Daily Endocrinology Research Analysis

BACKGROUND: Sodium glucose co-transporter 2 inhibitors (SGLT2i) are associated with a small-magnitude but higher risk of diabetic ketoacidosis (DKA). However, objectively identifying patients at lowest and highest risk of DKA is challenging. METHODS: We developed a prediction model using outpatient prescription data from Ontario, Canada and externally validated it using data from Denmark. We included adults with type 2 diabetes mellitus who were newly prescribed an SGLT2i. Our candidate predictors in the model were based on prior work and included the following: Sex, insulin use, prior DKA, dementia, hemoglobin A1C, and creatinine. Our outcome was 1-year risk of hospitalization with DKA. We calculated a risk score using an adaptation of penalized regression for each patient reported test characteristics in Ontario (derivation cohort) and Denmark (external validation cohort). RESULTS: We identified 322,135 in Ontario and 43,377 adults in Denmark who had type 2 diabetes mellitus and received an SGLT2i. The absolute risk of DKA within 1-year was 0.28 % (N = 916) in Ontario and 0.23 % (N = 101) in Denmark. Using data from Ontario, the risk score for each variable were as follows: Insulin use = 4 points, A1C > 9 % = 4 points and prior DKA = 19 points. All other variables received zero points. The overall model AUC was 63 % in Ontario and 66 % in Denmark (external validation set). Within Ontario, at a score threshold of zero, the risk of DKA was 0.19 % and the PPV was 0.3 % and the sensitivity was 100 % and similar results were observed in Denmark. For adults with a score of 19 or higher, the risk of DKA was 35-fold higher but false positives were common yielding a PPV of 6.7 % and sensitivity was lower at 3 %. In Denmark, adults with a score of 19 or higher had a risk of 11 % and the PPV was 10.2 % and sensitivity was 5 %. CONCLUSION: Adults with a score of 0 (that is, simply a lack of DKA history, lack of insulin therapy, and A1c < 9 %) can be reassured that 99.8 % will not experience DKA in the subsequent year. In contrast, for adults with a score of 19 or higher the one-year risk of DKA is approximately 9 %, but false positives and false negatives are common and thus more work is needed to improve the predictive performance of the model.

BACKGROUND: The incidence of adrenal incidentalomas (AIs) is increasing yearly. The early discovery of AIs is helpful to better manage adrenal diseases, especially subclinical primary aldosteronism, Cushing's syndrome and pheochromocytoma. METHODS: In this multicenter retrospective study, a total of 778 patients from three different medical centers were assessed. The two-stage cascade network consisted of a 3D Res-Unet network for adrenal gland segmentation and a classifier for determining the presence of AIs. The segmentation network was mainly evaluated by the Dice similarity coefficient (DSC), and the classifier was evaluated by the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity. The Delong test was used to compare the classification performance between the cascade network and manual segmentation. RESULTS: A total of 443 patients were randomly assigned in a 7:3 ratio, stratified sampling, to train and valid sets of the model development cohort, and 335 patients from the three centers were included in the test cohort. In the validation set, the AUC of the model for identifying left AI was 88.15%, and the AUC of the model for identifying right AI was 87.90%. There was no significant difference between model performance and manual segmentation of AIs ( CONCLUSIONS: The two-stage cascade network based on a deep learning algorithm can be used for automatic recognition of AIs in nonenhanced CT from different centers.

BACKGROUND: Diabetic foot complications, driven by microvascular dysfunction, remain a leading cause of morbidity and amputations. Early detection of microcirculatory and biomechanical alterations in vulnerable muscles, such as the extensor hallucis brevis (EHB), may contribute to risk stratification. However, noninvasive tools for quantifying these changes are lacking. METHODS: This cross-sectional study enrolled 90 participants stratified into healthy controls, uncomplicated type 2 diabetes (T2DM), and T2DM with microvascular complications (MC). Shear wave elastography (SWE) measured EHB stiffness (mean Young's modulus, Emean), while contrast-enhanced ultrasound (CEUS) assessed perfusion dynamics (transcapillary transit time [ΔAT], net enhancement intensity [ΔPI]). Diagnostic accuracy and reproducibility were evaluated via ROC analysis and intra-class correlation coefficients (ICC). RESULTS: Emean increased progressively across groups (control: 11.88 kPa; T2DM: 15.78 kPa; T2DM+MC: 18.57 kPa; P < 0.01). T2DM+MC exhibited prolonged ΔAT (89.5 s vs. 50.5 s in controls) and reduced ΔPI (5.0 dB vs. 7.0 dB; P < 0.01). ROC analysis demonstrated high diagnostic accuracy for ΔAT (AUC = 0.970), Emean (AUC = 0.947), and ΔPI (AUC = 0.931) in detecting MC. Both SWE and CEUS showed excellent reproducibility (ICC > 0.80). CONCLUSION: SWE and CEUS provide robust, noninvasive biomarkers for early diabetic microvascular complications. The EHB's unique susceptibility to stiffness and perfusion deficits highlights its clinical value, which may facilitate diabetic foot risk assessment and guide timely interventions to mitigate ulceration and amputations.