The use of predictive fall models for older adults receiving aged care, using routinely collected electronic health record data : a systematic review

Background: Falls in older adults remain a pressing health concern. With advancements in data analytics and increasing uptake of electronic health records, developing comprehensive predictive models for fall risk is now possible. We aimed to systematically identify studies involving the development and implementation of predictive falls models which used routinely collected electronic health record data in home-based, community and residential aged care settings. Methods: A systematic search of entries in Cochrane Library, CINAHL, MEDLINE, Scopus, and Web of Science was conducted in July 2020 using search terms relevant to aged care, prediction, and falls. Selection criteria included English-language studies, published in peer-reviewed journals, had an outcome of falls, and involved fall risk modelling using routinely collected electronic health record data. Screening, data extraction and quality appraisal using the Critical Appraisal Skills Program for Clinical Prediction Rule Studies were conducted. Study content was synthesised and reported narratively. Results: From 7,329 unique entries, four relevant studies were identified. All predictive models were built using different statistical techniques. Predictors across seven categories were used: demographics, assessments of care, fall history, medication use, health conditions, physical abilities, and environmental factors. Only one of the four studies had been validated externally. Three studies reported on the performance of the models. Conclusions: Adopting predictive modelling in aged care services for adverse events, such as falls, is in its infancy. The increased availability of electronic health record data and the potential of predictive modelling to document fall risk and inform appropriate interventions is making use of such models achievable. Having a dynamic prediction model that reflects the changing status of an aged care client is key to this moving forward for fall prevention interventions

Inhibition of mTOR delayed but could not prevent experimental collapsing focal segmental glomerulosclerosis

Contains fulltext : 220207.pdf (publisher's version ) (Open Access

The tetraspanin CD9 controls migration and proliferation of parietal epithelial cells and glomerular disease progression

International audienc

Extracellular Vesicle Isolation and Characterization from Periprosthetic Joint Synovial Fluid in Revision Total Joint Arthroplasty

Extracellular vesicles (EVs) comprise an as yet insufficiently investigated intercellular communication pathway in the field of revision total joint arthroplasty (RTJA). This study examined whether periprosthetic joint synovial fluid contains EVs, developed a protocol for their isolation and characterized them with respect to quantity, size, surface markers as well as documented their differences between aseptic implant failure (AIF) and periprosthetic joint infection (PJI). EV isolation was accomplished using ultracentrifugation, electron microscopy (EM) and nanoparticle tracking analysis evaluated EV presence as well as particle size and quantity. EV surface markers were studied by a bead-based multiplex analysis. Using our protocol, EM confirmed the presence of EVs in periprosthetic joint synovial fluid. Higher EV particle concentrations and decreased particle sizes were apparent for PJI. Multiplex analysis confirmed EV-typical surface epitopes and revealed upregulated CD44 and HLA-DR/DP/DQ for AIF, as well as increased CD40 and CD105. Our protocol achieved isolation of EVs from periprosthetic joint synovial fluid, confirmed by EM and multiplex analysis. Characterization was documented with respect to size, concentration and epitope surface signature. Our results indicate various differences between PJI and AIF EVs. This pilot study enables new research approaches and rising diagnostic opportunities in the field of RTJA