Evidence regarding the relationship between age-adjusted Charlson comorbidity index (ACCI) and in-hospital mortality is limited. Therefore, the present study investigated whether there was an independent association between ACCI and in-hospital mortality in critically ill patients with cardiogenic shock (CS) after adjusting for other covariates (age, sex, history of disease, scoring system, in-hospital management, vital signs at presentation, laboratory findings and vasopressors). ACCI, calculated retrospectively after hospitalization between 2008 and 2019, was derived from intensive care unit (ICU) admissions at the Beth Israel Deaconess Medical Center (Boston, MA, USA). Patients with CS were classified into two categories based on predefined ACCI scores (low, <8; high, ≥8). Based on baseline ACCI, the risk of in-hospital mortality in patients with CS was calculated using a multivariate Cox proportional risk model, and the threshold effect was calculated using a two-piece linear regression model. The in-hospital mortality rate was ~1.5 times greater in the ACCI high group compared with that in the ACCI low group [hazard ratio (HR)=1.45; 95% confidence interval (CI), 1.14 - 1.86]. Additional analysis showed that ACCI had a curvilinear association with in-hospital mortality risk in patients with CS, with a saturation effect predicted at 4.5. When ACCI was >4.5, the risk of in-hospital CS death increased significantly with increasing ACCI (HR=1.122; 95% CI, 1.054 - 1.194). Overall, ACCI was an independent predictor of in-hospital mortality in ICU patients with CS. A non-linear relationship was revealed between ACCI and in-hospital mortality, where in-hospital mortality increased significantly when ACCI was >4.5.

The relationship between the Charlson comorbidity index (CCI) and short-term readmission is as yet unknown. Therefore, we aimed to investigate whether the CCI was independently related to short-term readmission in patients with heart failure (HF) after adjusting for other covariates. From December 2016 to June 2019, 2008 patients who underwent HF were enrolled in the study to determine the relationship between CCI and short-term readmission. Patients with HF were divided into 2 categories based on the predefined CCI (low < 3 and high > =3). The relationships between CCI and short-term readmission were analyzed in multivariable logistic regression models and a 2-piece linear regression model. In the high CCI group, the risk of short-term readmission was higher than that in the low CCI group. A curvilinear association was found between CCI and short-term readmission, with a saturation effect predicted at 2.97. In patients with HF who had CCI scores above 2.97, the risk of short-term readmission increased significantly (OR, 2.66; 95% confidence interval, 1.566–4.537). A high CCI was associated with increased short-term readmission in patients with HF, indicating that the CCI could be useful in estimating the readmission rate and has significant predictive value for clinical outcomes in patients with HF.

Review question / Objective: The efficacy of risk prediction model for ISR. Condition being studied: Coronary heart disease (CHD), with high morbidity and high mortality rate, is still a serious public health concern around the world. PCI is fast becoming a key instrument in revascularization for patients with CHD, as well as an important technology in the management of CHD patients.1 Although the clinical application of coronary stents brought about a dramatic improvement in patients’ clinical and procedural outcomes, the mid-and long-term outcome of stent implantation remains significantly hampered by the risk of developing ISR with a prevalence rate of 3–20% over time. Predictive models have the advantage of formally combining risk factors to allow more accurate risk estimation. And it is essential to establish a model to predict ISR in patients with CAD and drug-eluting stents (DESs) implantation.However, predictive model performance needs further evaluation.