| Title: | Predicting Cardiovascular risk from retinal photographs and limited demographic data: An external validation of a New Zealand algorithm |
| Lead Institution: | University of Auckland |
| Principal investigator: | Dr Ehsan Vaghefi |
Application 86299
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
About
Retinal photographs are routinely taken from people with cardiovascular comorbidities (i.e. diabetes and hypertension). These images have the potential to be rapidly analyzed at low cost and used to improve cardiovascular risk prediction. This information could be made available immediately to the patient and their health care provider to both inform and motivate better risk management, allowing better targeting of expensive drugs to high-risk patients. The main modifiable factors for cardiovascular risk are diabetes and hypertension. However, to be effective, these factors must be proactively controlled by the patient, via positive change in lifestyle and medication uptake and compliance. Hence, it is essential to assess patient's cardiovascular risk early, and support the patient with education and medication, which will then significantly reduce the long term cost of intervention. Recent trials have demonstrated the benefits of several diabetes medications such as SGLT2i and GLP1-RA , as well as new lipid-targeted monoclonal antibodies therapies that inhibit PCSK9. Both SGLT2i and GLP1RA are currently under consideration for restricted access funding in New Zealand based on estimated CVD risk, while the PCSK9 inhibitors are currently unfunded. Each of these medications act to reduce CVD risk further, when added to existing standard therapies for glucose, blood pressure and lipid-lowering. The effectiveness and cost-effectiveness of these medications is directly proportional to patients' pre-treatment CVD risk, and therefore the accurate targeting of the medications requires accurate risk prediction.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 8230 | Examination of alternative eGFR definitions on the performance of deep learning models for detection of chronic kidney disease from fundus photographs | Songyang An (+4) | 2023 | PLOS ONE |
Enabling scientific discoveries that improve human health