Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol

BackgroundDistal radius (wrist) fractures are the second most common fracture admitted to hospital. The anatomical pattern of these types of injuries is diverse, with variation in clinical management, guidelines for management remain inconclusive, and the uptake of findings from clinical trials into routine practice limited. Robust predictive modelling, which considers both the characteristics of the fracture and patient, provides the best opportunity to reduce variation in care and improve patient outcomes. This type of data is housed in unstructured data sources with no particular format or schema. The “Predicting fracture outcomes from clinical Registry data using Artificial Intelligence (AI) Supplemented models for Evidence-informed treatment (PRAISE)” study aims to use AI methods on unstructured data to describe the fracture characteristics and test if using this information improves identification of key fracture characteristics and prediction of patient-reported outcome measures and clinical outcomes following wrist fractures compared to prediction models based on standard registry data.Methods and designAdult (16+ years) patients presenting to the emergency department, treated in a short stay unit, or admitted to hospital for >24h for management of a wrist fracture in four Victorian hospitals will be included in this study. The study will use routine registry data from the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), and electronic medical record (EMR) information (e.g. X-rays, surgical reports, radiology reports, images). A multimodal deep learning fracture reasoning system (DLFRS) will be developed that reasons on EMR information. Machine learning prediction models will test the performance with/without output from the DLFRS.DiscussionThe PRAISE study will establish the use of AI techniques to provide enhanced information about fracture characteristics in people with wrist fractures. Prediction models using AI derived characteristics are expected to provide better prediction of clinical and patient-reported outcomes following distal radius fracture

Outcomes of severe lower limb injury with Mangled Extremity Severity Score >= 7

AimsComplex fractures of the femur and tibia with associated severe soft tissue injury are often devastating for the individual. The aim of this study was to describe the two-year patient-reported outcomes of patients in a civilian population who sustained a complex fracture of the femur or tibia with a Mangled Extremity Severity Score (MESS) of >= 7, whereby the score ranges from 2 (lowest severity) to 11 (highest severity).MethodsPatients aged >= 16 years with a fractured femur or tibia and aMESS of >= 7 were extracted from the Victorian Orthopaedic Trauma Outcomes Registry (January 2007 to December 2018). Cases were grouped into surgical amputation or limb salvage. Descriptive analysis were used to examine return to work rates, three-level EuroQol five-dimension questionnaire (EQ-5D-3L), and Glasgow Outcome Scale-Extended (GOS-E) outcomes at 12 and 24 months post-injury.ResultsIn all, 111 patients were included: 90 (81%) patients who underwent salvage and 21 (19%) patients with surgical amputation. The mean age of patients was 45.8 years (SD 15.8), 93 (84%) were male, 37 (33%) were involved in motor vehicle collisions, and the mean MESS score was 8.2 (SD 1.4). Two-year outcomes in the cohort were poor: six (7%) patients achieved a GOS-E good recovery, the mean EQ-5D-3L summary score was 0.52 (SD 0.27), and 17 (20%) patients had returned to work.ConclusionA small proportion of patients with severe lower limb injury (MESS >= 7) achieved a good level of function 24 months post-injury. Further follow-up is needed to better understand the long-term trajectory of these patients, including delayed amputation, hospital readmissions, and healthcare utilization.Orthopaedics, Trauma Surgery and Rehabilitatio

Improving critical thinking using web based argument mapping exercises with automated feedback

Australasian Journal of Educational Technology252268-29

Twelve-month mortality and functional outcomes in hip fracture patients under 65 years of age

Abstract not availableC.L. Ekegren, E.R. Edwards, R. Page, R. Hau, R. de Steiger, A. Bucknill, S. Liew, A. Oppy, B.J. Gabb

Twelve-month work-related outcomes following hip fracture in patients under 65 years of age

Abstract not availableChristina L. Ekegren, Elton R. Edwards, Andrew Oppy, Susan Liew, Richard Page, Richard de Steiger, Peter A. Cameron, Andrew Bucknill, Raphael Hau, Belinda J. Gabb