SP0117 CASE 2 DISCUSSANT: AUTOMATED (IMPLEMENTED IN ELECTRONIC MEDICAL RECORD SYSTEMS) SHORT-TERM FRACTURE RISK PREDICTION
Authors: Bo Abrahamsen
Journal: Annals of the Rheumatic Diseases (2019)

<h3></h3> Identification of patients at high risk of osteoporotic fractures is commonly based on fracture history, bone mineral density or a combination of the two. However, vertebral fractures are very often either missed or not acted upon by our health systems and DXA scanning to measure BMD in itself requires a coherent case finding strategy. Current EPR systems may have built-in features to capture signs of sepsis or warnings to alert clinicians to cases with a diagnosis of atrial fibrillation with no apparent prescription for anti-coagulation. Building on the same principle, EPR systems or even national databases can be set up with appropriate decision rules to alert physicians to diagnoses such as RA, AS or COPD that we know strongly increases the risk of osteoporosis and to flag out medication patterns known to predict a high risk of osteoporotic fractures. This can then be checked against the presence of referrals for DXA and/or prescriptions for anti-osteoporosis medications and a clinical decision aid triggered to alert healthcare professionals. The mechanism powering such a tool can be an existing clinical risk algorithm such as qFracture, FRAX, FREM, Garvan FRC or others, depending on the nature of routinely collected data, the setting and the timescale required, or a bespoke risk tool developed for the healthcare system in question. It is important to appreciate that risk tools need careful calibration to be appropriate across countries and clinics and that the trigger risk level is ultimately a trade-off between clinical judgment and health economics. Vertebral fractures represent a particular challenge. It has been established in the past that vertebral fractures often are not mentioned in radiology reports even if clearly present, or reported using unclear terminology that does not make it sufficiently clear to clinicians that a vertebral fracture is present. Moreover, even when vertebral fractures are called out in the radiology report it is not uncommon for this to go unnoticed. Opportunistic identification of vertebral fractures by software tools is a possible new avenue for narrowing the treatment gap in osteoporosis. With increasing integration of DICOM images into EPR systems, DICOMs of CT scans and other modalities containing spine images could be used either for automated case finding with alert boxes in the EPR system itself or employed in radiology departments to automatically populate reports with additional information about the presence of vertebral fractures. <h3>References:</h3> [1] Rubin KH A New Fracture Risk Assessment Tool (FREM) Based on Public Health Registries. J Bone Miner Res. 2018 Nov;33(11):1967-1979. [2] Reber KC Development of a risk assessment tool for osteoporotic fracture prevention: A claims data approach. Bone. 2018 May;110:170-176. [3] Hippisley-Cox J Predicting risk of osteoporotic fracture in men and women in England and Wales: prospective derivation and validation of QFractureScores. BMJ. 2009 Nov 19;339:b4229. [4] Moon RJ. Identification of patient profile for treatment. Best Pract Res Clin Endocrinol Metab. 2014 Dec;28(6):767-82. [5] Rubin KH Risk assessment tools to identify women with increased risk of osteoporotic fracture: complexity or simplicity? A systematic review. J Bone Miner Res. 2013 Aug;28(8):1701-17. <h3>Disclosure of Interests:</h3> None declared