Abdominal aortic calcification is associated with a higher risk of injurious fall-related hospitalizations in older Australian women

Gebre, Abadi Kahsu; Sim, Marc; Rodríguez, Alexander J.; Hodgson, Jonathan M.; Blekkenhorst, Lauren C.; Szulc, Paweł; Bondonno, Nicola P.; Zhu, Kun; Bondonno, Catherine P.; Kiel, Douglas P.; Schousboe, John T.; Prince, Richard; Lewis, Joshua R.

doi:10.1016/j.atherosclerosis.2021.05.003

Cited by 16 publications

(15 citation statements)

References 48 publications

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“…The potential mechanisms underlying the observed relationships between AAC and falls and fracture risk have been discussed. [7,8,11] Briefly, this may relate to impaired blood flow to the skeleton distal to the abdominal aorta negatively influencing BMD, which may contribute to increasing fracture risk. Vascular disease may also contribute to increased falls propensity, and subsequent fracture, via various mechanisms.…”

Section: Discussionmentioning

confidence: 99%

“…The HMDC records of all women were obtained from their baseline clinical visit in 1998 and over the next 14.5 years for hospitalized falls and 10 years for fracture-related hospitalization to enable comparisons to previous work in this cohort. [7,8] Diagnosis codes were defined using the ICD-9-CM codes 1998 to 1999 [21] mapped to the ICD-10 Australian Modification (ICD-10-AM) for 1999 to 2013. [22] Hip and fracture-related hospitalizations were identified using the following ICD-10 codes: S02, S12, S22, S32, S42, S52, S62, S72, S82, S92, M80, T02, T08, T10, T12, and T14.2.…”

Section: Fall and Fracture-related Hospitalizationmentioning

confidence: 99%

“…We also analyzed the relationship of AAC with fall and fracture outcomes using two alternative categorizations of ML-AAC24 used previously. [4,7] Specifically, we compared AAC presence (ML-AAC24 ≥ 1) and absence (ML-AAC24 = 0), as well as across three groups of AAC extent (low [ML-AAC24 ≤ 1], moderate [ML-AAC24 ≥ 2 to <6], and extensive [ML-AAC24 ≥ 6]).…”

Section: Additional Analysesmentioning

confidence: 99%

“…[2,3] More extensive AAC is associated with an increased risk of cardiovascular disease (CVD) and related events, CVD and all-cause mortality, [4][5][6] as well as lower BMD, poorer muscle strength, and increased risk for injurious falls and fracture. [7][8][9][10][11] A major limitation for the clinical assessment of AAC is that it needs to be manually scored by a trained expert, a time-consuming task that limits the possibility for AAC assessment to be scaled and incorporated into routine clinical assessment, especially as part of osteoporosis screening. To address this limitation, machine learning (ML) approaches have been investigated to automate the identification of AAC and AAC-24 scoring from lateral spine images.…”

Section: Introductionmentioning

confidence: 99%

“…These scores were validated against future major adverse cardiovascular events, cardiovascular, and all-cause mortality from Hologic and GE machines. [13] Given that extensive AAC is associated with poorer musculoskeletal outcomes (e.g., falls [7] and fracture [8] ), determining whether these relationships remain comparable after automation (i.e., ML-AAC24) is a critical step to enhance the clinical utility of lateral spine images at the time of BMD measurement. Therefore, this study examined whether ML-AAC24 is related to longterm falls and fracture risk in community-dwelling older women, representing a high-risk population.…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learning Assessed Abdominal Aortic Calcification is Associated with Long-Term Fall and Fracture Risk in Community-Dwelling Older Australian Women

