冠状动脉重建术患者住院死亡率共病指数的发展。

Renxi Li, Stephen Huddleston
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引用次数: 0

摘要

背景:对于心肌血运重建术,冠状动脉旁路移植术(CAGB)和经皮冠状动脉介入治疗(PCI)是两种常见的方式,但住院死亡率很高。合并症指数可用于预测死亡率,或与其他协变量一起用于开发计分系统。本研究旨在为接受冠状动脉血管重建术的患者制定具体的合并症指标。方法:在2015-2020年第四季度的国家住院患者样本数据库中确定接受CABG或PCI治疗的患者。结果:LCMI对死亡率的判别接近充分(c-statistic=0.691, 95% CI=0.682-0.701),与合并合并症的多变量回归(c-statistic=0.685, 95% CI=0.675-0.694)相当。LCMI辨别力明显优于Elixhauser共病指数(ECI) (c-statistic=0.621, 95% CI=0.611 ~ 0.631),并可通过调整年龄进一步改善(c-statistic=0.721, 95% CI=0.712 ~ 0.730)。所有模型均校正良好(Brier评分=0.021-0.022)。LPMI中度区分住院死亡率(c-statistic=0.666, 95% CI=0.660 ~ 0.672),且显著优于ECI (c-statistic=0.610, 95% CI=0.604 ~ 0.616)。LPMI优于全共病多变量回归(c-statistic=0.658, 95% CI=0.652-0.663)。年龄调整后,LPMI歧视显著增加,接近充分(c-statistic=0.695, 95% CI=0.690-0.701)。所有模型均校正良好(Brier评分=0.025-0.026)。结论:LCMI和LPMI能有效判别和预测院内死亡率。这些指标经过验证并优于ECI。这些指标可以标准化共病测量,作为ECI的替代方案,以帮助重复和比较研究中的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of Comorbidity Index for in-hospital mortality for patients who underwent coronary artery revascularization.

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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