Abstract
AbstractBackgroundFor 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.MethodsPatients 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.ResultsMortality 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).ConclusionsLCMI 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.
Publisher
Cold Spring Harbor Laboratory