In 2018, an estimated 11 million Chinese were affected by coronary heart disease (CHD) [1
]. The aging population and increasing prevalence of cardiovascular disease risk factors [2
] will lead to a growing burden of CHD [4
]. The population affected by CHD is predicted to increase to 22.6 million by 2030 [6
]. The mortality rate of CHD in China reached 1.39 million in 2013 [7
It is generally believed that hypertension, diabetes, smoking and hyperlipidemia are the risk factors for CHD [8
]. In addition, renal insufficiency (RI) is also one of the important risk factors for CHD [11
]. Many studies had shown that in the patients with CHD, RI was an independent predictor for short- and long-term prognosis [12
]. Moreover, studies have confirmed that even mild RI increases the risk of adverse outcomes during hospitalization[15
].But the information about the attributable risk associated to mild RI in patients with ACS for MACEs is scarce too. Therefore, the purpose of this study is not only to evaluate the impact of mild RI on hospitalization outcomes, but also to analyze the attributable risk of mild RI on adverse outcomes during hospitalization.
Details of the design and methodology of the CCC-ACS project have been published [19
]. Briefly, it is a national, hospital-based quality improvement project with an ongoing database, aiming to increase adherence to ACS guidelines in China and to improve patient outcomes. It was launched in 2014 as a collaborative initiative of the American Heart Association (AHA) and Chinese Society of Cardiology (CSC). 240 hospitals were recruited representing the diversity of ACS care in hospitals in China, including 160 tertiary hospitals and 80 secondary hospitals. Clinical data were collected via a web-based data collection platform (Oracle Clinical Remote Data Capture, Oracle Corporation). Trained data abstractors entered the data elements abstracted from medical charts. Eligible patients were consecutively reported to the CCC-ACS database for each month before the middle of the following month. Around 5% of reported cases were randomly selected and compared with the original medical records. An audit by a third party was performed to ensure that cases were reported consecutively rather than selectively. This research has been registered in https://clinicaltrials.gov
(NCT02306616). All methods were performed in accordance with the relevant guidelines and regulations.
A total of 92,509 inpatients with ACS, identified based on their principal diagnosis at discharge, were enrolled from 240 hospitals across China from November 2014 to December 2019. Based on the eGFR, all the patients were further divided into ≥90 ml/min·1.73m2 group, 60-89 ml/min·1.73m2 group, 45-59 ml/min 1.73 m2 group, 30-44 ml/min 1.73 m2 group and < 30 ml/min 1.73 m2 group. Institutional review board approval was granted for the aggregate data set for research and quality improvement by the Ethics Committee of Beijing Anzhen Hospital, Capital Medical University. No informed consent was required.
Difinition of mild RI
Mild RI is defined as eGFR 60-89 ml / min · 1.73m2.
Definition of in-hospital outcomes
Major adverse cardiovascular events (MACEs) were defined as a combination of death, heart failure, cardiac arrest, and cardiac shock.
Definition of other Variables
The ACS classification was based on the primary diagnosis at discharge in the medical record. Non-ST-segment elevation ACS was defined as non-ST-segment elevation myocardial infarction (STEMI) or unstable angina. Hypertension was defined as having a history of hypertension, receiving antihypertensive therapy, or having a systolic blood pressure≥140mmHg or diastolic blood pressure≥90mmHg at admission. Diabetes mellitus was defined as having a previous or new diagnosis of diabetes mellitus, receiving oral hypoglycemic drug therapy or insulin therapy, or having a HBA1C≥6.5%. Hyperlipidemia was defined as having a history of hyperlipidemia, receiving lipid-lowering drugs, or having a serum LDL-C≥1.8mmol/L at admission. Current smoking was defined as smoking in the preceding 1 year according to the medical records of the patients.
Creatinine was collected on the day of admission.The baseline eGFR was calculated using the Modification of Diet in Renal Diseases (MDRD) equation for Chinese patients: eGFR (mL/min๒1.73m2
)=175 x SCr (mg/dl)-1.234
(x 0.79 for women) [20
All the laboratory testing values were the values tested the first time after admission.
Continuous variables with normal distribution were presented as mean±standard deviation, and ANOVA analysis was used for univariate comparison. On the other hand, those with non-normal distribution were represented as median and interquartile range, and Wilcoxon-Mann-Whitney test was used for univariate comparison. The categorical variables were reported as number of cases and percentages, and the chi-square test was used for univariate comparison. A multivariate logistic regression model was used to determine the association between the eGFR and in-hospital outcomes by controlling for potential confounders. Candidate adjustment factors included age, history of hypertension, diabetes mellitus, heart failure, atrial fibrillation, stroke, previous PCI or CABG, ACS type, heart failure at admission, cardiogenic shock at admission, cardiac arrest at admission, Killip class at admission, systolic pressure at admission, taking antiplatelet drugs before admission, taking β-blocker before admission and taking ACEI/ARB before admission, HB at admission, LDL-C at admission. The attributable risk (AR)[21
] was calculated to investigate the effect of mild RI on MACEs during hospitalization.
The subscript i denotes each exposure level; pi is the proportion of the study population in the exposure level i, and OR is the odds ratio for the exposure level i compared with the unexposed (reference) level.
For data with missing values lower than 15% (Additional file:Table S2
), the sequential regression multiple imputation method implemented by IVEware software version 0.2 (Survey Research Center, University of Michigan, Ann Arbor, MI, USA) was used to imputed the missing values.
SPSS software version 22.0 (IBM Inc, Armonk, NY, USA) was used to analyze the data. For all analyses, P<0.05 was considered as statistically significant.
In this large, hospital-based registry for patients with ACS, eGFR was significantly associated with the risk of MACEs during hospitalization. Moreover, the attributable risk of mild RI to MACEs was higher than that of moderate to severe RI, which is especially obvious in patients with NSTE-ACS.
