Background: Hip fracture patients in the aging population frequently present with various comorbidities, whilst preservation of independency and activities of daily living can be challenging. Thus, an interdisciplinary orthogeriatric treatment of these patients has recognized a growing acceptance in the last years. As there is still limited data on the impact of this approach, the present study aimed to evaluate the long-term outcome in elderly hip fracture patients, by comparing the treatment of a hospital with integrated orthogeriatric care (OGC) with a conventional trauma care (CTC). Methods: We conducted a retrospective, two-center, cohort study. In two maximum care hospitals all patients presenting with a hip fracture at the age of ≥ 70 years were consecutively assigned within a 1 year period and underwent follow-up examination 12 months after surgery. Patients treated in hospital site A were treated with an interdisciplinary orthogeriatric approach (co-managed care), patients treated in hospital B underwent conventional trauma care. Main outcome parameters were 1 year mortality, readmission rate, requirement of care (RC) and personal activities of daily living (ADL). Results: A total of 436 patients were included (219 with OGC / 217 with CTC). The mean age was 83.55 (66–99) years for OGC and 83.50 (70–103) years for CTC (76.7 and 75.6% of the patients respectively were female). One year mortality rates were 22.8% (OGC) and 28.1% (CTC; p = 0.029), readmission rates were 25.7% for OGC compared to 39.7% for CTC (p = 0.014). Inconsistent data were found for activities of daily living. After 1 year, 7.8% (OGC) and 13.8% (CTC) of the patients were lost to follow-up. Conclusions: Interdisciplinary orthogeriatric management revealed encouraging impact on the long-term outcome of hip fracture patients in the aging population. The observed reduction of mortality, requirements of care and readmission rates to hospital clearly support the health-economic impact of an interdisciplinary orthogeriatric care on specialized wards. Trial registration: The study was approved and registered by the bavarian medical council (BLAEK: 7/11192) and the local ethics committee of munich university (Reg. No. 234–16) and was conducted as a two-center, cohort study at a hospital with integrated orthogeriatric care and a hospital with conventional trauma care.

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.

Multimorbidity and polypharmacy are common challenges in the treatment of older trauma patients. Therefore, various integrated care models were developed over the last few years, merging the expertise of geriatricians and trauma surgeons. The aim of this study was to evaluate, if the number of prescriptions of potentially inappropriate medication (PIM) could be reduced in these patients by an interdisciplinary co-managed concept compared to conventional trauma care. We conducted a retrospective, dual-center cohort study, including all patients aged 70 years and older admitted with a fracture of the hip or the proximal humerus within the study period. Patients were treated in the universities department of trauma surgery with two different hospital sites, one with conventional trauma care (CTC) and the other one with a certified orthogeriatric trauma unit (OGC). Based on the STOPP/START criteria by O´Mahony et al., PIMs were defined, which should be avoided in (ortho)geriatric patients. Medical records of each patient were analyzed at discharge. Besides patients basic information, all prescribed drugs, changes in the medication plan and who carried out these changes were collected. For statistical analysis based on the data quality and distribution, the t test, Mann–Whitney U test and the Chi-square test were used. A total of 95 patients were included, 73 of them females, with an average age of 82.59 years (SD ± 6.96). Mean length of hospital stay was 12.98 at CTC and 13.36 days at OGC (p = 0.536). Among conventional care (41 patients), prescription of one or more PIMs was found in 85.4% of the patients, whereas at the orthogeriatric ward (54 patients) only in 22.2% (p < 0.001). Besides that, changes in medication were made for 48.1% of the patients during their stay on the orthogeriatric ward. Our findings show that an integrated care concept can reduce the number of prescriptions of PIMs significantly and potentially avoids adverse drug reactions and additional burdens in older trauma 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.