Journal of Atherosclerosis and Thrombosis
Online ISSN : 1880-3873
Print ISSN : 1340-3478
ISSN-L : 1340-3478
Original Article
Serum Albumin-to-Creatinine Ratio: A Novel Predictor of Pulmonary Infection in Patients with ST-Segment Elevation Myocardial Infarction Undergoing Percutaneous Coronary Intervention
Siyu Kong, Shijie Yu, Weibin He, Yu He, Weikun Chen, Yeshen Zhang, Yining Dai, Hailing Li, Yuling Zhan, Jiyang Zheng, Xuxi Yang, Pengcheng He, Chongyang Duan, Ning Tan, Yuanhui Liu
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Keywords: ST-segment elevation myocardial infarction, Percutaneous coronary intervention, Serum albumin-to-creatinine ratio, Pulmonary infection, Prognosis
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2024 Volume 31 Issue 12 Pages 1680-1691

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Abstract

Aim: In patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI), a low serum albumin-to-creatinine ratio (sACR) is associated with elevated risk of poor short- and long-term outcomes. However, the relationship between sACR and pulmonary infection during hospitalization in patients with STEMI undergoing PCI remains unclear.

Methods: A total of 4,507 patients with STEMI undergoing PCI were enrolled and divided into three groups according to sACR tertile. The primary outcome was pulmonary infection during hospitalization, and the secondary outcome was in-hospital major adverse cardiovascular events (MACE) including stroke, in-hospital mortality, target vessel revascularization, recurrent myocardial infarction, and all-cause mortality during follow-up.

Results: Overall, 522 (11.6%) patients developed pulmonary infections, and 223 (4.9%) patients developed in-hospital MACE. Cubic spline models indicated a non-linear, L-shaped relationship between sACR and pulmonary infection (P=0.039). Receiver operating characteristic curve analysis indicated that sACR had good predictive value for both pulmonary infection (area under the ROC curve [AUC]=0.73, 95% CI=0.70–0.75, P＜0.001) and in-hospital MACE (AUC=0.72, 95% CI=0.69–0.76, P＜0.001). Kaplan-Meier survival analysis indicated that higher sACR tertiles were associated with a greater cumulative survival rate (P＜0.001). Cox regression analysis identified lower sACR as an independent predictor of long-term all-cause mortality (hazard ratio [HR]=0.96, 95% CI=0.95–0.98, P＜0.001).

Conclusions: A low sACR was significantly associated with elevated risk of pulmonary infection and MACE during hospitalization, as well as all-cause mortality during follow-up among patients with STEMI undergoing PCI. These findings highlighted sACR as an important prognostic marker in this patient population.

See editorial vol. 31: 1662-1663

1.Introduction

Infection, a severe complication among hospitalized patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI), is associated with high mortality risk, hospitalization duration, and cost1, 2). Notably, most infections in these patients are pulmonary and are associated with a 10-fold elevated 30-day mortality rate3-6). Therefore, a simple and readily available pathophysiological biomarker of pulmonary infection is required to detect high risk patients as early as possible and ultimately improve their short- and long-term prognoses. Although several risk factors and scores are available to predict infection in patients with STEMI undergoing PCI, they are often complicated and are not easily applied4, 7-10).

Low serum albumin (SA) and renal impairment are frequently observed in patients with STEMI undergoing PCI, and are associated with poor prognoses11-13). Low SA is associated with elevated risk of infection in critically ill patients14) and has good predictive value for pulmonary infection11, 12, 15, 16). Serum creatinine (sCr) is the most commonly used indicator of renal injury. Renal injury in patients with STEMI has been associated with poor survival17). Some studies have also indicated that high sCr levels were associated with nosocomial infection18, 19). Elevated sCr levels may be used for in-hospital pulmonary infection prevention in patients with stroke20). Therefore, sCr may be a potential biomarker for pulmonary infection in patients with STEMI undergoing PCI. The combination of SA and sCr biomarkers can be used to assess the levels of inflammation, nutritional status, and kidney function, thus providing a comprehensive overview of patient condition and addressing the limitations of SA, whose levels vary under the influence of many factors21). Recent studies have indicated that the serum albumin-to-creatinine ratio (sACR) had greater prognostic value than SA and sCr levels alone in patients with acute myocardial infarction22, 23). Moreover, low sACR predicts poor short- and long-term outcomes in patients with STEMI undergoing PCI23, 24). However, the relationship between sACR and the occurrence of pulmonary infection during hospitalization in patients with STEMI undergoing PCI has not been clearly established.

