Cardiology hospital admission risk prediction: training, internal validation and technical implementation in the electronic health record.
The Cardiology Hospital Admission Risk Prediction model showed strong discrimination for predicting cardiac events.
Cardiology hospital admission risk prediction: training, internal validation and technical implementation in the electronic health record.
Rising healthcare demand is increasingly outpacing available outpatient capacity in cardiology, where follow-up is often scheduled at fixed intervals despite substantial variation in individual patient risk.
Rising healthcare demand is increasingly outpacing available outpatient capacity in cardiology, where follow-up is often scheduled at fixed intervals despite substantial variation in individual patient risk.
We developed and validated a machine-learning model as part of the Cardiology Hospital Admission Risk Prediction (Cardiology Hospital Admission Risk Prediction) program.
Accurate risk estimation using routinely collected electronic health record (electronic health record) data may support more individualized follow-up planning by identifying patients at very low risk of mortality or unplanned hospitalization, in whom follow-up intervals could be safely extended.
The retrospective baseline cohort comprised 307 792 outpatient visits from 52 989 unique patients at Amsterdam UMC.
The primary endpoint was a composite of unplanned cardiac hospitalization or all-cause death within 2 years; the 1-year composite endpoint served as a secondary outcome.
This study shows that machine-learning applied to routine electronic health record data can deliver clinically meaningful, visit-level risk stratification for cardiology outpatients.
The successful electronic health record integration of Cardiology Hospital Admission Risk Prediction enables prospective evaluation of data-driven follow-up strategies aimed at reducing outpatient clinic burden through safe de-intensification of follow-up for low-risk patients.