The new network PLP study is launched to validate machine learning model predicting newly diagnosed MDD patients within 1 year after initiation of beta-blocker for cardiovascular disease. The algorithm was developed on the Korean National Health Insurance Service - National Sample Cohort.
Background: Incident depression has been reported to be associated with poor prognosis in patients with cardiovascular disease (CVD), which might be caused by beta-blocker therapy. Since early detection and intervention for depression can alleviate the risk of future CVD risks in these patients, prediction model to identify high-risk patients for subsequent MDD may lead better outcome in patients requiring beta-blocker therapy.
Since we’re aiming to submit the paper to the Special Issue of Journal of Personalized Medicine (Deadline: 20 December 2020), please let us know as soon as possible if you want to participate.
And please provide any comments or suggestions.
Hi @SCYou! You can count in the IQVIA team. Can you upload your protocol to the Documents folder on your repo? That would be really helpful for us to understand what databases are most appropriate for this study.
Thank you so much @krfeeney! We’ve uploaded the protocol to the github. The detailed medical background for this research will be updated in the protocol soon.
You can count in Singapore as well. Do you have any ATLAS json files for the cohort definitions? How many no of patients do you require under T and O cohort for a data source to be eligible for participating in this study?
