Diabetics exposed to consistently high blood glucose levels can develop serious secondary complications, including heart disease, stroke, blindness, kidney failure and ulcers that require the amputation of toes, feet or legs.
In order to predict which diabetic patients have a high risk for these complications, physicians may use mathematical models. For example, the UKPDS Risk Engine calculates a diabetic patient’s risk of coronary heart disease and stroke — based on their age, sex, ethnicity, smoking status, time since diabetes diagnosis and other variables.
But this strategy doesn’t provide the accuracy needed by doctors. In response, a research team at Duke University has developed machine-learning computer algorithms to search for patterns and correlations in EHR data from approximately 17,000 diabetic patients in the Duke health system.
The group, led by Ricardo Henao, an assistant research professor in electrical and computer engineering, has demonstrated more accurate predictions than the UKPDS Risk Engine. A recent news story explains:
“This new model can project whether a patient will require amputation within a year with almost 90 percent accuracy, and can correctly predict the risks of coronary artery disease, heart failure and kidney disease in four out of five cases. The model looks at what was typed into a patient’s chart — diagnosis codes, medications, laboratory tests — and picks up on which pieces of information in the EHR are correlated with the development of a comorbidity in the following year.”
The Duke researchers plan to improve the model by training their machine-learning algorithms on a larger data set of diabetic patients from additional hospitals.
However, relying on EHR data has drawbacks. For instance, a patient’s EHR may be incomplete, particularly if the patient doesn’t consistently see the same doctors. Another major challenge is gaining access to the medical records for research. The Duke team had to contact all 17,000 patients to get their informed consent and may encounter similar challenges for a larger scale project.
This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.
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