Historical data from electronic health records (EHRs) could be used to identify the risk factors that will help healthcare providers better recognize which diabetic patients are most likely to have low blood sugar, according to new research.
Low blood sugar, known as hypoglycemia, occurs in 20 to 60 percent of patients with diabetes. It has considerable negative effects on a person's mental and physical health, including the cardiovascular system, according to the researchers. Per a new study, the strongest predictors of hypoglycemia are recent infections, using insulin other than long-acting insulin, recent occurrences of hypoglycemia, and dementia.
To this end, predictive risk model was developed and tested by researchers from Regenstrief Institute, Indiana University School of Medicine and Merck, and according to officials, is the first to combine nearly all known and readily assessed risk factors for hypoglycemia.
Researchers noted that many patients with diabetes, especially those with recurring episodes of low blood sugar, are unaware when it occurs, despite the risk of serious adverse events including cognitive impairment, coma and death. Being able to identify patients at high risk may provide an opportunity to intervene and prevent hypoglycemia as well as long-term consequences, they contend.
As such, in the retrospective cohort study, researchers gathered data from 10 years of EHRs covering nearly 39,000 patients with diabetes who received outpatient care at Eskenazi Health in central Indiana. Study participants were 56 percent female, 40 percent African-American and 39 percent uninsured. The researchers used laboratory tests, diagnostic codes and natural language processing to identify episodes of hypoglycemia.
The scientists found that natural language processing (NLP) was useful in identifying hypoglycemia, because there were not always laboratory tests to confirm the episode. Instead, hypoglycemia was often recorded only in narrative clinical notes. The study authors believe that their risk prediction model, incorporating NLP, could be useful to researchers, clinical administrators and those who are measuring population health.
"This study has implications for clinical support," said Michael Weiner, M.D., director of the Regenstrief Institute William M. Tierney Center for Health Services Research and the senior author of the study. "The predictive model could lead to changes in practice as well as new strategies to help patients lower their risk of hypoglycemia."
Dr. Weiner and his team are now studying the implementation of a clinical decision support tool that uses information from EHRs to alert clinicians when their patients have hypoglycemia risk factors. What’s more, they are conducting an outpatient study that uses wearable devices to monitor and record the actions and continuous glucose levels of people with diabetes. The goal is to identify patterns that allow healthcare providers to predict hypoglycemia earlier, officials noted.
