ABSTRACT
The neutrophil percentage to albumin ratio (NPAR) has been associated with prognosis of various cardiovascular diseases, but its role in acute type A aortic dissection (AAAD) mortality remains unclear. The aim of this study was to investigate the relationship between preoperative NPAR and in‐hospital mortality in AAAD patients. Clinical data from patients who underwent AAAD surgery at the Cardiac Medical Center of Fujian Province between January 2020 and April 2024 were retrospectively analyzed. Patients were categorized into three groups based on NPAR tertiles. Univariate and multivariate logistic regression analyses were employed to identify factors contributing to in‐hospital mortality. The predictive performance of NPAR was assessed using ROC curve analysis. The results revealed that out of 813 AAAD patients meeting the inclusion criteria, 137 (16.9%) died in hospital. Multivariate logistic regression analysis indicated that compared to the low tertile group, the odds ratios (95% CI) for in‐hospital mortality in the middle and high tertile groups were (OR 3.041, 95% CI: 1.502–6.158, p = 0.002) and (OR 6.586, 95% CI: 3.324–13.049, p<0.001), respectively. Additionally, cardiopulmonary bypass time (OR 1.010, 95% CI: 1.007‐1.013, p<0.001) and mechanical ventilation time (OR 1.115, 95% CI: 1.082–1.150, p<0.001) were also independently associated with in‐hospital mortality in AAAD patients. The area under the curve for NPAR was 0.708 (95% CI: 0.676–0.739) (p<0.001), with an optimal cut‐off value of 24.105, yielding a sensitivity of 73.7% and a specificity of 64.8%. In conclusion, higher preoperative NPAR may be independently associated with increased in‐hospital mortality, suggesting its potential as a novel indicator for monitoring AAAD patients.
Keywords: acute type A aortic dissection, in‐hospital mortality, neutrophil percentage‐to‐albumin ratio, neutrophils, prognosis
1. Introduction
Acute type A aortic dissection (AAAD) is a fatal cardiovascular event involving the ascending aorta [1]. It is caused by a tear in the intimal layer, and has an acute onset, rapid progress, and high mortality [1]. It is reported that the mortality rate of AAAD patients within 48 h after onset is 1%–2% [2]. At present, emergency surgical repair remains the only effective treatment for AAAD patients [3]. The guidelines of the American Heart Association also recommend, as early as possible, surgical intervention for AAAD patients [3]. In spite of advances in surgical management and techniques of AAAD, the in‐hospital mortality of AAAD patients receiving surgical treatment remains high, about 18%–25% [4]. Timely and accurate prediction of adverse prognosis is essential for better management of AAAD. Thus, it is important to find reliable and easily accessible predictive biomarkers to help clinicians identify high‐risk AAAD patients for early interventions, thereby reducing in‐hospital mortality and improving prognosis.
Inflammatory reaction is the main pathophysiological feature of the occurrence and development of AAAD [5]. As a typical effector, neutrophils can participate in the inflammatory process by secreting matrix metalloproteinases (MMP), which leads to further deterioration of aortic dissection (AD) [6]. Albumin has a variety of capabilities, including regulating osmotic pressure, antioxidation and anti‐inflammatory, is a significant inhibitor of platelet activation and aggregation, and is linked to the regulation of AD inflammatory state [7, 8]. Recent research combined the two indicators and proved that the neutrophil percentage to albumin ratio (NPAR) could be used as a predictor of the prognosis of many cardiovascular diseases. Recent studies have shown that admission NPAR was an independent predictor of all‐cause mortality in acute myocardial infarction (AMI) [9]. In another study, NPAR was independently associated with all‐cause mortality in heart failure patients [10]. In addition, studies have shown that higher NPAR was independently associated with increased risk of 30‐day, 60‐day, and 365‐day all‐cause mortality in coronary heart disease (CHD) patients [11]. However, no study has examined how NPAR level on admission relates to in‐hospital mortality in AAAD patients. Therefore, it was the purpose of this study to determine whether NPAR has potential value in predicting the in‐hospital mortality of AAAD patients.
2. Methods
2.1. Study Population
Records of patients over 18 years old who were diagnosed as AAAD by computed tomographic angiography (CTA) or magnetic resonance angiography (MRA) and underwent surgical repair at the Cardiac Medical Center of Fujian Province from January 2020 to April 2024 were analyzed retrospectively in this study. The exclusion criteria for this study were as follows: (1) Have been used drugs that affect blood cell counts, like aspirin, antibiotics and glucocorticoids in the past 2 weeks; (2) Previous history of malignant tumor; (3) Combined with chronic liver and kidney failure; and (4) Suffering from autoimmune diseases. All patients were admitted to the intensive care unit (ICU) postoperatively. This study was approved by the Ethics Committee of Fujian Medical University Union Hospital (Approval No: 2019KY019) and was in accordance with the Declaration of Helsinki. Informed consent waivers were obtained because the data were anonymous.
2.2. Data Collection and Definition
The demographic information, vital signs on admission, complications, intraoperative conditions, laboratory indicators, mechanical ventilation time, and postoperative complications of all enrolled patients were collected from electronic medical records. There was a collection of demographic information, including age, gender, body mass index (BMI), history of cardiac surgery, smoking, and drinking. Admission vital signs include systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate. Complications included hypertension, diabetes mellitus, CHD, and Marfan's syndrome. Intraoperative conditions included operating time, CPB time, aortic cross‐clamping clamp time, and surgical type. Laboratory indicators were obtained from the first laboratory results after admission, including neutrophil percentage, albumin, hemoglobin (Hb), lymphocyte, platelet (PLT), white blood cell (WBC), alanine aminotransferase (ALT), aspartate transferase (AST), creatinine (Cr), and blood urea nitrogen (BUN).