2.Aim

The present study was designed to evaluate the predictive value of sACR for pulmonary infection and major adverse cardiovascular events (MACE) in patients with STEMI undergoing PCI.

3.Methods

3.1 Study Population

Patients with STEMI were consecutively enrolled from multiple hospitals in various provinces in Southern China between 2010 and 2020. On the basis of international guidelines, STEMI was diagnosed according to the presence of the following: (1) typical chest pain or symptoms of myocardial ischemia, (2) electrocardiographic ST-segment elevation in more than two adjacent leads or new left bundle branch block, and (3) elevated levels of biochemical markers of myocardial necrosis25). The exclusion criteria included the following: (1) hospital readmission, (2) hemodialysis at admission, (3) death within 24 h after admission, (4) infection before STEMI diagnosis, (5) cardiac surgery, (6) lack of PCI treatment, and (7) missing crucial laboratory data regarding SA or sCr levels. The trial was approved by the Guangdong Provincial People’s Hospital ethics committee and adhered to the Declaration of Helsinki.

3.2 Data Collection

Within 24 h after admission, routine blood samples were collected and tested for SA, hemoglobin, white blood cell count, blood lipids, fasting glucose, electrolytes, troponin I/T, cardiac enzymes, sCr, and other routine tests. Patient demographic information, medical history, risk factors, admission status, electrocardiographic results, and coronary revascularization data were also collected. The sACR was calculated according to SA and sCr levels at admission. Laboratory tests followed standard procedures. Medication prescriptions and PCI technicalities were at the discretion of the cardiologists and interventionalists, respectively, and were in alignment with contemporary guidelines26).

3.3 Endpoints and Definitions

The primary outcome was the incidence of pulmonary infection during hospitalization. The secondary outcome was in-hospital MACE, including all-cause mortality, recurrent myocardial infarction, target vessel revascularization, and stroke, as well as follow-up all-cause mortality. Pulmonary infections were diagnosed by an experienced clinician according to typical medical imaging, symptoms, clinical signs, or laboratory biomarkers indicative of infection27). The ICD-10-CM codes at discharge were used to confirm pulmonary infection diagnoses when the evidence was insufficient. Trained nurses conducted at least a 1-year follow-up via telephone or office visits.

3.4 Statistical Analyses

The patients were divided into three groups (T1, T2, and T3) according to sACR tertiles at admission. Continuous variables are presented as means±standard deviations or interquartile ranges, and were compared with the independent t-test or Kruskal-Wallis test, whereas categorical variables are presented as percentages. Pearson’s chi-squared test and Fisher’s exact test were used to compare differences among groups. One-way analysis of variance was used to analyze parametric characteristics, whereas the Kruskal-Wallis H test was applied to compare nonparametric features. Receiver operating characteristic curves were used to evaluate the predictive value of sACR for pulmonary infection and in-hospital MACE, and to determine the optimal cutoff value. A cubic spline model was established to observe the effect of sACR on pulmonary infection. A logistic regression model was used to construct the cubic splines with the numbers of knots set to 32.050 and 42.369, on the basis of the sACR distribution. The Kaplan-Meier method and log-rank tests were applied to estimate the cumulative survival of participants in the three groups, and a Cox proportional hazards model was used to identify the association between sACR tertile and survival. The Cox proportional hazards models were adjusted for relevant variables including age, sex, smoking rate, anemia, hypertension, hyperlipidemia, Killip classification, diabetes mellitus, multi-vessel stenosis, prior PCI, prior myocardial infarction, white blood cell count, and left ventricular ejection fraction. Statistical analysis was performed in SAS 9.4 (SAS Institute, Cary, NC), and P values below 0.05 were considered statistically significant.

