Contrasting the relationship of serum uric acid/albumin ratio on quantitative flow ratio with other multiple composite parameters in patients with suspected coronary artery disease

A total of 410 suspected coronary heart disease patients who underwent coronary angiography (CAG) and QFR testing in the Cardiovascular Department of the First People's Hospital of Jingzhou City from September 2022 to March 2023 were collected.

Inclusion criteria: (1) Age 18 years and above; (2) Completion of coronary angiography examination; (3) Presence of clinical symptoms of coronary artery stenosis, such as chest pain, angina, etc.; (4) Quantitative assessment of coronary artery stenosis is required using the QFR technique.

Exclusion criteria: (1) Patients with unsatisfactory coronary angiography images (coronary ostial lesions, severe vessel tortuosity, diffuse long lesions, poor coronary artery image quality, lack of two images with a difference of more than 25 degrees, overlapping target lesions, excessive shrinkage or inadequate filling of contrast agent); (2) Patients with acute coronary syndrome (including acute myocardial infarction and unstable angina), coronary artery occlusion, diagnosed congenital coronary artery anomalies, or myocardial bridge; (3) Patients with severe valve disease, severe heart failure, cardiogenic shock, known history of liver or kidney failure (abnormal liver function defined as chronic liver disease, such as liver fibrosis or bilirubin > 2 times the upper limit of normal or alanine aminotransferase > 3 times the upper limit of normal, abnormal kidney function defined as chronic dialysis or kidney transplantation or glomerular filtration rate < 30 ml/min); (4) Patients with active infection, chronic inflammatory conditions, autoimmune diseases, hematologic disorders, and malignant tumors; (5) Patients with a history of medication use that may affect uric acid levels and those with missing data; (6) Patients with a history of previous coronary artery intervention surgery.

Based on the inclusion and exclusion criteria, consecutive coronary heart disease patients who underwent CAG and QFR were enrolled, and a final total of 257 cases were included (Fig. 1). According to the critical value of QFR, the suspected coronary heart disease patients in this study were divided into two groups: QFR ≤ 0.8 (n = 83) and QFR > 0.8 (n = 174).

This study met the review criteria set by our ethics committee and was approved by the ethics committee. As the study was retrospective in design, the ethics committee exempted the acquisition of informed consent.

Population demographic characteristics and laboratory data were extracted from the hospital's inpatient system. The collected demographic characteristics encompassed gender, age, smoking status, medical history, and a history of previous coronary artery intervention. The medical history included conditions such as hypertension, diabetes, stroke, liver and kidney diseases, as well as hematological disorders. The laboratory data comprised comprehensive assessments including routine blood tests, lipid profile, liver function, renal function, and electrolyte levels, all obtained upon admission.

Prior to undergoing CAG, peripheral venous blood samples were meticulously collected from patients who had observed a minimum 8-h fasting period overnight. Standardized methods were employed to meticulously analyze these samples, ensuring the avoidance of any potential storage-related influences. The study incorporated the calculation of five composite parameters as follows: UAR = uric acid/albumin ratio; MHR = monocyte / HDL-cholesterol ratio; SII = neutrophil × platelet/lymphocyte; SIRI = neutrophil × monocyte/lymphocyte; AISI = neutrophil × platelet × monocyte/lymphocyte.

All patients underwent coronary angiography according to standardized protocols, and the subsequent QFR analysis was performed collaboratively by a team of four physicians. With the assistance of a skilled imaging specialist, two experienced cardiologists utilized the AngioPlus system (developed by Bodong Medical Imaging Technology Co., Ltd., Shanghai, China) to collectively evaluate the degree of coronary artery stenosis for each patient (Fig. 2). Additionally, a fourth clinical physician was assigned to meticulously validate the data. In cases where suboptimal image quality was observed, manual adjustments were made following established procedural guidelines.

Subsequently, the contrast agent flow velocity was determined using frame counting methodology derived from the coronary angiography images. A contrast agent flow model was then employed to calculate the quantitative flow ratio (QFR) values obtained from the analysis. QFR, as a quantitative indicator, effectively reflected the functional severity of coronary artery stenosis. The target vessel for further analysis was selected based on the presence of the most severe stenosis. QFR values were quantified to assess the degree of functional stenosis in the target vessel and compared accordingly. Consistent with previous research [22], a QFR value of ≤ 0.80 was considered indicative of functionally significant coronary artery stenosis.

Data analysis was conducted using SPSS software version 27.0. The normality of continuous variables was assessed using the Kolmogorov–Smirnov test. Continuous variables were reported as mean ± standard deviation (SD) or median (interquartile range), while categorical variables were presented as frequencies (percentages). The differences in continuous variables between the two groups were evaluated using an independent t-test for normally distributed variables and the Mann–Whitney U test for non-normally distributed variables. Categorical variables were compared using the chi-square test.

A total of 257 suspected coronary artery disease patients were enrolled in the study, comprising 83 individuals in the QFR ≤ 0.80 group and 174 individuals in the QFR > 0.80 group. The prevalence of hypertension in the QFR ≤ 0.80 group was significantly higher than that in the QFR > 0.80 group (p < 0.001) (Table 1).

