Prognostic value of the C-reactive protein to albumin ratio in patients undergoing primary percutaneous coronary intervention for ST-segment elevation myocardial infarction

Association of high-sensitivity C-reactive protein to albumin ratio with all-cause and cardiac death in coronary heart disease individuals: A retrospective NHANES study

BACKGROUND

This research aimed to explore the association of high-sensitivity C-reactive protein to albumin ratio (CAR) with death events in community-based patients with coronary heart disease (CHD).

METHODS

624 CHD participants were followed for 36 months using data from the 2015-2018 National Health and Nutrition Examination Survey (NHANES). The CAR was dichotomized at 0.075 mg/g to stratify inflammation levels. Relationships between CAR, high-sensitivity C-reactive protein (hsCRP), albumin (ALB) and all-cause and cardiac death in all participants and subgroups were analyzed using restricted cubic spline (RCS), Kaplan-Meier survival curves and Cox proportional hazards models.

RESULTS

Both CAR and hsCRP showed positive correlations with all-cause and cardiac death risk while ALB exhibited a U-shaped correlation with all-cause death risk but a negative correlation with cardiac death risk. The high-CAR group had higher risks of all-cause (P = 0.04) and cardiac death (P = 0.02). The hazard ratios (HR) (95% confidence intervals (CI)) for all-cause death was 1.77 (1.15-2.74) (P = 0.010), while it was 2.99 (1.44-6.22) (P = 0.003) for cardiac death. No significant interaction was observed in subgroup analyses.

CONCLUSIONS

A CAR threshold of 0.075 mg/g effectively distinguished between high and low inflammation risks. Elevated CAR significantly increased the risk of all-cause and cardiac death in community CHD patients.

Assessment of the Relationship Between C-Reactive Protein-to-Albumin Ratio and Culprit Lesion Location in Patients With ST-Segment Elevation Myocardial Infarction

Aims/Background The C-reactive protein to albumin ratio (CAR) has traditionally been employed to assess inflammatory status in non-cardiac diseases. Recent clinical studies suggest that CAR is a valuable indicator of inflammation in atherosclerotic coronary artery diseases. However, its relationship with ST-segment elevation myocardial infarction (STEMI) remains unclear. This study aimed to investigate the relationship between CAR and the exact location of the culprit lesion in STEMI patients and its potential clinical implications. Methods A retrospective analysis was conducted on patients who presented with STEMI and were treated with primary percutaneous coronary intervention (PCI) within 12 hours of symptom onset between November 2018 and November 2023. Based on coronary angiography (CAG) findings, patients were categorized into three groups according to the culprit vessel: left anterior descending artery (LAD) (n = 218), left circumflex artery (LCX) (n = 31), and right coronary artery (RCA) (n = 153). Three patients with ramus occlusion were excluded from the subgroup analysis. Furthermore, based on the lesion location within the culprit vessel, patients were divided into proximal (n = 122), middle (n = 222), and distal (n = 61) segment groups. Clinical baseline characteristics and laboratory results were recorded. Statistical analyses, including analysis of variance (ANOVA), the Kruskal-Wallis H-test, Fisher's exact test, and the chi-square test, were performed based on variable types and distribution. Correlation analysis was conducted using Spearman's rank correlation coefficient. The receiver operating characteristic (ROC) curve was applied to determine the optimal cut-off value for CAR. A p-value < 0.05 was considered statistically significant. Results A total of 405 patients were included in the study. CAR and left ventricular ejection fraction (LVEF) showed significant differences across groups stratified by culprit vessels (p = 0.001 for CAR; p < 0.001 for LVEF) and lesion location within the vessels (p < 0.001 for CAR and LVEF). CAR values were higher in more proximally located lesions (r = 0.218, p < 0.001), while LVEF showed an inverse relationship (r = -0.203, p < 0.001). ROC curve analysis showed that CAR could predict proximal- and mid-vessel lesions in STEMI patients, with a cut-off value of 26.16 (area under the curve [AUC]: 0.662, 95% confidence interval [CI]: 0.59-0.74, p < 0.001). Conclusion CAR is an easily calculable and reliable biomarker associated with culprit lesion location in STEMI patients, providing potential clinical utility in risk stratification and disease assessment.

