Redeployment of specialist surgeons in the COVID-19 pandemic in a general hospital: critical issues and suggestions.

BACKGROUND AND AIM: to gain experience and highlight any margins for improvement, we outlined the role played by specialist surgeons (with particular reference to orthopedic surgeons), redeployed in treating COVID-19 patients in the Emergency Department of a general hospital, with severe overcrowding due to the massive and continuous arrival of patients Methods: "on the field" experience of the Authors is reported, followed by a narrative review of the literature, mainly on the topic of health-personnel redeployment during an emergency Results: a brief chronological discussion of the progressive reorganization of the hospital, in relation to the progress of the epidemic in the area, is reported, with specific reference to the experience of orthopedic and other branches specialist surgeons, that was characterized by a high degree of uncertainty about what to do, worsened by organizational difficulties due to the incessant arrival of patients and subsequent overcrowding. Observations relating to the critical aspects that have emerged and the various solutions proposed or implemented, if they have been identified, as well as the problems still open, are then made and compared to current literature. CONCLUSIONS: The most significant aspect that we have tried to outline is the organizational difficulty, due to the rapid and unpredictable change in the situation: greater efficiency and flexibility, seen as the ability to overcome bureaucratic, logistical, regulatory or budgetary obstacles that prevent the rapid changes made necessary by the epidemic, could perhaps help to face better any subsequent pandemic wave, like the fierce one ongoing at the present moment.

Surgical treatment of peri-prosthetic femur fractures with dedicated NCB plates: our experience.

BACKGROUND AND AIM OF THE WORK: The incidence of periprostethic femur fractures has increased over the last years; the treatment includes an open reduction and internal fixation or revision implant. The treatment of these fractures can be complex, expensive and associated with risks of systemic and local complications. METHODS: We evaluated clinical and radiological results in patients treated in our department for periprosthetic femoral fractures from 2011 to 2017. We included 52 cases of periprosthetic fractures regardless of their classification with a mean follow-up of 2 years. The analisys of the result was performed using Harris Hip Score and searching for radiographic signs of loosening, infections or mechanical failure of the implants. RESULTS: There was no evidence of septic complications or mechanical failure in cases treated. The average HHS was equal to 92 points with a certificate pain relief and a sufficient independence in daily living activities. CONCLUSIONS: The treatment of periprosthetic fractures is complex: it depends on type of fracture, on stability of the stem and on the bone quality. A right classification of the fractures, a good experience of the surgeon in prosthetic and trauma surgery is the basis for the best treatment.

Three-year National report from the Gruppo Italiano di Ortogeriatria (GIOG) in the management of hip-fractured patients.

BACKGROUND: Hip fractures (HF) are a major issue worldwide. We aimed at evaluating the practices in delivering care to patients with HF among several Italian Orthogeriatric centers. METHODS: The study took place from February 2016 to July 2018. Seven performance indicators (pre-surgical cognitive assessment, surgery performed ≤ 48 h from fracture, removal of urinary catheter/absence of delirium/start of physiotherapy on the first post-operative day, prescription of bone protection at discharge, and discharge toward rehabilitation) were collected. RESULTS: The 14 participating hospitals totally recruited 3.017 patients. Patients were old (median age 86 years; Inter Quartile Range [IQR] 80-90), mostly females (77%). Nearly 55% of them were already impaired in mobility and about 10% were nursing home residents. Median time-to-surgery was 41 h (IQR 23-62). Models of care greatly varied among centers, only 49.3% of patients being co-managed by geriatricians and orthopedics. There was high variability across centers in four indicators ("pre-surgical cognitive assessment", "bone protection prescription", "use of urinary catheter" and "start of physiotherapy"), moderate in two indicators ("surgery performed ≤ 48 h from fracture" and "discharge toward rehabilitation" and low in one ("absence of delirium on day following surgery"). Comparison with international studies suggests very different ways of providing care to HF Italian patients. CONCLUSIONS: The study results suggest high inter-center variability in the key-performance indicators, and different approaches in providing care to our HF patients in comparison to other countries. A National debate on the topic is required in Italy to harmonize practices of orthogeriatric care.

Recognition of morphometric vertebral fractures by artificial neural networks: analysis from GISMO Lombardia Database.

BACKGROUND: It is known that bone mineral density (BMD) predicts the fracture's risk only partially and the severity and number of vertebral fractures are predictive of subsequent osteoporotic fractures (OF). Spinal deformity index (SDI) integrates the severity and number of morphometric vertebral fractures. Nowadays, there is interest in developing algorithms that use traditional statistics for predicting OF. Some studies suggest their poor sensitivity. Artificial Neural Networks (ANNs) could represent an alternative. So far, no study investigated ANNs ability in predicting OF and SDI. The aim of the present study is to compare ANNs and Logistic Regression (LR) in recognising, on the basis of osteoporotic risk-factors and other clinical information, patients with SDI≥1 and SDI≥5 from those with SDI = 0. METHODOLOGY: We compared ANNs prognostic performance with that of LR in identifying SDI≥1/SDI≥5 in 372 women with postmenopausal-osteoporosis (SDI≥1, n = 176; SDI = 0, n = 196; SDI≥5, n = 51), using 45 variables (44 clinical parameters plus BMD). ANNs were allowed to choose relevant input data automatically (TWIST-system-Semeion). Among 45 variables, 17 and 25 were selected by TWIST-system-Semeion, in SDI≥1 vs SDI = 0 (first) and SDI≥5 vs SDI = 0 (second) analysis. In the first analysis sensitivity of LR and ANNs was 35.8% and 72.5%, specificity 76.5% and 78.5% and accuracy 56.2% and 75.5%, respectively. In the second analysis, sensitivity of LR and ANNs was 37.3% and 74.8%, specificity 90.3% and 87.8%, and accuracy 63.8% and 81.3%, respectively. CONCLUSIONS: ANNs showed a better performance in identifying both SDI≥1 and SDI≥5, with a higher sensitivity, suggesting its promising role in the development of algorithm for predicting OF.