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1.
Int J Infect Dis ; 108: 282-288, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1351700

ABSTRACT

AIM: The aim of this study was to determine the usefulness of COVID-GRAM and CURB-65 scores as predictors of the severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Caucasian patients. METHODS: This was a retrospective observational study including all adults with SARS-CoV-2 infection admitted to Hospital Universitario Marqués de Valdecilla from February to May 2020. Patients were stratified according to COVID-GRAM and CURB-65 scores as being at low-medium or high risk of critical illness. Univariate analysis, multivariate logistic regression models, receiver operating characteristic curve, and area under the curve (AUC) were calculated. RESULTS: A total of 523 patients were included (51.8% male, 48.2% female; mean age 65.63 years (standard deviation 17.89 years)), of whom 110 (21%) presented a critical illness (intensive care unit admission 10.3%, 30-day mortality 13.8%). According to the COVID-GRAM score, 122 (23.33%) patients were classified as high risk; 197 (37.7%) presented a CURB-65 score ≥2. A significantly greater proportion of patients with critical illness had a high COVID-GRAM score (64.5% vs 30.5%; P < 0.001). The COVID-GRAM score emerged as an independent predictor of critical illness (odds ratio 9.40, 95% confidence interval 5.51-16.04; P < 0.001), with an AUC of 0.779. A high COVID-GRAM score showed an AUC of 0.88 for the prediction of 30-day mortality, while a CURB-65 ≥2 showed an AUC of 0.83. CONCLUSIONS: The COVID-GRAM score may be a useful tool for evaluating the risk of critical illness in Caucasian patients with SARS-CoV-2 infection. The CURB-65 score could be considered as an alternative.


Subject(s)
COVID-19 , Adult , Aged , Female , Humans , Male , Prognosis , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index
2.
Med Glas (Zenica) ; 18(2): 384-393, 2021 08 01.
Article in English | MEDLINE | ID: covidwho-1257270

ABSTRACT

Aim To identify laboratory tests for early detection and the development of more severe illness and death in COVID-19 hospitalized patients. Methods A prospective study was done on 66 hospitalized COVID-19 patients (males: 54.5%; mean age 70.1 ± 9.6 years) who were stratified into: moderate (n=36; 54.5%), severe (n=12; 18.2%), and critically ill (n=18; 27.3%). Besides clinical findings, a wide spectrum of laboratory parameters was monitored at admission and control during the first seven days of hospitalization and used to predict progression from non-severe to severe illness and to predict the final outcome. Results Critically ill patients showed a higher control value of white blood cell count, C-reactive protein, lactate dehydrogenase, ferritin, but lower lymphocyte count and O2 saturation. Patients with fatal outcome (23; 34.85%) showed a higher control value of neutrophil, lactate dehydrogenase, ferritin, and lower lymphocyte and O2 saturation. Progression from moderate to severe or critical illness was predicted by increasing lactate dehydrogenase (95% CI 0.5803 to 0.8397;p=0.003729), increase in ferritin (95% CI 0.5288 to 0.8221;p=0.03248), and by drop in O2 saturation (95% CI 0.5498 to 0.8179;p=0.01168). A fatal outcome was predicted by increase in ferritin (95% CI 0.5059 to 0.8195;p=0.04985), as well as by drop in O2 saturation (95% CI 0.5916 to 0.8803; p=0.001861). Conclusion Increase in ferritin, and drop in O2 saturation could be the most important prognostic parameters for the development of more severe clinical illness and death in COVID-19 hospitalized patients.


Subject(s)
COVID-19 , Aged , COVID-19/diagnosis , COVID-19/mortality , Female , Hospitalization , Humans , Leukocyte Count , Lymphocyte Count , Male , Middle Aged , Prospective Studies , Retrospective Studies , Severity of Illness Index
3.
Front Endocrinol (Lausanne) ; 12: 652639, 2021.
Article in English | MEDLINE | ID: covidwho-1231330

ABSTRACT

Obesity has been recognized as an independent risk factor for critical illness and major severity in subjects with coronavirus disease 2019 (COVID-19). The role of fat distribution, particularly visceral fat (often linked to metabolic abnormalities), is still unclear. The adipose tissue represents a direct source of cytokines responsible for the pathological modifications occurring within adipose tissue in obese subjects. Adipokines are a crucial connection between metabolism and immune system: their dysregulation in obesity contributes to chronic low-grade systemic inflammation and metabolic comorbidities. Therefore the increased amount of visceral fat can lead to a proinflammatory phenotypic shift. This review analyzes the interrelation between obesity and COVID-19 severity, as well as the cellular key players and molecular mechanisms implicated in adipose inflammation, investigating if adipose tissue can constitute a reservoir for viral spread, and contribute to immune activation and cytokines storm. Targeting the underlying molecular mechanisms might have therapeutic potential in the management of obesity-related complications in COVID-19 patients.


