Reduced Awareness for Osteoporosis in Hip Fracture Patients Compared to Elderly Patients Undergoing Elective Hip Replacement.

Background: Osteoporotic fractures are associated with a loss of quality of life, but only few patients receive an appropriate therapy. Therefore, the present study aims to investigate the awareness of musculoskeletal patients to participate in osteoporosis assessment and to evaluate whether there are significant differences between acute care patients treated for major fractures of the hip compared to elective patients treated for hip joint replacement.; Methods: From May 2015 to December 2016 patients who were undergoing surgical treatment for proximal femur fracture or total hip replacement due to osteoarthritis and were at risk for an underlying osteoporosis (female > 60 and male > 70 years) were included in the study and asked to complete a questionnaire assessing the awareness for an underlying osteoporosis. ASA Score, FRAX Score, and demographic information have also been examined. Results: In total 268 patients (female = 194 (72.0%)/male = 74 (28%)), mean age 77.7 years (±7.7) undergoing hip surgery were included. Of these, 118 were treated for fracture-related etiology and 150 underwent total hip arthroplasty in an elective care setting. Patients were interviewed about their need for osteoporosis examination during hospitalization. Overall, 76 of 150 patients receiving elective care (50.7%) considered that an examination was necessary, whereas in proximal femur fracture patients the awareness was lower, and the disease osteoporosis was assessed as threatening by significantly fewer newly fractured patients. By comparison, patients undergoing trauma surgery had a considerably greater risk of developing another osteoporotic fracture than patients undergoing elective surgery determined by the FRAX® Score (p ≤ 0.001).; Conclusions: The patients' motivation to endure additional osteoporosis diagnostic testing is notoriously low and needs to be increased. Patients who underwent acute care surgery for a fragility proximal femur fracture, although acutely affected by the potential consequences of underlying osteoporosis, showed lower awareness than the elective comparison population that was also on average 6.1 years younger. Although elective patients were younger and at a lower risk, they seemed to be much more willing to undergo further osteoporosis assessment. In order to better identify and care for patients at risk, interventions such as effective screening, early initiation of osteoporosis therapy in the inpatient setting and a fracture liaison service are important measures.

Development of a Machine Learning-Based Model to Predict Timed-Up-and-Go Test in Older Adults.

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.

Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole-Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study.

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.

Reduced Awareness for Osteoporosis in Distal Radius Fracture Patients Compared to Patients with Proximal Femur 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.

[Pharmacotherapy of osteoporosis: evidence-based clinical practice]. / Evidenzbasierte medikamentöse Therapie der Osteoporose.

Since the foundation of the DVO ("Dachverband Osteologie") in 1999, it has been the continuous ambition to implement evidence-based medicine in the clinical course for the effective diagnosis and treatment of osteoporosis. This aim was addressed by the implementation of general guidelines (DVO) in 2003. These guidelines (DVO) offer strategies for diagnosis and treatment of osteoporosis in postmenopausal women, osteoporosis in elderly women and glucocorticoid-induced osteoporosis. Evidence-based treatment of osteoporosis recommends the supplementation with calcium and vitamin D, considering antiresorptive therapy antifracture efficacy is proven for the bisphosphonates alendronate, ibandronate and risedronate on the one hand and the SERM raloxifen on the other hand. On the basis of valid data from randomized, placebo-controlled clinical trials, new therapy regimen like rhPTH1-34 (teriparatide) or strontium ranelate fulfill the recommendation criteria for the guidelines (DVO). Effective treatment of glucocorticoid-induced osteoporosis includes supplemental calcium and vitamin D and antiresorptive agents like the bisphosphonates alendronate, risedronate and etidronate according to the guidelines (DVO).

[The successful development of bisphosphonates in the therapy of osteoporosis]. / Die Entwicklung der Bisphosphonate-ein Meilenstein in der Osteoporosetherapie.

In the last 30 years bisphosphonates have been used in the treatment of different bone diseases. Bisphosphonates in their oral pharmaceutical form are an established and approved medication in osteoporosis treatment for years. Latest research developed more potent bisphosphonates in different application forms: oral and parenteral. Therefore, their prospects for medical treatment could be enlarged to the fields of oncology, hematology, and osteology. Bisphosphonates are innovative in case of adjuvant therapy as well as in adequate pain therapy. In the medical treatment of osteoporosis and metabolic osteopathies bisphosphonates could be rightly denoted as groundbreaking.