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Abstract

Background

The C-reactive protein–albumin–lymphocyte index integrates inflammatory and nutritional markers. However, its role in acute coronary syndrome remains unclear.

Methods

We retrospectively analyzed 765 patients with acute coronary syndrome admitted to the Beijing Anzhen Hospital between January 2021 and December 2023. The C-reactive protein–albumin–lymphocyte index, neutrophil-to-lymphocyte ratio, C-reactive protein, and systemic immune–inflammation index were calculated from admission laboratories. Severe acute coronary syndrome was defined as ST-elevation myocardial infarction; mild acute coronary syndrome included non–ST-elevation myocardial infarction and unstable angina. Multivariable logistic regression was used to identify the predictors of severe acute coronary syndrome. Discriminative performance was assessed using receiver operating characteristic analysis with DeLong’s test.

Results

Lower C-reactive protein–albumin–lymphocyte index values were significantly associated with severe acute coronary syndrome (adjusted odds ratio: 0.91; 95% confidence interval: 0.86–0.95; p < 0.001) after adjustment for conventional risk factors. In receiver operating characteristic analysis, the C-reactive protein–albumin–lymphocyte index (area under the curve: 0.696; 95% confidence interval: 0.658–0.735) outperformed C-reactive protein (area under the curve: 0.599), neutrophil-to-lymphocyte ratio (area under the curve: 0.624), and systemic immune–inflammation index (area under the curve: 0.560) in discriminating severe from mild acute coronary syndrome (all p ≤ 0.001 for pairwise comparisons).

Conclusions

The C-reactive protein–albumin–lymphocyte index may help identify patients with more severe acute coronary syndrome and offers superior discrimination compared with established inflammatory and nutritional markers. Prospective studies are needed for validation.

Keywords

C-reactive protein–albumin–lymphocyte index acute coronary syndrome severity inflammatory–nutritional biomarkers risk stratification composite prognostic marker

Introduction

Acute coronary syndrome (ACS) is a collective term for a group of serious cardiovascular emergencies, including unstable angina and both non–ST-elevation and ST-elevation myocardial infarctions, characterized by a sudden reduction in coronary blood flow and high mortality risk.^1,2 Despite advances in reperfusion therapy and secondary prevention, early risk stratification remains critical. Patients presenting with higher initial severity experience substantially greater morbidity and mortality and may benefit from more aggressive management strategies. Traditional risk scores such as Global Registry of Acute Coronary Events (GRACE) and Thrombolysis in Myocardial Infarction (TIMI) incorporate clinical and laboratory parameters but require comprehensive data collection and may not fully capture the interplay between systemic inflammation and nutritional status.^3,4

Increasing evidence indicates that dysregulated inflammatory responses and malnutrition both contribute to adverse outcomes in cardiovascular disease.^5–7 Simple hematologic and biochemical markers—including C‐reactive protein (CRP), neutrophil‐to‐lymphocyte ratio (NLR), serum albumin, and lymphocyte count—have each been linked to adverse prognosis in ACS. Composite indices such as the Prognostic Nutritional Index and inflammation‐based Glasgow Prognostic Score integrate these dimensions, but their predictive performance in coronary events has been modest. Recently, the CRP–albumin–lymphocyte (CALLY) index (calculated as albumin × lymphocyte count/CRP) has been proposed as a novel marker combining both nutritional and inflammatory information; initial studies in oncology and chronic inflammatory disorders suggest that CALLY offers superior prognostic value compared with single‐parameter scores.^8–11

To date, however, the clinical relevance of the CALLY index in ACS has not been systematically evaluated. It remains unclear whether lower CALLY values are associated with a greater degree of angiographic stenosis, higher Killip class, or elevated GRACE scores at presentation.^12–14 Understanding this relationship could provide clinicians with a rapid, cost‐effective tool for early risk assessment and guide decision‐making regarding invasive versus conservative management. Accordingly, this single‐center retrospective study aimed to investigate the association between the CALLY index and ACS severity.

Methods

Study design and population

This single-center retrospective study enrolled consecutive patients with ACS admitted to the Department of Cardiology, Beijing Anzhen Hospital, between January 2021 and December 2023. ACS was defined according to the current European Society of Cardiology guidelines, including unstable angina, non–ST-segment–elevation myocardial infarction (NSTEMI), and ST-segment–elevation myocardial infarction (STEMI). The inclusion criteria were as follows: (a) age ≥18 years; (b) diagnosis of ACS according to the current European Society of Cardiology guidelines; and (c) availability of complete laboratory data (serum albumin, high-sensitivity CRP (hs-CRP), lymphocyte, and neutrophil counts) within 24 h of admission. The exclusion criteria were as follows: (a) active infection at presentation; (b) known malignancy; (c) chronic inflammatory or autoimmune disease; (d) severe hepatic dysfunction (alanine aminotransferase or aspartate aminotransferase >3× the upper limit of normal values) or renal dysfunction (estimated glomerular filtration rate <30 mL/min/1.73 m²); or (e) missing key clinical or laboratory data necessary for CALLY index calculation or severity classification. Eligible patients were stratified into mild and severe ACS groups according to the following clinical diagnosis: severe ACS was defined as STEMI, while mild ACS included NSTEMI and unstable angina (Figure 1).

