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1.
Dis Markers ; 2022: 3528312, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35242241

RESUMO

BACKGROUND: SARS-CoV-2 is responsible for COVID-19, a clinically heterogeneous disease, ranging from being completely asymptomatic to life-threating manifestations. An unmet clinical need is the identification at disease onset or during its course of reliable biomarkers allowing patients' stratification according to disease severity. In this observational prospective cohort study, patients' immunologic and laboratory signatures were analyzed to identify independent predictors of unfavorable (either death or intensive care unit admission need) or favorable (discharge and/or clinical resolution within the first 14 days of hospitalization) outcome. METHODS: Between January and May 2021 (third wave of the pandemic), we enrolled 139 consecutive SARS-CoV-2 positive patients hospitalized in Northern Italy to study their immunological and laboratory signatures. Multiplex cytokine, chemokine, and growth factor analysis, along with routine laboratory tests, were performed at baseline and after 7 days of hospital stay. RESULTS: According to their baseline characteristics, the majority of our patients experienced a moderate to severe illness. At multivariate analysis, the only independent predictors of disease evolution were the serum concentrations of IP-10 (at baseline) and of C-reactive protein (CRP) after 7 days of hospitalization. Receiver-operating characteristic (ROC) curve analysis confirmed that baseline IP - 10 > 4271 pg/mL and CRP > 2.3 mg/dL at 7 days predict a worsening in clinical conditions (87% sensitivity, 66% specificity, area under the curve (AUC) 0.772, p < 0.001 and 83% sensitivity, 73% specificity, AUC 0.826, p < 0.001, respectively). CONCLUSIONS: According to our results, baseline IP-10 and CRP after 7 days of hospitalization could be useful in driving clinical decisions tailored to the expected disease trajectory in hospitalized COVID-19 patients.


Assuntos
Biomarcadores/sangue , COVID-19/imunologia , Quimiocina CXCL10/sangue , Proteínas do Tecido Nervoso/sangue , Idoso , Área Sob a Curva , Proteína C-Reativa , COVID-19/sangue , COVID-19/mortalidade , Feminino , Hospitalização , Humanos , Itália , Masculino , Pessoa de Meia-Idade , Gravidade do Paciente , Prognóstico , Estudos Prospectivos
2.
Sci Rep ; 10(1): 20731, 2020 11 26.
Artigo em Inglês | MEDLINE | ID: mdl-33244144

RESUMO

Clinical features and natural history of coronavirus disease 2019 (COVID-19) differ widely among different countries and during different phases of the pandemia. Here, we aimed to evaluate the case fatality rate (CFR) and to identify predictors of mortality in a cohort of COVID-19 patients admitted to three hospitals of Northern Italy between March 1 and April 28, 2020. All these patients had a confirmed diagnosis of SARS-CoV-2 infection by molecular methods. During the study period 504/1697 patients died; thus, overall CFR was 29.7%. We looked for predictors of mortality in a subgroup of 486 patients (239 males, 59%; median age 71 years) for whom sufficient clinical data were available at data cut-off. Among the demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model in a further subgroup of patients, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were identified as independent predictors of mortality. In conclusion, the CFR of hospitalized patients in Northern Italy during the ascending phase of the COVID-19 pandemic approached 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk.


Assuntos
COVID-19/epidemiologia , COVID-19/mortalidade , Pandemias , SARS-CoV-2/genética , Fatores Etários , Idoso , Idoso de 80 Anos ou mais , COVID-19/virologia , Comorbidade , Feminino , Humanos , Itália/epidemiologia , Tempo de Internação , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Reação em Cadeia da Polimerase Via Transcriptase Reversa , Fatores de Risco , Fatores Sexuais , Fumar , Taxa de Sobrevida
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