The percentage of neutrophils was defined as the percentage of neutrophils in white blood cells. A neutrophil percentage divided by serum albumin concentration was used to calculate NPAR. The calculation formula was as follows: neutrophil percentage (%) × 100/albumin (g/dL). In‐hospital mortality was the primary outcome measure of the current study, and the postoperative complications, such as gastrointestinal bleeding, pulmonary infection, acute renal injury (AKI), multiple organ dysfunction syndrome (MODS), and arrhythmia, were the secondary outcome measures. In addition, the length of stay in ICU and length of hospital stay were recorded. In‐hospital mortality was defined as any death during postoperative hospital stay after AAAD surgery [12]. In cases of gastrointestinal bleeding, fecal occult blood tests or vomit that contains blood were used for diagnosis [13]. Pulmonary infection referred to the presence of pneumonia, or atelectasis on radiograph, and positive sputum bacterial culture [14]. AKI was defined as an increase in serum creatinine by ≥0.3 mg/dl within 48 h, a ≥1.5‐fold increase from baseline within 7 days, or a reduction in urine output to <0.5 mL/kg/h for 6 h or longer [15]. MODS was defined as a sequential organ failure assessment (SOFA) ≥ 6 over 2 successive days, at least 48 h after hospital admission [16]. Arrhythmia referred to any clinically significant cardiac arrhythmia, such as atrial fibrillation, supraventricular tachycardia, or cardiac arrest [17].
2.3. Study Endpoints
The primary endpoint was in‐hospital mortality. The secondary endpoints were postoperative complications and the duration of ICU and hospital stays. The postoperative complications consisted of gastrointestinal hemorrhage, pulmonary infection, AKI, MODS, and arrhythmia.
2.4. Statistical Analysis
SPSS Version 25.0 and MedCalc version 19.2.1 were used for statistical analysis. The normality of continuous variables was assessed using the Kolmogorov–Smirnov test. Variables with a Kolmogorov–Smirnov test result of p≥0.05 were considered normally distributed. If the continuous variables in this study were normally distributed, they were expressed as mean ± standard deviation (SD), and the difference between groups was analyzed using a one‐way analysis of variance (ANOVA). If the variable did not follow a normal distribution, it was indicated by median and quartile range, and comparisons between groups were conducted using Kruskal–Wallis tests. A frequency or percentage was used to express categorical variables, and for comparing groups, the Chi‐square test or Fisher's exact test were used. After adjusting for possible confounding factors, we used multivariate logistic regression analysis to determine the independent correlation between NPAR and in‐hospital mortality. These confounding factors were judged by statistical significance and clinical judgment in univariate analysis. The first tertile was defined as reference, odds ratio (OR) and 95% confidence interval (CI) have been used to calculate the results. The study also conducted subgroup analysis on the relationship between NPAR and in‐hospital mortality, and calculated the p value for interaction to confirm whether the relationship between NPAR and in‐hospital mortality was different in each subgroup classified by demographic information (such as age and BMI), complications (such as hypertension), vital signs (such as SBP, DBP, heart rate), and laboratory test results (such as Hb, lymphocytes, PLT, WBC, ALT, AST, Cr, BUN). ROC curves were used to evaluate the predictive value of neutrophil percentage, albumin and NPAR in predicting in‐hospital mortality of AAAD patients. Area under curve (AUC), sensitivity and specificity were calculated. In order to compare AUC between different indicators, we used the Delong test. All tests were two‐sided, and the differences were considered statistically significant when p<0.05.
3. Results
3.1. Subject Characteristics
This study included 813 AAAD patients (Figure 1). This study included 629 males and 184 females, with a mean age of 52.50 ± 11.55 years. Using tertiles of admission NPAR, patients were split into three groups (T1:<21.87; T2: 21.87–24.81; T3:>24.81). The baseline characteristics for patients stratified by NPAR tertiles were summarized in Table 1. A total of 269 patients were included in T1 group, 274 patients in T2 group, and 270 patients in T3 group. In comparison with T1 and T2 groups, T3 patients were older (p<0.001), had longer mechanical ventilation (p<0.001), had less hypertension (p = 0.009), and had lower SBP (p = 0.007). In the laboratory test results, compared with T1 and T2 groups, patients in T3 group had lower Hb (p<0.001) and PLT (p<0.001), higher WBC counts (p<0.001), and higher Cr levels (p = 0.028). There were no significant differences among the three groups in BMI, diabetes, CHD, Marfan's syndrome, history of cardiac surgery, smoking, drinking, DBP, heart rate, surgical type, operating time, CPB time, aortic cross‐clamping time, ALT, AST, and BUN (p>0.05).
FIGURE 1.
The flow chart for the selection process of patients with AAAD. AAAD, acute type A aortic dissection.
TABLE 1.
Baseline characteristics stratified by preoperative NPAR.
