History of Coronary Heart Disease Increases the Mortality Rate of Coronavirus Disease 2019 (COVID-19) Patients: A Nested Case-Control Study Based on Publicly Reported Confirmed Cases in Mainland China

Tian Gu; Qiao Chu; Zhangsheng Yu; Botao Fa; Anqi Li; Lei Xu; Ruijun Wu; Yaping He

The total number of comorbidities was defined as the summation of comorbidities, ranging from zero to four or above. Kaplan-Meier curve was plotted to check the proportional hazard assumption and the Pearson correlation test was used to rule out the multicollinearity concern before fitting any model. Univariate weighted Cox models were performed for each comorbidity. The multivariate weighted Cox model was used to determine if pre-existing comorbidity yielded prognostic hazard information. We included those comorbidities that were marginally significant (p<0.1) in the univariate analysis to the multivariate model. Other than the common risk factors (age and gender), the multivariate model also adjusted for early period of pandemic (after vs. before January 11 th , 2020 when no-intervention was taken by the government) [22] . Although matching was based on age and gender, we adjusted for the matching covariates, since the matching was broken with inverse probability weighting [32] . A separate multivariate model was built by using the total number of pre-existing comorbidities as an ordinal predictor, adjusting for the same covariates. Hazard ratios (HRs) from the weighted Cox model were reported along with 95% confidence intervals (CIs) and p-values. Sensitivity analysis was performed using multivariate logistic regression to provide estimated odds ratio (ORs), which includes the same covariates as the multivariate weighted Cox model.