Triglyceride-glucose index and hsCRP-to-albumin ratio as predictors of major adverse cardiovascular events in STEMI patients with hypertension

This study aimed to evaluate the association between the triglyceride-glucose (TyG) index and high-sensitivity C-reactive protein-to-albumin ratio (hsCAR) and the prognosis of patients with STEMI and hypertension. A total of 699 patients diagnosed with STEMI and hypertension were included in this study database. Compared to the low TyG index group (< 7.8), the high TyG index group (≥ 7.8) was associated with an increased risk of MACE (HR 2.09, 95% CI = 1.58-2.77; P < 0.001). Similarly, a higher hsCAR (≥ 0.15) was linked to an increased risk of MACE (HR 1.46, 95% CI = 1.12-1.90; P = 0.005). Subsequently, we categorized the population into four groups based on the defined cutoff points. Compared to the low TyG-low hsCAR subgroup, the other three subgroups demonstrated an elevated risk of MACE. Among patients treated with PCSK9 and SGLT2 inhibitors, the combined effect of the TyG index and hsCAR on MACE was attenuated. Finally, The combined TyG index and hsCAR model exhibited optimal performance (AUC = 0.71, 95% CI = 0.67-0.75; P < 0.001). This study demonstrates that the TyG index and hsCAR provide strong combined predictive power. The synergistic utilization offers a comprehensive approach to cardiovascular risk assessment.

CAR, mGPS and hs-mGPS: What is among them the best gero-biomarker for age-related diseases? And for what clinical application?

The identification of biomarkers linked to the onset, progression, and prevention of age-related diseases (ARD), in the era of personalized medicine, represents the best goal of geroscience. Geroscience has the fundamental role of exploring and identifying the biological mechanisms of aging to suggest interventions capable of stopping/delaying the many pathological conditions and disabilities related to age. Therefore, it has become its key priority, as well as that of clinical practice and research, based on identifying and validating a range of biomarkers, geromarkers, which can be used to diagnostic, prognostic, or predictive clinical purposes. Indeed, geromarkers have, the potential to predict ARD trajectories and facilitate targeted interventions to slow down the related disabilities. Here our attention is paid to the inflammatory indexes (CAR, mGPS, hs-mGPS) linked to the relationship between the plasma levels of two inflammatory analytes, the typical positive protein of the acute phase, and the negative one, i.e. c-reactive protein (CRP) and albumin, respectively. These indexes allow us to understand the magnitude of the two main mechanisms predicted to influence the aging process, including inflammation and immunosenescence, as well as the degree of ARD severity. Evidence on their relationship with ARD is widely reported and discussed, to understand which can represent the best ARD geromarker, and its clinical application.

The value of D-dimer to lymphocyte ratio in predicting clinical outcomes after percutaneous coronary intervention in ST-segment elevation myocardial infarction patients: A retrospective study

BACKGROUND

D-dimer to lymphocyte ratio (DLR) is a novel composite metric. This study investigated the association between DLR and major adverse cardiovascular events (MACEs) in patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention.

MATERIALS AND METHODS

This retrospective study included 683 STEMI cases treated between January 2018 and June 2021 at a single center. DLR was calculated for each patient. Receiver operating characteristic curves assessed the predictive value of in-hospital and long-term MACEs, with calculated AUC. Based on the optimal DLR cutoff value, the population was categorized into groups for clinical characteristic analysis. Multivariate logistic and COX regression analyses determined factors independently associated with MACEs. Kaplan-Meier estimation method and log-rank tests assessed event-free survival among different DLR groups. Spearman's test explored the correlation between DLR and Gensini score.

RESULTS

DLR demonstrated an AUC of 0.792 for predicting in-hospital MACEs and 0.708 for long-term MACEs in patients with STEMI. Multivariate logistic regression analysis revealed that a high DLR (cutoff value, 0.47) independently increased the risk of MACEs during hospitalization in patients with STEMI (P = 0.003; odds ratio: 3.015; 95 % CI: 1.438-6.321). Multivariate COX regression showed that a high DLR (cutoff value, 0.34) independently predicted MACEs during long-term follow-up in patients with STEMI (P = 0.011; hazard ratio: 1.724; 95 % CI: 1.135-2.619). Furthermore, DLR exhibited a positive correlation with the Gensini score (P < 0.001).

CONCLUSIONS

DLR is a valuable predictor for MACEs occurrence in patients with STEMI during hospitalization and long-term follow-up after PCI.

C-reactive protein to albumin ratio may predict in-hospital mortality in non-ST elevation myocardial infarction

Objective: The authors investigated the value of novel inflammatory markers, systemic immune-inflammation index and C-reactive protein to albumin ratio (CAR), to predict in-hospital mortality in patients with non-ST elevation myocardial infarction (NSTEMI). Materials & methods: A total of 308 patients who underwent percutaneous coronary intervention because of NSTEMI were retrospectively included in the study. Killip classification, Thrombolysis in Myocardial Infarction score, SYNTAX score, and CAR and systemic immune-inflammation index values were calculated. Results: CAR (cutoff: 0.0864; sensitivity: 94.1%; specificity: 40.5%; p = 0.008) and Killip classification (cutoff: 2.5; sensitivity: 64.7%; specificity: 8.9%; p = 0.001) were found to be significantly higher in determining in-hospital mortality. Conclusion: This study revealed that CAR is an inexpensive and significant factor in predicting in-hospital mortality in patients undergoing percutaneous coronary intervention for NSTEMI.