| NPAR | ||||
|---|---|---|---|---|
| Variable | T1 (<21.87) N = 269 | T2 (21.87–24.81) N = 274 | T3 (>24.81) N = 270 | p |
| Age (years) | 49.78 ± 11.75 | 53.05 ± 11.07 | 54.64 ± 11.33 | <0.001 |
| Male | 227 (84.4) | 221 (80.7) | 181 (67.0) | <0.001 |
| BMI (kg/m2) | 25.26 ± 4.30 | 24.97 ± 3.90 | 24.44 ± 3.92 | 0.059 |
| Hypertension | 158 (58.7) | 136 (49.6) | 124 (45.9) | 0.009 |
| Diabetes mellitus | 14 (5.2) | 9 (3.3) | 7 (2.6) | 0.249 |
| CHD | 2 (0.7) | 1 (0.4) | 4 (1.5) | 0.359 |
| Marfan's syndrome | 15 (5.6) | 13 (4.7) | 13 (4.8) | 0.887 |
| Previous cardiac surgery | 18 (6.7) | 16 (5.8) | 12 (4.4) | 0.522 |
| Smoker | 140 (52.0) | 130 (47.4) | 124 (45.9) | 0.334 |
| Drinker | 119 (44.2) | 110 (40.1) | 100 (37.0) | 0.232 |
| SBP (mmHg) | 144.39 ± 27.82 | 138.27 ± 27.18 | 137.10 ± 31.34 | 0.007 |
| DBP (mmHg) | 76.19 ± 15.91 | 75.15 ± 16.77 | 73.79 ± 15.97 | 0.228 |
| Heart rate | 82.34 ± 16.05 | 81.59 ± 15.92 | 81.71 ± 17.00 | 0.850 |
| Operating time (min) | 290.0 (255.0–335.0) | 301.0 (263.0–348.0) | 310.0 (265.0–365.0) | 0.254 |
| CPB time (min) | 150.0 (118.0–185.0) | 153.0 (122.0–190.0) | 151.0 (124.0–185.0) | 0.727 |
| Aortic cross‐clamping time (min) | 65.0 (48.0–98.0) | 75.0 (53.0–102.0) | 69.0 (50.0–96.0) | 0.520 |
| Mechanical ventilation time (days) | 1.8 (0.9–3.4) | 2.0 (1.1–4.4) | 2.9 (1.5–8.3) | <0.001 |
| Surgical type | 0.365 | |||
| Simple aortic surgeries | 249 (92.6) | 255 (93.1) | 256 (94.8) | |
| Combined CABG | 11 (4.1) | 15 (5.5) | 9 (3.3) | |
| Combined valve surgery | 9 (3.3) | 3 (1.1) | 4 (1.5) | |
| Combined CABG and valve surgery | 0 | 1 (0.4) | 1 (0.4) | |
| Laboratory tests | ||||
| Neutrophil percentage (%) | 76.60 (65.50–83.50) | 86.30 (81.40–90.40) | 91.40 (86.90–97.80) | <0.001 |
| Albumin (g/dL) | 4.01 ± 0.52 | 3.69 ± 0.31 | 3.29 ± 0.34 | <0.001 |
| Hb (g/dL) | 13.16 ± 1.98 | 12.98 ± 1.75 | 12.22 ± 2.01 | <0.001 |
| Lymphocyte (109/L) | 1.30 (0.92–1.74) | 0.92 (0.68–1.31) | 0.95 (0.63–1.86) | <0.001 |
| PLT (109/L) | 198.93 ± 69.61 | 184.41 ± 68.44 | 170.64 ± 72.18 | <0.001 |
| WBC (109/L) | 11.63 ± 4.22 | 12.35 ± 3.60 | 12.95 ± 4.56 | 0.001 |
| ALT (IU/L) | 27.00 (19.00–36.00) | 27.00 (17.00–38.00) | 27.00 (19.00–41.00) | 0.074 |
| AST (IU/L) | 24.00 (19.00–38.50) | 26.00 (20.00–39.00) | 27.00 (20.00–43.00) | 0.148 |
| Cr (µmol/L) | 87.00 (70.00–119.00) | 85.50 (69.00–118.50) | 89.00 (69.00–131.00) | 0.028 |
| BUN (mmol/L) | 5.40 (4.30–7.00) | 6.50 (5.20–8.60) | 7.30 (5.30–9.80) | 0.083 |
The values in bold indicate statistically significant p‐values, meaning p < 0.05.
Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; BUN, blood urea nitrogen; CHD, coronary heart disease; CPB, cardiopulmonary bypass; Cr, creatinine; DBP, diastolic blood pressure; Hb, hemoglobin; NPAR, neutrophil percentage to albumin ratio; PLT, platelet; SBP, systolic blood pressure; WBC, white blood cell.
3.2. Relationship between NPAR Values and In‐Hospital Outcomes
As shown in Table 2, 137 patients died during hospitalization (in‐hospital mortality 16.9%). Compared with T1 and T2 groups, T3 group had a higher in‐hospital mortality of 33.0% (p <0.001). In addition, the incidence of AKI (p <0.001) and MODS (p = 0.004) in T3 group was significantly higher than that in T1 and T2 groups, and the stay time in ICU was significantly longer (p <0.001). The overall incidence of postoperative pulmonary infection was 27.3% (222 out of 813). Specifically, the incidence was 25.7% (69 out of 269) in group T1, 26.6% (73 out of 274) in group T2, and 29.6% (80 out of 270) in group T3. However, the difference in incidence among the three groups was not statistically significant (p >0.05). Additionally, no significant differences were observed among the three groups in the incidence of postoperative gastrointestinal hemorrhage, arrhythmia and hospital length of stay (p >0.05).
TABLE 2.
In‐Hospital Outcomes stratified by preoperative NPAR.
| NPAR | |||||
|---|---|---|---|---|---|
| Variable | Total | T1 (<21.87) N = 269 | T2 (21.87–24.81) N = 274 | T3>24.81 N = 270 | p |
| In‐hospital mortality | 137 (16.9) | 6 (2.2) | 42 (15.3) | 89 (33.0) | <0.001 |
| Gastrointestinal hemorrhage | 78 (9.6) | 23 (8.6) | 26 (9.5) | 29 (10.7) | 0.687 |
| Pulmonary infection | 222 (27.3) | 69 (25.7) | 73 (26.6) | 80 (29.6) | 0.558 |
| AKI | 175 (21.5) | 36 (13.4) | 52 (19.0) | 87 (32.2) | <0.001 |
| MODS | 28 (3.4) | 2 (0.7) | 10 (3.6) | 16 (5.9) | 0.004 |
| Arrhythmia | 17 (2.1) | 3 (1.1) | 4 (1.5) | 10 (3.7) | 0.074 |
| ICU stay (days) | 6.0 (4.0–10.0) | 5.0 (4.0–9.0) | 6.0 (4.0–10.0) | 8.0 (4.0–15.0) | <0.001 |
| hospital length of stay (days) | 20.0 (15.0–27.0) | 19.0 (15.0–26.0) | 20.0 (15.0–26.0) | 21.0 (15.0–29.0) | 0.069 |
The values in bold indicate statistically significant p‐values, meaning p < 0.05.
