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一种基于人工智能的新型病例发现策略,用于系统性识别和管理骨质疏松症患者或有不同脆性骨折风险的个体。

A novel case-finding strategy based on artificial intelligence for the systematic identification and management of individuals with osteoporosis or at varying risk of fragility fracture.

作者信息

Voltan Gianpaolo, Di Giovannantonio Gennaro, Carretta Giovanni, Vianello Stefano, Contessa Cristina, Veronese Nicola, Brandi Maria Luisa

机构信息

Centre for Metabolic Bone Diseases, Health Authority of Venice Province, Noale, Venice, Italy.

Health Authority of Venice Province, Mirano, Venice, Italy.

出版信息

Arch Osteoporos. 2024 May 31;19(1):45. doi: 10.1007/s11657-024-01403-5.

Abstract

UNLABELLED

An artificial intelligence-based case-finding strategy has been developed to systematically identify individuals with osteoporosis or at varying risk of fragility fracture. This strategy has the potential to close the critical care gap in osteoporosis treatment in primary care, thereby lessening the societal burden imposed by fragility fractures.

BACKGROUND

Osteoporotic fractures represent a major cause of morbidity and, in older adults, a precursor of disability, loss of independence, poor quality of life and premature death. Despite the detrimental health impact, osteoporosis remains largely underdiagnosed and undertreated worldwide. Subjects at risk for osteoporosis-related fractures are identified either via organised screening or case finding. In the absence of a population-based screening policy, subjects at high risk of fragility fractures are opportunistically identified when a fracture occurs or because of other clinical risk factors (CRFs) for osteoporotic fracture and areal bone mineral density (aBMD) measured by dual-energy X-ray absorptiometry (DXA).

PURPOSE

This paper describes the development of a novel case-finding strategy, named Osteoporosis Diagnostic and Therapeutic Pathway (ODTP), which enables to identify subjects with osteoporosis or at varying risk of fragility fracture. This strategy is based on a specifically designed software tool, named "Bone Fragility Query" (BFQ), which analyses the electronic health record (EHR) databases of General Practitioners (GPs) to systematically identify individuals who should be prescribed DXA-BMD measurement, vertebral fracture assessment (VFA) and anti-osteoporosis medications (AOM).

CONCLUSIONS

The ODTP through BFQ tool is a feasible, convenient and time-saving osteoporosis model of care for GPs during routine clinical practice. It enables GPs to shift their focus from what to do (clinical guidelines) to how to do it in the primary health care setting. It also allows a systematic approach to primary and secondary prevention of fragility fractures, thereby overcoming clinical inertia and contributing to closing the gap between evidence and practice for the management of osteoporosis in primary care.

摘要

未标注

已开发出一种基于人工智能的病例发现策略,用于系统地识别骨质疏松症患者或有不同脆性骨折风险的个体。该策略有可能缩小初级保健中骨质疏松症治疗方面的关键护理差距,从而减轻脆性骨折带来的社会负担。

背景

骨质疏松性骨折是发病的主要原因,在老年人中,是残疾、失去独立生活能力、生活质量差和过早死亡的先兆。尽管对健康有不利影响,但骨质疏松症在全球范围内仍大多未得到诊断和治疗。与骨质疏松症相关骨折风险的受试者通过有组织的筛查或病例发现来识别。在没有基于人群的筛查政策的情况下,当发生骨折时或由于骨质疏松性骨折的其他临床风险因素(CRF)以及通过双能X线吸收法(DXA)测量的面积骨密度(aBMD),机会性地识别出脆性骨折高风险受试者。

目的

本文描述了一种名为骨质疏松症诊断与治疗路径(ODTP)的新型病例发现策略的开发,该策略能够识别骨质疏松症患者或有不同脆性骨折风险的个体。该策略基于一个专门设计的软件工具,名为“骨脆性查询”(BFQ),它分析全科医生(GP)的电子健康记录(EHR)数据库,以系统地识别应开具DXA - BMD测量、椎体骨折评估(VFA)和抗骨质疏松药物(AOM)处方的个体。

结论

通过BFQ工具的ODTP是全科医生在日常临床实践中可行、方便且省时的骨质疏松症护理模式。它使全科医生能够将重点从做什么(临床指南)转移到在初级卫生保健环境中如何做。它还允许对脆性骨折进行系统的一级和二级预防,从而克服临床惰性,并有助于缩小初级保健中骨质疏松症管理的证据与实践之间的差距。

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