New clinical model for predicting hip fractures could save lives

Researchers at Uppsala University have developed a clinical model that can accurately predict the risk of hip fractures in the elderly. The model is based on analyses of data from the entire Swedish population and can identify patients at high risk, without the need to measure skeletal strength. This can speed up the process for doctors and allow patients to receive preventive treatment. This is shown in a new study published in the journal eClinicalMedicine.

Around 15,000 hip fractures occur in Sweden each year. This causes both pain in patients and renders them more dependent on either relatives, friends or health care staff. As many as 25 percent of those affected die within the first year, which means that the mortality rate is higher than for incidents such as a stroke or a heart attack. This means that if you can predict who will be affected, you can take preventive action and save lives.

The new study is based on register data collected from the entire Swedish population. For five years, the researchers followed all people living in Sweden who were at least 50 years old in order to identify factors that increased the risk of hip fractures.

“The most surprising result was that we could predict hip fractures so accurately without using bone density, which has traditionally been an important factor. This means that more people can be identified in time and offered preventive treatment,” explains Peter Nordström, Professor and Consultant Physician who leads the research group.

Bone densitometry, which is currently the most commonly used method to assess fracture risk, has several limitations. It is a time-consuming examination that requires expensive equipment, and is therefore not available to all doctors. The model that has now been developed is based instead on variables that are easier to collect in clinical settings, such as diagnoses and medical treatments. This allows health care staff to carry out risk assessments without needing access to bone densitometry equipment.

“A major advantage of our model is that it is based on data already available in the clinic, which allows us to identify at-risk groups more quickly and easily. This in turn enables us to start preventive interventions earlier, such as medication for osteoporosis, and prevent serious complications that occur in hip fractures,” continues Nordström.

Based on 19 variables

The research model is based on 19 variables, with the strongest predictors—apart from advanced age—being the use of home-help services and diagnoses such as Parkinson’s disease and dementia. The model showed that women with home-help services had a 5-year risk of almost 8% of suffering a hip fracture, while the corresponding figure for men was 5%.

“A key insight from our research is that frailty, which home-help services can reflect, is a strong indicator of hip fractures. It helps us see that it’s not just about bone density but about the whole health situation of each individual,” notes Anna Nordström, one of the researchers behind the study.

Treatment can be started earlier

An important key finding of the study was that the researchers were able to establish a risk threshold for when treatment with bone-strengthening drugs should be considered. If a person has a 3 percent or higher risk of hip fracture within five years, preventive medication may be beneficial. According to the model, 36 women or 52 men would therefore need treatment to prevent a hip fracture.

“This model can have a major impact on elderly people, especially those who have not had their bone density measured. It gives us a tool to identify at-risk groups early and take preventive measures before a hip fracture occurs,” says Nordström.

The study has also been validated among individuals with a foreign background and proved to be equally accurate there. The results were recently published and the research may lead to new guidelines on how health care providers should manage the risk of hip fractures in older people.

The 19 factors on which the free-to-use algorithm is based are: age, gender, osteoporosis, Parkinson’s disease, alcohol abuse, dementia, hip fracture in siblings, previous fracture, rheumatoid arthritis, chronic lung obstructive disease, renal disease, stroke, depression or antidepressants, psychosis or neuroleptics, Swedish background, low income, nursing home resident, home-help services, and oral corticosteroids.