Significant differences in laboratory parameters were observed between the QFR ≤ 0.80 and QFR > 0.80 groups, including glycated hemoglobin (p = 0.040), total cholesterol (p = 0.022), low-density lipoprotein cholesterol (p = 0.007), platelet count (p = 0.034), neutrophil count (p < 0.001), lymphocyte count (p = 0.028), monocyte count (p = 0.001), uric acid (p = 0.001), and albumin (p < 0.001).

Regarding the composite parameters, statistically significant differences were observed between the QFR ≤ 0.80 and QFR > 0.80 groups for UAR (9.19 ± 2.47 vs 7.61 ± 1.91; p < 0.001), MHR (0.46 ± 0.19 vs 0.37 ± 0.16; p < 0.001), SII (674.98 ± 332.30 vs 571.43 ± 255.82; p = 0.006), SIRI (1.53 ± 0.83 vs 1.29 ± 1.10; p = 0.047), and AISI (340.22 ± 242.10 vs 243.97 ± 151.97; p < 0.001). Notably, UAR, MHR, and AISI demonstrated a stronger correlation with QFR ≤ 0.80 (Fig. 3).

The analysis of the ROC curve revealed that the area under the curve (AUC) for UAR was determined to be 0.701 (95% confidence interval [CI]: 0.633–0.770; p < 0.001). In comparison to UA, UAR continued to demonstrate superior performance (Figs. 4, 5 and 6). Furthermore, the combined parameters MHR × UAR and AISI × UAR did not exhibit a significant enhancement in AUC when compared to UAR alone (Table 2).

Through the analysis of the ROC curve, we determined the optimal cutoff values for five composite parameters, namely UAR, MHR, SII, SIRI, and AISI. These cutoff values were used to transform the continuous variables into binary variables. To address multicollinearity issues, we selected Hypertension, Hemoglobin A1c (HbA1c), Low-Density Lipoprotein Cholesterol (LDL-c), MHR, SII, SIRI, AISI, and UAR as independent variables for logistic regression analysis (Table 3). Our findings revealed a significant association between elevated levels of UAR (Odds Ratio [OR]: 3.876, 95%CI: 2.196–6.841, p < 0.001), MHR (OR: 2.751, 95% CI: 1.606–4.712, p < 0.001), SII (OR: 1.976, 95% CI: 1.121–3.483, p < 0.018), SIRI (OR: 2.444, 95% CI: 1.425–4.193, p < 0.001), and AISI (OR: 2.695, 95% CI: 1.539–4.719, p < 0.001) with a significantly increased risk of QFR ≤ 0.80 (refer to Table 2). Furthermore, in the multivariate analysis, elevated levels of UAR (OR: 3.085, 95% CI: 1.722–5.527, p < 0.001) and AISI (OR: 2.269, 95% CI: 1.256–4.099, p = 0.007) remained significantly associated with the risk of QFR ≤ 0.80.

When considering these variables as continuous predictors, univariate analysis demonstrated a significant association between UAR (OR: 1.412, 95% CI: 1.231–1.620, p < 0.001), e^MHR (OR: 1.394, 95% CI: 1.151–1.687, p < 0.001), lnSII (OR: 1.001, 95% CI: 1.000–1.002, p = 0.008), lnAISI (OR: 2.695, 95% CI: 1.539–4.719, p = 0.001), and QFR ≤ 0.80. In the multivariable analysis, UAR (OR: 1.373, 95% CI: 1.187–1.587, p < 0.001) and AISI (OR: 2.217, 95% CI: 1.309–3.757, p < 0.001) remained significantly associated with QFR ≤ 0.80 (refer to Table 4).

Our study has several limitations that should be acknowledged. Firstly, it is important to note that our study design was retrospective and cross-sectional in nature. The utilization of QFR technology in coronary angiography images demands a high level of technical expertise and adherence to standardized protocols to ensure accurate data collection. Consequently, the sample size in our study was relatively small. To address these limitations and further explore the relationships between the parameters under investigation, it would be valuable to extend the study duration and collaborate with multiple centers to conduct large-scale, multicenter cohort studies. Secondly, it is worth mentioning that our study did not include a comparison between these parameters and patients with multi-vessel coronary artery disease. Additionally, we were unable to compare the severity of vascular lesions within the same patient due to the limitations of QFR evaluation in certain coronary vessels. These factors contribute to a gap in our understanding of the associations between these parameters and more complex coronary artery disease presentations. This study did not include complex lesions such as coronary ostial disease, severe vessel tortuosity, and diffuse long lesions. Therefore, the findings of this study may not represent patients with more severe and complex coronary artery disease. Thirdly, it is important to acknowledge that our study did not incorporate C-reactive protein (CRP) and its related composite inflammatory parameters, despite their potential relevance. A previous study [43] highlighted the association between neutrophil count, SII, CRP, albumin, and the CRP-to-albumin ratio (CAR) with the occurrence of microvascular angina (MVA) in univariate analysis. Future research should consider the inclusion of these inflammatory markers to provide a more comprehensive assessment of their impact on coronary artery disease. Another limitation of this study is that although it analyzed the value of serological markers in assessing the disease, it did not incorporate treatment regimens to analyze patient prognostic outcomes. This calls for further clinical information to be provided through cohort studies that consider treatment regimens. Lastly, it is essential to recognize that our study did not account for the potential influence of oral medications that patients may have been taking prior to admission. These medications could have introduced confounding factors that may have affected the blood markers under investigation. Future studies should consider controlling for medication history to ensure a more accurate evaluation of the parameters of interest.