Development of Comorbidity Index for In-hospital Mortality for Patients Underwent Coronary Artery Revascularization
Insights
New Li CABG Mortality Index (LCMI) and Li PCI Mortality Index (LPMI) effectively predict in-hospital mortality after coronary artery revascularization, outperforming the Elixhauser Comorbidity Index (ECI). Age adjustment further enhances predictive accuracy for clinical application.
Area of Science:
- Cardiology
- Medical Informatics
Background:
- Coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) are common myocardial revascularization procedures with significant in-hospital mortality.
- Predictive indices for mortality are crucial for patient management and risk stratification in cardiovascular interventions.
Approach:
- Developed Li CABG Mortality Index (LCMI) and Li PCI Mortality Index (LPMI) using Elixhauser comorbidities from the National Inpatient Sample database (Q4 2015-2020).
- Utilized multivariable regression to assign weights to comorbidities for predicting in-hospital mortality in patients undergoing CABG or PCI.
- Randomly sampled patients into experimental (70%) and validation (30%) groups, excluding those under 40 for congenital heart defects.
Key Points:
- LCMI demonstrated adequate mortality prediction (c-statistic=0.691), outperforming the Elixhauser Comorbidity Index (ECI) (c-statistic=0.621).
- LPMI moderately predicted mortality (c-statistic=0.666), also showing superiority over ECI (c-statistic=0.610).
- Adjusting for age significantly improved both LCMI (c-statistic=0.721) and LPMI (c-statistic=0.695) predictive power, approaching adequacy.
Conclusions:
- LCMI and LPMI are effective and validated indices for predicting in-hospital mortality in patients undergoing coronary revascularization.
- These novel indices demonstrate superior performance compared to the existing ECI.
- Incorporating age adjustment enhances the predictive utility of LCMI and LPMI, suggesting strong potential for clinical application in risk assessment.
Background:
For myocardial revascularization, coronary artery bypass grafting (CAGB) and percutaneous coronary intervention (PCI) are two common modalities but with high in-hospital mortality. A comorbidity index is useful to predict mortality or can be used with other covariates to develop point-scoring systems. This study aimed to develop specific comorbidity indices for patients who underwent coronary artery revascularization.
Methods:
Patients who underwent CABG or PCI were identified in the National Inpatient Sample database between Q4 2015-2020. Patients of age<40 were excluded for congenital heart defects. Patients were randomly sampled into experimental (70%) and validation (30%) groups. Thirty-eight Elixhauser comorbidities were identified and included in multivariable regression to predict in-hospital mortality. Weight for each comorbidity was assigned and single indices, Li CABG Mortality Index (LCMI) and Li PCI Mortality Index (LPMI), were developed.
Results:
Mortality prediction by LCMI approached adequacy ( c -statistic=0.691, 95% CI=0.682-0.701) and was comparable to multivariable regression with comorbidities ( c -statistic=0.685, 95% CI=0.675-0.694). LCMI prediction performed significantly better than Elixhauser Comorbidity Index (ECI) ( c -statistic=0.621, 95% CI=0.611-0.631) and can be further improved by adjusting age ( c -statistic=0.721, 95% CI=0.712-0.730). LPMI moderately predicted in-hospital mortality ( c -statistic=0.666, 95% CI=0.660-0.672) and performed significantly better than ECI ( c -statistic=0.610, 95% CI=0.604-0.616). LPMI performed better than the all-comorbidity multivariable regression ( c -statistic=0.658, 95% CI=0.652-0.663). After age adjustment, LPMI prediction was significantly increased and was approaching adequacy ( c -statistic=0.695, 95% CI=0.690-0.701).
Conclusions:
LCMI and LPMI effectively predicted in-hospital mortality. These indices were validated and performed superior to ECI. The adjustment of age increased their predictive power to adequacy, implicating potential clinical application.
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