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Development of Comorbidity Index for in-hospital mortality for patients who underwent coronary artery
Renxi Li1,2, Stephen Huddleston3
1George Washington University School of Medicine and Health Sciences, Washington, DC, USA - renxili@gwu.edu.
Insights
New mortality indices, Li CABG Mortality Index (LCMI) and Li PCI Mortality Index (LPMI), effectively predict in-hospital mortality after coronary revascularization. These validated indices outperform the Elixhauser Comorbidity Index (ECI) for improved patient risk assessment.
Area of Science:
- Cardiology
- Medical Informatics
- Health Services Research
Background:
- Coronary artery bypass grafting (CAGB) and percutaneous coronary intervention (PCI) are common myocardial revascularization procedures.
- Both CAGB and PCI are associated with significant in-hospital mortality.
- Existing comorbidity indices can aid in predicting mortality but may lack specificity for revascularization patients.
Purpose of the Study:
- To develop and validate specific comorbidity indices for patients undergoing coronary artery revascularization.
- To assess the predictive performance of these new indices against existing measures.
- To provide tools for standardizing comorbidity assessment in cardiovascular surgery.
Main Methods:
- Utilized the National Inpatient Sample database (Q4 2015-2020) for patients undergoing CABG or PCI.
- Excluded patients under 40 with congenital heart defects.
- Developed Li CABG Mortality Index (LCMI) and Li PCI Mortality Index (LPMI) using multivariable regression on 38 Elixhauser comorbidities, with a 70% experimental and 30% validation split.
Main Results:
- LCMI demonstrated adequate discrimination for in-hospital mortality (c-statistic=0.691), outperforming the Elixhauser Comorbidity Index (ECI) (c-statistic=0.621).
- LPMI showed moderate discrimination (c-statistic=0.666), also significantly better than ECI (c-statistic=0.610).
- Both indices showed improved discrimination after age adjustment and were well-calibrated.
Conclusions:
- LCMI and LPMI are effective tools for discriminating and predicting in-hospital mortality in revascularization patients.
- These indices offer superior performance compared to the ECI.
- LCMI and LPMI can standardize comorbidity measurement, facilitating comparable research outcomes.
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 discriminate 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 discrimination 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 discrimination 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). All models were well-calibrated (Brier score=0.021-0.022). LPMI moderately discriminated 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 discrimination was significantly increased and was approaching adequacy (c-statistic=0.695, 95% CI=0.690-0.701). All models were well-calibrated (Brier score=0.025-0.026).
Conclusions:
LCMI and LPMI effectively discriminated and predicted in-hospital mortality. These indices were validated and performed superior to ECI. These indices can standardize comorbidity measurement as alternatives to ECI to help replicate and compare results across studies.
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