Fracture Risk Predictable From History Of Falls
You can read how researchers from the University of Southampton arrived at this conclusion in an early "in press" issue of a paper that was published online in the journal Bone.
Lead author Mark Edwards is a Clinical Research Fellow at Southampton's MRC Lifecourse Epidemiology Unit. He says in a statement:
"Nearly 60% of all hospital admissions due to fractures in England are the result of a fall."
"Fracture prediction is extremely important to allow us to target treatments to those at greatest risk: assessing falls history provides us a further tool with which to do so," he adds, having remarked earlier that, "in a clinical setting, asking whether a patient has fallen is quick and easy".
The chances that a person will fracture a bone when they fall depends on how strong their bones are and how heavily they fall (the forces that are applied to the bones).
Bone strength depends on bone density: the lower the density, the higher the risk of fracture. However, bones usually only break when inflicted with trauma (banged very hard), which in most cases is as a result of a fall.
Fracture risk assessment tools like the FRAX model exist, and they help clinicians assess, with quite a high level of accuracy, a patient's risk of fracture. Such tools require data on known risk factors like gender, age, smoking status, alcohol consumption, diseases, and family history, with or without bone density.
But not all such tools take into account the individual patient's fall history.
So for their study, Edwards and colleagues used data fom the Hertfordshire Cohort, a group of studies on men and women born in the English county of Hertfordshire between 1911 and 1939, that aims to find out as much as possible about how their inbuilt makeup (their genome) and their environment affect their health and aging.
The Hertfordshire Cohort is based at the MRC Lifecourse Epidemiology Unit at the University of Southampton. The Director of the Unit is Professor Cyrus Cooper, senior author of the Bone study.
The data set includes information on factors that affect fracture risk, such as gender, age, height, weight, smoking, alcohol, family history, and rheumatoid arthritis. It also contains information about previous fractures and falls, as well as bone density. And during follow ups, participants also reported any new fractures.
Equipped with this data, the researchers looked at how well the risk factors were able to predict the fractures reported at follow up.
When they looked at the risk factors similar to the ones used in the FRAX model, they found a good level of fracture prediction. And they were not surprised when the accuracy improved further by the addition of bone density data.
But they were quite surprised that accuracy was even further improved, especially for male participants, for whom predictive capacity went up another 6%, by the addition of fall history data.
And conversely, in more than 80% of men who had not fallen at all during the follow up, and also had no fractures, adding fall history to the model correctly reduced their predicted fracture risk.
Cooper says these findings show the value that data from "well-characterized population cohorts such as the Hertfordshire Cohort Study" bring to clinical decision-making.
"The enhanced fracture risk prediction facilitated through use of our findings will help reduce the ever-growing burden of fractures in the elderly," he adds.
Written by Catharine Paddock PhD