Subject(s)
COVID-19/complications , Obesity/complications , Abdominal Fat/pathology , Adipose Tissue/pathology , COVID-19/physiopathology , Humans , Inflammation/etiology , Obesity/physiopathology
4.
Infection ; 49(5): 1033-1038, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1220597

ABSTRACT

PURPOSE: Clinical scores to rapidly assess the severity illness of Coronavirus Disease 2019 (COVID-19) could be considered of help for clinicians. Recently, a specific score (named COVID-GRAM) for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection, based on a nationwide Chinese cohort, has been proposed. We routinely applied the National Early Warning Score 2 (NEWS2) to predict critical COVID-19. Aim of this study is to compare NEWS2 and COVID-GRAM score. METHODS: We retrospectively analysed data of 121 COVID-19 patients admitted in two Clinics of Infectious Diseases in the Umbria region, Italy. The primary outcome was critical COVID-19 illness defined as admission to the intensive care unit, invasive ventilation, or death. Accuracy of the scores was evaluated with the area under the receiver-operating characteristic curve (AUROC). Differences between scores were confirmed used Hanley-McNeil test. RESULTS: The NEWS2 AUROC curve measured 0.87 (standard error, SE 0.03; 95% CI 0.80-0.93; p < 0.0001). The COVID-GRAM score AUROC curve measured 0.77 (SE 0.04; 95% CI 0.68-0.85; p < 0.0001). Hanley-McNeil test showed that NEWS2 better predicted severe COVID-19 (Z = 2.03). CONCLUSIONS: The NEWS2 showed superior accuracy to COVID-GRAM score for prediction of critical COVID-19 illness.


Subject(s)
COVID-19 , Early Warning Score , Critical Illness , Humans , Retrospective Studies , SARS-CoV-2
5.
Int Immunopharmacol ; 97: 107685, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1188659

ABSTRACT

BACKGROUND: The 2019 Coronavirus (COVID-19) pandemic poses a huge threat internationally; however, the role of the host immune system in the pathogenesis of COVID-19 is not well understood. METHODS: Cytokine and chemokine levels and characterisation of immune cell subsets from 20 COVID-19 cases after hospital admission (17 critically ill and 3 severe patients) and 16 convalescent patients were determined using a multiplex immunoassay and flow cytometry, respectively. RESULTS: IP-10, MCP-1, MIG, IL-6, and IL-10 levels were significantly higher in acute severe/critically ill patients with COVID-19, whereas were normal in patients who had reached convalescence. CD8 T cells in severe and critically ill COVID-19 patients expressed high levels of cytotoxic granules (granzyme B and perforin)and was hyperactivated as evidenced by the high proportions of CD38. Furthermore, the cytotoxic potential of natural killer (NK) cells, and the frequencies of myeloid dendritic cells and plasmacytoid dendritic cells was reduced in patients with severe and critical COVID-19; however, these dysregulations were found to be restored in convalescent phases. CONCLUSION: Thus, elicitation of the hyperactive cytokine-mediated inflammatory response, dysregulation of CD8 T and NK cells, and deficiency of host myeloid and plasmacytoid DCs, may contribute to COVID-19 pathogenesis and provide insights into potential therapeutic targets and strategies.


Subject(s)
COVID-19/blood , COVID-19/immunology , Convalescence , Inflammation/etiology , ADP-ribosyl Cyclase 1/blood , Acute Disease , Adult , Aged , CD4-Positive T-Lymphocytes/immunology , CD8-Positive T-Lymphocytes/enzymology , CD8-Positive T-Lymphocytes/immunology , Chemokine CCL2/blood , Chemokine CXCL10/blood , Chemokine CXCL9/blood , Critical Illness , Cytokine Release Syndrome/blood , Cytokine Release Syndrome/immunology , Dendritic Cells/immunology , Female , Granzymes/metabolism , Humans , Interleukin-10/blood , Interleukin-6/blood , Killer Cells, Natural/enzymology , Killer Cells, Natural/immunology , Male , Membrane Glycoproteins/blood , Middle Aged , Perforin/metabolism
6.
Nat Rev Immunol ; 21(5): 319-329, 2021 05.
Article in English | MEDLINE | ID: covidwho-1171402