Figure 1.

Patient enrollment and study flow diagram.

Data collection

Demographic data (age, sex, and body mass index (BMI)), clinical history (hypertension, diabetes mellitus, dyslipidemia, smoking, and alcohol use), and vital signs on admission were retrieved from the hospital’s electronic medical record system. Laboratory parameters, including albumin, white blood cell count, neutrophils, lymphocytes, hemoglobin (Hb), platelet count, CRP, fasting blood glucose (FBG), lipid profile, creatinine, and cardiac biomarkers (high-sensitivity troponin I (hs-TnI), creatine kinase–myocardial band (CK-MB), and B-type natriuretic peptide), were recorded. Derived indices such as the CALLY index (albumin × lymphocyte count/CRP), NLR, and estimated glomerular filtration rate (eGFR) were calculated accordingly.

Biomarker calculation

The CALLY index was calculated by multiplying the serum albumin concentration (in g/L) by the lymphocyte count (in 10⁹/L) and then dividing that product by the hs-CRP level (in mg/L). NLR was calculated by dividing the neutrophil count (in 10⁹/L) by the lymphocyte count (in 10⁹/L). Systemic immune–inflammation index (SII) was calculated as platelet count × neutrophil count/lymphocyte count.

Statistical analysis

Continuous data were reported as mean ± SD or median (interquartile range), according to distribution. Categorical variables were described as number (percentage). Intergroup differences were assessed using the Student’s t-test or Mann–Whitney U test for continuous variables and the chi-square test for categorical variables.

Univariate logistic regression analyses were performed to assess the associations between each variable and severe ACS. Variables with clinical relevance or p < 0.1 in univariate analysis were included in the multivariable model. Multivariable logistic regression analysis was conducted to identify independent predictors of severe ACS. Variables with clinical relevance or p < 0.1 in the univariate analysis were included in the model. The results were expressed as odds ratios (ORs) with 95% confidence intervals (CIs).

Receiver operating characteristic (ROC) curves were constructed, and the area under the curve (AUC) was calculated to assess the discriminative performance of CRP, NLR, and the reversed CALLY index. The 95% CIs for the AUCs were computed using bootstrap resampling with 1000 iterations. In addition, a combined ROC curve was constructed using a multivariable logistic regression model incorporating the CALLY index, CRP, NLR, and SII. The predicted probabilities from this model were used as composite scores, and their discriminative ability was evaluated using AUC with 95% CIs. To ensure consistency in ROC curve interpretation, the CALLY index was reversed by taking its negative value (i.e. reversed CALLY = −CALLY), such that a higher reversed CALLY value corresponds to a higher risk of severe ACS.

A two-sided p < 0.05 was considered statistically significant. All analyses were performed using SPSS (version 26.0) and R (version 2.0) statistical packages.

Ethics statement

This study was conducted in accordance with the Declaration of Helsinki (1975, as revised in 2024). The study protocol was approved by the Institutional Review Board of the Department of Cardiology, Beijing Anzhen Hospital, which waived the requirement for informed consent due to the retrospective design.

Results

Patient characteristics

A total of 765 patients diagnosed with ACS were included in the study: 233 patients in the mild ACS group (mild group) and 532 in the severe ACS group (severe group). The baseline demographic and clinical characteristics of the two groups are summarized in Table 1. There was no significant difference in age, sex distribution, or BMI between the two groups (p = 0.100, 0.682, and 0.078, respectively). However, the prevalence rates of hypertension and diabetes mellitus were significantly higher in the severe group than in the mild group (71.05% vs. 62.23%, p = 0.025 and 54.32% vs. 33.91%, p < 0.001, respectively). The frequencies of dyslipidemia, current smoking, and alcohol use did not differ significantly between the two groups (p > 0.05 for all). In terms of vital signs, the severe group had slightly higher systolic blood pressure (130.01 ± 9.57 vs. 128.40 ± 10.73 mmHg, p = 0.015) and diastolic blood pressure (77.70 ± 6.37 vs. 76.40 ± 6.03 mmHg, p = 0.007) than the mild group. Heart rate was marginally elevated in the severe group, but the difference did not reach statistical significance (p = 0.056).