4.Results

4.1 Participant Characteristics

Between January 2010 and February 2020, 5,361 patients were diagnosed with STEMI. According to the inclusion and exclusion criteria, 4,507 patients were finally enrolled. The flowchart is shown in Fig.1.

Fig.1. Flow chart of participants

STEMI, ST-segment elevation myocardial infarction; PCI, percutaneous coronary intervention; SA, serum albumin; sCr, serum creatinine; sACR, serum albumin-to-creatinine ratio

The patients were divided into three groups according to sACR tertile (T1=1502, T2=1503, and T3=1502). The demographic and biochemical data for the eligible population are shown in Table 1.

Table 1.Comparison of baseline characteristics among groups, stratified by sACR tertile

variables T1 T2 T3 P value
Age 65.99±11.58 60.80±11.85 56.97±12.15 ＜.001
Male, n (%) 1296 (86.3%) 1330 (88.5%) 1116 (74.3%) ＜.001
SBP, mm Hg 121.27±23.96 122.56±21.23 126.33±20.34 ＜.001
DBP, mm Hg 72.91±14.02 74.55±13.62 77.67±13.59 ＜.001
Heart rate 82.79±18.69 79.24±14.85 79.57±14.64 ＜.001
Killip classification
I 826 (55.0%) 1103 (73.4%) 1223 (81.4%) ＜.001
II 379 (25.2%) 309 (20.6%) 228 (15.2%) .
III 150 (10.0%) 44 (2.9%) 25 (1.7%) .
IV 146 (9.7%) 47 (3.1%) 26 (1.7%) .
Medical history, n (%)
Current smoker 558 (37.2%) 671 (44.7%) 616 (41.0%) ＜.001
Hypertension 914 (60.9%) 694 (46.2%) 646 (43.0%) ＜.001
Atrial fibrillation 64 (4.3%) 36 (2.4%) 31 (2.1%) ＜.001
Diabetes mellitus 498 (33.2%) 342 (22.8%) 388 (25.8%) ＜.001
Hyperlipidemia 114 (8.2%) 172 (13.2%) 188 (16.9%) ＜.001
COPD 57 (3.8%) 18 (1.2%) 11 (0.7%) ＜.001
Prior-MI 347 (23.1%) 361 (24.0%) 181 (12.1%) ＜.001
Prior-PCI 212 (15.3%) 171 (13.1%) 115 (10.3%) 0.001
Prior stroke 156 (10.4%) 71 (4.7%) 40 (2.7%) ＜.001
Laboratory measurements
Serum albumin, g/L 32.50±4.34 35.90±3.56 38.38±3.72 ＜.001
sCr, mg/dL 1.68±1.40 0.97±0.11 0.76±0.12 ＜.001
eGFR, mL/min 55.25±19.98 86.73±12.69 117.37±25.30 ＜.001
LVEF, % 48.58±12.49 53.20±10.72 54.51±10.07 ＜.001
Hemoglobin, g/L 127.57±22.40 136.93±18.12 139.47±17.76 ＜.001
Anemia 714 (47.7%) 415 (27.8%) 259 (17.3%) ＜.001
WBC 11.89±4.54 11.26±3.80 11.44±3.55 ＜.001
Total cholesterol, mmol/L 4.57±1.24 4.85±1.15 5.15±1.25 ＜.001
LDL-C, mmol/L 2.98±1.00 3.19±0.94 3.41±1.02 ＜.001
Medication use
Aspirin 1457 (97.0%) 1474 (98.1%) 1475 (98.2%) 0.052
Clopidogrel 1366 (91.0%) 1280 (85.3%) 1108 (73.8%) ＜.001
Statins 1430 (95.3%) 1474 (98.1%) 1464 (97.5%) ＜.001
β -blockers 1138 (75.8%) 1222 (81.3%) 1212 (80.7%) ＜.001
ACEI/ARB 1128 (75.1%) 1184 (78.8%) 1238 (82.4%) ＜.001
Angiography
Number of stents 1.57±1.31 1.50±2.68 1.35±1.13 0.004
Multi-vessel stenosis 1180 (78.6%) 991 (65.9%) 857 (57.1%) ＜.001
Length of stents, mm 40.42±26.33 36.70±24.15 34.19±22.12 ＜.001
Contrast volume, mL 124.58±43.78 120.03±42.89 116.02±42.56 ＜.001
Mechanical support use
Thrombus aspiration 381 (25.4%) 410 (27.3%) 445 (29.6%) 0.032
IABP 308 (20.5%) 107 (7.1%) 107 (7.1%) ＜.001
CPAP 190 (13.7%) 37 (2.8%) 13 (1.2%) ＜.001

