Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 3 de 3
Filter
Add filters

Database
Language
Document Type
Year range
1.
Front Cardiovasc Med ; 8: 686328, 2021.
Article in English | MEDLINE | ID: covidwho-1370986

ABSTRACT

Although sporadic studies have shown that myoglobin may have better prognostic performance than other cardiac markers in COVID-19, a comprehensive comparative study is lacking. Herein, we retrospectively analyzed the clinical and laboratory data of COVID-19 patients admitted to the Guanggu Campus of Wuhan Tongji Hospital from February 9, 2020 to March 30, 2020, intending to compare the prognostic accuracy of three commonly used cardiac markers on COVID-19 mortality. Our results revealed that abnormal increases in myocardial biomarkers were associated with a significantly increased risk of in-hospital mortality with COVID-19. Interestingly, myoglobin, a non-cardiac-specific biomarker, also expressed in skeletal myocytes, had even higher prognostic accuracy than cardiac-specific biomarkers such as high-sensitivity troponin I (hs-TnI) and creatine kinase-MB (CK-MB). More importantly, multivariate Cox analysis showed that myoglobin, rather than hs-TnI or CK-MB, was independently prognostic for in-hospital mortality in COVID-19. These results were further confirmed by subgroup analyses of patients with severe and critical illnesses and those without a history of cardiovascular disease. Our findings suggest that myoglobin may be a reliable marker of illness reflecting general physiological disturbance and help to assess prognosis and treatment response in patients with COVID-19.

2.
J Med Virol ; 93(5): 2908-2917, 2021 05.
Article in English | MEDLINE | ID: covidwho-1196524

ABSTRACT

The aim is to explore the relation between inflammation-associated factors and in-hospital mortality and investigate which factor is an independent predictor of in-hospital death in patients with coronavirus disease-2019. This study included patients with coronavirus disease-2019, who were hospitalized between February 9, 2020, and March 30, 2020. Univariate Cox regression analysis and least absolute shrinkage and selection operator regression (LASSO) were used to select variables. Multivariate Cox regression analysis was applied to identify independent risk factors in coronavirus disease-2019. A total of 1135 patients were analyzed during the study period. A total of 35 variables were considered to be risk factors after the univariate regression analysis of the clinical characteristics and laboratory parameters (p < .05), and LASSO regression analysis screened out seven risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were myoglobin (HR, 5.353; 95% CI, 2.633-10.882; p < .001), C-reactive protein (HR, 2.063; 95% CI, 1.036-4.109; p = .039), neutrophil count (HR, 2.015; 95% CI, 1.154-3.518; p = .014), interleukin 6 (Il-6; HR, 9.753; 95% CI, 2.952-32.218; p < .001), age (HR, 2.016; 95% CI, 1.077-3.773; p = .028), and international normalized ratio (HR, 2.595; 95% CI, 1.412-4.769; p = .002). Our results suggested that inflammation-associated factors were significantly associated with in-hospital mortality in coronavirus disease-2019 patients. C-reactive protein, neutrophil count, and interleukin 6 were independent factors for predicting in-hospital mortality and had a better independent predictive ability. We believe these findings may allow early identification of the patients at high risk for death, and can also assist in better management of these patients.


Subject(s)
COVID-19/mortality , Hospitalization/statistics & numerical data , Inflammation/blood , Aged , Aged, 80 and over , Biomarkers/blood , COVID-19/blood , COVID-19/diagnosis , Female , Hospital Mortality , Humans , Male , Middle Aged , ROC Curve , Retrospective Studies , Risk Factors , SARS-CoV-2 , Survival Rate
3.
Front Cardiovasc Med ; 7: 599096, 2020.
Article in English | MEDLINE | ID: covidwho-1069719

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has become a global threat. Increases in cardiac biomarkers are common and are associated with adverse outcomes in patients with COVID-19. Although these increases are more likely to occur in cases with concomitant cardiac disease, the differences in cardiac biomarker levels between patients with and without cardiac disease and their associations with in-hospital mortality are largely unknown. A consecutive serial of laboratory-confirmed COVID-19 cases was retrospectively enrolled. Clinical characteristics, laboratory results, and outcome data were collected. The levels of cardiac biomarkers were evaluated and compared by stratifying patients according to concomitant cardiac conditions and clinical classifications. The prognostic efficacy of cardiac biomarker levels on admission was also assessed. Among the overall study population and survived patients, the cardiac biomarker levels at both the early and late stages in cardiac patients were significantly higher than those in non-cardiac patients. However, their concentrations in cardiac patients were comparable to non-cardiac ones among non-survivors. The cardiac biomarker levels at the late stage of the disease were significantly decreased compared to those at the early stage among patients who were alive. Whereas, the late-stage biomarker levels were significantly increased in patients who ultimately died. Subgroup analysis illustrated that increases in cardiac biomarkers were closely related to the severity of the disease, and were prognostic for high risks of in-hospital mortality in non-cardiac, rather than in cardiac patients. Myo and NT-proBNP, rather than Hs-TnI and CK-MB, were independently associated with in-hospital mortality in the overall population and non-cardiac patients. However, these associations were not significant among cardiac patients. In conclusion, our results helped better understand the release pattern and prognostic performance of cardiac biomarkers in patients with COVID-19. Increased levels of Myo and NT-proBNP on admission could be useful markers for early identifying high-risk patients. However, special attention must be paid when implementing the prognostic function for cardiac patients.

SELECTION OF CITATIONS
SEARCH DETAIL