Aim
The aim of this project is to use lung function data from two hospitals and the UK Biobank, to find the minimum survivable levels for lung function and fitness tests. The minimum survivable values will reflect the bottom 1% of all measurements in the databases. We want to see if the amount each person’s lung function and fitness measurements are from these minimum survivable values, can help predict disease and survival better than the current methods.
Rationale
Doctors often use lung function tests to check how well our lungs work. These tests measure:
* How fast air moves through the lungs
* How big the lungs are
* How fit we are.
These tests are used to diagnose lung diseases, monitor them over time, and see if someone is fit for surgery. When doctors look at the test results, they compare them to healthy people of the same age, height, and sex. This helps to understand if the lungs are working well. But there is some debate about the validity of these comparisons because:
1. There is not enough data from healthy people to derive accurate reference ranges.
2. It’s unclear if the comparisons should be different for different races or the same for everyone.
A new idea suggests comparing lung test results to the minimum level needed for survival instead of an ideal level. This method might be better at predicting long term survival and could give doctors more useful information about a patient’s disease and how long they might live.
Project duration
36 months
Public health impact
This work will identify the minimum survivable values for lung function and fitness tests. It will derive new measurements that reflect how far a person’s lung function and fitness are from these minimum values. If we find that these measurements are better at predicting clinically important outcomes such as mortality and clinical decline and are more closely associated with respiratory symptoms than currently used methods, which involve complex reference equations, then these measurements could simplify the interpretation of lung function and fitness tests. This could improve the accuracy of diagnosis and focus disease management on outcomes patients care about, i.e. risk of death and rate of decline.