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a clinical syndrome caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Patients with severe disease show hyperactivation of the immune system, which can affect multiple organs besides the lungs. Here, we propose that SARS-CoV-2 infection induces a process known as immunothrombosis, in which activated neutrophils and monocytes interact with platelets and the coagulation cascade, leading to intravascular clot formation in small and larger vessels. Microthrombotic complications may contribute to acute respiratory distress syndrome (ARDS) and other organ dysfunctions. Therapeutic strategies aimed at reducing immunothrombosis may therefore be useful. Several antithrombotic and immunomodulating drugs have been proposed as candidates to treat patients with SARS-CoV-2 infection. The growing understanding of SARS-CoV-2 infection pathogenesis and how it contributes to critical illness and its complications may help to improve risk stratification and develop targeted therapies to reduce the acute and long-term consequences of this disease.


Subject(s)
COVID-19/immunology , COVID-19/pathology , Cytokine Release Syndrome/pathology , Venous Thrombosis/immunology , Venous Thrombosis/pathology , Blood Coagulation/immunology , Blood Platelets/immunology , Critical Illness/therapy , Cytokine Release Syndrome/immunology , Endothelium, Vascular/pathology , Fibrinolytic Agents/therapeutic use , Humans , Immunity, Innate/immunology , Lung/blood supply , Lung/pathology , Lung/virology , Monocytes/immunology , Neutrophils/immunology , SARS-CoV-2/immunology , SARS-CoV-2/pathogenicity , Venous Thrombosis/prevention & control
7.
Kidney Int Rep ; 6(4): 905-915, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1169160

ABSTRACT

INTRODUCTION: Acute kidney injury (AKI) is an important complication in COVID-19, but its precise etiology has not fully been elucidated. Insights into AKI mechanisms may be provided by analyzing the temporal associations of clinical parameters reflecting disease processes and AKI development. METHODS: We performed an observational cohort study of 223 consecutive COVID-19 patients treated at 3 sites of a tertiary care referral center to describe the evolvement of severe AKI (Kidney Disease: Improving Global Outcomes stage 3) and identify conditions promoting its development. Descriptive statistics and explanatory multivariable Cox regression modeling with clinical parameters as time-varying covariates were used to identify risk factors of severe AKI. RESULTS: Severe AKI developed in 70 of 223 patients (31%) with COVID-19, of which 95.7% required kidney replacement therapy. Patients with severe AKI were older, predominantly male, had more comorbidities, and displayed excess mortality. Severe AKI occurred exclusively in intensive care unit patients, and 97.3% of the patients developing severe AKI had respiratory failure. Mechanical ventilation, vasopressor therapy, and inflammatory markers (serum procalcitonin levels and leucocyte count) were independent time-varying risk factors of severe AKI. Increasing inflammatory markers displayed a close temporal association with the development of severe AKI. Sensitivity analysis on risk factors of AKI stage 2 and 3 combined confirmed these findings. CONCLUSION: Severe AKI in COVID-19 was tightly coupled with critical illness and systemic inflammation and was not observed in milder disease courses. These findings suggest that traditional systemic AKI mechanisms rather than kidney-specific processes contribute to severe AKI in COVID-19.

8.
Clin Neurophysiol ; 132(7): 1733-1740, 2021 07.
Article in English | MEDLINE | ID: covidwho-1163547

ABSTRACT

OBJECTIVE: The aim was to characterize the electrophysiological features and plasma biomarkers of critical illness polyneuropathy (CIN) and myopathy (CIM) in coronavirus disease 2019 (COVID-19) patients with intensive care unit acquired weakness (ICUAW). METHODS: An observational ICU cohort study including adult patients admitted to the ICU at Uppsala University Hospital, Uppsala, Sweden, from March 13th to June 8th 2020. We compared the clinical, electrophysiological and plasma biomarker data between COVID-19 patients who developed CIN/CIM and those who did not. Electrophysiological characteristics were also compared between COVID-19 and non-COVID-19 ICU patients. RESULTS: 111 COVID-19 patients were included, 11 of whom developed CIN/CIM. Patients with CIN/CIM had more severe illness; longer ICU stay, more thromboembolic events and were more frequently treated with invasive ventilation for longer than 2 weeks. In particular CIN was more frequent among COVID-19 patients with ICUAW (50%) compared with a non-COVID-19 cohort (0%, p = 0.008). Neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAp) levels were higher in the CIN/CIM group compared with those that did not develop CIN/CIM (both p = 0.001) and correlated with nerve amplitudes. CONCLUSIONS: CIN/CIM was more prevalent among COVID-19 ICU patients with severe illness. SIGNIFICANCE: COVID-19 patients who later developed CIN/CIM had significantly higher NfL and GFAp in the early phase of ICU care, suggesting their potential as predictive biomarkers for CIN/CIM.


