March 31, 2021
3 minutes to read
Source / Disclosures
Disclosures: Colhoun reports that she has received grants and personal fees from Eli Lilly and Novo Nordisk, grants from AstraZeneca, Pfizer and Regeneron, and institutional fees from Novartis and Sanofi Aventis, and is a shareholder of Roche Pharmaceuticals. Please see the study for relevant financial information from all other authors.
A prediction model that takes into account recent hospitalizations, co-morbidities and drug exposure can help determine the individual risk of COVID-19 intensive care admission and mortality for people with diabetes, according to data from the ‘study.
Hélène M. Colhoun
Hélène M. Colhoun, FRCP, The AXA Chair in Medical Informatics and Life Course Epidemiology at the University of Edinburgh, Scotland, and colleagues wrote that the risks for people with diabetes vary, and that a model including factors medical history and demographic information may be a better predictor than a model that only takes into account the demographics and type of diabetes of the patient.
“We have shown that, among people with diabetes, the risk of severe disease varies widely and is predictable,” the researchers wrote. “This idea should inform protection policies and vaccine prioritization strategies.”
Researchers conducted a population cohort study in Scotland from March 1 to July 31, 2020. There were 5,463,300 residents in Scotland 3 weeks before the start of the study period, of which 319,349 had diabetes. Researchers collected data on all COVID-19 cases, intensive care admissions and deaths in Scotland during the study period. Intensive care included all admissions to an intensive care unit or high dependency unit. The data was linked to the National Diabetes Registry to identify people with diabetes who contracted COVID-19.
COVID-19 risk factors in diabetes
Among people with diabetes in Scotland, 0.9% tested positive for COVID-19 in the first 5 months of the pandemic, and 0.3% died or were treated in an intensive care unit. Of those who died or admitted to intensive care, 89.9% were aged 60 or over. Among people without diabetes, 0.1% died or were treated in intensive care for COVID-19.
After adjusting for age and sex, people with diabetes had an increased risk of being admitted to intensive care or dying from COVID-19 (OR = 1.4; 95% CI: 1.3-1 , 49; P <.0001 the likelihood of severe covid results was similar for men and women. compared to people without diabetes those with type ci:>P P
Older age, male sex, and longer duration of diabetes were associated with an increased risk of COVID-19 ICU admission or death. Diabetics who lived in a residential care home had a very high risk of COVID-19 intensive care treatment or death (OR = 16.57; 95% CI: 14.33-19.17; P <.0001>
“More than a third of people with diabetes who developed fatal COVID-19 or treated in an intensive care unit lived in residential care homes, underscoring the critical importance of protecting these vulnerable people during the remainder of the pandemic The researchers wrote.
The risk of COVID-19 intensive care treatment or death was higher for people with diabetes who were admitted to hospital within the past 5 years for any reason (OR = 3.31; 95 CI %, 2.79-3.92; P <.0001 there was also an increased risk for people with elevated hba1c ci:>P P <.0001 taking antihypertensive drugs was associated with a lower risk of covid-19 intensive care or death ci:>P = 0.0006), while there was an increased risk for people who took NSAIDs (OR = 1.85; 95% CI: 1.63-2.1; P P P <.0001 the risk of covid-19 intensive care treatment or death was also higher for people exposed to more types drugs other than those used diabetes in past years ci>P <.0001>
Predicting individual risk of COVID-19
The researchers used the results to create a cross-validated COVID-19 outcome prediction model that included age; sex; duration and type of diabetes; comorbidities; clinical measures, such as HbA1c, BMI, estimated glomerular filtration rate, and systolic BP; and drug exposures. This model had a higher C statistic (0.85; 95% CI, 0.83-0.86) than a baseline model which only included age, sex and type and duration of diabetes ( C-statistic = 0.76; 95% CI 0.75 to 0.77).
“This level of predictive accuracy refutes the idea that all people with diabetes have a similar risk,” the researchers wrote. “The variables retained in the model are those which are the most predictive and not necessarily causal; some of the most valuable predictors include the number of hospital admissions in the past 5 years and the number of diabetes and non-diabetic drugs, which have not been evaluated in other studies on the diabetes COVID-19. “
The researchers used the prediction model to produce the Shiny app, which converts the absolute risk score in the prediction model and converts it to a COVID age, which is the age of a person without diabetes who has the same absolute risk.
“The Shiny app has been provided for illustration purposes only, to allow a better understanding of how a prediction model translates into COVID age overall in people with diabetes,” the researchers wrote. “External validation, regulatory approval and appropriate licensing would be required before this application can be used in clinical practice. “