Abbreviations: AKI, acute kidney injury; ICU, intensive care unit; MODS, multiple organ dysfunction syndrome; NPAR, neutrophil percentage to albumin ratio.
3.3. Univariate and Multivariate Logistic Regression Analysis
The univariate logistic regression analysis of in‐hospital mortality of AAAD patients showed that age, operating time, CPB time, aortic cross‐clamping time, mechanical ventilation time, PLT, WBC, AST, and NPAR were the factors related to in‐hospital mortality (p <0.05). After adjusting for all covariates, multivariate logistic regression analysis revealed that CPB time (OR 1.010, 95% CI: 1.007–1.013, p <0.001), mechanical ventilation time (OR 1.115, 95% CI: 1.082–1.150, p <0.001), NPAR were significantly correlated with in‐hospital mortality. With group T1 as a reference, the OR (95% CI) values of group T2 and group T3 were 3.041 (1.502–6.158) (p = 0.002) and 6.586 (3.324–13.049), respectively (p <0.001). In addition, the p value of NPAR trend was less than 0.001, indicating that as NPAR increases, in‐hospital mortality increases as well. A summary of the results was shown in Table 3.
TABLE 3.
Univariate and multivariate analysis of variables associated with in‐hospital mortality.
| Variable | Univariate | Multivariate | ||
|---|---|---|---|---|
| OR (95% CI) | p | OR (95% CI) | p | |
| Age (years) | 1.030 (1.013–1.047) | 0.001 | 1.006 (0.986–1.026) | 0.569 |
| Male | 1.272 (0.834–1.939) | 0.264 | ||
| BMI (kg/m2) | 1.027 (0.983–1.074) | 0.235 | ||
| Hypertension | 0.717 (0.496–1.038) | 0.078 | ||
| Diabetes mellitus | 0.986 (0.371–2.624) | 0.978 | ||
| CHD | 1.988 (0.382–10.354) | 0.414 | ||
| Marfan's syndrome | 1.017 (0.441–2.344) | 0.969 | ||
| Previous cardiac surgery | 0.454 (0.160–1.288) | 0.138 | ||
| Smoker | 0.743 (0.513–1.077) | 0.116 | ||
| Drinker | 0.984 (0.677–1.431) | 0.933 | ||
| SBP (mmHg) | 0.995 (0.989–1.002) | 0.156 | ||
| DBP (mmHg) | 1.004 (0.992–1.015) | 0.536 | ||
| Heart rate | 1.008 (0.997–1.020) | 0.132 | ||
| Surgical type | ||||
| Simple aortic surgeries | 1.0 (ref) | |||
| Combined CABG | 1.682 (0.790, 3.580) | 0.177 | ||
| Combined valve surgery | 1.402 (0.446, 4.409) | 0.563 | ||
| Combined CABG and valve surgery | — | 0.999 | ||
| Operating time (min) | 1.005 (1.003–1.007) | <0.001 | ||
| CPB time (min) | 1.009 (1.006–1.012) | <0.001 | 1.010 (1.007–1.013) | <0.001 |
| Aortic cross‐clamping time (min) | 1.004 (1.001–1.007) | 0.025 | ||
| Mechanical ventilation time (days) | 1.141 (1.109–1.175) | <0.001 | 1.115 (1.082–1.150) | <0.001 |
| Laboratory tests | ||||
| Hb (g/dL) | 0.966 (0.881–1.059) | 0.463 | ||
| Lymphocyte (109/L) | 0.974 (0.916–1.035) | 0.392 | ||
| PLT (109/L) | 0.994 (0.991–0.997) | <0.001 | 0.999 (0.996–1.002) | 0.516 |
| WBC (109/L) | 1.064 (1.021–1.109) | 0.004 | 1.031 (0.979–1.086) | 0.248 |
| ALT (IU/L) | 1.004 (0.998–1.011) | 0.216 | ||
| AST (IU/L) | 1.006 (1.001–1.011) | 0.027 | 1.004 (0.997–1.010) | 0.235 |
| Cr (µmol/L) | 1.001 (0.999–1.003) | 0.384 | ||
| BUN (mmol/L) | 1.003 (0.995–1.011) | 0.529 | ||
| NPAR | ||||
| <21.87 | 1.0 (ref) | 1.0 (ref) | ||
| 21.87–24.81 | 3.297 (1.755–6.194) | <0.001 | 3.041 (1.502–6.158) | 0.002 |
| >24.81 | 7.806 (4.294–14.191) | <0.001 | 6.586 (3.324–13.049) | <0.001 |
| p for trend | <0.001 | <0.001 | ||
The values in bold indicate statistically significant p‐values, meaning p < 0.05.
Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; BUN, blood urea nitrogen; CHD, coronary heart disease; CPB, cardiopulmonary bypass; Cr, creatinine; DBP, diastolic blood pressure; Hb, hemoglobin; NPAR, neutrophil percentage to albumin ratio; PLT, platelet; SBP, systolic blood pressure; WBC, white blood cell.
3.4. Subgroup Analysis
In order to verify the consistency of the correlation between NPAR and in‐hospital mortality, a subgroup analysis was conducted on AAAD patients. Interaction analysis showed that there was no significant interaction between subgroups classified by age, BMI, hypertension, SBP, DBP, heart rate, Hb, lymphocytes, PLT, WBC, ALT, AST, Cr, and BUN (p >0.05). The results were shown in Table 4.
TABLE 4.
