Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions.
David E HamiltonJeremy AlbrightMilan SethIan PainterCharles MaynardRavi S HiraDevraj SukulHitinder S GurmPublished in: European heart journal (2024)
Using common pre-procedural risk factors, the BMC2 machine learning models accurately predict post-PCI outcomes. Utilizing patient feedback, the BMC2 models employ a patient-centred tool to clearly display risks to patients and providers (https://shiny.bmc2.org/pci-prediction/). Enhanced risk prediction prior to PCI could help inform treatment selection and shared decision-making discussions.
Keyphrases
- machine learning
- coronary artery disease
- case report
- percutaneous coronary intervention
- acute coronary syndrome
- acute myocardial infarction
- risk factors
- end stage renal disease
- antiplatelet therapy
- st segment elevation myocardial infarction
- newly diagnosed
- artificial intelligence
- chronic kidney disease
- coronary artery
- ejection fraction
- big data
- peritoneal dialysis
- heart failure
- coronary artery bypass grafting
- prognostic factors
- physical activity
- deep learning
- minimally invasive
- patient reported outcomes
- left ventricular
- ultrasound guided
- aortic valve
- weight loss
- insulin resistance
- glycemic control