C-Reactive Protein to Albumin Ratio as a Predictor of Contrast-Induced Nephropathy After Carotid Angiography

This study evaluated the role of the C-reactive protein (CRP)/albumin ratio (CAR) in estimating the probability of occurring contrast-induced nephropathy (CIN) after carotid artery angiography (CAAG). Patients (n = 410) who had CAAG for carotid artery stenosis (CAS) were included in this study. A spike in serum creatinine was used to define CIN within 72 h of the procedure (>.5 mg/dL or >25% above baseline). CAR was calculated by dividing the CRP by the albumin level. Patients with CIN had higher numbers of white blood cells (P = .002), numbers of neutrophils (P = .007), neutrophil-lymphocyte ratios (P = .026), high-sensitivity CRP levels (P < .001), and CAR levels (P < .001) than those without CIN. They were also older (P < .001) and more likely to have diabetes mellitus (P = .006) and hypertension (P = .016). According to receiver operator characteristic curve (ROC) analysis, the CAR value has a 75% sensitivity and a 68% specificity for identifying CIN at a cutoff of 1.8. Also, NLR and CRP predicted CIN with 71% sensitivity and 67% specificity, 71% sensitivity and 66% specificity at the best cutoff values of 1.96 and 7.91, respectively. According the present study, in patients with CAS, the development of CIN after CAAG is independently correlated with CAR at admission.

Non-linear Association of CAR with all-Cause and Cardiovascular Mortality in Coronary Heart Disease: A Retrospective Cohort Study from NHANES

OBJECTIVE

To investigate the relationship between C-reactive protein and albumin ratios (CAR) and all-cause and cardiovascular disease(CVD)-specific mortality in individuals with coronary heart disease(CHD).

METHODS

The data from 1895 patients were extracted from the National Health and Nutrition Examination Survey (NHANES) database from 1999-2010. We used weighted COX regression analyses to explore the association between CAR, all-cause, and CVD-specific mortality. Restricted cubic spline(RCS) regression models and threshold effects analysis were used to analyze nonlinear relationships. Subgroup analyses were also performed to explore these relationships further.

RESULTS

During a mean follow-up of 115.78 months, 61.48% of deaths occurred, and 21.85% were due to CVD. After adjusting for potential confounders, each 1-unit increase in CAR was associated with a 65% increase in all-cause mortality and a 67% increase in CVD-specific mortality. The RCS model revealed a non-linear association between CAR and the risk of all-cause mortality and CVD-specific mortality in CHD patients (all non-linear P < 0.001). Threshold effects analysis identified inflection points in regression models of all-cause mortality (0.04, P < 0.001) and CVD-specific mortality (0.05, P = 0.0024). The interaction tests found sex, smoking and diabetes influenced the association between CAR and all-cause mortality and sex, smoking and HF influenced its association with CVD-specific mortality (all P < 0.05).

CONCLUSION

There was a nonlinear association between CAR and all-cause mortality and CVD mortality in patients with CHD, with a higher hazard ratio before the inflection point. Sex, smoking, diabetes, and HF might have an effect on the associations between CAR and death risks.

High levels of high-sensitivity C reactive protein to albumin ratio can increase the risk of cardiovascular disease

BACKGROUND

The high levels of C reactive protein (CRP) to albumin ratio (CAR) is thought to increase the risk of poor outcomes for cancer and cardiovascular disease (CVD). However, the association between CAR and CVD in the Chinese community population has not been investigated.

OBJECTIVE

The aim of this study was to investigate the association between CAR and CVD in the Chinese community population.

METHODS

A total of 62 067 participants without a history of CVD or cancer were included in this study. Kaplan-Meier survival curves were used to calculate the cumulative incidence of endpoint events in CAR quartile groups, and the results were tested by log-rank test. Fine-Gray model was used to analyse the competing risk of death. C-index, Net Reclassification Index (NRI) and Integrated Discrimination Improvement Index (IDI) of different indicators were calculated to distinguish the predictive performance of different indicators.

RESULTS

During an average follow-up period of 10.3±2.1 years, 4025 participants developed CVD. In multivariable Cox regression analysis, compared with Q1 group, model 3 showed that the hazard ratio (HR) (95% confidence interval (95%CI)) of CVD in Q4 group was 1.26 (1.15 to 1.38) (p<0.01), and the HR (95% CI) per 1 SD increase was 1.06 (1.03 to 1.08) (p<0.01). The C-index, continuous NRI and IDI for predicting 10-year CVD were 73.48%, 0.1366 (0.1049 to 0.1684) (p<0.01) and 0.0002 (0.0001 to 0.0004) (p<0.01), respectively, which were higher than those of hs-CRP (C-index:0.7344, NRI:0.0711, IDI: 0.0001) and albumin (C-index:0.7339, NRI: -0.0090, IDI: 0.0000).

