review and meta-analysis of obesity and COVID-19 outcomes. Sci. Rep. 2021;11:7193. doi: 10.1038/s41598-021-86694-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Favre G., Legueult K., Pradier C., Raffaelli C., Ichai C., Iannelli A., Redheuil A., Lucidarme O., Esnault V. Visceral fat is associated to the severity of COVID-19. Metabolism. 2021;115:154440. doi: 10.1016/j.metabol.2020.154440. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Goehler A., Hsu T.H., Seiglie J.A., Siedner M.J., Lo J., Triant V., Hsu J., Foulkes A., Bassett I., Khorasani R., et al. Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction. Open Forum Infect. Dis. 2021;8:ofab275. doi: 10.1093/ofid/ofab275. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Bunnell K.M., Thaweethai T., Buckless C., Shinnick D.J., Torriani M., Foulkes A.S., Bredella M.A. Body composition predictors of outcome in patients with COVID-19. Int. J. Obes. 2021;45:2238–2243. doi: 10.1038/s41366-021-00907-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Nikolic M., Simovic S., Novkovic L., Jokovic V., Djokovic D., Muric N., Bazic Sretenovic D., Jovanovic J., Pantic K., Cekerevac I. Obesity and sleep apnea as a significant comorbidities in COVID-19—A case report. Obes. Res. Clin. Pract. 2021;15:281–284. doi: 10.1016/j.orcp.2021.04.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Chait A., den Hartigh L.J. Adipose Tissue Distribution, Inflammation and Its Metabolic Consequences, Including Diabetes and Cardiovascular Disease. Front. Cardiovasc. Med. 2020;7:22. doi: 10.3389/fcvm.2020.00022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Chaudry O., Grimm A., Friedberger A., Kemmler W., Uder M., Jakob F., Quick H.H., von Stengel S., Engelke K. Magnetic Resonance Imaging and Bioelectrical Impedance Analysis to Assess Visceral and Abdominal Adipose Tissue. Obesity. 2020;28:277–283. doi: 10.1002/oby.22712. [DOI] [PubMed] [Google Scholar]
- 36.Yang J., Zheng Y., Gou X., Pu K., Chen Z., Guo Q., Ji R., Wang H., Wang Y., Zhou Y. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: A systematic review and meta-analysis. Int. J. Infect. Dis. 2020;94:91–95. doi: 10.1016/j.ijid.2020.03.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Pijls B.G., Jolani S., Atherley A., Derckx R.T., Dijkstra J.I.R., Franssen G.H.L., Hendriks S., Richters A., Venemans-Jellema A., Zalpuri S., et al. Demographic risk factors for COVID-19 infection, severity, ICU admission and death: A meta-analysis of 59 studies. BMJ Open. 2021;11:e044640. doi: 10.1136/bmjopen-2020-044640. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Attaway A.H., Scheraga R.G., Bhimraj A., Biehl M., Hatipoglu U. Severe covid-19 pneumonia: Pathogenesis and clinical management. BMJ. 2021;372:n436. doi: 10.1136/bmj.n436. [DOI] [PubMed] [Google Scholar]
- 39.Sharma G., Volgman A.S., Michos E.D. Sex Differences in Mortality From COVID-19 Pandemic: Are Men Vulnerable and Women Protected? JACC Case Rep. 2020;2:1407–1410. doi: 10.1016/j.jaccas.2020.04.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Salah H.M., Mehta J.L. Hypothesis: Sex-Related Differences in ACE2 Activity May Contribute to Higher Mortality in Men Versus Women With COVID-19. J. Cardiovasc. Pharmacol. Ther. 2021;26:114–118. doi: 10.1177/1074248420967792. [DOI] [PubMed] [Google Scholar]
- 41.Jimenez-Solem E., Petersen T.S., Hansen C., Hansen C., Lioma C., Igel C., Boomsma W., Krause O., Lorenzen S., Selvan R., et al. Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients. Sci. Rep. 2021;11:3246. doi: 10.1038/s41598-021-81844-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Mejia F., Medina C., Cornejo E., Morello E., Vasquez S., Alave J., Schwalb A., Malaga G. Oxygen saturation as a predictor of mortality in hospitalized adult patients with COVID-19 in a public hospital in Lima, Peru. PLoS ONE. 2020;15:e0244171. doi: 10.1371/journal.pone.0244171. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Ostergaard L. SARS CoV-2 related microvascular damage and symptoms during and after COVID-19: Consequences of capillary transit-time changes, tissue hypoxia and inflammation. Physiol. Rep. 2021;9:e14726. doi: 10.14814/phy2.14726. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Del Valle D.M., Kim-Schulze S., Huang H.H., Beckmann N.D., Nirenberg S., Wang B., Lavin Y., Swartz T.H., Madduri D., Stock A., et al. An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat. Med. 2020;26:1636–1643. doi: 10.1038/s41591-020-1051-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Leisman D.E., Ronner L., Pinotti R., Taylor M.D., Sinha P., Calfee C.S., Hirayama A.V., Mastroiani F., Turtle C.J., Harhay M.O., et al. Cytokine elevation in severe and critical COVID-19: A rapid systematic review, meta-analysis, and comparison with other inflammatory syndromes. Lancet Respir. Med. 2020;8:1233–1244. doi: 10.1016/S2213-2600(20)30404-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Tan L., Wang Q., Zhang D., Ding J., Huang Q., Tang Y.Q., Wang Q., Miao H. Lymphopenia predicts disease severity of COVID-19: A descriptive and predictive study. Signal Transduct. Target Ther. 2020;5:33. doi: 10.1038/s41392-020-0148-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Sanchez-Ramirez D.C., Normand K., Zhaoyun Y., Torres-Castro R. Long-Term Impact of COVID-19: A Systematic Review of the Literature and Meta-Analysis. Biomedicines. 2021;9:900. doi: 10.3390/biomedicines9080900. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Pelosi P., Tonelli R., Torregiani C., Baratella E., Confalonieri M., Battaglini D., Marchioni A., Confalonieri P., Clini E., Salton F., et al. Different Methods to Improve the Monitoring of Noninvasive Respiratory Support of Patients with Severe Pneumonia/ARDS Due to COVID-19: An Update. J. Clin. Med. 2022;11:1704. doi: 10.3390/jcm11061704. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Ambrosino P., Lanzillo A., Maniscalco M. COVID-19 and Post-Acute COVID-19 Syndrome: From Pathophysiology to Novel Translational Applications. Biomedicines. 2021;10:47. doi: 10.3390/biomedicines10010047. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Marini E., Buffa R., Contreras M., Magris M., Hidalgo G., Sanchez W., Ortiz V., Urbaez M., Cabras S., Blaser M.J., et al. Effect of influenza-induced fever on human bioimpedance values. PLoS ONE. 2015;10:e0125301. doi: 10.1371/journal.pone.0125301. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Xu Z., Liu Y., Yan C., Yang R., Xu L., Guo Z., Yu A., Cheng X., Ma L., Hu C., et al. Measurement of visceral fat and abdominal obesity by single-frequency bioelectrical impedance and CT: A cross-sectional study. BMJ Open. 2021;11:e048221. doi: 10.1136/bmjopen-2020-048221. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data presented in this study are available on request from the corresponding author.