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1.
Mayo Clin Proc Innov Qual Outcomes ; 2021 Apr 12.
Article in English | MEDLINE | ID: covidwho-1804780

ABSTRACT

OBJECTIVE: To conduct a comprehensive evaluation of coagulation profiles - via traditional and whole blood thromboelastometry tests - in COVID-19 positive vs. COVID-19 negative patients admitted to medical wards for acute pneumonia. PATIENTS AND METHODS: We enrolled all consecutive patients admitted to Internal Medicine wards of Padova University Hospital between 7 March and 30 April 2020 for COVID-19-related pneumonia (cases) vs. non-COVID-19 pneumonia (controls). A group of healthy subjects acted as baseline for thromboelastometry parameters. RESULTS: Fifty-six cases (mean age 64±15 yrs, M/F 37/19) and 56 controls (mean age 76±11 yrs, M/F 35/21) were enrolled. Cases and controls showed markedly hypercoagulable thromboelastometry profiles vs. healthy subjects, mainly characterized by a significantly shorter propagation phase of coagulation (Clot Formation Time, CFT) and significantly increased maximum clot firmness (MCF) (p <0.001 in all comparisons). COVID-19 patients with pneumonia had significantly shorter CFT and higher MCF (p <0.01 and <0.05, respectively in all comparisons) vs. controls. CONCLUSION: Patients admitted to internal medicine wards for COVID-19 pneumonia presented a markedly prothrombotic state, which seems peculiar to COVID-19 rather than pneumonia itself.

2.
Brain Hemorrhages ; 2(2): 76-83, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1597171

ABSTRACT

COVID-19 patients have presented with a wide range of neurological disorders, among which stroke is the most devastating. We have reviewed current studies, case series, and case reports with a focus on COVID-19 patients complicated with stroke, and presented the current understanding of stroke in this patient population. As evidenced by increased D-dimer, fibrinogen, factor VIII and von Willebrand factor, SARS-CoV-2 infection induces coagulopathy, disrupts endothelial function, and promotes hypercoagulative state. Collectively, it predisposes patients to cerebrovascular events. Additionally, due to the unprecedented strain on the healthcare system, stroke care has been inevitably compromised. The underlying mechanism between COVID-19 and stroke warrants further study, so does the development of an effective therapeutic or preventive intervention.

3.
Metabol Open ; 11: 100101, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1275587

ABSTRACT

The recognition of the rare but serious and potentially lethal complication of vaccine induced thrombotic thrombocytopenia (VITT) raised concerns regarding the safety of COVID-19 vaccines and led to the reconsideration of vaccination strategies in many countries. Following the description of VITT among recipients of adenoviral vector ChAdOx1 vaccine, a review of similar cases after Ad26.COV2·S vaccination gave rise to the question whether this entity may constitute a potential class effect of all adenoviral vector vaccines. Most cases are females, typically younger than 60 years who present shortly (range: 5-30 days) following vaccination with thrombocytopenia and thrombotic manifestations, occasionally in multiple sites. Following initial incertitude, concrete recommendations to guide the diagnosis (clinical suspicion, initial laboratory screening, PF4-polyanion-antibody ELISA) and management of VITT (non-heparin anticoagulants, corticosteroids, intravenous immunoglobulin) have been issued. The mechanisms behind this rare syndrome are currently a subject of active research and include the following: 1) production of PF4-polyanion autoantibodies; 2) adenoviral vector entry in megacaryocytes and subsequent expression of spike protein on platelet surface; 3) direct platelet and endothelial cell binding and activation by the adenoviral vector; 4) activation of endothelial and inflammatory cells by the PF4-polyanion autoantibodies; 5) the presence of an inflammatory co-signal; and 6) the abundance of circulating soluble spike protein variants following vaccination. Apart from the analysis of potential underlying mechanisms, this review aims to synopsize the clinical and epidemiologic features of VITT, to present the current evidence-based recommendations on diagnostic and therapeutic work-up of VITT and to discuss new dilemmas and perspectives that emerged after the description of this entity.

4.
Comput Struct Biotechnol J ; 19: 3640-3649, 2021.
Article in English | MEDLINE | ID: covidwho-1272373

ABSTRACT

Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort, resulting in a data matrix containing 3,065 readings for 124 types of measurements over 52 days. A machine learning model was established to predict the disease progression based on the cohort consisting of training, validation, and internal test sets. A panel of eleven routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 98% in the discovery set. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.70, 0.99, 0.93, and 0.93, respectively. Our model captured predictive dynamics of lactate dehydrogenase (LDH) and creatine kinase (CK) while their levels were in the normal range. This model is accessible at https://www.guomics.com/covidAI/ for research purpose.