Table 1.

Baseline demographics and clinical characteristics.

Variable	Mild (n = 233)	Severe (n = 532)	p-value
Demographics
Age (years)	61.14 ± 8.20	58.87 ± 9.40	0.100
Male sex, n (%)	131 (56.22%)	307 (57.71%)	0.682
BMI (kg/m²)	24.70 ± 2.48	25.05 ± 2.72	0.078
Comorbidities
Hypertension, n (%)	145 (62.23%)	378 (71.05%)	0.025
Diabetes mellitus, n (%)	79 (33.91%)	289 (54.32%)	<0.001
Dyslipidemia, n (%)	183 (78.54%)	433 (81.39%)	0.849
Current smoker, n (%)	67 (28.76%)	177 (33.27%)	0.255
Alcohol use, n (%)	42 (18.03%)	111 (20.86%)	0.402
Vital signs
SBP (mmHg)	128.40 ± 10.73	130.01 ± 9.57	0.015
DBP (mmHg)	76.40 ± 6.03	77.70 ± 6.37	0.007
Heart rate (bpm)	69.04 ± 8.85	70.44 ± 10.34	0.056

BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure.

Laboratory and inflammatory indices

Laboratory data revealed several statistically significant differences between the mild and severe groups (Table 2). Compared with the mild group, patients in the severe group had significantly lower levels of albumin (34.33 ± 4.54 vs. 38.77 ± 4.09 g/L, p < 0.001), Hb (126.82 ± 16.58 vs. 143.12 ± 13.73 g/L, p < 0.001), and platelet counts (186.58 ± 62.97 vs. 207.97 ± 54.86 × 10⁹/L, p < 0.001). Lymphocyte counts were also significantly lower in the severe group (1.39 ± 0.71 vs. 1.93 ± 0.85 × 10⁹/L, p < 0.001). Conversely, inflammatory markers were elevated in the severe group. Both NLR and CRP levels were significantly higher (p < 0.001), along with a lower CALLY index (2.07 ± 2.98 vs. 3.29 ± 3.84, p < 0.001). In terms of lipid metabolism, patients in the severe group exhibited lower levels of high-density lipoprotein cholesterol (0.84 ± 0.37 vs. 0.97 ± 0.34 mmol/L, p < 0.001) and total cholesterol (3.24 ± 1.54 vs. 3.61 ± 1.40 mmol/L, p = 0.001) as well as a slight but statistically significant reduction in low-density lipoprotein cholesterol and triglyceride levels. Glycemic control and renal function also differed between the two groups. In the severe group, the FBG level was lower (p = 0.007), and the eGFR was mildly reduced (p = 0.011). Although CK-MB levels were significantly elevated in the severe group (p < 0.001), no significant difference was observed in hs-TnI (p = 0.673).

Table 2.

Laboratory parameters and inflammatory indices.

Variable	Mild (n = 233)	Severe (n = 532)	p-value
Laboratory tests
Albumin (g/L)	38.77 ± 4.09	34.33 ± 4.54	<0.001
WBC count (×10⁹/L)	6.28 ± 2.09	5.95 ± 2.34	0.059
Neutrophil count (×10⁹/L)	3.95 ± 1.44	3.77 ± 1.51	0.112
Lymphocyte count (×10⁹/L)	1.93 ± 0.85	1.39 ± 0.71	<0.001
Hb (g/L)	143.12 ± 13.73	126.82 ± 16.58	<0.001
PLT count (×10⁹/L)	207.97 ± 54.86	186.58 ± 62.97	<0.001
FBG (mmol/L)	6.54 ± 3.37	5.81 ± 3.56	0.007
LDL-C (mmol/L)	1.97 ± 0.97	1.82 ± 1.04	0.047
HDL-C (mmol/L)	0.97 ± 0.34	0.84 ± 0.37	<0.001
TC (mmol/L)	3.61 ± 1.40	3.24 ± 1.54	0.001
TG (mmol/L)	1.62 ± 0.76	1.46 ± 0.87	0.009
Cr (μmol/L)	70.51 ± 25.36	66.60 ± 28.32	0.059
eGFR (mL/min/1.73 m²)	80.47 ± 24.46	75.30 ± 28.62	0.011
hs-TnI (ng/L)	41.64 ± 30.35	40.65 ± 30.55	0.673
CK-MB (U/L)	20.85 ± 7.56	25.73 ± 8.06	<0.001
BNP (pg/mL)	296.01 ± 149.44	258.88 ± 218.79	0.001
Derived indices
CALLY index	3.29 ± 3.84	2.07 ± 2.98	<0.001
NLR	2.90 ± 3.28	4.06 ± 7.72	<0.001
CRP (mg/L)	3.89 ± 2.18	5.26 ± 3.80	<0.001

