
PMC:7402624 / 9116-10037
Annnotations
LitCovid-PD-MONDO
To examine potential associations between these clinical features, we performed correlation analysis (Fig. 1C and fig. S1C). This analysis revealed correlations between different COVID-19 disease severity metrics, as well as clinical features or interventions associated with more severe disease (e.g., D-dimer, vasoactive medication) (Fig. 1C and fig. S1C). WBC and PMN also correlated with metrics of disease severity (e.g., APACHE III), as well as with IL-6 levels (Fig. 1C and fig. S1C). Other relationships were also apparent, including correlations between age or mortality and metrics of disease severity and many other correlations between clinical measures of disease, inflammation, and co-morbidities (Fig. 1C and fig. S1C). Thus, COVID-19 patients presented with varied pre-existing comorbidities, complex clinical phenotypes, evidence of inflammation in many patients, and clinically altered leukocyte counts.
LitCovid-PD-GO-BP
To examine potential associations between these clinical features, we performed correlation analysis (Fig. 1C and fig. S1C). This analysis revealed correlations between different COVID-19 disease severity metrics, as well as clinical features or interventions associated with more severe disease (e.g., D-dimer, vasoactive medication) (Fig. 1C and fig. S1C). WBC and PMN also correlated with metrics of disease severity (e.g., APACHE III), as well as with IL-6 levels (Fig. 1C and fig. S1C). Other relationships were also apparent, including correlations between age or mortality and metrics of disease severity and many other correlations between clinical measures of disease, inflammation, and co-morbidities (Fig. 1C and fig. S1C). Thus, COVID-19 patients presented with varied pre-existing comorbidities, complex clinical phenotypes, evidence of inflammation in many patients, and clinically altered leukocyte counts.
LitCovid-PubTator
To examine potential associations between these clinical features, we performed correlation analysis (Fig. 1C and fig. S1C). This analysis revealed correlations between different COVID-19 disease severity metrics, as well as clinical features or interventions associated with more severe disease (e.g., D-dimer, vasoactive medication) (Fig. 1C and fig. S1C). WBC and PMN also correlated with metrics of disease severity (e.g., APACHE III), as well as with IL-6 levels (Fig. 1C and fig. S1C). Other relationships were also apparent, including correlations between age or mortality and metrics of disease severity and many other correlations between clinical measures of disease, inflammation, and co-morbidities (Fig. 1C and fig. S1C). Thus, COVID-19 patients presented with varied pre-existing comorbidities, complex clinical phenotypes, evidence of inflammation in many patients, and clinically altered leukocyte counts.
LitCovid-sentences
To examine potential associations between these clinical features, we performed correlation analysis (Fig. 1C and fig. S1C). This analysis revealed correlations between different COVID-19 disease severity metrics, as well as clinical features or interventions associated with more severe disease (e.g., D-dimer, vasoactive medication) (Fig. 1C and fig. S1C). WBC and PMN also correlated with metrics of disease severity (e.g., APACHE III), as well as with IL-6 levels (Fig. 1C and fig. S1C). Other relationships were also apparent, including correlations between age or mortality and metrics of disease severity and many other correlations between clinical measures of disease, inflammation, and co-morbidities (Fig. 1C and fig. S1C). Thus, COVID-19 patients presented with varied pre-existing comorbidities, complex clinical phenotypes, evidence of inflammation in many patients, and clinically altered leukocyte counts.
LitCovid-PD-CHEBI
To examine potential associations between these clinical features, we performed correlation analysis (Fig. 1C and fig. S1C). This analysis revealed correlations between different COVID-19 disease severity metrics, as well as clinical features or interventions associated with more severe disease (e.g., D-dimer, vasoactive medication) (Fig. 1C and fig. S1C). WBC and PMN also correlated with metrics of disease severity (e.g., APACHE III), as well as with IL-6 levels (Fig. 1C and fig. S1C). Other relationships were also apparent, including correlations between age or mortality and metrics of disease severity and many other correlations between clinical measures of disease, inflammation, and co-morbidities (Fig. 1C and fig. S1C). Thus, COVID-19 patients presented with varied pre-existing comorbidities, complex clinical phenotypes, evidence of inflammation in many patients, and clinically altered leukocyte counts.
LitCovid-PD-FMA-UBERON
To examine potential associations between these clinical features, we performed correlation analysis (Fig. 1C and fig. S1C). This analysis revealed correlations between different COVID-19 disease severity metrics, as well as clinical features or interventions associated with more severe disease (e.g., D-dimer, vasoactive medication) (Fig. 1C and fig. S1C). WBC and PMN also correlated with metrics of disease severity (e.g., APACHE III), as well as with IL-6 levels (Fig. 1C and fig. S1C). Other relationships were also apparent, including correlations between age or mortality and metrics of disease severity and many other correlations between clinical measures of disease, inflammation, and co-morbidities (Fig. 1C and fig. S1C). Thus, COVID-19 patients presented with varied pre-existing comorbidities, complex clinical phenotypes, evidence of inflammation in many patients, and clinically altered leukocyte counts.