Case presentation: A 33-year-old female presented with recurrent hypoglycemia. Endogenous hyperinsulinemia was confirmed by a prolonged fast, however serial imaging was negative. Incidental finding of an ovarian mass gave rise to the suspicion of an insulin-producing ovarian tumor. Subsequent multimodality pancreatic imaging remained negative, requiring more invasive investigations. The tumor was localized by specialized arteriography using calcium stimulation to support the diagnosis of an insulinoma. However, repeated negative imaging led to further delays in definitive management, with worsening hypoglycemia. The surgery was finally performed three years after the initial presentation with successful removal of the tumor using intra-operative ultrasound.
Clinical discussion: It is important to emphasize that preoperative radiological imaging is useful to localize pancreatic lesions. However, most insulinomas could only be detected intraoperatively. The absence of suggestive radiological evidence should not deter surgeons from proceeding with definitive surgical intervention.
Conclusion: The case highlights the importance of a multidisciplinary approach in the management of a complicated case.
OBJECTIVE: This study analyses the effect of estimating EGP for ICU patients with very low SI (severe insulin resistance) and its impact on identified, model-based insulin sensitivity identification, modeling accuracy, and model-based glycemic clinical control.
METHODS: Using clinical data from 717 STAR patients in 3 independent cohorts (Hungary, New Zealand, and Malaysia), insulin sensitivity, time of insulin resistance, and EGP values are analyzed. A method is presented to estimate EGP in the presence of non-physiologically low SI. Performance is assessed via model accuracy.
RESULTS: Results show 22%-62% of patients experience 1+ episodes of severe insulin resistance, representing 0.87%-9.00% of hours. Episodes primarily occur in the first 24 h, matching clinical expectations. The Malaysian cohort is most affected. In this subset of hours, constant model-based EGP values can bias identified SI and increase blood glucose (BG) fitting error. Using the EGP estimation method presented in these constrained hours significantly reduced BG fitting errors.
CONCLUSIONS: Patients early in ICU stay may have significantly increased EGP. Increasing modeled EGP in model-based glycemic control can improve control accuracy in these hours. The results provide new insight into the frequency and level of significantly increased EGP in critical illness.
METHODS: We performed a single-blind, cross-over design study. Twenty-five healthy young men performed three exercise protocols as follows: 1) no blood flow restriction exercise (control group), 2) resistance exercise at 40% of arterial occlusion pressure (AOP) (low group), and 3) resistance exercise at 70% of AOP (high group). Blood lactate, GH, testosterone, and IGF-1 levels were measured at four time points.
RESULTS: There were no differences in the indices before exercise. The blood flow restriction exercise under different pressures had different effects on each index and there was an interactive effect. GH levels were significantly higher in the high group than in the other groups after exercise. Immediately after exercise, IGF-1 and testosterone levels were significantly higher in the high group than in the other groups. At 15 minutes after exercise, testosterone levels were significantly higher in the high group than in the other groups.
CONCLUSIONS: Low-intensity resistance exercise combined with blood flow restriction effectively increases GH, IGF-1, and testosterone levels in young men. Increasing the cuff pressure results in greater levels of hormone secretion.
OBJECTIVE: This study examines how patients with diabetes mellitus responded towards their clinical treatments, where the probability distribution of patients and the types of treatment received were derived from the Rasch probabilistic model.
METHODS: This is a retrospective study wherein data were collected from patients' medical records at a local public hospital in Selangor, Malaysia. Clinical and demographic information such as fasting blood glucose, hemoglobin A1c (HbA1c), family history, type of diabetes (type 1 or type 2), types of medication (oral or insulin), compliance with treatments, gender, race and age were chosen as the agents of measurement.
RESULTS: The use of Rasch analysis in the present study helped to compare the patients' responses towards the DM treatments and identify the types of treatment they received. Results from the Wright map show that a majority of the diabetes mellitus patients who were diagnosed with type 2 diabetes have no controlled readings of HbA1c during their first and second visits to the medical center. However, patients with a family history of diabetes mellitus who took oral medication have controlled readings of fasting blood glucose based on the probabilistic outcomes of the treatment received by the patients.
CONCLUSION: Controlled readings were found only in the readings of fasting blood glucose during the first and second visits, followed by family history, types of medication received, and compliance with the treatment. This study has recommended that type 2 patients with diabetes without a family history of diabetes mellitus need to exercise more control over the readings of HbA1c.