Subgroup analysis of the associations between preoperative NPAR and in‐hospital mortality.
| Variable | N | NPAR | p for interaction | ||
|---|---|---|---|---|---|
| <21.87 | 21.87–24.81 | >24.81 | |||
| Age (years) | 0.289 | ||||
| <53 | 398 | 1.0 (ref) | 1.806 (0.747–4.367) | 6.579 (2.973–14.560) | |
| ≥53 | 415 | 1.0 (ref) | 5.437 (2.029–14.571) | 9.478 (3.643–24.658) | |
| BMI (kg/m2) | 0.642 | ||||
| <24 | 338 | 1.0 (ref) | 4.341 (1.408–13.387) | 8.500 (2.903–24.889) | |
| 24≤BMI<28 | 313 | 1.0 (ref) | 2.735 (0.940–7.963) | 9.794 (3.631–26.417) | |
| ≥28 | 162 | 1.0 (ref) | 3.293 (1.080–10.041) | 6.321 (2.088–19.135) | |
| Hypertension | 0.183 | ||||
| No | 395 | 1.0 (ref) | 6.210 (2.097–18.387) | 11.167 (3.870–32.223) | |
| Yes | 418 | 1.0 (ref) | 1.973 (0.864–4.507) | 6.540 (3.103–13.783) | |
| SBP (mmHg) | 0.171 | ||||
| <140.00 | 405 | 1.0 (ref) | 6.386 (2.159–18.889) | 11.733 (4.075–33.786) | |
| ≥140.00 | 408 | 1.0 (ref) | 1.939 (0.848–4.433) | 6.299 (2.979–13.317) | |
| DBP (mmHg) | 0.572 | ||||
| <74.00 | 393 | 1.0 (ref) | 3.275 (1.340–8.008) | 6.765 (2.898–15.794) | |
| ≥74.00 | 420 | 1.0 (ref) | 3.317 (1.363–8.073) | 8.967 (3.859–20.834) | |
| Heart rate | 0.338 | ||||
| <80.00 | 387 | 1.0 (ref) | 2.750 (1.175–6.437) | 4.620 (2.039–10.470) | |
| ≥80.00 | 426 | 1.0 (ref) | 3.972 (1.545–10.210) | 12.854 (5.284–31.269) | |
| Hb (g/dL) | 0.154 | ||||
| <12.90 | 380 | 1.0 (ref) | 2.689 (1.030–7.021) | 5.739 (2.342–14.066) | |
| ≥12.90 | 433 | 1.0 (ref) | 3.765 (1.630–8.697) | 10.312 (4.579–23.226) | |
| Lymphocyte (109/L) | 0.140 | ||||
| <1.05 | 406 | 1.0 (ref) | 2.534 (1.064–6.032) | 6.078 (2.619–14.106) | |
| ≥1.05 | 407 | 1.0 (ref) | 3.140 (1.196–8.242) | 8.010 (3.377–18.998) | |
| PLT (109/L) | 0.477 | ||||
| <176.00 | 404 | 1.0 (ref) | 5.784 (1.942–17.225) | 13.502 (4.719–38.637) | |
| ≥176.00 | 409 | 1.0 (ref) | 2.202 (0.980–4.947) | 4.933 (2.284–10.656) | |
| WBC (109/L) | 0.295 | ||||
| <12.06 | 406 | 1.0 (ref) | 3.610 (1.490–8.746) | 6.847 (2.882–16.266) | |
| ≥12.06 | 407 | 1.0 (ref) | 2.986 (1.215–7.338) | 8.286 (3.600–19.070) | |
| ALT (IU/L) | 0.499 | ||||
| <27.00 | 393 | 1.0 (ref) | 7.037 (2.030–24.390) | 16.370 (4.878–54.934) | |
| ≥27.00 | 420 | 1.0 (ref) | 2.273 (1.061–4.869) | 5.395 (2.659–10.945) | |
| AST (IU/L) | 0.208 | ||||
| <26.00 | 387 | 1.0 (ref) | 4.354 (1.406–13.479) | 10.236 (3.454–30.335) | |
| ≥26.00 | 426 | 1.0 (ref) | 2.795 (1.294–6.035) | 6.363 (3.085–13.125) | |
| Cr (µmol/L) | 0.986 | ||||
| <87.00 | 399 | 1.0 (ref) | 3.024 (1.234–7.409) | 6.243 (2.640–14.759) | |
| ≥87.00 | 414 | 1.0 (ref) | 3.592 (1.479–8.725) | 9.333 (4.050–21.511) | |
| BUN (mmol/L) | 0.197 | ||||
| <6.3 | 406 | 1.0 (ref) | 2.208 (0.831–5.866) | 7.667 (3.204–18.349) | |
| ≥6.3 | 407 | 1.0 (ref) | 3.512 (1.479–8.339) | 6.633 (2.877–15.291) | |
Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; BUN, blood urea nitrogen; Cr, creatinine; DBP, diastolic blood pressure; Hb, hemoglobin; NPAR, neutrophil percentage to albumin ratio; PLT, platelet; SBP, systolic blood pressure; WBC, white blood cell.
3.5. Sensitivity and Specificity of Neutrophil Percentage, Albumin, and NPAR in Predicting In‐Hospital Mortality
The predictive values of neutrophil percentage, albumin, and NPAR for in‐hospital mortality of AAAD patients were shown in Table 5 and Figure 2. Results indicated that the AUC of NPAR was 0.708 (95% CI: 0.676–0.739) (p <0.001), the optimal cutoff value was 24.105, with a sensitivity of 73.7% and a specificity of 64.8%. The AUC of neutrophil percentage and albumin were 0.649 (95% CI: 0.615–0.682) (p<0.001) and 0.622 (95% CI: 0.588–0.656) (p<0.001), respectively. Based on the Delong test results, the AUC of NPAR was significantly higher than that of neutrophil percentage (p = 0.004) and albumin (p<0.001), and NPAR showed higher predictive value in predicting hospital mortality.
TABLE 5.
Diagnostic value of neutrophil percentage, albumin, and NPAR for in‑hospital mortality.
| Variable | AUC | 95% CI | Cut‑off value | Sensitivity (%) | Specificity (%) |
|---|---|---|---|---|---|
| Neutrophil percentage (%) | 0.649 | 0.615–0.682 | 85.108 | 78.1 | 51.6 |
| Albumin (g/dL) | 0.622 | 0.588–0.656 | 3.705 | 77.4 | 48.5 |
| NPAR | 0.708 | 0.676–0.739 | 24.105 | 73.7 | 64.8 |
Abbreviations: AUC, area under the curve; NPAR, neutrophil percentage to albumin ratio.
