BMC2 Data Presented via Poster on Merging Machine Learning and Patient Preference during AHA 2022

An illustration of a brain's circuitry hovers over a tablet device.
Elizabeth Walker

David E. Hamilton, MD, presented the poster “Merging Machine Learning and Patient Preference: A Contemporary, Comprehensive, Patient-Centered Tool for Risk Prediction Prior to Percutaneous Coronary Intervention” during the American Heart Association Scientific Sessions 2022 held November 5th – 7th in Chicago.

Dr. Hamilton and his colleagues hypothesized that using machine learning models would allow predictions of common post-PCI complications using pre-procedural factors.

The team engaged a panel of patients who underwent PCI procedures at BMC2 hospitals to rank the importance of common PCI complications. A separate group of 66 adults underwent a semiquantitative survey to assess a preferred list of outcomes and model display.  

Using data, the team identified percentages of:

  • acute kidney injury
  • new need for dialysis
  • major bleeding
  • transfusion
  • overall rate of mortality 

They then designed an XGBoost machine learning model that accurately predicts individualized post-PCI outcomes.

Patients, including BMC2 PCI Patient Advisory Council members, provided feedback to help create a patient-centered tool. The tool displays risks to patients and providers, allowing them access to enhanced risk prediction before PCI. This tool could help inform shared decision-making discussions and treatment selection. 

The study co-authors are Jeremy Albright, PhD; Milan Seth, MS; Devraj Sukul, MD, MSc; and Hitinder S. Gurm, MD. You can view the poster on