Cook, Curtiss B.

Abstract Coccidioidomycosis is an endemic fungal infection caused by the soil-dwelling Coccidioides. One prior study showed that persons with diabetes mellitus (DM) with elevated glucose at the time of coccidioidomycosis had poorer coccidioidal outcomes compared to persons without DM. We aimed to assess the association between hemoglobin A1C (A1C) and outcomes among persons with coccidioidomycosis and coexisting DM. We conducted a retrospective case-control study of patients with DM (cases) and without DM (controls) with coccidioidomycosis to assess the effect of glycemic control on outcomes in patients with DM. From January 1, 2017, to October 13, 2022, we identified 138 DM patients with coccidioidomycosis and compared them with 283 non-DM control patients with coccidioidomycosis. Compared with controls, cases were more likely to require hospitalization (OR 1.20, P < .001), have a lung cavity (OR 1.36, P < .001) and cavitary complications (OR 1.09, P = .001), require surgical management (OR 1.07, P = .016), and experience relapsed infection (OR 1.09, P = .041). Among the cases, when baseline A1C was assessed as a continuous variable, each 1-unit increase of A1C had a significant effect on coccidioidomycosis-related hospitalizations (A1C OR 1.59, P = .005) and the presence of cavities (OR 1.42, P = .01). Controlling for Charlson Comorbidity Index did not change the significance of A1C influence on outcomes of hospitalization and lung cavities. DM patients with A1C levels ≤8.0 at presentation did not have increased adverse coccidioidomycosis outcomes. In summary, glycemic control profoundly impacts the outcomes of diabetic patients with coccidioidomycosis.

AbstractIntroductionInpatient hyperglycemia is an established independent risk factor among several patient cohorts for hospital readmission. This has not been studied after kidney transplantation. Nearly one‐third of patients who have undergone a kidney transplant reportedly experience 30‐day readmission.MethodsData on first‐time solitary kidney transplantations were retrieved between September 2015 and December 2018. Information was linked to the electronic health records to determine diagnosis of diabetes mellitus and extract glucometric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30‐day readmission. We report the average performance of the models on the testing set on bootstrapped partitions of the data to ensure statistical significance.ResultsThe cohort included 1036 patients who received kidney transplantation; 224 (22%) experienced 30‐day readmission. The machine learning algorithm was able to predict 30‐day readmission with an average area under the receiver operator curve (AUC) of 78% with (76.1%, 79.9%) 95% confidence interval (CI). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient‐hyperglycemia, inpatient‐hypoglycemia, minimum and maximum glucose values among those with higher 30‐day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper‐ and hypoglycemia, the recipient and donor body mass index (BMI) values, presence of delayed graft function, and African American race as the most predictive risk factors of 30‐day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed.ConclusionsSuboptimal glucose metrics during hospitalization after kidney transplantation are associated with an increased risk for 30‐day hospital readmission. Optimizing hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30‐day readmission.

Diabetes Technology Society organized an expert consensus panel to develop metrics for research in the use of continuous glucose monitors (CGMs) in a hospital setting. The experts met virtually in small groups both before and after an April 13, 2023 virtual meeting of the entire panel. The goal of the panel was to develop consensus definitions in anticipation of greater use of CGMs in hospital settings in the future. Establishment of consensus definitions of inpatient analytical metrics will be easier to compare outcomes between studies. Panelists defined terms related to 10 dimensions of measurements related to the use of CGMs including (1) hospital hypoglycemia, (2) hospital hyperglycemia, (3) hospital time in range, (4) hospital glycemic variability, (5) hospital glycemia risk index, (6) accuracy of CGM devices and reference methods for CGMs in the hospital, (7) meaningful time blocks for hospital glycemic goals, (8) hospital CGM data sufficiency, (9) using CGM data for insulin dosing, and (10) miscellaneous factors. The panelists voted on 51 proposed recommendations. Based on the panel vote, 51 recommendations were classified as either strong (43) or mild (8). Additional research is needed on CGM performance in the hospital. This consensus report is intended to support that type of research intended to improve outcomes for hospitalized people with diabetes.

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.

Abstract Autoimmune polyglandular syndromes (APS) are rare disorders characterized by auto‐destruction of endocrine and non‐endocrine organs by organ‐specific antibody‐directed T‐lymphocytic infiltration. This case highlights a 29‐year‐old Caucasian man with vitiligo found to have significant neurological abnormalities in the setting of newly diagnosed pernicious anemia and thyroid autoimmune disease.