FIGURE 2.
ROC curves of neutrophil percentage, albumin, and NPAR for in‐hospital mortality. AUC, the area under the curve; NPAR, neutrophil percentage to albumin ratio; ROC curve, the receiver operating characteristic curve.
4. Discussion
As far as we know, this is the first study to examine the relationship between NPAR and in‐hospital mortality in AAAD patients. The results of our study were summarized below. First, AAAD patients had a 16.9% in‐hospital mortality rate, which was similar to previous studies [18]. Patients with higher preoperative NPAR have higher incidence of AKI and MODS, and longer ICU stay. Second, after adjusting for potential confounders, AAAD patients with higher preoperative NPAR had an increased risk of in‐hospital mortality. From the results of subgroup analysis, there were no significant interactions between the subgroups of patients. In addition, CPB time and mechanical ventilation time also correlated with in‐hospital mortality of AAAD patients. Finally, the ROC curves showed that in comparison with the percentage of neutrophils or albumin alone, NPAR was a better predictor of in‐hospital mortality.
NPAR is a comprehensive reflection of the neutrophil percentage and albumin. In this study, AAAD patients with higher preoperative NPAR had a significantly higher risk of in‐hospital mortality than those with lower NPAR, which was consistent with the findings of NPAR in the prognostic value of acute myocardial infarction, heart failure, and CHD [9, 10, 11]. The underlying mechanism for NPAR levels in association with in‐hospital mortality in AAAD patients is unclear. According to previous studies, inflammation plays a crucial role in the occurrence, progression and prognosis of AD [2]. Lafçi et al. [19] found that neutrophil‐to‐lymphocyte ratio (NLR) as an indicator of inflammation was also independently associated with mortality in AAAD patients, thereby underscoring the pivotal role of the inflammatory response in the progression of AD.
As a result of inflammation, the media layer of the aorta degenerates and arterial walls remodel, resulting in a fragile aortic wall and an increased risk of rupture. As an inflammatory marker, NPAR was closely related to the inflammatory process. Thus, we tried to explain the relationship between NPAR levels and in‐hospital mortality in AAAD. Neutrophils were the major type of WBC [20], as a classic cellular effector, neutrophils played a crucial role in mediating inflammatory responses of AD [21]. Previous studies have shown that high neutrophil‐to‐platelet ratio (NPR) and NLR were independently linked to poor prognosis in acute aortic dissection (AAD) [22, 23]. A study by Liu et al. [24] revealed that neutrophil count was linked to in‐hospital mortality in AAAD patients. There are probably several reasons for that. First, neutrophils play a pivotal role in mediating the inflammatory response during the acute phase of AAAD [23]. The aortic wall dissection induces localized ischemia and necrosis, which triggers the release of damage‐associated molecular patterns (DAMPs) and activates Toll‐like receptors (TLRs) and the NF‐κB signaling pathway, consequently promoting neutrophil infiltration [23]. Second, activated neutrophils secrete matrix metalloproteinases (MMPs) such as MMP‐9, along with reactive oxygen species (ROS) and pro‐inflammatory cytokines including interleukin‐6 (IL‐6) and tumor necrosis factor‐alpha (TNF‐α) [24, 25]. These mediators contribute to the degradation of the aortic wall's extracellular matrix (ECM), thereby compromising vascular structural integrity [24, 25]. Furthermore, the generation of neutrophil extracellular traps (NETs) exacerbates endothelial injury and promotes thrombotic complications [26]. Finally, neutrophil activation leads to the production of substantial reactive oxygen intermediates, which induce apoptosis in aortic smooth muscle cells and further degrade the ECM [27]. This cascade of events exacerbates vascular endothelial injury, thereby heightening the risk of aortic dissection and coarctation, and consequently, elevating the in‐hospital mortality rate [27].
The protein albumin is a major component of plasma and plays various roles such as anti‐inflammatory, antioxidant, anticoagulant, and antiplatelet aggregation [28]. Albumin levels were associated with poor prognosis in a wide range of cardiovascular diseases. A previous study found that lower albumin levels were independently associated with increased in‐hospital mortality in both type A and type B AAD [29]. A previous study on first episode acute myocardial infarction (AMI) showed that low albumin level at admission was independently correlated with all‐cause mortality [30]. A meta‐analysis demonstrated that low serum albumin levels were at high risk for all‐cause mortality among patients with acute coronary syndrome (ACS), even after adjusting for the confounding factors [31]. This may be due to the following reasons: First, albumin functions as a primary circulating antioxidant, effectively scavenging reactive oxygen species (ROS) and protecting the vascular endothelium against oxidative stress‐induced damage [32]. Consequently, diminished albumin levels may augment oxidative damage to the aortic wall [32]. Second, albumin plays a crucial role in inhibiting platelet aggregation and augmenting antithrombin III activity. A reduction in albumin levels may elevate the risk of thrombosis, thereby exacerbating aortic false lumen thrombosis or postoperative thrombotic events [33]. Furthermore, albumin serves as an essential nutritional biomarker and modulates vascular permeability through maintenance of colloid osmolality [34]. Low albumin levels may signify malnutrition, peripheral edema, or organ dysfunction in patients [34]. Finally, lower albumin levels may reflect more severe blood loss and catabolic reactions and lower potential to scavenge oxygen free radicals, likewise suggesting the presence of microcirculatory dysfunction and tissue damage after CPB [35, 36]. These states are strongly associated with an increased risk of death in AAAD [35, 36].
