Medical historical past could assist predict COVID-19 threat in individuals with diabetes

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March 31, 2021

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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 in Roche Pharmaceuticals. In the study you will find all relevant financial information from all other authors.

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A predictive model that takes into account recent hospital stays, comorbidities, and drug exposure can help determine individual risk of COVID-19 critical care admission and mortality for people with diabetes, according to study data.

Helen M. Colhoun

Helen 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 a model with anamnesis factors and demographic information may be a better predictor than a model that uses factors only takes into account the demographics and type of diabetes of the patient.

In adults with diabetes in Scotland, recent hospitalizations, high HbA1c, low systolic blood pressure and prescriptions of NSAIDs, PPIs and anticoagulants were all associated with an increased risk of severe COVID-19. Infographic content was derived from McGurnaghan SJ et al. Lancet Diabetes Endocrinol. 2020; doi: 10.1016 / S2213-8587 (20) 30405-8.

“We have shown that the risk of serious illness in diabetics is very variable and predictable,” the researchers wrote. “This finding should influence screening policies and vaccine prioritization strategies.”

The researchers conducted a population cohort study in Scotland from March 1 to July 31, 2020. Three weeks prior to the start of the study period, Scotland had a population of 5,463,300, of whom 319,349 had diabetes. The researchers collected data on all COVID-19 cases, intensive care units and deaths in Scotland during the study period. The critical care included all admissions to an intensive care unit or a unit with high dependency. The data was linked to the national diabetes registry to identify people with diabetes who became infected with COVID-19.

COVID-19 risk factors in diabetes

Of those 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 were admitted to intensive care, 89.9% were 60 years of age or older. In people without diabetes, 0.1% died or were treated with critical care for COVID-19.

After adjusting for age and gender, people with diabetes had an increased risk of being admitted to intensive care or of dying from COVID-19 (OR = 1.4; 95% CI, 1.3-1.49; P <0, 0001). The likelihood of serious COVID results was similar for men and women. Compared to people without diabetes, people with type 1 diabetes (OR = 2.4; 95% CI, 1.82-3.16; P <0.0001) and type 2 diabetes (OR = 1, 37; 95% CI, 1.28-1.47; P <0.0001)) had higher chances of COVID-19 intensive care or mortality.

Older age, males, and longer duration of diabetes were associated with an increased risk of admission or mortality from COVID-19 intensive care. People with diabetes who lived in a nursing home were at a very high increased risk for COVID-19 treatment or mortality (OR = 16.57; 95% CI, 14.33-19.17; P <0.0001) .

“More than a third of people with diabetes who developed COVID-19 in the intensive care unit lived in nursing homes and emphasized the critical importance of protecting such vulnerable people during the remainder of the pandemic,” the researchers wrote.

The risk of treatment for COVID-19 in intensive care or for death was higher in people with diabetes who were hospitalized for any reason in the past 5 years (OR = 3.31; 95% CI, 2.79 -3.92; P <0.0001). There was also an increased risk for people with high HbA1c (OR = 1.01; 95% CI, 1.006-1.014; P <0.0001) or low systolic blood pressure (OR = 0.986; 95% CI, 0.982-0 , 99; P <) .0001). Use of antihypertensive drugs was associated with a lower risk of COVID-19 critical care or mortality (OR = 0.8; 95% CI, 0.71-0.91; P = 0.0006), while an increased risk for Passed people taking NSAIDs (OR) = 1.85; 95% CI, 1.63-2.1; P <0.0001), proton pump inhibitors (OR = 1.41; 95% CI, 1.25-1.59; P <0.0001) and anticoagulants (OR = 1.66; 95% CI, 1.47) -1.89; P <0.0001). The risk for treatment or mortality for COVID-19 in the intensive care unit was also higher in people who had been exposed to more other types of drugs in the past 3 years than for diabetes (OR = 1.14; 95% CI, 1.08 -1.2; P <0.0001)).

Prediction of the individual risk of COVID-19

The researchers used the results to create a cross-validated COVID-19 outcome prediction model that included age. Sex; Diabetes duration and type; Comorbidities; clinical measures such as HbA1c, BMI, estimated glomerular filtration rate, and systolic blood pressure; and drug exposures. This model had a higher C statistic (0.85; 95% CI, 0.83-0.86) than a base model that only included age, gender, and type and duration of diabetes (C statistic = 0.76 ; 95% CI, 0.75-0.77).

“This accuracy of prediction belies the belief that all people with diabetes are at similar risk,” the researchers wrote. “The variables retained in the model are those that are most predictive and not necessarily most causal. Some of the most valuable predictors include the number of hospitalizations over the past 5 years, as well as the number of diabetes and non-diabetes drugs that were not assessed in other diabetes COVID-19 studies. “

The researchers used the predictive model to create the Shiny app, which converts the absolute risk score in the predictive model to a COVID age, which is the age of a person without diabetes who has the same absolute risk.

“The Shiny app was provided for illustrative purposes only, to provide a better understanding of how a predictive model translates into COVID age in the broadest sense of people with diabetes,” the researchers wrote. “External validation, regulatory approval and licensing would be required before this app can be used in clinical practice.”

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