The subjects of this study were patients with ACS. In the present study, we found the proportion of patients with eGFR < 90 ml / min · 1.73m2
was 54.05%, and the proportion of patients with mild RI (eGFR 60-89 ml / min · 1.73m2
) was 36.17%. This was similar to the foreign literature report: in a study including 20,604 patients with ACS in New Zealand, 53.3%, 23.3%, 1.7% and 1.4% of patients combined with CKD stages 2, 3, 4 and 5 respectively [23
]. In other studies with relative small sample size, the proportion of ACS patients with RI is also similar [24
]. It can be seen that the proportion of patients with RI is large in ACS population and which is mainly mild RI.
The present study also found that RI was an independent risk factor for MACEs during hospitalization, and there was a gradual correlation between them. In the multivariate regression analysis, the odds ratio(95% CI) of MACEs in the patients with 60-89 ml/min·1.73m2
, 30-44 ml/min·1.73m2
and <30 ml/min·1.73m2
were 1.27, 1.65, 2.04 and 2.23. We can see from this data that the worse the renal function in the patients with ACS, the higher risk of MACEs during hospitalization, which is consistent with the foreign literature reports [13
]. Therefore, we always pay more attention to the ASC patients with severe RI in clinical work, while often ignore the ACS patients with mild RI.
However, it also can be seen from the data of the present study that only the slightly decrease of eGFR (60-89 ml / min·1.73m2
) in the patients with ACS, the risk of MACEs increased 1.27 times compared to those with normal renal function (eGFR ≥ 90ml / min·1.73m2
). Smith GL et al. followed up 118,753 patients with AMI for 10 years. They found that even mild impairment of renal function (eGFR 66-74 ml/min.1.73 m2
) could increase 10-year mortality risk of patients to 10% compared to patients with normal renal function [27
]. El menyar et al. retrospectively analyzed 6518 patients with ACS and found that ACS patients with mild RI (eGFR 60-89 ml/min.1.73 m2
) increased the risk of in-hospital mortality by 2.1 times compared with ACS patients with normal renal function[15
].Other studies with relatively small sample size also found that mild RI was related to the short-term and long-term prognosis in ACS patients [27
]. However, only regression analysis was used to analyze the relationship between mild RI and prognosis of ACS patients in these studies. And this was not be reported in previous literature that used attributable risk to reveal the relationship between mild RI and hospital outcomes in ACS population. Attributable risk is reflected in the total chance of a disease (or death) in the population exposed to a certain factor, the part that really attributable to the exposure factor. The public health significance of this index is that for the exposed population, if the exposure factors are eliminated, the number of morbidity (or death) per unit population can be reduced. In the present study, the attributable risk of eGFR 60-89ml/min·1.73m2
was 7.78%, eGFR 45-59 ml/min·1.73m2
was 4.69%, eGFR 30-44 ml/min·1.73m2
was 4.46% and eGFR<30 ml/min·1.73m2
was 3.36%. That is to say, among the ACS patients, 7.78% of MACEs during hospitalization was caused by mild RI. It can be seen that in the ACS population in the present study, although the risk of MACEs during hospitalization with mild RI was lower than that of moderate and severe RI, its attributable risk is far greater than that of moderate and severe RI. The occurrence of this phenomenon is attributed to the proportion of ACS patients with mild RI far greater than that of ACS patients with moderate and severe RI. For example, the patients with mild RI accounts for 36.17% of all the ACS patients in the present study. Therefore, even if the creatinine is slightly increased, we should pay enough attention and carry out early intervention to avoid the occurrence of adverse events in the hospital. Overall, attributable risks can allow for optimal allocation of resources toward the prevention of MACEs associated with ACS and suggest that primary prevention strategies may be needed at mild RI.
We even found that in the NSTE-ACS population, the attributable risk of eGFR 60-89 ml/min·1.73m2
to MACEs during hospitalization was as high as 10.91%. While that is 7.18% in patients with STEMI. In the present study, the proportion of NST-ACS patients with RI compared with STEMI patients with RI was 58.43% vs. 51.14%. Moreover, 34.95% of patients with STEMI had mild RI, while that of NST-ACS patients was 38%. It can be seen that patients with NSTE-ACS are more likely to have a poor basic renal function than patients with STEMI, which is similar to Gupta’s conclusion [13
]. They analyzed 3,187,404 patients. In the ACS subgroup, the percentages of STEMI in non CKD, CKD and ESRD patients were 34.5%, 22.3% and 16.6% respectively, while the percentages of NSTE-ACS were 65.5%, 77.7% and 83.4%. The occurrence of this phenomenon may be related to the presence of microinflammation, vascular calcification caused by abnormal bone metabolism, etc. in patients with RI, which leads to the progression of chronic invasive plaque of coronary artery and chronic obstruction, eventually lead to NSTE-ACS[30
]. It is precisely because the basic renal function of patients with NSTE-ACS is worse than that of patients with STEMI, and the percentage of patients with mild RI is higher, so the attribution risk of mild RI to MACEs during hospitalization is higher in patients with NSET-ACS. This also suggests that once NSTE-ACS patients with mild RI, we need to be highly vigilant and actively prevent the occurrence of MACEs.
The current study has the following limitations: (1) Because cardiologists usually avoid to do coronary angiography in patients with RI because of a high risk. Therefore, the population for assessing the severity of coronary artery lesions in our study, may not be represented all the ACS patients. (2) Because the data in our study are obtained from CCC-ACS database, which does not be included the data of calcium and phosphorus metabolism, inflammation, the current study couldn’t adjust the impact of the above factors on the short-term outcomes in patients with ACS. (3) The outcome of our study was limited to hospital events and follow-up data were not available.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.