On the other hand, we found that patients who had higher preoperative NPAR were more likely to experience AKI and MODS, and longer stays in the ICU, which was similar to the results of another study [37]. This may be because the occurrence of AKI and MODS is inextricably linked to the activation of inflammatory cells, oxidative stress and the increase of oxygen free radicals, and NPAR can reflect the severity of inflammation and oxidative stress reaction to a certain extent [38]. Existing literature has established that mortality in patients with AAAD is primarily driven by two interrelated factors: progression of the underlying aortic pathology and postoperative multi‐organ failure [39]. In our study, while no statistically significant correlation between the NPAR and pulmonary infections was observed, there was an observable trend indicating a higher incidence of infections in the cohort with elevated NPAR. This suggested that systemic inflammatory responses may exacerbate postoperative immune imbalances, thereby heightening susceptibility to infection [40]. In addition, we noticed that in addition to NPAR, CPB time and mechanical ventilation time were also independent predictors of in‐hospital mortality among AAAD patients, which was consistent with the study of Bhamidipati et al. [41] This may be due to the existence of inflammation and ischemia‐reperfusion during CPB. In this case, the function of important organs such as lung, liver and kidney will deteriorate due to cell damage, vasodilation and increased capillary filtration [42, 43]. Moreover, patients with long mechanical ventilation had worse physical condition and were often accompanied by weakness and cognitive decline [44]. These factors may increase the in‐hospital mortality risk among AAAD patients.
It was found that NPAR could better predict in‐hospital mortality in AAAD than neutrophil percentage or albumin alone. As a potential novel biomarker, NPAR can be obtained from admission laboratory results quickly and conveniently, which is practical and simple. Existing literature has established CRP and NLR as biomarkers closely linked to systemic inflammatory responses, demonstrating prognostic value in cardiovascular disease outcomes [45]. However, CRP alone reflects only the intensity of inflammation, and NLR is limited to cellular ratios without accounting for the organism's nutritional status [45]. The NPAR emerges as a novel composite indicator, uniquely integrating acute inflammatory status with nutritional reserve capacity. A decrease in albumin is often accompanied by increased oxidative stress, coagulation disorders, and inadequate organ perfusion, mechanisms that are closely linked to postoperative complications of AAAD, such as AKI and multiple organ dysfunction syndrome MODS. Consequently, this comprehensive feature may render NPAR more advantageous in evaluating systemic inflammatory load and the risk of organ damage in AAAD patients. In light of its inexpensive cost, high availability, and ability in predicting in‐hospital mortality, NPAR may be a suitable clinical risk assessment tool for AAAD. Therefore, it is recommended that this indicator be included in risk stratification when making a clinical monitoring protocol.
This present study had some potential limitations. First, this was a single‐center retrospective study, which may result in selection bias. In the future, there is still a need for multicenter prospective studies to validate the conclusions of this study. Second, the percentage of neutrophils and the concentrations of serum albumin for our study were obtained through the first blood test after admission. However, since these indicators were dynamic, random error was inevitable from using only the first blood results, which made it impossible to dynamically observe NPAR. Additionally, this study did not account for the potential influence of additional inflammatory biomarkers, such as C‐reactive protein (CRP) and erythrocyte sedimentation rate (ESR). It is advisable for future research to investigate the synergistic effects of these markers in conjunction with the NPAR to further substantiate the independent predictive value of NPAR and to comprehensively evaluate the influence of inflammatory status on the prognosis of AAAD. Finally, the study only discussed how NPAR was associated with poor prognosis of AAAD patients during hospitalization, and there is a need for additional long‐term follow‐up studies to determine whether preoperative NPAR predicts long‐term patient outcome.
5. Conclusions
This study demonstrated that elevated NPAR on admission was independently related to the increased risk of in‐hospital mortality of AAAD patients. In addition, the practicability, availability and low cost of NPAR make it a valuable biomarker, which plays a critical rolein predicting the in‐hospital mortality of AAAD patients. For AAAD patients with high preoperative NPAR, more attention and closer monitoring should be given in clinical practice.
Author Contributions
We would like to acknowledge the valuable contributions made by each author to this article. Liangwan Chen and Yanjuan Lin conceived the whole study. Xuecui Zhang drafted the original manuscript and prepared the tables and figures. Lingyu Lin and Yanchun Peng were responsible for reviewing and editing the manuscript. Sailan Li and Xizhen Huang were in charge of data collecting. Xuecui Zhang and Lingyu Lin analyzed the data. All authors have contributed to the revision and refinement of the article, ensuring its clarity, coherence, and accuracy. We would like to express our gratitude to each author for their dedication, hard work, and invaluable contributions to this article.
Ethics Statement
This study was approved by the Ethics Committee of Fujian Medical University Union Hospital (Approval No: 2019KY019) and was by the Declaration of Helsinki.
Consent
Informed consent waivers were obtained because the data were anonymous.
Conflicts of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Xuecui Zhang and Lingyu Lin are the first co‐authors.
Liangwan Chen and Yanjuan Lin are co‐corresponding authors.
Funding: This research was sponsored by the Fifth Batch of Hospital Key Discipline Construction Projects (No. 2022YYZDXK01), Fujian Provincial Department of Finance Project (No. 2023CZ005), and Laboratory of Cardio‐Thoracic Surgery (Fujian Medical University), Fujian Province University.
Contributor Information
Liangwan Chen, Email: fjxhlwc@163.com.
Yanjuan Lin, Email: fjxhyjl@163.com.
Data Availability Statement
Full data set available from the corresponding author. However, reanalysis of the full data needs to be approved by Fujian Medical University Union Hospital.
