Bone marrow edema (BME) is a descriptive term for a common finding in magnetic resonance imaging (MRI). Although pain is the major symptom, BME differs in terms of its causal mechanisms, underlying disease, as well as treatment and prognosis. This complexity together with the lack of evidence-based guidelines, frequently makes the identification of underlying conditions and its management a major challenge. Unnecessary multiple consultations and delays in diagnosis as well as therapy indicate a need for interdisciplinary clinical recommendations. Therefore, an interdisciplinary task force was set up within our large osteology center consisting of specialists from internal medicine, endocrinology/diabetology, hematology/oncology, orthopedics, pediatrics, physical medicine, radiology, rheumatology, and trauma surgery to develop a consenus paper. After review of literature, review of practical experiences (expert opinion), and determination of consensus findings, an overview and an algorithm were developed with concise summaries of relevant aspects of the respective underlying disease including diagnostic measures, clinical features, differential diagnosis and treatment of BME. Together, our single-center consensus review on the management of BME may help improve the quality of care for these patients.

Background: Assessment of the physical frailty of older patients is of great importance in many medical disciplines to be able to implement individualized therapies. For physical tests, time is usually used as the only objective measure. To record other objective factors, modern wearables offer great potential for generating valid data and integrating the data into medical decision-making. Objective: The aim of this study was to compare the predictive value of insole data, which were collected during the Timed-Up-and-Go (TUG) test, to the benchmark standard questionnaire for sarcopenia (SARC-F: strength, assistance with walking, rising from a chair, climbing stairs, and falls) and physical assessment (TUG test) for evaluating physical frailty, defined by the Short Physical Performance Battery (SPPB), using machine learning algorithms. Methods: This cross-sectional study included patients aged >60 years with independent ambulation and no mental or neurological impairment. A comprehensive set of parameters associated with physical frailty were assessed, including body composition, questionnaires (European Quality of Life 5-dimension [EQ 5D 5L], SARC-F), and physical performance tests (SPPB, TUG), along with digital sensor insole gait parameters collected during the TUG test. Physical frailty was defined as an SPPB score≤8. Advanced statistics, including random forest (RF) feature selection and machine learning algorithms (K-nearest neighbor [KNN] and RF) were used to compare the diagnostic value of these parameters to identify patients with physical frailty. Results: Classified by the SPPB, 23 of the 57 eligible patients were defined as having physical frailty. Several gait parameters were significantly different between the two groups (with and without physical frailty). The area under the receiver operating characteristic curve (AUROC) of the TUG test was superior to that of the SARC-F (0.862 vs 0.639). The recursive feature elimination algorithm identified 9 parameters, 8 of which were digital insole gait parameters. Both the KNN and RF algorithms trained with these parameters resulted in excellent results (AUROC of 0.801 and 0.919, respectively). Conclusions: A gait analysis based on machine learning algorithms using sensor soles is superior to the SARC-F and the TUG test to identify physical frailty in orthogeriatric patients.

Osteoporosis-associated fractures are of increasing importance in trauma surgery. Systematic diagnostics and treatment of osteoporosis during a hospital stay, however, remain inadequate. Therefore, a specific algorithm for diagnosing and treating osteoporosis in trauma surgery patients was developed based on the DVO (German Osteology Society) guideline for osteoporosis from 2014. In a first step, the individuals’ age and risk profile for osteoporosis is identified considering specific fractures indicating osteoporosis and risk factors assessed by a specific questionnaire. In addition, physical activity, risk of falls, dietary habits and the individuals’ medication are considered. Basic osteoporosis laboratory tests, a bone densitometry by dual-energy X-ray absorptiometry (DXA) and, if needed, X-rays of the spine are carried out to identify prevalent vertebral body fractures. Based on the treatment algorithm adapted to the new guidelines for osteoporosis in the majority of proximal femoral fractures, treatment of osteoporosis could already be indicated without prior DXA. In case of preexisting glucocorticoid therapy, a history of previous fractures or other risk factors according to the risk questionnaire, the threshold of treatment has to be adjusted given the table of T-scores. The treatment algorithm for diagnosing and treating osteoporosis in in-patient trauma surgery patients can help identify high-risk patients systematically and efficiently. As a result, osteoporosis-associated fractures or failure of osteosynthesis could be reduced, yet a prospective validation of the algorithm has to be completed.