Subject(s)
COVID-19/complications , Muscular Diseases/etiology , Polyneuropathies/etiology , Aged , Biomarkers/blood , COVID-19/physiopathology , Critical Illness , Female , Humans , Intensive Care Units , Length of Stay/statistics & numerical data , Male , Middle Aged , Muscle Weakness/etiology , Muscular Diseases/blood , Muscular Diseases/physiopathology , Polyneuropathies/blood , Polyneuropathies/physiopathology , Prospective Studies , Respiration, Artificial/statistics & numerical data , Thromboembolism/etiology
9.
Lancet Digit Health ; 3(5): e286-e294, 2021 05.
Article in English | MEDLINE | ID: covidwho-1152741

ABSTRACT

BACKGROUND: Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. METHODS: We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. FINDINGS: 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796-0·828) to 0·846 (0·815-0·852; p<0·0001) on internal testing and 0·731 (0·712-0·738) to 0·792 (0·780-0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755-0·786) to 0·805 (0·800-0·820; p<0·0001) on internal testing and 0·707 (0·695-0·729) to 0·752 (0·739-0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001). INTERPRETATION: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19. FUNDING: Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.


Subject(s)
Artificial Intelligence , COVID-19/physiopathology , Prognosis , Radiography, Thoracic , Adult , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index , Tomography, X-Ray Computed , United States , Young Adult
10.
Medicine (Baltimore) ; 100(12): e25083, 2021 Mar 26.
Article in English | MEDLINE | ID: covidwho-1150005

ABSTRACT

ABSTRACT: The purpose of this study was to investigate the predictive value of combined clinical and imaging features, compared with the clinical or radiological risk factors only. Moreover, the expected results aimed to improve the identification of severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) patients who may have critical outcomes.This retrospective study included laboratory-confirmed SARS-COV-2 cases between January 18, 2020, and February 16, 2020. The patients were divided into 2 groups with noncritical illness and critical illness regarding severity status within the hospitalization. Univariable and multivariable logistic regression models were used to explore the risk factors associated with clinical and radiological outcomes in patients with SARS-COV-2. The ROC curves were performed to compare the prediction performance of different factors.A total of 180 adult patients in this study included 20 critical patients and 160 noncritical patients. In univariate logistic regression analysis, 15 risk factors were significantly associated with critical outcomes. Of importance, C-reactive protein (1.051, 95% confidence interval 1.024-1.078), D-dimer (1.911, 95% CI, 1.050-3.478), and CT score (1.29, 95% CI, 1.053-1.529) on admission were independent risk factors in multivariate analysis. The combined model achieved a better performance in disease severity prediction (P = .05).CRP, D-dimer, and CT score on admission were independent risk factors for critical illness in adults with SARS-COV-2. The combined clinical and radiological model achieved better predictive performance than clinical or radiological factors alone.


Subject(s)
COVID-19/epidemiology , COVID-19/physiopathology , Diagnostic Techniques and Procedures/statistics & numerical data , Adult , Aged , C-Reactive Protein/analysis , Female , Fibrin Fibrinogen Degradation Products/analysis , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , ROC Curve , Retrospective Studies , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Tomography, X-Ray Computed
11.
Biomarkers ; 26(5): 417-424, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1146879

