Advancement Of A Personalised Approach To Minimising Infective Complications In Cancer Care
Funder
National Health and Medical Research Council
Funding Amount
$265,138.00
Summary
Managing infections in patients with cancer have become more difficult and unpredictable because of new generation cancer therapies. Measuring the response of the immune system (immune profiling) will allow us to predict which patients will develop infection so that action such as vaccination can be taken to reduce their risk. This program will refine immune profiling to personalise infection care for cancer patients and to introduce it into hospital practice.
The Prediction And Prevention Of Caesarean Section For Slow Progress In Labour
Funder
National Health and Medical Research Council
Funding Amount
$227,261.00
Summary
8% of all births are by caesarean section (CS) for slow labour. Bringing on labour just before the due date reduces the chance of CS but we can’t do this for all women. We have a way to predict high risk of CS for slow labour to select women who may benefit from bringing on the labour. We will perform a study where the women at high risk have can have the labour brought on slightly early. The project could result in 6,000 fewer CSs in Australia alone and prevent many complications of CS.
Acute Severe Ulcerative Colitis - Clinical And Translational Studies
Funder
National Health and Medical Research Council
Funding Amount
$340,891.00
Summary
One in five patients with severe ulcerative colitis, a condition resulting in damage to the large bowel, may require surgery to remove the bowel. This project aims to find out how best to avoid surgery using a drug called infliximab which targets the immune system to reduce bowel damage. This study also aims to find changes in the immune system that cause ulcerative colitis and identify which patients are more likely to avoid surgery with infliximab thereby minimising side effects and costs.
Derivation, Transportability, And Usefulness Of Clinical Prediction Rules For Low Back Pain
Funder
National Health and Medical Research Council
Funding Amount
$333,515.00
Summary
Low back pain (LBP) is a very common and expensive health condition worldwide. To help clinicians manage LBP and other health conditions, clinical prediction rules can be used to identify patients at risk of prolonged suffering and prescribe appropriate treatments. This research program will determine the best methods to develop these rules and measure the effect they have on the burden of LBP. The findings will also contribute to improving the management of many other health conditions.
Using The Results Of Genome Wide Association Studies To Reduce The Burden Of Disease: A Case For Type II Diabetes?
Funder
National Health and Medical Research Council
Funding Amount
$332,255.00
Summary
Only half of those with type II diabetes (T2D) have been diagnosed. The delay allows for the progression of associated problems like blindness, cardiovascular disease and heart failure. Advances in genetics have helped identify genes increasing the risk of T2D. Using this information, we see whether we can predict if someone will develop the disease. We then determine whether a test at birth followed by preventive measures reduces the problems associated with T2D and helps people live longer.
Depression In Primary Care: Investigating Burden And Identifying Opportunities For Change
Funder
National Health and Medical Research Council
Funding Amount
$316,449.00
Summary
This research follows a cohort of primary care patients with depressive symptoms over 10 years. Data provided by participants will be used to identify the risk factors for chronic depression and this information will be used to develop novel ways to manage this common, but complex, condition in the general practice setting.
Risk Prediction For Surgical Site Infections Following Prosthetic Joint Replacement Surgery
Funder
National Health and Medical Research Council
Funding Amount
$376,449.00
Summary
With an ageing population the number of patients undergoing total joint replacement surgery is rapidly increasing. Surgical site infections are one of the most devastating complications of this surgery and are associated with patient suffering and significant healthcare costs. This research aims to identify those patients at greatest risk of infection and to investigate strategies to aid clinical judgment for early diagnosis of surgical site infection.