CONCLUSION

High levels of CAR can increase the risk of CVD and the predictive performance of CAR for CVD is better than that of hs-CRP or albumin alone.

A novel inflammatory biomarker, high-sensitivity C-reactive protein-to-albumin ratio, is associated with 5-year outcomes in patients with type 2 diabetes who undergo percutaneous coronary intervention

BACKGROUND

Patients with coronary artery disease (CAD) combined with diabetes have a higher risk of cardiovascular events, and high-sensitivity C-reactive protein (hs-CRP)-to-albumin ratio (CAR) is a novel inflammatory biomarker. However, whether the CAR can identify high-risk patients with CAD and type 2 diabetes (T2DM) remains unclear.

METHODS

The present study was based on a prospective and observational cohort with 10,724 individuals who undergo percutaneous coronary intervention (PCI) in Fu Wai Hospital throughout the year 2013 consecutively enrolled. The primary endpoint was all-cause mortality. The secondary endpoint was cardiac mortality. CAR was calculated with the formula: hs-CRP (mg/L)/albumin (g/L). According to the optimal cut-off value of CAR for all-cause mortality, patients were divided into higher CAR (CAR-H) and lower CAR (CAR-L) groups.

RESULTS

A total of 2755 patients with T2DM who underwent PCI and received dual antiplatelet therapy were finally enrolled. During a follow-up of 5 years (interquartile range: 5.0-5.1 years), 126 (4.6%) all-cause mortalities and 74 (2.7%) cardiac mortalities were recorded. In the multivariable Cox model, CAR-H was associated with a higher risk of all-cause mortality (hazard ratio [HR]: 1.634, 95% confidence interval [CI] 1.121-2.380, p = 0.011) and cardiac mortality (HR: 1.733, 95% CI 1.059-2.835, p = 0.029) compared with CAR-L. When comparing the predictive value, CAR was superior to hs-CRP for all-cause mortality (area under the curve [AUC] 0.588 vs. 0.580, p = 0.002) and cardiac mortality (AUC 0.602 vs. 0.593, p = 0.004).

CONCLUSION

In this real-world cohort study, a higher level of CAR was associated with worse 5-year outcomes among diabetic patients with PCI.

C-Reactive Protein/Albumin Ratio as a Novel Predictor of Contrast Induced Nephropathy in Patients With Stable Angina Pectoris

The relationship between C-reactive protein (CRP) to albumin ratio (CAR) and contrast-induced nephropathy (CIN) in patients with acute coronary syndrome has been reported. However, the relevance of CAR in patients with stable angina pectoris (SAP) has not been clarified. We hypothesized that CAR might predict the development of CIN in patients with SAP undergoing coronary angiography (CAG). Patients (n = 554) with SAP who underwent CAG were included in the study. CIN was defined as a ≥25% increase in serum creatinine compared with baseline value within 72 h of CAG. Participants were divided into two groups: CIN (n = 87) and non-CIN (n = 467). Age, CRP, CAR, mean corpuscular volume (MCV), urea, uric acid, contrast medium volume, the percent of percutaneous coronary intervention were significantly greater, whereas albumin and high-density lipoprotein were significantly lower in the CIN group than non-CIN group (p < .05, for all). Multivariate analysis showed that CAR was the only independent predictor for CIN (odds ratio = 7.065, 95% confidence interval (CI); 3.279-15.221, p < .001). Receiver operating characteristic ROC analysis showed that a CAR ≥ 0.1164 could predict CIN (sensitivity of 71% and specificity of 72%; area under curve = 0.736; 95% CI: 0.677-0.795, p < .001). CAR was significantly greater in patients who developed CIN and this independently predicted CIN.

From Classic to Modern Prognostic Biomarkers in Patients with Acute Myocardial Infarction

Despite all the important advances in its diagnosis and treatment, acute myocardial infarction (AMI) is still one of the most prominent causes of morbidity and mortality worldwide. Early identification of patients at high risk of poor outcomes through the measurement of various biomarker concentrations might contribute to more accurate risk stratification and help to guide more individualized therapeutic strategies, thus improving prognoses. The aim of this article is to provide an overview of the role and applications of cardiac biomarkers in risk stratification and prognostic assessment for patients with myocardial infarction. Although there is no ideal biomarker that can provide prognostic information for risk assessment in patients with AMI, the results obtained in recent years are promising. Several novel biomarkers related to the pathophysiological processes found in patients with myocardial infarction, such as inflammation, neurohormonal activation, myocardial stress, myocardial necrosis, cardiac remodeling and vasoactive processes, have been identified; they may bring additional value for AMI prognosis when included in multi-biomarker strategies. Furthermore, the use of artificial intelligence algorithms for risk stratification and prognostic assessment in these patients may have an extremely important role in improving outcomes.