ABSTRACT

BACKGROUND: About 20% of ICU patients with COVID-19 require renal replacement therapy (RRT). Mid-regional pro-adrenomedullin (MR-proADM) might be used for risk assessment. This study investigates MR-proADM for RRT prediction in ICU patients with COVID-19. METHODS: We analysed data of consecutive patients with COVID-19, requiring ICU admission at a university hospital in Germany between March and September 2020. Clinical characteristics, details on AKI, and RRT were assessed. MR-proADM was measured on admission. RESULTS: 64 patients were included (49 (77%) males). Median age was 62.5y (54-73). 47 (73%) patients were ventilated and 50 (78%) needed vasopressors. 25 (39%) patients had severe ARDS, and 10 patients needed veno-venous extracorporeal membrane oxygenation. 29 (45%) patients required RRT; median time from admission to RRT start was 2 (1-9) days. MR-proADM on admission was higher in the RRT group (2.491 vs. 1.23 nmol/l; p = 0.002) and showed the highest correlation with renalSOFA. ROC curve analysis showed that MR-proADM predicts RRT with an AUC of 0.69 (95% CI: 0.543-0.828; p = 0.019). In multivariable logistic regression MR-proADM was an independent predictor (OR: 3.813, 95% CI 1.110-13.102, p<0.05) for RRT requirement. CONCLUSION: AKI requiring RRT is frequent in ICU patients with COVID-19. MR-proADM on admission was able to predict RRT requirement, which may be of interest for risk stratification and management.


Subject(s)
Acute Kidney Injury/therapy , Adrenomedullin/metabolism , COVID-19/prevention & control , Critical Illness/therapy , Protein Precursors/metabolism , Renal Replacement Therapy/methods , SARS-CoV-2/isolation & purification , Acute Kidney Injury/diagnosis , Aged , Biomarkers/metabolism , COVID-19/virology , Cohort Studies , Female , Germany , Hospitals, University , Humans , Intensive Care Units , Male , Middle Aged , Predictive Value of Tests , ROC Curve , SARS-CoV-2/physiology
12.
Int J Med Sci ; 18(8): 1768-1777, 2021.
Article in English | MEDLINE | ID: covidwho-1145693

ABSTRACT

Aim: In other respiratory infectious diseases, obesity may be associated with a poor outcome. For coronavirus disease 2019 (COVID-19), the association between obesity and severity or prognosis requires further analysis. Methods: This was a retrospective, single-center study. Hospitalized patients were recruited in Renmin Hospital of Wuhan University from January 2, 2020 to February 20, 2020. The data of body mass index (BMI) was obtained from follow-up of surviving patients. According to BMI, normal weight was defined as 18.5-23.9 kg/m2, overweight as 24.0-27.9 kg/m2 and obesity as > 28.0 kg/m2. Results: A total of 463 patients were enrolled, of which 242 (52.3%) patients were in the normal weight group; 179 (38.7%) were in the overweight group; and 42 (9.1%) were in the obesity group. Compared to the normal group, obese patients were more likely to have a higher heart rate; lower finger oxygen saturation; higher levels of white blood cells, neutrophil counts, basophil counts, intravenous glucose, triacylglycerol, uric acid, alanine aminotransferase, creatine kinase-MB, CD19+ cell counts and percentage; and lower levels of monocyte percentage, high density lipoprotein and CD3+ cell percentage. In addition, the proportions of hypertension (21.5% vs. 42.6%) and severe+critical illness (47.8 vs. 81.0 %) were significantly higher in the obesity group than those in normal group. However, no significant differences were observed between the normal and obesity groups in critical illness, organ damage and defined endpoint (mechanical ventilation or intensive care unit). Multiple logistic regression showed that obesity increased the risk of developing severe+critical illness (Odd ratio 3.586, 95% CI 1.550-8.298, P=0.003) in patients with COVID-19, and did not affect the risk of critical illness, organ damage and endpoints. Overweight did not affect the risk of severity, organ damage or endpoint in patients with COVID-19. Conclusion: Obesity may be a risk factor for developing severity in patients with COVID-19.


Subject(s)
COVID-19/complications , Obesity/complications , Aged , CD4 Lymphocyte Count , COVID-19/blood , COVID-19/diagnostic imaging , Female , Humans , Male , Middle Aged , Obesity/blood , Obesity/diagnostic imaging , Radiography, Thoracic , Retrospective Studies , Tomography, X-Ray Computed
13.
Endocr Pract ; 27(5): 484-493, 2021 May.
Article in English | MEDLINE | ID: covidwho-1141759

