Artificial intelligence modelling to assess the risk of cardiovascular disease in oncology patients.
Samer S Al-DroubiEiman JahangirKarl M KochendorferMarianna KriveMichal Laufer-PerlDan GilonTochukwu M OkwuosaChristopher P GansJoshua H ArnoldShakthi T BhaskarHesham A YasinJacob KrivePublished in: European heart journal. Digital health (2023)
Predictive models are ready for translation into oncology practice to identify and care for patients who are at risk of cardiovascular disease. The models are being integrated with electronic health record application as a report of patients who should be referred to cardio-oncology for monitoring and/or tailored treatments. Models operationally support cardio-oncology practice. Limited validation identified 86% of the lymphoma and 58% of the kidney cancer patients with major risk for cardiotoxicity who were not referred to cardio-oncology.
Keyphrases
- palliative care
- cardiovascular disease
- artificial intelligence
- end stage renal disease
- healthcare
- electronic health record
- newly diagnosed
- ejection fraction
- chronic kidney disease
- primary care
- machine learning
- prognostic factors
- type diabetes
- peritoneal dialysis
- quality improvement
- coronary artery disease
- papillary thyroid
- cardiovascular risk factors
- smoking cessation
- young adults
- diffuse large b cell lymphoma
- cardiovascular events
- clinical decision support
- health insurance