Dalla Via,

Gebre,

Smith

et al. 2023

Journal of Bone and Mineral Research

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Abdominal aortic calcification (AAC), a recognized measure of advanced vascular disease, is associated with higher cardiovascular risk and poorer long‐term prognosis. AAC can be assessed on dual‐energy X‐ray absorptiometry (DXA)‐derived lateral spine images used for vertebral fracture assessment at the time of bone density screening using a validated 24‐point scoring method (AAC‐24). Previous studies have identified robust associations between AAC‐24 score, incident falls and fractures. However, a major limitation of manual AAC assessment is that it requires a trained expert. Hence, we have developed an automated machine‐learning algorithm for assessing AAC‐24 scores (ML‐AAC24). In this prospective study, we evaluated the association between ML‐AAC24 and long‐term incident falls and fractures in 1,023 community‐dwelling older women (mean age, 75 ± 3 years) from the Perth Longitudinal Study of Ageing Women. Over 10 years of follow‐up, 253 (24.7%) women experienced a clinical fracture identified via self‐report every 4–6 months and verified by X‐ray, and 169 (16.5%) women had a fracture hospitalization identified from linked hospital discharge data. Over 14.5 years, 393 (38.4%) women experienced an injurious fall requiring hospitalization identified from linked hospital discharge data. After adjusting for baseline fracture risk, women with moderate to extensive AAC (ML‐AAC24 ≥ 2) had a greater risk of clinical fractures (HR 1.42, 95%CI 1.10–1.85) and fall‐related hospitalization (HR 1.35, 95%CI 1.09–1.66), compared to those with low AAC (ML‐AAC24 ≤ 1). Similar to manually assessed AAC‐24, ML‐AAC24 was not associated with fracture hospitalizations. The relative hazard estimates obtained using machine learning were similar to those using manually assessed AAC‐24 scores. In conclusion, this novel automated method for assessing AAC, that can be easily and seamlessly captured at the time of bone density testing, has robust associations with long‐term incident clinical fractures and injurious falls. However, the performance of the ML‐AAC24 algorithm needs to be verified in independent cohorts.This article is protected by copyright. All rights reserved.

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Section: Discussionmentioning

confidence: 99%

Section: Fall and Fracture-related Hospitalizationmentioning

confidence: 99%

Section: Additional Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine-Learning Assessed Abdominal Aortic Calcification is Associated with Long-Term Fall and Fracture Risk in Community-Dwelling Older Australian Women

Dalla Via,

Gebre,

Smith

et al. 2023

Journal of Bone and Mineral Research

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Cardiovascular Safety of Antifracture Medications in Patients With Osteoporosis: A Narrative Review of Evidence From Randomized Studies

Rodríguez

Abrahamsen

2021

JBMR Plus

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Osteoporosis and cardiovascular (CV) disease share common risk factors and pathophysiology. Low bone mineral density (BMD) and fractures appear to increase the risk for multiple CV diseases. Equally, prevalent CV disease appears to predispose to bone loss and increase fracture rates. This relationship has naturally provoked the hypothesis that stopping bone loss may result in some CV benefit. Secondary analyses of safety and adverse event data from many randomized controlled trials (RCTs) have attempted to clarify this putative association. Recently, the discontinuation of odanacatib (anti‐cathepsin K monoclonal antibody) over stroke concerns and the imbalance in ischemic events in romosozumab‐treated (anti‐sclerostin monoclonal antibody) women compared to bisphosphonate‐treated women, has provided further justification to better characterize potential CV benefits and harms of osteoporosis medications. This review delves into the seminal, and other major RCTs of osteoporosis medications and, using both published data and additional information provided on trial registration pages, examines the evidence for CV safety and harms of these medications. Accepted and emerging “off‐target” effects are explored for validity, biological plausibility, and clinical importance. A brief research agenda is provided to stimulate the next wave of clinical development and CV understanding of osteoporosis medications. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

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Abdominal aortic calcification is an independent predictor of perioperative blood loss in posterior spinal fusion surgery

Guven,

Evangelisti,

Schönnagel

et al. 2024

Eur Spine J

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Abdominal aortic calcification is associated with a higher risk of injurious fall-related hospitalizations in older Australian women

Cited by 16 publications

References 48 publications

Machine-Learning Assessed Abdominal Aortic Calcification is Associated with Long-Term Fall and Fracture Risk in Community-Dwelling Older Australian Women

Machine-Learning Assessed Abdominal Aortic Calcification is Associated with Long-Term Fall and Fracture Risk in Community-Dwelling Older Australian Women

Cardiovascular Safety of Antifracture Medications in Patients With Osteoporosis: A Narrative Review of Evidence From Randomized Studies

Abdominal aortic calcification is an independent predictor of perioperative blood loss in posterior spinal fusion surgery

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