CALLY: C-reactive protein–albumin–lymphocyte; CRP: C-reactive protein; FBG: fasting blood glucose; eGFR: estimated glomerular filtration rate; Hb: hemoglobin; NLR: neutrophil-to-lymphocyte ratio; WBC: white blood cell; PLT: platelet; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; TC: total cholesterol; TG: triglycerides; CK-MB: creatine kinase–myocardial band; BNP: B-type natriuretic peptide; hs-TnI: high-sensitivity troponin I.

Multivariable analysis of independent risk factors for severe ACS

Univariate analysis identified several variables significantly associated with severe ACS (Table S1). Notably, diabetes mellitus, CALLY index, NLR, and CRP demonstrated strong associations with disease severity. Multivariable logistic regression analysis revealed that diabetes mellitus (OR: 2.11, 95% CI: 1.52–2.94, p < 0.001) and lower CALLY index (OR: 0.91, 95% CI: 0.86–0.95, p < 0.001) were independent predictors severe ACS. Age, sex, BMI, and hypertension showed no statistically significant association after adjustment, although hypertension demonstrated a nonsignificant trend toward increased risk (OR: 1.39, 95% CI: 0.98–1.96, p = 0.06) (Table 3).

Table 3.

Multivariable logistic regression analysis of factors associated with severe ACS.

Variable	OR	95% CI lower	95% CI upper	p-value
Age	0.984	0.966	1.002	0.072
Sex (male)	0.891	0.622	1.271	0.523
BMI	1.047	0.980	1.121	0.108
Hypertension	1.389	0.983	1.958	0.060
Diabetes mellitus	2.110	1.521	2.941	<0.001
eGFR	0.995	0.989	1.000	0.049
CALLY index	0.906	0.863	0.949	<0.001

BMI: body mass index; CALLY: C-reactive protein–albumin–lymphocyte; CI: confidence interval; eGFR: estimated glomerular filtration rate; OR: odds ratio.

Discriminative performance of inflammatory markers for predicting severe ACS

Pairwise comparison of ROC curves (Table 4, Figure 2) demonstrated the CALLY index had significantly superior discriminative performance compared with other inflammatory and nutritional markers. Specifically, the AUC for CALLY was significantly higher than those for NLR (p = 0.001), CRP (p < 0.001), and SII (p < 0.001). Additionally, NLR exhibited a higher AUC than SII (p < 0.001), while the AUC differences between CRP and NLR (p = 0.395) and between CRP and SII (p = 0.180) were not statistically significant. These findings further support the superior predictive value of the CALLY index for severe ACS.

Table 4.

Discriminative performance of the CALLY index, CRP, and NLR in predicting severe ACS.

Marker	AUC	95% CI lower	95% CI upper
CALLY	0.696	0.658	0.735
CRP	0.599	0.558	0.641
NLR	0.624	0.582	0.667
SII	0.560	0.517	0.603

ACS: acute coronary syndrome; AUC: area under the curve; CALLY: C-reactive protein–albumin–lymphocyte; CI: confidence interval; CRP: C-reactive protein; NLR: neutrophil-to-lymphocyte ratio; SII: systemic immune–inflammation index.

Figure 2.

ROC curves of CALLY index, CRP, NLR, and SII for predicting severe ACS. ACS: acute coronary syndrome; CALLY: C-reactive protein–albumin–lymphocyte; CRP: C-reactive protein; NLR: neutrophil-to-lymphocyte ratio; ROC: receiver operating characteristic; SII: systemic immune–inflammation index.

Discussion

In this study, we systematically evaluated the association between the CALLY index and clinical severity in patients with ACS. Our findings revealed that lower CALLY scores were significantly associated with more severe ACS presentations, as defined by clinical diagnosis. Additionally, we compared the predictive performance of the CALLY index with commonly used inflammatory markers, including CRP, NLR, and SII. Among these indices, the CALLY index demonstrated the highest discriminatory ability for identifying severe ACS cases, as reflected by the largest AUC and significant pairwise comparisons. These results underscore the superior prognostic value of the CALLY index for risk stratification in patients with ACS.

