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I want to talk about a new continuous glucose monitoring (CGM) metric known as glycemic risk index, or GRI. You may ask why we need another metric. We currently have multiple CGM metrics, including time in range, time below range, time above range, mean glucose, glucose management indicator (GMI), and coefficient of variation, and it seems like an overwhelming number of ways to look at the same data.
The problem is that no single metric tells you exactly what is happening with the patient. For instance, a patient could be at a target time in range of 70%, but that could mean that 30% of that patient’s time is spent too low or even very low, which is a very serious problem, versus if 30% of their time was spent in a somewhat but not very high range, which requires less immediate attention.
Dr. David Klonoff and colleagues, including me, decided to see if one number could be used to identify which patients needed more immediate attention and which needed less. He asked 330 clinicians to evaluate 225 CGM tracings and rank their clinical status in terms of these metrics: very low glucose and low glucose hypoglycemia, very high glucose and high glucose hyperglycemia, time in range, mean glucose, and coefficient of variation.
Then he took all the data and analyzed it in complex ways that I barely understood and came up with one number, the GRI, that captures what the clinicians considered important. The analysis showed that the clinician rankings depended primarily on two components: One related to hypoglycemia, which gives more weight to very low glucose than to low glucose hypoglycemia; and the other related to hyperglycemia, which gives greater weight to very high glucose than to high glucose.
These two components were combined into a single glycemic risk index, the GRI, that corresponds closely to the clinician rankings of the overall quality of glycemia. In terms of numbers, the best GRI is in the zero to 20th percentile and the worst in the 81st to 100th percentile. The GRI grid that is provided in the paper enables users to track sequential changes within an individual over time and compare groups of individuals.
As I said initially, at first I wasn’t sure of the utility of adding yet another number to the mix, but I realized that for triaging what I hope will be increasing amounts of CGM data in a health care system, this could help identify those patients who need the most urgent assistance. It can also help providers have an overall sense of how a patient is doing and whether or not they are improving.
The GRI is not yet in general use and needs to be studied to see if it is actually helpful in clinical practice; however, I like the concept. Given the need to increase provider understanding of CGM metrics overall, I think it is a good way for providers to identify which patients need further analysis of their CGM data for potential treatment modifications.
Thank you.
Anne L. Peters, MD, is a professor of medicine at the University of Southern California and director of the USC clinical diabetes programs. She has published more than 200 articles, reviews, and abstracts, and three books, on diabetes, and has been an investigator for more than 40 research studies.
A version of this article first appeared on Medscape.com.
I want to talk about a new continuous glucose monitoring (CGM) metric known as glycemic risk index, or GRI. You may ask why we need another metric. We currently have multiple CGM metrics, including time in range, time below range, time above range, mean glucose, glucose management indicator (GMI), and coefficient of variation, and it seems like an overwhelming number of ways to look at the same data.
The problem is that no single metric tells you exactly what is happening with the patient. For instance, a patient could be at a target time in range of 70%, but that could mean that 30% of that patient’s time is spent too low or even very low, which is a very serious problem, versus if 30% of their time was spent in a somewhat but not very high range, which requires less immediate attention.
Dr. David Klonoff and colleagues, including me, decided to see if one number could be used to identify which patients needed more immediate attention and which needed less. He asked 330 clinicians to evaluate 225 CGM tracings and rank their clinical status in terms of these metrics: very low glucose and low glucose hypoglycemia, very high glucose and high glucose hyperglycemia, time in range, mean glucose, and coefficient of variation.
Then he took all the data and analyzed it in complex ways that I barely understood and came up with one number, the GRI, that captures what the clinicians considered important. The analysis showed that the clinician rankings depended primarily on two components: One related to hypoglycemia, which gives more weight to very low glucose than to low glucose hypoglycemia; and the other related to hyperglycemia, which gives greater weight to very high glucose than to high glucose.
These two components were combined into a single glycemic risk index, the GRI, that corresponds closely to the clinician rankings of the overall quality of glycemia. In terms of numbers, the best GRI is in the zero to 20th percentile and the worst in the 81st to 100th percentile. The GRI grid that is provided in the paper enables users to track sequential changes within an individual over time and compare groups of individuals.
As I said initially, at first I wasn’t sure of the utility of adding yet another number to the mix, but I realized that for triaging what I hope will be increasing amounts of CGM data in a health care system, this could help identify those patients who need the most urgent assistance. It can also help providers have an overall sense of how a patient is doing and whether or not they are improving.
The GRI is not yet in general use and needs to be studied to see if it is actually helpful in clinical practice; however, I like the concept. Given the need to increase provider understanding of CGM metrics overall, I think it is a good way for providers to identify which patients need further analysis of their CGM data for potential treatment modifications.
Thank you.
Anne L. Peters, MD, is a professor of medicine at the University of Southern California and director of the USC clinical diabetes programs. She has published more than 200 articles, reviews, and abstracts, and three books, on diabetes, and has been an investigator for more than 40 research studies.
A version of this article first appeared on Medscape.com.
I want to talk about a new continuous glucose monitoring (CGM) metric known as glycemic risk index, or GRI. You may ask why we need another metric. We currently have multiple CGM metrics, including time in range, time below range, time above range, mean glucose, glucose management indicator (GMI), and coefficient of variation, and it seems like an overwhelming number of ways to look at the same data.
The problem is that no single metric tells you exactly what is happening with the patient. For instance, a patient could be at a target time in range of 70%, but that could mean that 30% of that patient’s time is spent too low or even very low, which is a very serious problem, versus if 30% of their time was spent in a somewhat but not very high range, which requires less immediate attention.
Dr. David Klonoff and colleagues, including me, decided to see if one number could be used to identify which patients needed more immediate attention and which needed less. He asked 330 clinicians to evaluate 225 CGM tracings and rank their clinical status in terms of these metrics: very low glucose and low glucose hypoglycemia, very high glucose and high glucose hyperglycemia, time in range, mean glucose, and coefficient of variation.
Then he took all the data and analyzed it in complex ways that I barely understood and came up with one number, the GRI, that captures what the clinicians considered important. The analysis showed that the clinician rankings depended primarily on two components: One related to hypoglycemia, which gives more weight to very low glucose than to low glucose hypoglycemia; and the other related to hyperglycemia, which gives greater weight to very high glucose than to high glucose.
These two components were combined into a single glycemic risk index, the GRI, that corresponds closely to the clinician rankings of the overall quality of glycemia. In terms of numbers, the best GRI is in the zero to 20th percentile and the worst in the 81st to 100th percentile. The GRI grid that is provided in the paper enables users to track sequential changes within an individual over time and compare groups of individuals.
As I said initially, at first I wasn’t sure of the utility of adding yet another number to the mix, but I realized that for triaging what I hope will be increasing amounts of CGM data in a health care system, this could help identify those patients who need the most urgent assistance. It can also help providers have an overall sense of how a patient is doing and whether or not they are improving.
The GRI is not yet in general use and needs to be studied to see if it is actually helpful in clinical practice; however, I like the concept. Given the need to increase provider understanding of CGM metrics overall, I think it is a good way for providers to identify which patients need further analysis of their CGM data for potential treatment modifications.
Thank you.
Anne L. Peters, MD, is a professor of medicine at the University of Southern California and director of the USC clinical diabetes programs. She has published more than 200 articles, reviews, and abstracts, and three books, on diabetes, and has been an investigator for more than 40 research studies.
A version of this article first appeared on Medscape.com.