Use of Fracture Risk Assessment Tool in clinical practice and Fracture Risk Assessment Tool future directions.

Fracture Risk Assessment Tool is a free, online fracture risk calculator which can be used to predict 10-year fracture risk for women and men over age 50 years. It incorporates seven clinical risk factors and bone density to give a 10-year risk of major osteoporotic fracture and hip fracture. This dynamic tool can be used with patients at the bedside to help guide treatment decisions. There are some limitations to Fracture Risk Assessment Tool, with the most central limitation being the fact that inputs are binary. Much research has been done to try to refine Fracture Risk Assessment Tool to allow for more accurate risk prediction, and this article describes the data for adjusting Fracture Risk Assessment Tool depending on the clinical scenario such as the dose of glucocorticoid use, presence of diabetes and others. Recently, the new FRAXplus tool has been developed to address many of these concerns and will likely replace the old Fracture Risk Assessment Tool in the future. At the current time, it is available in beta form.

Methods for Refining the FRAX® Tool in Patients with Low Bone Density to Help Improve the Accuracy of Osteoporotic Fracture Risk PredictionMany patients who have low bone density develop fragility fractures, even those whose bone density is not yet within the osteoporosis range. Thus, in patients with low bone density, the health care team should estimate the risk of fracture to decide which patients should take medications to prevent fractures. Factors such as age, body mass index, steroid use, family history and other clinical factors can influence the fracture risk, in addition to bone density. There is an online calculator called the Fracture Risk Assessment Tool (FRAX®) which allows patients and doctors to integrate these risk factors with bone density in order to estimate the 10 year risk of osteoporotic fractures. FRAX® asks a series of yes/no questions about the patient's risks for fracture, and also takes into account the patient's country of residence, age, gender, race and bone density at the femur neck. However, there are some important limitations of this calculator. For example, we think that steroid medications increase the risk of fractures, and the higher the dose, the higher the risk of fractures. However, FRAX® only allows a "yes" or "no" input to the steroid use question. This paper aims to descibe methods for refining the FRAX® calculation to make the fracture risk prediction more accurate. For example, it describes a mathematical adjustment to FRAX® to account for the dose of steroids used. It also reviews methods for FRAX® adjustment for diabetes type 1 and 2, and severity of rheumatoid arthritis, among other considerations. Importantly, there is a new FRAX® tool that is currently in beta testing which will also further refine the accuracy of fracture risk prediction.

Cyclic Cushing syndrome due to an ectopic pituitary adenoma.

BACKGROUND: A 39-year-old man was referred to an endocrinology clinic for evaluation of his Cushing syndrome. He had gained 20 kg over 5 years and complained of intermittent headaches and easy bruisability. His medical history included a left foot fracture associated with minimal trauma 2 years earlier, hypertension, and stable Crohn disease with no use of exogenous glucocorticoids for at least 10 years. INVESTIGATIONS: Measurements of plasma adrenocorticotropic hormone, 24 h urine free cortisol excretion, late-night salivary cortisol, serum cortisol levels before and after corticotropin-releasing hormone administration during a dexamethasone suppression/corticotropin-releasing hormone-stimulation test, pituitary MRI, and inferior petrosal sinus sampling. DIAGNOSIS: Cyclic Cushing syndrome secondary to an ectopic pituitary adenoma. MANAGEMENT: The cyclic nature of Cushing syndrome was suggested by the absence of hypercortisolemia during inferior petrosal sinus sampling, and was confirmed by multiple 24 h urine free cortisol measurements. The patient underwent transsphenoidal surgery, during which a 5 mm firm, round, midline sphenoid sinus lesion was identified and resected. In preoperative imaging studies, this lesion had been interpreted as being a mucosal polyp. At microscopic examination, the lesion was found to be a pituitary adenoma, which stained diffusely with antiadrenocorticotropic-hormone antibodies. Explorations of the sella and pituitary did not reveal any abnormalities. Postoperatively, the patient became hypocortisolemic and his cushingoid features resolved. His adrenal function normalized 3 months after surgery. At 18 months, the patient continued to be symptom-free with normal levels of urinary-free cortisol and midnight salivary cortisol.

Changing patterns in diagnosis and therapy of acromegaly over two decades.

BACKGROUND: The increased morbidity and mortality of acromegaly makes early diagnosis and therapy critical. However, whether the type of medical professional who first diagnoses acromegaly, the major complaint prompting medical attention, or the management paradigms used in the setting of novel medical therapies have changed over time has not been well explored. OBJECTIVES: Our objective was to identify the medical professional who first suspected acromegaly and the complaint prompting the diagnosis, and if these have changed. Additional goals were to assess the interval from symptom onset to diagnosis of acromegaly and to compare treatment trends over consecutive decades. DESIGN: This was a case-record retrospective study. SETTING: The study was performed in a neuroendocrine clinical center at a tertiary care center. SUBJECTS: A total of 100 patients (45 men and 55 women) with acromegaly referred from 1985-2005 was included in the study. RESULTS: Acral changes (24%) and headaches (20%) were most prevalent presenting symptoms prompting diagnosis. Eighteen percent reported no symptoms of acromegaly at diagnosis. The primary care physician most often initiated the evaluation (44%). Comorbidities were more prevalent in older patients (P = 0.001). The interval between symptom onset and diagnosis decreased, compared with previous reports. Radiation therapy was used less frequently in the decade after 1994 than in the prior (16 vs. 33%; P < 0.05). CONCLUSIONS: The primary care doctor plays the major role in diagnosis of acromegaly. The increased use of brain magnetic resonance imaging may contribute to the many incidentally discovered cases and to the shortened time interval to diagnosis. Presumably due to the availability of new medical therapies, the use of radiation therapy has decreased.