Ten years ago today, we launched the Garvan Fracture Risk Calculator (GRX). This is one of our significant contributions to osteoporosis and bone health internationally. On this 10th year anniversary occasion, I reflect our work that led to the development and implementation of GRX.
Over 10 years ago, in a weekly lab meeting my doctoral student (Dr. Nguyen D. Nguyen) and I decided that we would translate our findings into a tool that can be used by doctors and patients alike for predicting the absolute risk of fracture for an individual. I called the concept “individualized risk assessment,” and wrote an editorial in Osteoporosis International to articulate my idea. Nguyen was an ideal person to do the work because he was a clinician and a very competent statistician. (It’s hilarious that some people in the field mistaken him as my son — NO. There are almost 35% of people in Vietnam carrying “Nguyen” as their surname).
Actually, my idea of ‘individualization‘ was not new; I learned it from colleagues in the cancer research field. At the time, cancer researchers were busily developing nomograms for predicting the risk of having cancer, and it appeared that these nomograms worked well for many cases. Why these probabilistic tools work well? Now, we know that highly experienced clinicians can also make good prognosis, and that is a fact. Unfortunately, their predictions are highly variables and less consistent, or in scientific language, clinicians’ predictions are irreproducible. But reproducibility is a bedrock of science. So, scientifically, we cannot rely on clinician’s personal judgment. Statistical prognostic models have been shown to outperform clinical judgment, because these models can objectively incorporate many risk data. Moreover, any prognosis from a statistical model is unbiased, consistent, and completely reproducible.
At the time we were working on the development of the Garvan Fracture Risk Calculator, clinicians still diagnosed osteoporosis based on a bone mineral density (BMD) measurement. Any post-menopausal woman or man aged 50 yr and older with a BMD T-score being less than -2.5 is classified as having “osteoporosis”. A T-score of -2.48 is not osteoporotic, but a T-score of -2.51 is. The diagnosis was (and still is) dichotomous, simple and clean.
I thought the above classification is absurd. Nguyen and I had observed that over 50% of women and 70% of men with a fracture did not have “osteoporisis” (i.e., their BMD T-scores were above the -2.5 threshold). We were intrigued by this fact (and have published a paper in JCEM) and asked what other factors contributed to fracture? Nguyen did a sophisticated analysis called “Bayesian Model Averging” and he found that apart from old age and low BMD, the number of falls and the number of prior fractures were very important risk factors for fracture. When he presented the result in a lab meeting, I said to myself: this makes sense! We then wrote a series of papers to describe our predictive models and how they could be used for individualized fracture risk assessment. I decided to send the papers to Osteoporosis International, because the journal was (and still is) a highly clinically oriented venue. In the papers, we made a point that:
“The ultimate aim of developing a prognostic model is to provide clinicians and each individual with their risk estimate to guide clinical decisions. At present, individuals with low bone mineral density (i.e., T-scores being less than -2.5) or with a history of prior low trauma fracture are recommended for therapeutic intervention. This recommendation is logical and appropriate, since these individuals – as shown in this study and previous studies – have higher risk of fracture, and treatment can reduce their risk of fracture. However, because fracture is a multifactorial event, there is more than one way that an individual can attain the risk conferred by either low BMD or a prior fracture. Indeed, virtually all women aged 70 years with BMD T-scores less than -1.5 and all 80-year-old men with BMD T-scores less than -1.0 can be considered ‘high risk’. On the other hand, no 60 year old men or women without a prior fracture and a fall are considered high risk, even when their BMD T-scores are below -2.5. This demonstrates the informativeness of a multivariable prognostic model, and the limitation of a risk stratification-based approach for risk assessment for an individual.”
We also made another point re the uniqueness of fracture risk:
“Each individual is important and unique. […] Prognosis is about imparting information of fracture risk to an individual and each individual is a unique case, because there exists no ‘average individual’ in the population. The more risk factors are considered, the greater likelihood of uniqueness of an individual’s profile being defined. Therefore, by modeling risk factors in their continuous scale the present models can be uniquely tailored to an individual.”
One year later after the publication of our papers, the FRAX model — developed under the sponsorship of the World Health Organization — was published. So, doctors and patients in the world now have at least two tools to assess their own risk of fracture in their convenience. The two models, Garvan and FRAX, have helped transform the management of osteoporosis worldwide.
Our Garvan Fracture Risk Calculator has been a success. Many research groups around the world have validated the model in their populations, and they found that the model could be used to guide osteoporosis management in their local setting. The name “Garvan” has imprinted in the bone field through hundred of publications and thousands of references.
