The study findings from the Garavan Institute of Medical Research were published in the JAMA Network Open Journal. The study was conducted on more than 300,000 Danish people aged 50 or older who had suffered fractures. In patients with fractures closer to the center of the body (for example, in the hip, spine, upper arm or leg), the researchers found a higher mortality rate than expected for the general population of the same age.
If people with fractures also had multiple or complicated health conditions, the mortality rate was again higher. The researchers found that certain groups of conditions were associated with increased mortality, suggesting that this information could be used by physicians to uncover patients who may need more intensive medical care.
“This is an important study that could change the way medical treatment is provided to older adults,” said Professor Jacqueline Center, head of the Clinical Studies and Epidemiology Lab and lead author of the study. “This could potentially lead to a new way of thinking about how we view people with fractures at the site of fractures in light of their specific underlying health conditions. Typically, the management of health conditions such as osteoporosis, heart disease or diabetes can be addressed. Considered in terms of individual illness.”
However, these new findings suggest that looking at clusters of underlying conditions may indicate that someone is at high risk of poor outcomes, over and above the risk from the condition being treated. The researchers found that chronic health conditions at the time of fracture were naturally divided into five specific groups for men and four for women: a relatively healthy group that typically had only one or no health conditions, a cardiovascular group, a diabetes group, and a cancer group with an additional liver/inflammatory group for the men.
Visiting scientist Robert Blank said, “It is not enough to count other diseases. Their severity and their combination must also be taken into account. Many patients with prior cancer history, for example, what we call cancer clusters.” , but the cancer cluster included virtually everyone who had evidence of advanced cancer. Similar sorting by severity was observed in the other groups as well.”
Interestingly, a fracture located closer to the center of the body, such as the bones of the hip, vertebrae or upper arm, has a higher risk of death. In contrast, in the healthy group who had more distant fractures in the body, such as the hand and forearm, there was almost no increase in mortality. The presence of specific clusters of health conditions in people increased the likelihood of death after these fractures, much higher than after fractures or health conditions alone.
For example, men in the cancer cluster had a 41% higher mortality rate after hip fracture than men in the same age group in the general community. And diabetes was not associated with increased mortality in otherwise healthy people, but diabetes in combination with heart, vascular or kidney disease. “This research highlights that there is an interaction between a group of fractures and a patient’s health condition – their underlying health – and this may be a good way to identify those at risk,” said Dr. Thatch from Garavan. Tran, first author of the study.
“We can identify those who will fare poorly. Importantly, the findings of this research may be applicable to many disease settings in which sentinel events occur with pre-existing health disorders.” As to why the connection exists between several underlying conditions, the type of fracture and poor outcome, the Professor Center thinks it may be an interaction with the bone and the immune system. ,
“Bone is not dormant. When you break a bone, you get increased bone turnover and associated inflammatory factors and I suspect that in underlying diseases, this process is not well controlled and the fractures are caused by underlying health problems. can promote.” The next steps are to determine whether these clusters are evident in other diseases – if they are universal – and whether they can be used as predictive tools for clinicians.