The clinical significance of the CALLY index lies in its integration of inflammation (CRP), nutritional status (albumin), and immune competence (lymphocytes), all of which are pathophysiologically linked to the progression and severity of coronary artery disease (CAD). Chronic inflammation, reflected by elevated CRP, promotes endothelial dysfunction and atherosclerosis, while hypoalbuminemia is associated with malnutrition and poor clinical outcomes in cardiovascular disease. Lymphocyte count is a marker of immune system integrity and stress response, and lymphopenia has been linked to adverse cardiac events. Therefore, the CALLY index captures the interplay between these factors, serving as a composite marker that reflects the systemic environment influencing atherosclerotic burden and outcomes in patients with ACS.

Previous studies have explored inflammation-based composite indices in cardiovascular disease, including HALP, CALLY, and oxidative stress markers, showing their prognostic relevance in coronary syndromes.^5–7 In addition, other ratio-based indices derived from routine blood tests have attracted growing clinical interest for predicting CAD severity. For example, the gamma-glutamyl transferase-to-albumin ratio has been reported to be significantly associated with CAD burden, as assessed by coronary computed tomography angiography.¹⁵ For instance, the NLR has been associated with adverse outcomes in ACS.^12,16 Similarly, CRP, a marker of systemic inflammation, is known to be correlated with plaque instability and cardiovascular events.^17–19 The CALLY index, which incorporates both nutritional and inflammatory markers, may offer a more comprehensive reflection of systemic stress and immune status.

Recent oncologic studies have reported the prognostic utility of the CALLY index in hepatocellular carcinoma and other malignancies; however, its application in cardiovascular disease has been largely overlooked.^20–22 Our findings indicate that a lower CALLY score is independently associated with severe ACS, even after adjusting for traditional risk factors such as diabetes and BMI. Notably, in ROC analysis, the CALLY index outperformed both CRP and NLR, highlighting its potential superiority in risk stratification. Furthermore, because a lower CALLY index reflects both heightened inflammation and compromised nutritional status, clinicians may consider closer monitoring or earlier therapeutic interventions in patients with low CALLY scores. Although our study did not evaluate treatment responses, the CALLY index could guide individualized treatment intensity and follow-up frequency in clinical practice. This warrants further investigation in prospective studies. In line with this, a recent study in patients with heart failure reported that lower CALLY index values were independently associated with prolonged hospital stay, highlighting the potential of this composite marker to reflect not only disease severity but also recovery trajectory.²³ Given that both inflammation and nutritional status influence clinical outcomes in ACS, future research should examine whether the CALLY index similarly predicts the length of hospital stay in this population.

Nevertheless, this study has some limitations. First, this was a single-center retrospective study, and potential selection bias cannot be excluded. Second, the cross-sectional nature of the analysis precludes causal inference. Third, we did not assess long-term outcomes such as mortality or readmission, which would provide further validation of the CALLY index as a prognostic marker.

Future prospective, multicenter studies are warranted to validate our findings and explore the temporal relationship between CALLY dynamics and ACS progression. Additionally, integrating the CALLY index into existing risk models may enhance predictive performance and guide individualized clinical decision-making.

Conclusion

This retrospective observational study suggests that the CALLY index—a composite indicator integrating serum albumin, lymphocyte count, and CRP levels—may be associated with the severity of ACS. Patients with lower CALLY index values tended to present with more severe ACS, indicating its potential as a simple and accessible tool for early risk stratification. Further prospective, multicenter studies with larger cohorts are warranted to confirm these associations and clarify the clinical utility of the CALLY index.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605251381579 - Supplemental material for Association between the C-reactive protein–albumin–lymphocyte index and severity of acute coronary syndrome: A retrospective study

Supplemental material, sj-pdf-1-imr-10.1177_03000605251381579 for Association between the C-reactive protein–albumin–lymphocyte index and severity of acute coronary syndrome: A retrospective study by Tingting Wu, Xin Zhao, Chenchen Tu, Xiantao Song and Qiumei Cao in Journal of International Medical Research

Footnotes

Acknowledgements

The authors thank all the clinicians and staff at Beijing Anzhen Hospital for their assistance in data collection.

Author contributions

Tingting Wu and Qiumei Cao contributed to the conceptualization of the study. Xin Zhao and Chenchen Tu were responsible for the methodology. Formal analysis and investigation were conducted by Tingting Wu and Xiantao Song. Tingting Wu and Chenchen Tu drafted the original manuscript, while Qiumei Cao and Xin Zhao participated in the review and editing process. Funding acquisition was managed by Qiumei Cao. Resources were provided by Xiantao Song and Qiumei Cao. Qiumei Cao supervised the entire project.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Declaration of conflicting interests

The authors declared no potential conﬂicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

ORCID iD

Qiumei Cao

Supplemental material

Supplemental material for this article is available online.

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