ABSTRACT

Vitamin D is known not only for its importance for bone health but also for its biologic activities on many other organ systems. This is due to the presence of the vitamin D receptor in various types of cells and tissues, including the skin, skeletal muscle, adipose tissue, endocrine pancreas, immune cells, and blood vessels. Experimental studies have shown that vitamin D exerts several actions that are thought to be protective against coronavirus disease (COVID-19) infectivity and severity. These include the immunomodulatory effects on the innate and adaptive immune systems, the regulatory effects on the renin-angiotensin-aldosterone-system in the kidneys and the lungs, and the protective effects against endothelial dysfunction and thrombosis. Prior to the COVID-19 pandemic, studies have shown that vitamin D supplementation is beneficial in protecting against risk of acquiring acute respiratory viral infection and may improve outcomes in sepsis and critically ill patients. There are a growing number of data connecting COVID-19 infectivity and severity with vitamin D status, suggesting a potential benefit of vitamin D supplementation for primary prevention or as an adjunctive treatment of COVID-19. Although the results from most ongoing randomized clinical trials aiming to prove the benefit of vitamin D supplementation for these purposes are still pending, there is no downside to increasing vitamin D intake and having sensible sunlight exposure to maintain serum 25-hydroxyvitamin D at a level of least 30 ng/mL (75 nmol/L) and preferably 40 to 60 ng/mL (100-150 nmol/L) to minimize the risk of COVID-19 infection and its severity.


Subject(s)
COVID-19 , Pandemics , Humans , Pandemics/prevention & control , SARS-CoV-2 , Vitamin D , Vitamins
14.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue ; 33(2): 145-149, 2021 Feb.
Article in Chinese | MEDLINE | ID: covidwho-1138768

ABSTRACT

OBJECTIVE: To explore the correlation between early inflammation indicators and the severity of coronavirus disease 2019 (COVID-19). METHODS: A retrospective study was conducted. Patients with COVID-19 admitted to Wenzhou Central Hospital from January 17 to February 14, 2020 were enrolled. The general information, chest CT before admission, the first laboratory parameters and chest CT within 24 hours after admission were collected. Patients were followed up for 30 days after the first onset of dyspnea or pulmonary imaging showed that the lesions progressed more than 50% within 24 to 48 hours (according to the criteria for severe cases) as the study endpoint. According to the endpoint, the patients were divided into two groups: mild type/common type group and severe/critical group, and the differences in general information and inflammation index of the two groups were compared. Logistic regression was used to analyze the inflammation index and the severity of COVID-19. Receiver operating characteristic (ROC) curve was draw to evaluate the predictive value of early inflammation indicators for severe/critical in patients with COVID-19. RESULTS: A total of 140 patients with COVID-19 were included, 74 males and 66 females; the average age was (45±14) years old; 6 cases (4.3%) of mild type, 107 cases (76.4%) of common type, and 22 cases (15.7%) of severe type, 5 cases (3.6%) were critical. There were significantly differences in ages (years old: 43±13 vs. 57±13), the proportion of patients with one chronic disease (17.7% vs. 55.6%), C-reactive protein [CRP (mg/L): 7.3 (2.3, 21.0) vs. 40.1 (18.8, 62.6)], lymphocyte count [LYM (×109/L): 1.3 (1.0, 1.8) vs. 0.8 (0.7, 1.1)], the neutrophil/lymphocyte ratio [NLR: 2.1 (1.6, 3.0) vs. 3.1 (2.2, 8.8)] and multilobularinltration, hypo-lymphocytosis, bacterial coinfection, smoking history, hyper-tension and age [MuLBSTA score: 5.0 (3.0, 5.0) vs. 5.0 (5.0, 7.0)] between mild/common group and severe/critical group (all P < 0.05). Univariate Logistic regression analysis showed that CRP, NLR, MuLBSTA score, age, and whether chronic diseases were associated with the severity of COVID-19 [odds ratio (OR) and 95% confidence interval (95%CI) were 1.037 (1.020-1.055), 1.374 (1.123-1.680), 1.574 (1.296-1.911), 1.082 (1.042-1.125), 6.393 (2.551-16.023), respectively, all P < 0.01]. Further multivariate Logistic regression analysis showed that CRP and MuLBSTA score were risk factors for the development of COVID-19 to severe/critical cases [OR and 95%CI were 1.024 (1.002-1.048) and 1.321 (1.027-1.699) respectively, both P < 0.05]. ROC curve analysis showed that the area under the curve for CRP and MuLBSTA score to predict severe/critical cases were both 0.818, and the best cut-off points were 27.4 mg/L and 6.0 points, respectively. CONCLUSIONS: CRP and MuLBSTA score are related to the severity of COVID-19, and may have good independent predictive ability for the development of severe/critical illness.


