Institutional case volume-incorporated mortality risk prediction model for cardiac surgery
Related Experiment Video
Updated: Nov 4, 2025
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Seohee Lee1, Eun Jin Jang2, Junwoo Jo3
1Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehak-Ro Jongno-Gu, Seoul, 03080, South Korea.
Insights
A new cardiac surgery risk model accurately predicts mortality by including hospital case volume. This model improves upon existing methods by accounting for surgical experience, enhancing patient outcome predictions.
Area of Science:
- Cardiovascular Surgery
- Medical Informatics
- Health Services Research
Background:
- Existing cardiac surgery risk models often overlook surgical experience, specifically institutional case volume.
- Institutional case volume is a critical factor influencing patient outcomes in high-risk cardiac procedures.
Purpose of the Study:
- To develop and validate a novel risk prediction model for in-hospital and 1-year mortality after cardiac surgery.
- To incorporate institutional case volume as a key predictor in the risk model.
Main Methods:
- A cohort of 57,804 adult cardiac surgery patients (2009-2016) was randomly split into derivation and validation sets.
- A risk prediction model for in-hospital and 1-year mortality was developed using the derivation cohort.
- Model performance was rigorously evaluated in the independent validation cohort.
Main Results:
- The developed model showed good predictive performance with c-statistics of 0.76 for in-hospital mortality and 0.74 for 1-year mortality in both cohorts.
- Institutional case volume was identified as a significant predictor variable within the model.
- Hospitals were categorized by annual case volume (≤50, 50-100, 100-200, >200 cases).
Conclusions:
- A validated risk prediction model incorporating institutional case volume for cardiac surgery has been successfully developed.
- This new model offers improved accuracy in predicting both in-hospital and 1-year mortality.
- The findings highlight the importance of considering surgical case volume in risk stratification for cardiac surgery patients.
Background:
Most risk prediction models predicting short-term mortality after cardiac surgery incorporate patient characteristics, laboratory data, and type of surgery, but do not account for surgical experience. Considering the impact of case volume on patient outcome after high-risk procedures, we attempted to develop a risk prediction model for mortality after cardiac surgery that incorporates institutional case volume.
Methods:
Adult patients who underwent cardiac surgery from 2009 to 2016 were identified. Patients who underwent cardiac surgery (n = 57,804) were randomly divided into the derivation cohort (n = 28,902) or the validation cohorts (n = 28,902). A risk prediction model for in-hospital mortality and 1-year mortality was developed from the derivation cohort and the performance of the model was evaluated in the validation cohort.
Results:
The model demonstrated fair discrimination (c-statistics, 0.76 for in-hospital mortality in both cohorts; 0.74 for 1-year mortality in both cohorts) and acceptable calibration. Hospitals were classified based on case volume into 50 or less, 50-100, 100-200, or more than 200 average cardiac surgery cases per year and case volume was a significant variable in the prediction model.
Conclusions:
A new risk prediction model that incorporates institutional case volume and accurately predicts in-hospital and 1-year mortality after cardiac surgery was developed and validated.