The Garvan tool is a key component of “Know Your Bones” that helps people self-assess their bone health. (You can click on the above link to have your test now!)
However, more work remains to be done to optimize the tool’s prognostic performance. I propose approaches to improve the accuracy of existing predictive models by incorporating new markers such as genetic factors, bone turnover markers, trabecular bone score, and time-variant factors. I believe that new and more refined models for individualized fracture risk assessment will help identify those most likely to sustain a fracture, those most likely to benefit from treatment, and encouraging them to modify their risk profile to decrease risk. My team and I are busily working toward that ideal.
There have been more than 100 articles on the Garvan model in the literature, including ours. Here are some of my recent reviews that I have written for journals and books:
Nguyen TV. Individualized Fracture Risk Assessment: State-of-the-Art and Room for Improvement. Osteoporosis and Sarcopenia 2018; in-press.
Nguyen TV. Individualized Assessment of Fracture Risk: Contribution of “Osteogenomic Profile”. J Clin Densitom 2017;20:353-359.
Nguyen TV, Eisman JA. Assessment of fracture risk: population association vs individual prediction. J Bone Miner Res 2017 Dec 27.
Nguyen TV, Eisman JA. Genetic profiling and individualized assessment of fracture risk. Nature Review Endocrinolology 2013 Mar;9(3):153-61.
Nguyen TV, Center JR, Eisman JA. Individualized fracture risk assessment: progresses and challenges. Curr Opin Rheumatol. 2013 Jul;25(4):532-41.
Nguyen TV, Eisman JA. Genetics and the individualized prediction of fracture. Curr Osteoporos Rep 2012 Sep;10(3):236-44.
Nguyen TV. Mapping translational research into individualized prognosis of fracture risk. International Journal of Rheumatic Diseases 2008; 11:347-358.
Bich H. Tran, Jacqueline R. Center, Tuan V. Nguyen. Translational genetics of osteoporosis: from population association to individualized risk assessment. In Primer on the Metabolic Bone Diseases and Disorders of Mineral Metabolism, Seventh Edition, Ed: Clifford Rosen. ASBMR 2017 Edition.
Nguyen TV, Eisman JA. Pharmacogenetics and pharmacogenomics of osteoporosis: personalized medicine outlook. In Genetics of Bone Biology and Skeletal Disease, Edited by RJ Thakker, MP Whyte, JA Eisman, T Igarashi. Academic Press Amsterdam 2017 Edition.
Nguyen TV. Individualized Progress of Fractures in Men. In Osteoporosis in Men – the effect of gender on skeletal health, 2nd Ed, Edited by ES Orwoll, JP Biezikian, and D Vanderschueren. Academic Press, 2011.
This is the Press Release 10 years ago:
WEB-BASED TOOL TO PREDICT RISK OF BONE FRACTURE
Scientists from Garvan have developed a fracture risk calculator using data, accumulated over 17 years, from the internationally recognised Dubbo Osteoporosis Epidemiology Study. A paper describing the tool and its methodology was published today in the prestigious international journal, Osteoporosis International.
Media Release: 07 March 2008
It will soon be possible for anyone over 60 to predict their individual risk of bone fracture with the aid of a simple web-based tool, developed by the Sydney-based Garvan Institute of Medical Research.
The tool will be accessible online from the end of March at http://www.fractureriskcalculator.com
Each person has a unique risk profile, a combination of five factors including sex, age, weight, history of prior fracture, number of falls in the past 12 months and bone mineral density.
Scientists from Garvan developed the tool using data, accumulated over 17 years, from the internationally recognised Dubbo Osteoporosis Epidemiology Study. A paper describing the tool and its methodology was published online today in the prestigious international journal, Osteoporosis International.
“The biggest challenge at the moment is how to develop prognostic tools that allow individuals and their doctors to predict risk of fracture” said Professor John Eisman, Director of Garvan’s Bone and Mineral Research Program.
Associate Professor Tuan Nguyen, whose team at Garvan developed the tool, said “We have kept our model simple and easy to use. In addition to the web-based version, it is also available on paper as a nomogram, a simple graph which is easy for a clinician to complete.”
The prognostic tool was developed in two stages. First, people from the Dubbo epidemiological study were separated into ‘low risk’ and ‘high risk’ categories. Their risk factors were combined in a statistical model, allowing scientists to derive the weighting for each risk factor. For these analyses, the Dubbo population was split into two halves. Scientists derived the prognostic model from one half and then applied the model to the other half to ensure that it accurately predicted their fracture risk.
This Tool has the potential to allow individuals to make informed judgments about their actual risk of having an osteoporotic fracture and what steps they may wish to take to reduce that risk.