English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

BACKGROUND Coronavirus disease 2019 (COVID-19) has been declared a global pandemic. The overwhelming volume of confirmed cases makes prevention and control a challenge. Despite this, numerous knowledge gaps remain in the diagnosis, management and prognostication of this novel coronavirus infection.  METHODS This retrospective cohort study included patients with polymerase chain reaction confirmed COVID-19. Binary logistic regression was used to determine the association between the cardiac biomarkers and in-hospital mortality. ROC, AUC and cutoff analyses were used to determine optimal cutoff values for the cardiac biomarkers.  RESULTS Out of 224 rRT-PCR confirmed cases, 90 subjects with a complete panel of cardiac biomarkers were included in this study. Sixtysix (77.6%) subjects survived, five were still admitted while 19 (22.4%) expired. Logistic regression analysis showed that creatine kinase and hsTnI were independently associated with inhospital mortality (OR, 4.103 [95% CI, 1.241-13.563], p = 0.021; and OR, 7.899 [95% CI, 2.430-25.675], p = 0.001, respectively). ROC curve analysis showed that hsTnI was a good predictor for in-hospital mortality (AUC, 0.829 [95% CI, 0.735-0.923], p = &lt;0.001). Optimal cutoff point derived from the ROC curve for hsTnI was 0.010 ng/ml (J, 0.574) with a sensitivity of 84% (TPR, 0.842 [95%CI, 0.604-0.966]), specificity of 73% (TNR, 0.732 [95% CI, 0.614-0.386]) , an adjusted negative predictive value of 99% (Known prevalence*adjusted NPV, 0.989), a positive likelihood ratio of 20% (LR+, 3.147 [95% CI, 2.044-4.844]).  CONCLUSION High sensitivity troponin I level was a good tool in significantly predicting in-hospital mortality.

The Role of Cardiac Biomarkers as Predictors of In-hospital Mortality among COVID patients in a Tertiary Hospital in Metro Manila, Philippines