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New Tool Identifies Diabetes Patients at Risk for Low Blood Sugar Emergencies

New Tool Identifies Diabetes Patients at Risk for Low Blood Sugar Emergencies
Author Information (click to view)

Kaiser Permanente


Kaiser Permanente (click to view)

Kaiser Permanente

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A team led by Kaiser Permanente researchers has developed and validated a practical tool for identifying diabetes patients who are at the highest risk for being admitted to an emergency department or hospital due to severe hypoglycemia, or very low blood sugar. Their results are published in JAMA Internal Medicine.

Advances in care and improved treatment options have reduced the risk of long-term complications and death for more than 25 million Americans who have diabetes, which is characterized by high blood sugar. At the same time, patients sometimes experience dangerously low blood sugar levels while taking diabetes medications, especially after skipping a meal or exercising harder than usual.

“Sometimes a person with diabetes is unaware that their blood sugar is dropping and can progress quickly into severe hypoglycemia, which has been associated with falls, automobile accidents, heart attacks, coma, and even death,” said Andrew J. Karter, PhD, senior research scientist with the Kaiser Permanente Division of Research and the study’s lead author. “Hypoglycemia is often preventable with the proper clinical attention, and we believe this tool will help focus that attention on the patients who most need it.”

With an estimated 100,000 hypoglycemia-related adverse events resulting in emergency room visits each year in the United States, hypoglycemia is now one of the most frequent adverse events in patients with type 2 diabetes. Older patients and those with a longer history of diabetes are particularly susceptible, noted Karter.

The researchers developed the hypoglycemia risk stratification tool by identifying 156 possible risk factors for hypoglycemia and collecting data from more than 200,000 patients with type 2 diabetes receiving care from Kaiser Permanente in Northern California. Using machine-learning analytical techniques, they developed a model to predict a patient’s 12-month risk of hypoglycemia-related emergency department or hospital use.

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