A recent study from Case Western Reserve University used national data on U.S. military veterans with diabetes to test and modify a widely accepted model for predicting the risk of heart failure in patients with diabetes. .

This model, called the WATCH-DM score, is used to predict the likelihood of heart failure in people with diabetes within five years.

However, the influence of social determinants of health, such as housing, diet, and patient neighborhood, is overlooked, so researchers used a multifactorial summary score, the Social Deprivation Index, to adjust the WATCH-DM score. (SDI) was used.

Introduced by the Robert Graham Center, a group of clinical researchers, the SDI can use food, housing, transportation, and community conditions to quantify the level of disadvantage in a particular area. Previous research has demonstrated that this score is directly proportional to the level of health disparities observed in a community.

The study identified approximately 1 million U.S. veterans with type 2 diabetes who did not have heart failure and were being treated as outpatients in Veterans Affairs health care settings nationwide in 2010.

The researchers used patients’ ZIP codes to obtain the SDI and entered it into a risk calculator to determine how likely they were to be hospitalized for heart failure within five years.

The hospitalization rate for heart failure in the entire cohort of over 1 million patients was 5.39%, but this incidence ranged from 3% (most socially disadvantaged) to 11% (most disadvantaged). Ta.

The researchers found that depending on a patient’s other clinical information, adding SDI to a risk prediction model could double the patient’s odds of developing heart failure over the next five years.

The team of researchers then optimized the WATCH-DM scores for each SDI group using statistical correction factors to improve predictive accuracy across the full range of social determinants of health.

“We found that adding SDI enhanced the risk-predicting ability of the WATCH-DM score,” said Salil Deo, associate professor of surgery at Case Western Reserve School of Medicine, who led the study. “These results highlight the need to include social determinants of health in future clinical risk prediction algorithms. This will improve algorithm accuracy and benefit patients by improving health outcomes. I guess.”

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We hope that our research will encourage healthcare professionals to adopt a holistic approach when treating patients in the future. Understanding and quantifying social inequalities is the first step to ensuring that they do not impact patient health. ”

Salil Deo, Associate Professor, Department of Surgery, Case Western Reserve School of Medicine