A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.

BACKGROUND: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. METHODS: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. RESULTS: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. CONCLUSION: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Pancreatic Neuroendocrine Tumor With Humoral Hypercalcemia and High Tumor PD-L1 Score.

Pancreatic neuroendocrine tumors (NETs) are a heterogeneous group of neoplasms. They can be functioning tumors with secretion of a variety of peptide hormones, or nonfunctioning tumors with metastases to the liver at the time of diagnosis. Well-differentiated tumors tend to be slow-growing and characterized by low tumor mutational burden (TMB) and lower propensity to express PD-L1. Hypercalcemia due to malignancy can occur in about 20% to 30% of patients with cancer. The secretion of parathyroid hormone-related protein (PTH-rP) is among the causes of malignant hypercalcemia and has seldom been associated with hypercalcemia of NETs. Although the therapeutic landscape for neuroendocrine neoplasms has evolved substantially over the past decade, the role of immunotherapy has not yet been completely explored in this group of patients. We present a rare case of a metastatic pancreatic NET with high TMB, high PD-L1 tumor proportion score, and high PTH-rP related hypercalcemia.