Subject(s)
COVID-19 , Adult , Female , Humans , Inflammation , Male , Middle Aged , Prognosis , ROC Curve , Retrospective Studies , SARS-CoV-2
15.
J Thorac Dis ; 13(2): 1215-1229, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1134641

ABSTRACT

BACKGROUND: To develop machine learning classifiers at admission for predicting which patients with coronavirus disease 2019 (COVID-19) who will progress to critical illness. METHODS: A total of 158 patients with laboratory-confirmed COVID-19 admitted to three designated hospitals between December 31, 2019 and March 31, 2020 were retrospectively collected. 27 clinical and laboratory variables of COVID-19 patients were collected from the medical records. A total of 201 quantitative CT features of COVID-19 pneumonia were extracted by using an artificial intelligence software. The critically ill cases were defined according to the COVID-19 guidelines. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select the predictors of critical illness from clinical and radiological features, respectively. Accordingly, we developed clinical and radiological models using the following machine learning classifiers, including naive bayes (NB), linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), K-nearest neighbor (KNN), kernel support vector machine (k-SVM), and back propagation neural networks (BPNN). The combined model incorporating the selected clinical and radiological factors was also developed using the eight above-mentioned classifiers. The predictive efficiency of the models is validated using a 5-fold cross-validation method. The performance of the models was compared by the area under the receiver operating characteristic curve (AUC). RESULTS: The mean age of all patients was 58.9±13.9 years and 89 (56.3%) were males. 35 (22.2%) patients deteriorated to critical illness. After LASSO analysis, four clinical features including lymphocyte percentage, lactic dehydrogenase, neutrophil count, and D-dimer and four quantitative CT features were selected. The XGBoost-based clinical model yielded the highest AUC of 0.960 [95% confidence interval (CI): 0.913-1.000)]. The XGBoost-based radiological model achieved an AUC of 0.890 (95% CI: 0.757-1.000). However, the predictive efficacy of XGBoost-based combined model was very close to that of the XGBoost-based clinical model, with an AUC of 0.955 (95% CI: 0.906-1.000). CONCLUSIONS: A XGBoost-based based clinical model on admission might be used as an effective tool to identify patients at high risk of critical illness.

17.
Adv Exp Med Biol ; 1321: 97-107, 2021.
Article in English | MEDLINE | ID: covidwho-1114239

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has resulted in worldwide research efforts to recognize people at greatest risk of developing critical illness and dying. Growing numbers of reports have connected obesity to more severe COVID-19 illness and death. Although the exact mechanism by which obesity may lead to severe COVID-19 outcomes has not yet been determined, the mechanisms appear to be multifactorial. These include mechanical changes of the airways and lung parenchyma, systemic and airway inflammation, and general metabolic dysfunction that adversely affect pulmonary function and/or response to treatment. As COVID-19 continues to spread worldwide, clinicians should carefully monitor and manage obese patients for prompt and targeted treatment.


Subject(s)
COVID-19 , Humans , Lung , Obesity/complications , Obesity/epidemiology , Pandemics , SARS-CoV-2
18.
Blood Adv ; 5(5): 1164-1177, 2021 03 09.
Article in English | MEDLINE | ID: covidwho-1105683

ABSTRACT

Pathologic immune hyperactivation is emerging as a key feature of critical illness in COVID-19, but the mechanisms involved remain poorly understood. We carried out proteomic profiling of plasma from cross-sectional and longitudinal cohorts of hospitalized patients with COVID-19 and analyzed clinical data from our health system database of more than 3300 patients. Using a machine learning algorithm, we identified a prominent signature of neutrophil activation, including resistin, lipocalin-2, hepatocyte growth factor, interleukin-8, and granulocyte colony-stimulating factor, which were the strongest predictors of critical illness. Evidence of neutrophil activation was present on the first day of hospitalization in patients who would only later require transfer to the intensive care unit, thus preceding the onset of critical illness and predicting increased mortality. In the health system database, early elevations in developing and mature neutrophil counts also predicted higher mortality rates. Altogether, these data suggest a central role for neutrophil activation in the pathogenesis of severe COVID-19 and identify molecular markers that distinguish patients at risk of future clinical decompensation.


