Clinical Registries, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong, Victoria, Australia.
PLoS One. 2021 Sep 23;16(9):e0257361. doi: 10.1371/journal.pone.0257361. eCollection 2021.
Distal 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.
Adult (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.
The 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.
桡骨远端（腕部）骨折是第二大常见的住院骨折。这些类型损伤的解剖模式多种多样，临床管理方式存在差异，管理指南尚无定论，临床试验结果在常规实践中的应用也有限。强大的预测模型，同时考虑骨折和患者的特征，为减少护理差异和改善患者结局提供了最佳机会。这种类型的数据存储在没有特定格式或模式的非结构化数据源中。“使用人工智能（AI）补充模型从临床注册数据预测骨折结局以实现基于证据的治疗（PRAISE）”研究旨在使用 AI 方法处理非结构化数据，以描述骨折特征，并测试是否使用这些信息可以提高对关键骨折特征的识别能力，并预测腕部骨折患者报告的结局测量和临床结局，与基于标准注册数据的预测模型相比。
本研究将纳入维多利亚州四家医院因腕部骨折在急诊科就诊、在短期留观病房治疗或住院 24 小时以上的成年（16 岁及以上）患者。该研究将使用维多利亚州骨科创伤结局登记处（VOTOR）的常规登记数据，以及电子病历（EMR）信息（例如 X 光片、手术报告、放射学报告、图像）。将开发一种多模态深度学习骨折推理系统（DLFRS），该系统可对 EMR 信息进行推理。机器学习预测模型将测试有无 DLFRS 输出的性能。
PRAISE 研究将确立使用 AI 技术为腕部骨折患者提供有关骨折特征的增强信息。使用 AI 衍生特征的预测模型有望更好地预测桡骨远端骨折后的临床和患者报告结局。