sACR, serum albumin-to-creatinine ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; COPD, chronic obstructive pulmonary disease; MI, myocardial infarction; PCI, percutaneous coronary intervention; sCr, serum creatinine; eGFR, estimated glomerular filtration rate; WBC, white blood cell; LVEF, left ventricular ejection fraction; LDL-C, low-density lipoprotein cholesterol; ACEI/ARB, angiotensin converting enzyme inhibitor/angiotensin receptor blocker; IABP, intra-aortic balloon pump; CPAP, continuous positive airway pressure

The mean patient age was 61.25±12.42 years, and men made up 83.03% (3742/4507) of the cohort. The lower sACR group had a lower smoking rate, and higher percentages of advanced age, male sex, high Killip class, high blood pressure and lipid levels, low hemoglobin levels, diabetes, prior myocardial infarction, PCI, multi-vessel stenosis, and use of intra-aortic balloon pumps or continuous positive airway pressure.

4.2 Associations of sACR with Pulmonary Infection and In‑Hospital MACE

A total of 522 (11.6%) patients developed pulmonary infections, and 223 (4.9%) developed MACE during hospitalization. Higher sACR tertiles were corrected with lower rates of pulmonary infection (22.8%, 7.3%, and 4.7% for tertiles 1–3, respectively; P＜0.001) and in-hospital MACE (10.1%, 2.7%, and 2.1% for tertiles 1–3, respectively; P＜0.001) (Fig.2).

Fig.2. Incidence of in-hospital events among sACR groups

sACR, serum albumin-to-creatinine ratio; MACE major adverse cardiovascular events. ＊＊＊P＜0.001.

After adjustment for confounders, multivariate logistic regression analysis indicated that a higher sACR was associated with lower risks of both pulmonary infection (T2 vs T1: odds ratio [OR]=0.49, 95% confidence interval [CI]=0.36–0.68, P＜0.001 and T3 vs. T1: OR=0.32, 95% CI=0.21–0.49, P＜0.001) and in-hospital MACE (T2 vs. T1: OR=0.35, 95% CI=0.22–0.56, P＜0.001 and T3 vs. T1: OR=0.34, 95% CI=0.20–0.60, P＜0.001). Similar results were found when sACR was used as a continuous variable (Table 2). When eGFR was included, and SA and sCr were excluded, a higher sACR remained associated with a lower risk of pulmonary infection (OR=0.92, 95% CI=0.89–0.94, P＜0.001).

Table 2.Multivariable logistic regression analysis of sACR as a categorical variable and continuous variable

Variables Pulmonary infection In-hospital MACE
OR 95%CI P value OR 95%CI P value
Model 1
T1 Reference Reference
T2 0.49 0.36-0.68 ＜0.001 0.35 0.22-0.56 ＜0.001
T3 0.32 0.21-0.49 ＜0.001 0.34 0.20-0.60 ＜0.001
Model 2
sACR＊ 0.96 0.94-0.97 ＜0.001 0.95 0.92-0.97 ＜0.001

Adjusted for age, sex, WBC, smoking rate, anemia, serum albumin, sCr, current smoking status, diabetes mellitus, hypertension, hyperlipidemia, prior MI, prior PCI, LVEF, and multi-vessel stenosis. sACR, serum albumin-to-creatinine ratio; MACE, major adverse cardiovascular events; OR, odds ratio; CI, confidence interval; sCr, serum creatinine; WBC, white blood cell; MI, myocardial infarction; PCI, percutaneous coronary intervention; LVEF, left ventricular ejection fraction

sACR＊ as a continuous variable

Nonlinear, L-shaped associations between sACR and pulmonary infection emerged in the cubic spline models (P=0.039) (Fig.3).

