Patient characteristics contribute to COVID-19 survival
Demographic factors influence survival outlook in patients with the coronavirus disease 2019 (COVID-19), according to a recent study.
“These analyses provide a preliminary picture of how key demographic characteristics and a range of comorbidities, a priori selected as being of interest in COVID-19, are jointly associated with poor outcomes,” researchers said. “These initial results may be used subsequently to inform the development of prognostic models. We caution against interpreting our estimates as causal effects.”
Researchers accessed the primary care records of 17,278,392 adult patients, from which 10,926 COVID-19-related deaths were retrieved. The resulting overall cumulative incidence of death within 90 days after study initiation was <0.01 percent in participants aged 18–39 years. This grew to 0.67 percent and 0.44 percent in males and females ≥80 years of age, respectively. [Nature 2020;doi:10.1038/s41586-020-2521-4]
Cox proportional hazards modelling confirmed the strong influence of age on mortality risk. Patients who were ≥80 years of age were more than 20 times as likely to die of COVID-19 as their counterparts who were 20 to 30 years younger (adjusted hazard ratio [HR], 20.61, 95 percent confidence interval [CI], 18.72–22.70).
Comorbidities had the same effect. Diabetes, severe asthma, respiratory diseases, chronic heart diseases, stroke or dementia, liver diseases, weakened kidney function, autoimmune and immunosuppressive conditions, haematological malignancies, and neurological diseases all increased the risk of death among COVID-19 patients.
Obesity likewise worsened outlook (adjusted HR, 1.92, 95 percent CI, 1.72–2.13), as did being of South Asian (adjusted HR, 1.44, 95 percent CI, 1.32–1.58), black (adjusted HR, 1.48, 95 percent CI, 1.30–1.69), or mixed (adjusted HR, 1.43, 95 percent CI, 1.11–1.85) ethnicity
Importantly, the degree of deprivation also correlated with mortality. Patients who belonged to the most deprived quintile were almost twice as likely to die of COVID-19 than comparators who were least deprived (adjusted HR, 1.80, 95 percent CI, 1.69–1.91).
This interaction could not be “explained by pre-existing disease or clinical risk factors, suggesting that other social factors may have an important role,” the researchers said.
“We have demonstrated—for the first time—that only a small part of the substantially increased risks of COVID-19 related death among nonwhite groups and among people living in more deprived areas can be attributed to existing disease,” they continued. Strategies and interventions for these populations are urgently needed.
In the present study, the researchers used a new data analytics platform called OpenSAFELY, which assigns a pseudonym to patient primary care records, allowing epidemiologists, researchers, and physicians to conduct near-real-time case analyses without sacrificing patient privacy.
“The open source reusable codebase on OpenSAFELY supports rapid, secure, and collaborative development of new analyses: [W]e are currently conducting expedited studies on the impact of various medical treatments and population interventions on the risk of COVID-19 infection, [intensive care] admission, and death, alongside other observational analyses,” the researchers said.
“OpenSAFELY is rapidly scalable for additional NHS patients’ records, with new data sources progressing,” they added.