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Derivation and validation of predictive indices for cardiac readmission after coronary and valvular surgery - A
Louise Y Sun1,2, Anna Chu2, Derrick Y Tam2,3,4
1Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Insights
New models can predict one-year cardiac readmission risk after coronary artery bypass grafting (CABG), aortic valve replacement (AVR), or combined procedures. These models utilize readily available electronic health record data for improved patient care.
Area of Science:
- Cardiovascular Surgery
- Health Informatics
- Predictive Analytics
Background:
- Cardiac surgery patients face significant risks of readmission.
- Accurate prediction of readmission is crucial for resource allocation and patient management.
- Existing models often lack generalizability or utilize limited data sources.
Purpose of the Study:
- To develop and validate predictive models for one-year cardiac readmission risk.
- To utilize routinely collected electronic medical record data for model derivation.
- To assess model performance after isolated coronary artery bypass grafting (CABG), aortic valve replacement (AVR), and combined CABG+AVR.
Main Methods:
- Retrospective cohort study using Canadian clinical registries and administrative databases.
- Development of Fine and Gray subdistribution hazard models within a competing-risk framework.
- External validation of models in an independent patient cohort.
Main Results:
- Models demonstrated good predictive performance: c-statistics ranged from 0.66 to 0.74.
- Post-CABG model: c-statistic 0.73 (derivation), 0.70 (validation).
- Post-AVR model: c-statistic 0.74 (derivation), 0.73 (validation).
- Post-CABG+AVR model: c-statistic 0.70 (derivation), 0.66 (validation).
Conclusions:
- Predicting one-year cardiac readmission after CABG, AVR, and combined procedures is feasible using multidimensional data.
- The derived models show improved discrimination compared to existing registry-based models.
- These models offer a parsimonious yet effective tool for risk stratification.
Objective:
To derive and validate models to predict the risk of a cardiac readmission within one year after specific cardiac surgeries using information that is commonly available from hospital electronic medical records.
Methods:
In this retrospective cohort study, we derived and externally validated clinical models to predict the likelihood of cardiac readmissions within one-year of isolated CABG, AVR, and combined CABG+AVR in Ontario, Canada, using multiple clinical registries and routinely collected administrative databases. For all adult patients who underwent these procedures, multiple Fine and Gray subdistribution hazard models were derived within a competing-risk framework using the cohort from April 2015 to March 2018 and validated in an independent cohort (April 2018 to March 2020).
Results:
For the model that predicted post-CABG cardiac readmission, the c-statistic was 0.73 in the derivation cohort and 0.70 in the validation cohort at one-year. For the model that predicted post-AVR cardiac readmission, the c-statistic was 0.74 in the derivation and 0.73 in the validation cohort at one-year. For the model that predicted cardiac readmission following CABG+AVR, the c-statistic was 0.70 in the derivation and 0.66 in the validation cohort at one-year.
Conclusions:
Prediction of one-year cardiac readmission for isolated CABG, AVR, and combined CABG+AVR can be achieved parsimoniously using multidimensional data sources. Model discrimination was better than existing models derived from single and multicenter registries.
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