Pregnancy and lactation-associated osteoporosis (PLO) with predominantly subsequent vertebral fracture is a rare but severe disease with an estimated incidence of 0.4 in 100,000. In the past, patients with PLO have been predominantly treated with oral and i.v. bisphosphonates to reduce subsequent fracture risk. Hereby, the use of bisphosphonates in premenopausal women is controversial, as bisphosphonates know to persist in bone for many years and can be exposed and circulate in maternal serum and subsequently pass the placenta barrier and may have a detrimental effect on fetal bone health. Here we report the effects of denosumab on the bone mineral density (BMD) and subsequent fracture risk in PLO. In this case presentation, denosumab was administered postpartum with 3000 IE vitamin D and 1000 mg of calcium daily in a patient with PLO and vertebral fracture of L1 and L4. After 18 months of treatment with denosumab, we could demonstrate a clinical significant increase of BMD at the lumbar spine, femoral neck, and total hip of 32.2%, 13.0%, and 11.5% respectively with no further subsequent fractures. As the patient had regular menstrual cycles and considered a further pregnancy, denosumab treatment was terminated and soon a second pregnancy occurred. After the second pregnancy, BMD decreased at the lumbar spine, femur neck, and total hip by −8.8%, −6.9%, and −7.0% respectively compared to the maximum values during treatment with denosumab, but was still significantly higher compared to baseline levels with no further fractures.

Purpose: The present study is aiming to evaluate patients’ awareness to participate in further diagnostics for osteoporosis and to find out if there are significant differences with regards to fracture site. Methods: Patients at risk for underlying osteoporosis (female >60 and male >70 years) undergoing surgical treatment for a distal radius fracture (DRF) or a proximal femur fracture (PFF) were asked to complete a questionnaire assessing the awareness for underlying osteoporosis. Furthermore, dual-X-ray absorptiometry (DXA) scans were analyzed. Results: Overall, 150 patients (w = 122/m = 28, mean age 79.9 years (±8.6)) were included, of these, 36 patients suffered a DRF and 114 patients a PFF. Of these, 68 out of the 150 patients (45.3%) considered that an examination was necessary, whereas in PFF patients the awareness was higher than in the DRF Group (41% vs. 32%). Conclusions: The patients’ willingness to undergo further diagnostics for osteoporosis was generally poor. DRFs are frequently accompanied by a lower limitation of quality of life compared to PFF, which might be causative for even poorer awareness in these patients. Especially younger patients (age 60–70 years) with a distal radius fracture seemed to underestimate osteoporosis.

Introduction: The measurement of physical frailty in elderly patients with orthopedic impairments remains a challenge due to its subjectivity, unreliability, time-consuming nature, and limited applicability to uninjured individuals. Our study aims to address this gap by developing objective, multifactorial machine models that do not rely on mobility data and subsequently validating their predictive capacity concerning the Timed-up-and-Go test (TUG test) in orthogeriatric patients. Methods: We utilized 67 multifactorial non-mobility parameters in a pre-processing phase, employing six feature selection algorithms. Subsequently, these parameters were used to train four distinct machine learning algorithms, including a generalized linear model, a support vector machine, a random forest algorithm, and an extreme gradient boost algorithm. The primary goal was to predict the time required for the TUG test without relying on mobility data. Results: The random forest algorithm yielded the most accurate estimations of the TUG test time. The best-performing algorithm demonstrated a mean absolute error of 2.7 s, while the worst-performing algorithm exhibited an error of 7.8 s. The methodology used for variable selection appeared to exert minimal influence on the overall performance. It is essential to highlight that all the employed algorithms tended to overestimate the time for quick patients and underestimate it for slower patients. Conclusion: Our findings demonstrate the feasibility of predicting the TUG test time using a machine learning model that does not depend on mobility data. This establishes a basis for identifying patients at risk automatically and objectively assessing the physical capacity of currently immobilized patients. Such advancements could significantly contribute to enhancing patient care and treatment planning in orthogeriatric settings.