Qualitative focus group interview study of communication between patients, dentists and physicians for efficient osteonecrosis of the jaw practices.

OBJECTIVES: To investigate problems and the current status of existing methods of communication between patients, dentists and physicians for the prevention and treatment of medication-related osteonecrosis of the jaw (MRONJ). DESIGN: A focus group interview study with a descriptive design using qualitative content analysis of transcripts. SETTING: Secondary care in Korea. PARTICIPANTS: 3 patient groups and 4 health professional groups in a total of 32 participants including patients with osteoporosis or bone metastasis, dentists and physicians. RESULTS: This study revealed that patients lacked medical knowledge of osteoporosis drugs, whereas dentists and physicians lacked each other's expertise. All patients reported undergoing dental treatments during the osteoporosis drug treatment, but dentists and physicians had different MRONJ experiences depending on their work setting in primary or secondary care. Patients expressed dissatisfaction with the current system of communication with health professionals via letter as they found this to be a slow process. Dentists and physicians reported the need for effective communication because they felt defensive when sending and receiving medical consults. CONCLUSIONS: Despite the low incidence of MRONJ among patients with osteoporosis, it is difficult to treat; thus, it is necessary to inform dentists, physicians and patients about the importance of MRONJ prevention. To this end, close communication among all involved stakeholders about osteoporosis drugs is required.

Bone Radiomics Score Derived From DXA Hip Images Enhances Hip Fracture Prediction in Older Women.

Dual-energy X-ray absorptiometry (DXA)-based bone mineral density testing is standard to diagnose osteoporosis to detect individuals at high risk of fracture. A radiomics approach to extract quantifiable texture features from DXA hip images may improve hip fracture prediction without additional costs. Here, we investigated whether bone radiomics scores from DXA hip images could improve hip fracture prediction in a community-based cohort of older women. The derivation set (143 women who sustained hip fracture [mean age 73 years, time to fracture median 2.1 years] versus 290 age-matched women [mean age 73 years] who did not sustain hip fracture during follow-up [median 5.5 years]) were split into the train set (75%) and the test set (25% hold-out set). Among various models using 14 selected features out of 300 texture features mined from DXA hip images in the train set, random forest model was selected as the best model to build a bone radiomics score (range 0 to 100) based on the performance in the test set. In a community-based cohort (2029 women, mean age 71 years) as the clinical validation set, the bone radiomics score was calculated using a model fitted in the train set. A total of 34 participants (1.7%) sustained hip fracture during median follow-up of 5.4 years (mean bone radiomics score 40 ± 16 versus 28 ± 12 in non-fractured, p < 0.001). A one-point bone radiomics score increment was associated with a 4% elevated risk of incident hip fracture (adjusted hazard ratio [aHR] = 1.04, p = 0.001) after adjustment for age, body mass index (BMI), previous history of fracture, and femoral neck T-score, with improved model fit when added to covariates (likelihood ratio chi-square 10.74, p = 0.001). The association between bone radiomics score with incident hip fracture remained robust (aHR = 1.06, p < 0.001) after adjustment for FRAX hip fracture probability. Bone radiomics scores estimated from texture features of DXA hip images have the potential to improve hip fracture prediction. © 2021 American Society for Bone and Mineral Research (ASBMR).

Machine Learning Applications in Endocrinology and Metabolism Research: An Overview.

Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have been actively deployed. Relevant studies are discussed to provide an overview of the methodology, main findings, and limitations of ML, with the goal of stimulating insights into future research directions. Clear, testable study hypotheses stem from unmet clinical needs, and the management of data quality (beyond a focus on quantity alone), open collaboration between clinical experts and ML engineers, the development of interpretable high-performance ML models beyond the black-box nature of some algorithms, and a creative environment are the core prerequisites for the foreseeable changes expected to be brought about by ML and artificial intelligence in the field of endocrinology and metabolism, with actual improvements in clinical practice beyond hype. Of note, endocrinologists will continue to play a central role in these developments as domain experts who can properly generate, refine, analyze, and interpret data with a combination of clinical expertise and scientific rigor.