5.
Comput Struct Biotechnol J ; 19: 2833-2850, 2021.
Article in English | MEDLINE | ID: covidwho-1240272

ABSTRACT

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

6.
Comput Struct Biotechnol J ; 19: 1863-1873, 2021.
Article in English | MEDLINE | ID: covidwho-1171610

ABSTRACT

Metabolic profiling in COVID-19 patients has been associated with disease severity, but there is no report on sex-specific metabolic changes in discharged survivors. Herein we used an integrated approach of LC-MS-and GC-MS-based untargeted metabolomics to analyze plasma metabolic characteristics in men and women with non-severe COVID-19 at both acute period and 30 days after discharge. The results demonstrate that metabolic alterations in plasma of COVID-19 patients during the recovery and rehabilitation process were presented in a sex specific manner. Overall, the levels of most metabolites were increased in COVID-19 patients after the cure relative to acute period. The major plasma metabolic changes were identified including fatty acids in men and glycerophosphocholines and carbohydrates in women. In addition, we found that women had shorter length of hospitalization than men and metabolic characteristics may contribute to predict the duration from positive to negative in non-severe COVID-19 patients. Collectively, this study shed light on sex-specific metabolic shifts in non-severe COVID-19 patients during the recovery process, suggesting a sex bias in prognostic and therapeutic evaluations based on metabolic profiling.

7.
Med Clin (Engl Ed) ; 156(7): 324-331, 2021 Apr 09.
Article in English | MEDLINE | ID: covidwho-1164195

ABSTRACT

BACKGROUND: The aim of this study was to evaluate hyperferritinemia could be a predicting factor of mortality in hospitalized patients with coronavirus disease-2019 (COVID-19). METHODS: A total of 100 hospitalized patients with COVID-19 in intensive care unit (ICU) were enrolled and classified into moderate (n = 17), severe (n = 40) and critical groups (n = 43). Clinical information and laboratory results were collected and the concentrations of ferritin were compared among different groups. The association between ferritin and mortality was evaluated by logistic regression analysis. Moreover, the efficiency of the predicting value was assessed using receiver operating characteristic (ROC) curve. RESULTS: The amount of ferritin was significantly higher in critical group compared with moderate and severe groups. The median of ferritin concentration was about three times higher in death group than survival group (1722.25 µg/L vs. 501.90 µg/L, p < 0.01). The concentration of ferritin was positively correlated with other inflammatory cytokines, such as interleukin (IL)-8, IL-10, C-reactive protein (CRP) and tumor necrosis factor (TNF)-α. Logistic regression analysis demonstrated that ferritin was an independent predictor of in-hospital mortality. Especially, high-ferritin group was associated with higher incidence of mortality, with adjusted odds ratio of 104.97 [95% confidence interval (CI) 2.63-4185.89; p = 0.013]. Moreover, ferritin had an advantage of discriminative capacity with the area under ROC (AUC) of 0.822 (95% CI 0.737-0.907) higher than procalcitonin and CRP. CONCLUSION: The ferritin measured at admission may serve as an independent factor for predicting in-hospital mortality in patients with COVID-19 in ICU.


ANTECEDENTES: El objetivo de este estudio fue evaluar si la hiperferritinemia podría ser un factor predictivo de la mortalidad en pacientes hospitalizados con enfermedad por coronavirus de 2019 (COVID-19). MÉTODOS: Se incluyó un total de 100 pacientes hospitalizados con COVID-19 en la unidad de cuidados intensivos (UCI), clasificándose como grupos moderado (n = 17), grave (n = 40) y crítico (n = 43). Se recopiló la información clínica y de laboratorio, comparándose los niveles de ferritina entre los diferentes grupos. Se evaluó la asociación entre ferritina y mortalidad mediante un análisis de regresión logística. Además, se evaluó la eficacia del valor predictivo utilizando la curva ROC (receiver operating characteristic). RESULTADOS: La cantidad de ferritina fue significativamente superior en el grupo de pacientes críticos en comparación con el grupo de pacientes graves. La media de concentración de ferritina fue cerca de 3 veces superior en el grupo de muerte que en el grupo de supervivientes (1.722,25 µg/L vs. 501,90 µg/L, p < 0,01). La concentración de ferritina guardó una correlación positiva con otras citoquinas inflamatorias tales como interleucina (IL)-8, IL-10, proteína C reactiva (PRC) y factor de necrosis tumoral (TNF)-α. El análisis de regresión logística demostró que la ferritina era un factor predictivo independiente de la mortalidad intrahospitalaria. En especial, el grupo de ferritina alta estuvo asociado a una mayor incidencia de la mortalidad, con un valor de odds ratio ajustado de 104,97 [intervalo de confianza (IC) del 95% 2,63-4.185,89; p = 0,013]. Además, el valor de ferritina tuvo una ventaja de capacidad discriminativa en el área bajo la curva ROC (AUC) de 0,822 (IC 95% 0,737-0,907] superior al de procalcitonina y PRC. CONCLUSIÓN: El valor de ferritina medido durante el ingreso puede servir de factor independiente para prevenir la mortalidad intrahospitalaria en los pacientes de COVID-19 en la UCI.

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