Subject(s)
COVID-19/immunology , Neutrophil Activation , Adult , Aged , Aged, 80 and over , Biomarkers/blood , COVID-19/blood , COVID-19/mortality , Critical Illness/epidemiology , Critical Illness/mortality , Cross-Sectional Studies , Female , Hospitalization , Humans , Machine Learning , Male , Middle Aged , Prognosis , SARS-CoV-2/immunology , Severity of Illness Index
19.
Int J Med Sci ; 18(6): 1474-1483, 2021.
Article in English | MEDLINE | ID: covidwho-1089156

ABSTRACT

Background: For coronavirus disease 2019 (COVID-19), early identification of patients with serious symptoms at risk of critical illness and death is important for personalized treatment and balancing medical resources. Methods: Demographics, clinical characteristics, and laboratory tests data from 726 patients with serious COVID-19 at Tongji Hospital (Wuhan, China) were analyzed. Patients were classified into critical group (n = 174) and severe group (n= 552), the critical group was sub-divided into survivors (n = 47) and non-survivors (n = 127). Results: Multivariable analyses revealed the risk factors associated with critical illness in serious patients were: Advanced age, high respiratory rate (RR), high lactate dehydrogenase (LDH) level, high hypersensitive cardiac troponin I (hs-cTnI) level, and thrombocytopenia on admission. High hs-cTnI level was the independent risk factor of mortality among critically ill patients in the unadjusted and adjusted models. ROC curves demonstrated that hs-cTnI and LDH were predictive factors for critical illness in patients with serious COVID-19 whereas procalcitonin and D-Dimer with hs-cTnI and LDH were predictive parameters in mortality risk. Conclusions: Advanced age, high RR, LDH, hs-cTnI, and thrombocytopenia, constitute risk factors for critical illness among patients with serious COVID-19, and the hs-cTnI level helps predict fatal outcomes in critically ill patients.


Subject(s)
COVID-19/metabolism , COVID-19/virology , SARS-CoV-2/pathogenicity , Troponin I/metabolism , Aged , COVID-19/pathology , Critical Illness , Humans , L-Lactate Dehydrogenase/genetics , L-Lactate Dehydrogenase/metabolism , Middle Aged , Prognosis , Retrospective Studies
20.
Nutr Metab Cardiovasc Dis ; 31(3): 745-755, 2021 03 10.
Article in English | MEDLINE | ID: covidwho-1065508

ABSTRACT

AIMS: As reported, hypertension may play an important role in adverse outcomes of coronavirus disease-2019 (COVID-19), but it still had many confounding factors. The aim of this study was to explore whether hypertension is an independent risk factor for critical COVID-19 and mortality. DATA SYNTHESIS: The Medline, PubMed, Embase, and Web of Science databases were systematically searched until November 2020. Combined odds ratios (ORs) with their 95% confidence interval (CIs) were calculated by using random-effect models, and the effect of covariates was analyzed using the subgroup analysis and meta-regression analysis. A total of 24 observational studies with 99,918 COVID-19 patients were included in the meta-analysis. The proportions of hypertension in critical COVID-19 were 37% (95% CI: 0.27 -0.47) when compared with 18% (95% CI: 0.14 -0.23) of noncritical COVID-19 patients, in those who died were 46% (95%CI: 0.37 -0.55) when compared with 22% (95% CI: 0.16 -0.28) of survivors. Pooled results based on the adjusted OR showed that patients with hypertension had a 1.82-fold higher risk for critical COVID-19 (aOR: 1.82; 95% CI: 1.19 - 2.77; P = 0.005) and a 2.17-fold higher risk for COVID-19 mortality (aOR: 2.17; 95% CI: 1.67 - 2.82; P < 0.001). Subgroup analysis results showed that male patients had a higher risk of developing to the critical condition than female patients (OR: 3.04; 95%CI: 2.06 - 4.49; P < 0.001) and age >60 years was associated with a significantly increased risk of COVID-19 mortality (OR: 3.12; 95% CI: 1.93 - 5.05; P < 0.001). Meta-regression analysis results also showed that age (Coef. = 2.3×10-2, P = 0.048) had a significant influence on the association between hypertension and COVID-19 mortality. CONCLUSIONS: Evidence from this meta-analysis suggested that hypertension was independently associated with a significantly increased risk of critical COVID-19 and inhospital mortality of COVID-19.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , Hypertension/epidemiology , Adult , Age Factors , Aged , Aged, 80 and over , Critical Illness , Female , Hospital Mortality , Humans , Hypertension/mortality , Male , Middle Aged , Risk Factors , SARS-CoV-2 , Severity of Illness Index
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