Fig.3.

Cubic spine models for the association between sACR and pulmonary infection

4.3 Predictive Value of sACR for In‑Hospital Clinical Outcomes

Receiver operating characteristic curve analysis demonstrated that sACR had good predictive value for both pulmonary infection (area under the ROC curve [AUC]=0.73, 95% CI=0.70–0.75, P＜0.001) and in-hospital MACE (AUC=0.72, 95% CI=0.69–0.76, P＜0.001) (Fig.4A–B). A comparison of the predictive performance of sACR, SA, and sCr for pulmonary infection demonstrated that sACR had the highest predictive performance (Table 3).

Fig.4.

A ROC curve of sACR for pulmonary infection among patients with STEMI undergoing PCI. B ROC curve of sACR for in-hospital MACE among patients with STEMI undergoing PCI. C ROC curve of sACR for pulmonary infection for subgroup analysis. D ROC curve of sACR for in-hospital MACE for subgroup analysis.

Table 3.Performance of sACR, SA, and sCr in predicting pulmonary infection

Variable Pulmonary infection
AUC 95% CI P value
sACR 0.73 0.70 0.75 ref
SA 0.67 0.64 0.69 ＜0.001
sCr 0.70 0.67 0.72 ＜0.001

sACR, serum albumin-to-creatinine ratio; SA, serum albumin; sCr, serum creatinine; AUC, area under the curve; OR, odds ratio; CI, confidence interval

The ideal sACR cutoff was 48.39, with a sensitivity of 63.6% and specificity of 73.4% for pulmonary infection. Stratified analyses confirmed sACR’s robust predictive value for pulmonary infection and in-hospital MACE in patients with STEMI undergoing PCI, regardless of their sex, age, estimated glomerular filtration rate, and presence of diabetes mellitus (Fig.4C–D).

4.4 The sACR for Long Term Follow-Up All-Cause Mortality

Among 4,507 participants, 3,926 continued to receive follow-up, thus yielding a follow-up rate of approximately 87%. Over a median 1.83-year follow-up, patients with low rather than high sACR exhibited greater risk of all-cause mortality (P＜0.001), according to Kaplan-Meier analysis (Fig.5).

Fig.5.

Kaplan-Meier survival curve of all-cause death for patients with STEMI undergoing PCI

After adjustment for potential confounders, Cox regression analysis demonstrated that a low sACR was independently associated with MACE (hazard ratio=0.96, 95% CI=0.95–0.97, P＜0.001) and all-cause mortality (hazard ratio=0.96, 95% CI=0.95–0.98, P＜0.001) (Table 4).

Table 4.Cox regression analysis of correlations between clinical outcomes and sACR.

Variables Unadjusted Adjusted
HR 95%CI P value HR 95%CI P value
Model1
T1 Reference Reference
T2 0.31 0.24~0.39 ＜0.001 0.67 0.50~0.88 0.005
T3 0.19 0.14~0.26 ＜0.001 0.59 0.40~0.87 0.008
Model2
sACR＊ 0.94 0.93~0.94 ＜0.001 0.96 0.95~0.98 ＜0.001

Risk factors adjusted by age, sex, WBC, smoking rate, anemia, serum albumin, sCr, current smoking status, diabetes mellitus, hypertension, hyperlipidemia, prior MI, prior PCI, LVEF, and multi-vessel stenosis. sACR, serum albumin-to-creatinine ratio; HR, hazard ratio; CI, confidence interval; sCr, serum creatinine; WBC, white blood cell; MI, myocardial infarction; PCI, percutaneous coronary intervention; LVEF, left ventricular ejection fraction

sACR＊ as a continuous variable

4.5 Subgroup Analysis

Multivariate regression analysis revealed that a low sACR was independently associated with pulmonary infection in patients with STEMI undergoing PCI in subgroups based on age, sex, eGFR, and diabetes mellitus (Table 5).

