Literature DB >> 35240349

Reverse engineering the FRAX algorithm: Clinical insights and systematic analysis of fracture risk.

Jules D Allbritton-King1, Julia K Elrod2, Philip S Rosenberg2, Timothy Bhattacharyya3.   

Abstract

The Fracture Risk Assessment Tool (FRAX) is a computational tool developed to predict the 10-year probability of hip fracture and major osteoporotic fracture based on inputs of patient characteristics, bone mineral density (BMD), and a set of seven clinical risk factors. While the FRAX tool is widely available and clinically validated, its underlying algorithm is not public. The relative contribution and necessity of each input parameter to the final FRAX score is unknown. We systematically collected hip fracture risk scores from the online FRAX calculator for osteopenic Caucasian women across 473,088 unique inputs. This dataset was used to dissect the FRAX algorithm and construct a reverse-engineered fracture risk model to assess the relative contribution of each input variable. Within the reverse-engineered model, age and T-Score were the strongest contributors to hip fracture risk, while BMI had marginal contribution. Of the clinical risk factors, parent history of fracture and ongoing glucocorticoid treatment had the largest additive effect on risk score. A generalized linear model largely recapitulated the FRAX tool with an R2 of 0.91. Observed effect sizes were then compared to a true patient population by creating a logistic regression model of the Study of Osteoporotic Fractures (SOF) cohort, which closely paralleled the effect sizes seen in the reverse-engineered fracture risk model. Analysis identified several clinically relevant observations of interest to FRAX users. The role of major osteoporotic fracture risk prediction in contributing to an indication of treatment need is very narrow, as the hip fracture risk prediction accounted for 98% of treatment indications for the SOF cohort. Removing any risk factor from the model substantially decreased its accuracy and confirmed that more parsimonious models are not ideal for fracture prediction. For women 65 years and older with a previous fracture, 98% of FRAX combinations exceeded the treatment threshold, regardless of T-score or other factors. For women age 70+ with a parent history of fracture, 99% of FRAX combinations exceed the treatment threshold. Based on these analyses, we re-affirm the efficacy of the FRAX as the best tool for fracture risk assessment and provide deep insight into the interplay between risk factors.
Copyright © 2022 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Bone mineral density; FRAX; Generalized linear model; Hip fracture risk; Osteoporosis

Mesh:

Year:  2022        PMID: 35240349      PMCID: PMC9035136          DOI: 10.1016/j.bone.2022.116376

Source DB:  PubMed          Journal:  Bone        ISSN: 1873-2763            Impact factor:   4.626


  31 in total

1.  The Fracture Risk Assessment Tool (FRAX®) predicts fracture risk in patients with chronic kidney disease.

Authors:  Reid H Whitlock; William D Leslie; James Shaw; Claudio Rigatto; Laurel Thorlacius; Paul Komenda; David Collister; John A Kanis; Navdeep Tangri
Journal:  Kidney Int       Date:  2018-12-20       Impact factor: 10.612

2.  FRAX-based intervention and assessment thresholds in seven Latin American countries.

Authors:  P Clark; E Denova-Gutiérrez; C Zerbini; A Sanchez; O Messina; J J Jaller; C Campusano; C H Orces; G Riera; H Johansson; J A Kanis
Journal:  Osteoporos Int       Date:  2017-12-23       Impact factor: 4.507

3.  Measured height loss predicts incident clinical fractures independently from FRAX: a registry-based cohort study.

Authors:  W D Leslie; J T Schousboe; S N Morin; P Martineau; L M Lix; H Johansson; E V McCloskey; N C Harvey; J A Kanis
Journal:  Osteoporos Int       Date:  2020-02-03       Impact factor: 4.507

4.  The accuracy of different FRAX tools in predicting fracture risk in Japan: A comparison study.

Authors:  Gang Xu; Norio Yamamoto; Katsuhiro Hayashi; Akihiko Takeuchi; Shinji Miwa; Kentaro Igarashi; Yuta Taniguchi; Yoshihiro Araki; Hirotaka Yonezawa; Sei Morinaga; Hiroyuki Tsuchiya
Journal:  J Orthop Surg (Hong Kong)       Date:  2020 Jan-Apr       Impact factor: 1.118

Review 5.  Pitfalls in the external validation of FRAX.

Authors:  J A Kanis; A Oden; H Johansson; E McCloskey
Journal:  Osteoporos Int       Date:  2011-11-26       Impact factor: 4.507

6.  Prevalence and Fracture Risk of Osteoporosis in Patients with Rheumatoid Arthritis: A Multicenter Comparative Study of the FRAX and WHO Criteria.

Authors:  Sang Tae Choi; Seong-Ryul Kwon; Ju-Yang Jung; Hyoun-Ah Kim; Sung-Soo Kim; Sang Hyon Kim; Ji-Min Kim; Ji-Ho Park; Chang-Hee Suh
Journal:  J Clin Med       Date:  2018-12-02       Impact factor: 4.241

7.  Do Additional Clinical Risk Factors Improve the Performance of Fracture Risk Assessment Tool (FRAX) Among Postmenopausal Women? Findings From the Women's Health Initiative Observational Study and Clinical Trials.

Authors:  Carolyn J Crandall; Joseph Larson; Jane A Cauley; John T Schousboe; Andrea Z LaCroix; John A Robbins; Nelson B Watts; Kristine E Ensrud
Journal:  JBMR Plus       Date:  2019-11-30

8.  Can Classification and Regression Tree Analysis Help Identify Clinically Meaningful Risk Groups for Hip Fracture Prediction in Older American Men (The MrOS Cohort Study)?

Authors:  Yi Su; Timothy C Y Kwok; Steven R Cummings; Benjamin H K Yip; Peggy M Cawthon
Journal:  JBMR Plus       Date:  2019-08-21

9.  A surrogate FRAX model for Pakistan.

Authors:  G Naureen; H Johansson; R Iqbal; L Jafri; A H Khan; M Umer; E Liu; L Vandenput; M Lorentzon; N C Harvey; E V McCloskey; J A Kanis
Journal:  Arch Osteoporos       Date:  2021-02-17       Impact factor: 2.617

10.  A surrogate FRAX model for the Kyrgyz Republic.

Authors:  O Lesnyak; A Zakroyeva; O Lobanchenko; H Johansson; E Liu; M Lorentzon; N C Harvey; E McCloskey; J A Kanis
Journal:  Arch Osteoporos       Date:  2020-05-06       Impact factor: 2.617

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