Department of Medicine, Section of Endocrinology, Diabetes, and Metabolism, The University of Chicago, 5841 South Maryland Ave, MC 1027, Chicago, IL, 60637, USA.
Weill Cornell Medicine, Clinical Population Health Sciences, New York, USA.
J Gen Intern Med. 2023 Dec;38(16):3451-3459. doi: 10.1007/s11606-023-08347-5. Epub 2023 Sep 15.
Osteoporotic fracture prediction calculators are poorly utilized in primary care, leading to underdiagnosis and undertreatment of those at risk for fracture. The use of these calculators could be improved if predictions were automated using the electronic health record (EHR). However, this approach is not well validated in multi-ethnic populations, and it is not clear if the adjustments for race or ethnicity made by calculators are appropriate.
To investigate EHR-generated fracture predictions in a multi-ethnic population.
Retrospective cohort study using data from the EHR.
An urban, academic medical center in Philadelphia, PA.
12,758 White, 7,844 Black, and 3,587 Hispanic patients seeking routine care from 2010 to 2018 with mean 3.8 years follow-up.
None.
FRAX and QFracture, two of the most used fracture prediction tools, were studied. Risk for major osteoporotic fracture (MOF) and hip fracture were calculated using data from the EHR at baseline and compared to the number of fractures that occurred during follow-up.
MOF rates varied from 3.2 per 1000 patient-years in Black men to 7.6 in White women. FRAX and QFracture had similar discrimination for MOF prediction (area under the curve, AUC, 0.69 vs. 0.70, p=0.08) and for hip fracture prediction (AUC 0.77 vs 0.79, p=0.21) and were similar by race or ethnicity. FRAX had superior calibration than QFracture (calibration-in-the-large for FRAX 0.97 versus QFracture 2.02). The adjustment factors used in MOF prediction were generally accurate in Black women, but underestimated risk in Black men, Hispanic women, and Hispanic men.
Single center design.
Fracture predictions using only EHR inputs can discriminate between high and low risk patients, even in Black and Hispanic patients, and could help primary care physicians identify patients who need screening or treatment. However, further refinements to the calculators may better adjust for race-ethnicity.
骨质疏松性骨折预测计算器在初级保健中未得到充分利用，导致骨折风险患者的诊断和治疗不足。如果使用电子健康记录（EHR）自动进行这些计算器的预测，那么使用情况可能会得到改善。然而，这种方法在多民族人群中尚未得到充分验证，并且尚不清楚计算器对种族或族裔的调整是否合适。
研究多民族人群中的 EHR 生成骨折预测。
使用 EHR 中的数据进行回顾性队列研究。
宾夕法尼亚州费城的一家城市学术医疗中心。
2010 年至 2018 年间，12758 名白人、7844 名黑人、3587 名西班牙裔患者在常规护理中接受调查，平均随访 3.8 年。
无。
研究了两种最常用的骨折预测工具，即 FRAX 和 QFracture。使用 EHR 中的数据在基线时计算主要骨质疏松性骨折（MOF）和髋部骨折的风险，并与随访期间发生的骨折数量进行比较。
黑人男性的 MOF 发生率为每 1000 名患者年 3.2 例，而白人女性的发生率为 7.6 例。FRAX 和 QFracture 对 MOF 预测的区分度相似（曲线下面积 AUC，0.69 对 0.70，p=0.08），对髋部骨折预测的 AUC 也相似（0.77 对 0.79，p=0.21），且与种族或族裔无关。FRAX 的校准优于 QFracture（大校准 FRAX 为 0.97，而 QFracture 为 2.02）。MOF 预测中使用的调整因素在黑人女性中通常是准确的，但在黑人男性、西班牙裔女性和西班牙裔男性中低估了风险。
单中心设计。
仅使用 EHR 输入的骨折预测可以区分高风险和低风险患者，即使在黑人或西班牙裔患者中也是如此，并且可以帮助初级保健医生识别需要筛查或治疗的患者。然而，计算器的进一步改进可能会更好地调整种族和族裔因素。