Table 5.Multivariable logistic regression analysis for sACR as a continuous variable, by subgroup

Variables Pulmonary infection
OR 95%CI P value Interaction P value
Age 0.858
≥ 65 0.94 0.92~0.96 ＜0.001
＜65 0.97 0.94~0.99 0.004
Gender 0.127
Male 0.95 0.93~0.97 ＜0.001
Female 0.96 0.93~0.99 0.008
eGFR 0.271
≤ 60 0.96 0.93~0.99 0.015
＞60 0.96 0.94~0.98 ＜0.001
Diabetes mellitus 0.112
Yes 0.96 0.94~0.99 0.003
No 0.95 0.93~0.97 ＜0.001

Adjusted for age, sex, WBC, smoking rate, anemia, serum albumin, sCr, current smoking status, diabetes mellitus, hypertension, hyperlipidemia, prior MI, prior PCI, LVEF, and multi-vessel stenosis. sACR, serum albumin-to-creatinine ratio; MACE, major adverse cardiovascular events; OR, odds ratio; CI, confidence interval; sCr, serum creatinine; WBC, white blood cell; MI, myocardial infarction; PCI, percutaneous coronary intervention; LVEF, left ventricular ejection fraction

5.Discussion

To our knowledge, this study is the first to demonstrate a relationship between sACR levels and pulmonary infection during hospitalization in patients with STEMI undergoing PCI. sACR was independently associated with both pulmonary infection and MACE during hospitalization, as well as long term all-cause mortality in these patients. Additionally, a low sACR level was found to serve as a useful biomarker for early-stage pulmonary infection and as an independent predictor of poor prognosis.

Inflammation, thrombosis, and malnutrition are known to contribute to adverse outcomes in patients with acute myocardial infarction28-31), and SA plays important roles in these pathophysiological events32, 33). SA is synthesized by the liver34) and has multiple protective roles, including anti-inflammatory, antioxidant, anticoagulant, anti-platelet aggregation, and anti-vascular endothelial cell apoptosis functions35, 36). Low SA is found in both acute and chronic illnesses, because of transcapillary leakage, as well as impaired synthesis associated with liver dysfunction, malnutrition, or inflammation37). Moreover, low SA is a risk factor for vascular endothelial injury and platelet activation, which are considered the key links of coronary atherosclerosis and thrombosis31, 35, 38). Previous studies have indicated that the level of SA was negatively correlated with in-hospital mortality among patients with STEMI39, 40). Low SA is a simple indicator that has been widely used as a quantitative measure of malnutrition. Among patients diagnosed with acute coronary syndrome, malnutrition is commonly observed, and may give rise to conditions including chronic low grade inflammation, high oxidative stress, endothelial dysfunction, and compromised immunity41-43). Malnutrition is independently associated with the development and progression of acute coronary syndrome42). Moreover, patients undergoing PCI with a lower prognostic nutritional index have higher risk of long-term mortality and MACE44). Malnutrition has been recognized as an independent factor associated with nosocomial infection45), because of chronic low grade inflammation and compromised immunity41). Low SA is also associated with the acquisition and severity of viral, bacterial, and fungal infections, as well as infectious complications in non-infective diseases46). Albumin influences endotoxin-induced inflammation via binding the lipid A portion, lipopolysaccharides, reactive oxygen species, and other bacterial products47, 48). Numerous studies have suggested that low serum albumin levels were associated with elevated infection rates in critically ill patients49). Furthermore, low SA contributes to plasma volume expansion and systemic inflammation regulation, which are closely associated with the development of pulmonary infection48, 50). Moreover, low SA indicates a poor nutritional reserve and consequently an elevated risk of pulmonary infection. Infections can lead to higher protein catabolism, thereby further decreasing SA and worsening clinical outcomes. Low SA has been confirmed to have good predictive value for pulmonary infection11, 12, 15, 16). However, the prognostic applications of low SA are limited by this biomarker’s susceptibility to influences from multiple factors51). Our study indicated that the SA level was associated with pulmonary infection but had limited prognostic value.

