Combining both cardiometabolic and social determinants of health improves the predictive power of models assessing outcomes in COVID-19
The combination of clinical markers of cardiometabolic disease and social determinants of health provides a better predictive model for adverse outcomes in patients with COVID-19 according to a retrospective analysis by researchers from Alabama, US.
Cardiometabolic diseases such as diabetes have become recognised as a risk factor for both infection and more severe disease in COVID-19. Moreover, it has also become clear that there increased risks of infection and more severe disease based on race, ethnicity and various socioeconomic determinants. Therefore, combined analyses that incorporate both cardiometabolic and social factors might enable a better understanding of how health disparities impact upon COVID-19 outcomes, yet such analyses are rarely undertaken. As a result, for the present study, the US team made use of electronic health records incorporating both clinical and social factors and sought to determine the ability of models based on parameters extracted from both factors, were able to predict the subsequent need for hospitalisation, intensive care unit (ICU) admission and mortality after infection with COVID-19.
Using these records, the researchers obtained clinical patient data (e.g., glucose levels, body mass index, blood pressure etc) and to calculate a cardiometabolic disease staging (CMDS) value, used to assign risk levels for diabetes, and all-cause and cardiovascular disease mortality. CMDS values range from 0 to 0.99 with higher values indicating a greater risk of developing diabetes. Individual-level social determinants of health (SDoH) included factors such as martial, employment and insurance status. A neighbourhood SDoH considered factors such as social vulnerability (an index of poverty) rurality and health care access. Using regression analysis, the researchers modelled the risk of being hospitalised, admitted to ICU and death, using a CMDS model only and then after addition of both individual and neighbourhood SDoH values to determine whether the predictive power of the model changed and which was assessed by measuring the area under the curve.
Cardiometabolic health and COVID-19-related outcomes
A total of 2,873 patients with a mean age of 58.3 years (40.9% male) were included in the analysis, of whom 13.9% were hospitalised, 13.7% admitted to an ICU and 14.8% who died.
Using the CMDS model, each one standard deviation increase in CMDS score was associated with hospitalisation (odds ratio, OR = 2.0, 95% CI 1.83 – 2.20), ICU admission (OR = 1.88) and death (OR = 1.69).
Based on individual level SDoH, patients with no insurance had a higher odds of being hospitalised (OR = 3.35), an ICU admission (OR = 2.99) and death (OR = 7.27). In addition, the analysis also showed that patients with high social vulnerability were more likely to be hospitalised or admitted to an ICU.
Interestingly, when the CMDS model alone was used to predict hospitalisation, it had an area under the curve (AUC) of 0.776. But when individual level SDoH was added to the model, the predictive power improved and the AUC increased to 0.815. However, when both individual and neighbourhood SDoH were added, the AUC increased slightly more (0.819) and in both instances, these differences were significant (p < 0.05). Similar improvements to the predictive power were seen when individual and neighbourhood SDoH were added for both ICU admission and mortality models.
The authors concluded that using both clinical and social factor data improved the predictability of models for determination of the risk of severe outcomes after infection with COVID-19. They added that incorporation of both clinical and social measures could help guide treatment, intervention and prevention efforts to improve both health and inequality.
Howell CR et al. Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID-19 outcomes Obesity (Silver Spring) 2022