Patients with STEMI and subsequent renal injury may have poor survival and adverse long-term renal outcomes19). The sCr, as a biomarker for renal injury, is associated with various traditional cardiovascular risk factors; this biomarker was proved to be correlated with elevated in-hospital and 1-year mortality after acute myocardial infarction51-54). Renal injury is an immediate and short-term risk factor for the development of nosocomial infection and sepsis. Furthermore, an estimated glomerular filtration rate＜30 mL/min/1.73 m2 (estimated according to sCr) is strongly associated with pulmonary infection in patients with stroke, probably because a weakened immune system is likely to occur concurrently with kidney disease. Additionally, the sCr level may serve as a potential biomarker for pulmonary infection in patients with STEMI undergoing PCI20, 55). Moreover, the combined use of SA and sCr levels can overcome the limitations of the SA in predicting patient outcomes21).

Both SA and sCr are associated with cardiovascular diseases and infections. Herein, we used sACR, a combined biomarker of SA and sCr, to enhance the comprehensive assessment of patient condition and provide a more effective therapeutic regimen, thereby overcoming the individual limitations of SA and sCr. Interestingly, the inverse relationship between SA and sCr in cardiovascular diseases suggested that sACR may have a stronger ability than SA or sCr alone to predict patient prognosis and complications. Notably, we confirmed the excellent predictive value of sACR for pulmonary infection and MACE in hospitalized patients, in contrast to that of SA or sCr alone.

Subgroup analyses indicated that the predictive value of sACR remained stable across various subgroups of patients with STEMI undergoing PCI. This consistency should aid in the early identification of high risk patients, and enable targeted interventions to improve treatment effectiveness and patient outcomes. Furthermore, we observed that sACR was independently associated with both short- and long-term prognoses of patients with STEMI undergoing PCI, in agreement with previous researches23, 24).

Nevertheless, this study had several limitations. Because this study was observational, potential bias was inevitable despite our efforts to include consecutive patients and adjust for potential confounders by using multivariate regression. Due to the missing data for TIMI flow and C reactive proteins, we did not include these variables. Additionally, our endpoint events included only long-term all-cause mortality. Because of their infrequent occurrence, we did not extensively investigate infections at other sites. Follow-up data on sACR and its trajectory during the chronic phase are highly valuable for assessing the effects on long-term mortality; however, our current dataset lacked this information, which therefore was not described in our study. Furthermore, the effects of sACR on prognosis of patients with STEMI without PCI remains unclear because of the small sample size of such patients. Therefore, prospective studies with larger sample sizes are needed in the future to confirm our research findings.

6.Conclusions

The sACR not only was independently associated with the risks of pulmonary infection and MACE during hospitalization and follow-up all-cause mortality, but also had good predictive value for these outcomes. Consequently, sACR was used to stratify patients with STEMI undergoing PCI according to their risk of pulmonary infection and MACE during hospitalization.

Abbreviations

STEMI, ST-segment elevation myocardial infarction;

PCI, percutaneous coronary intervention;

sACR, serum albumin-to-creatinine ratio;

MACE, major adverse cardiovascular events;

SA, serum albumin;

sCr, serum creatinine;

ROC, Receiver operating characteristic;

AUC, Area under the ROC curve;

OR, Odds ratio;

CI, Confidence interval;

Institutional Review Board Statement

This study was conducted according to the guidelines stipulated in the Declaration of Helsinki and approved by the research ethics committee of Guangdong Provincial People’s Hospital.

Informed Consent Statement

Not applicable

Data Availability Statement

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

Funding

This research was funded by the Shuangqing Talent Program Project of Guangdong Provincial People’s Hospital (Grant No. KJ012019095 to Y.H.L. and Grant No. KJ012019084 to P.C.H), and Basic and Applied Basic Research Fund of Guangdong Province (Grant No. 2023A1515220174 to Y.H.L).

Authors’ Contributions

YL, NT, and CD contributed to the conception and design of the work; YZ, YD, HL, YZ, YH, JZ, XY, and PH contributed to the acquisition of the data; SY and CD contributed to the analysis of the data; SK, SY, WH, and WC interpreted the data; SK and WH wrote the initial draft manuscript; CD, NT, and YL substantively revised it; YH contributed to the submission of the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

Conflicts of Interest

All authors declare no conflicts of interest.

References
 

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