References
- 1. Li G., Wu X. W., Lu W. H., et al., “High‐Sensitivity Cardiac Troponin T: A Biomarker for the Early Risk Stratification of Type‐A Acute Aortic Dissection?,” Archives of Cardiovascular Diseases 109 (2016): 163–170. [DOI] [PubMed] [Google Scholar]
- 2. Zhang Y., Chen T., Chen Q., Min H., Nan J., and Guo Z., “Development and Evaluation of an Early Death Risk Prediction Model After Acute Type A Aortic Dissection,” Annals of Translational Medicine 9 (2021): 1442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Hiratzka L. F., Bakris G. L., and Beckman J. A., “2010 ACCF/AHA/AATS/ACR/ASA/SCA/SCAI/SIR/STS/SVM Guidelines for the Diagnosis and Management of Patients With Thoracic Aortic Disease: Executive Summary. A Report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, American Association for Thoracic Surgery, American College of Radiology, American Stroke Association, Society of Cardiovascular Anesthesiologists, Society for Cardiovascular Angiography and Interventions, Society of Interventional Radiology, Society of Thoracic Surgeons, and Society for Vascular Medicine,” Catheterization and Cardiovascular Interventions 76 (2010): E43–E86. [DOI] [PubMed] [Google Scholar]
- 4. Pape L. A., Awais M., Woznicki E. M., et al., “Presentation, Diagnosis, and Outcomes of Acute Aortic Dissection: 17‐Year Trends From the International Registry of Acute Aortic Dissection,” Journal of the American College of Cardiology 66 (2015): 350–358. [DOI] [PubMed] [Google Scholar]
- 5. Zhou Z., Liu Y., Zhu X., et al., “Exaggerated Autophagy in Stanford Type A Aortic Dissection: A Transcriptome Pilot Analysis of Human Ascending Aortic Tissues,” Genes 11 (2020): 1187. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Xu Y., Fang H., Qiu Z., and Cheng X., “Prognostic Role of Neutrophil‐to‐Lymphocyte Ratio in Aortic Disease: A Meta‐Analysis of Observational Studies,” Journal of Cardiothoracic Surgery 15 (2020): 215. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Gu C., Li T., Jiang S., et al., “AMP‐Activated Protein Kinase Sparks the Fire of Cardioprotection Against Myocardial Ischemia and Cardiac Ageing,” Ageing Research Reviews 47 (2018): 168–175. [DOI] [PubMed] [Google Scholar]
- 8. Duan Z., Luo C., Fu B., and Han D., “Association Between Fibrinogen‐to‐Albumin Ratio and the Presence and Severity of Coronary Artery Disease in Patients With Acute Coronary Syndrome,” BMC Cardiovascular Disorders 21 (2021): 588. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Lin Y., Lin Y., Yue J., and Zou Q., “The Neutrophil Percentage‐to‐Albumin Ratio Is Associated With All‐Cause Mortality in Critically Ill Patients With Acute Myocardial Infarction,” BMC Cardiovascular Disorders 22 (2022): 115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Hu Z., Wang J., Xue Y., et al., “The Neutrophil‐to‐Albumin Ratio as a New Predictor of All‐Cause Mortality in Patients With Heart Failure,” Journal of Inflammation Research 15 (2022): 701–713. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Sun T., Shen H., Guo Q., et al., “Association Between Neutrophil Percentage‐to‐Albumin Ratio and All‐Cause Mortality in Critically Ill Patients With Coronary Artery Disease,” BioMed Research International 220 (2020): 8137576. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Xie E., Yang F., Luo S., et al., “Association Between Preoperative Monocyte to High‐Density Lipoprotein Ratio on In‐Hospital and Long‐Term Mortality in Patients Undergoing Endovascular Repair for Acute Type B Aortic Dissection,” Frontiers in Cardiovascular Medicine 8 (2022): 775471. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Huang K., Ji F., Xie Z., et al., “Artificial Liver Support System Therapy in Acute‐on‐Chronic Hepatitis B Liver Failure: Classification and Regression Tree Analysis,” Scientific Reports 9 (2019): 16462. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Peng S. Y., Wang J. W., Lau W. Y., et al., “Conventional Versus Binding Pancreaticojejunostomy After Pancreaticoduodenectomy: A Prospective Randomized Trial,” Annals of Surgery 245 (2007): 692–698. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Ostermann M., Kunst G., Baker E., Weerapolchai K., and Lumlertgul N., “Cardiac Surgery Associated AKI Prevention Strategies and Medical Treatment for CSA‐AKI,” Journal of Clinical Medicine 10 (2021): 5285. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Manson J., Cole E., De'Ath H. D., et al., “Early Changes Within the Lymphocyte Population Are Associated With the Development of Multiple Organ Dysfunction Syndrome in Trauma Patients,” Critical Care 20 (2016): 176. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Luo W., Sun J. J., Tang H., et al., “Association of Apoptosis‐Mediated CD4+ T Lymphopenia with Poor Outcome After Type A Aortic Dissection Surgery,” Frontiers in Cardiovascular Medicine 8 (2021): 747467. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Benedetto U., Dimagli A., Kaura A., et al., “Determinants of Outcomes Following Surgery for Type A Acute Aortic Dissection: The UK National Adult Cardiac Surgical Audit,” European Heart Journal 43 (2021): 44–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Lafçi G., Ciçek Ö. F., Uzun H. A., et al., “Relationship of Admission Neutrophil‐to‐Lymphocyte Ratio With in‐hospital Mortality in Patients With Acute Type I Aortic Dissection,” Turkish Journal of Medical Sciences 44, no. 2 (2014): 186–192. [PubMed] [Google Scholar]
- 20. Zhang T., Ye B., and Shen J., “Prognostic Value of Albumin‐Related Ratios in HBV‐Associated Decompensated Cirrhosis,” Journal of Clinical Laboratory Analysis 36 (2022): e24338. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Libby P., “Inflammation in Atherosclerosis,” Arteriosclerosis, Thrombosis, and Vascular Biology 32 (2012): 2045–2051. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Pang J., Liu J., Liang W., Yang L., and Wu L., “High Neutrophil‐to‐Platelet Ratio Is Associated With Poor Survival in Patients With Acute Aortic Dissection,” Disease Markers 2022 (2022): 5402507. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Kalkan M. E., Kalkan A. K., Gündeş A., et al., “Neutrophil to Lymphocyte Ratio: A Novel Marker for Predicting Hospital Mortality of Patients With Acute Type A Aortic Dissection,” Perfusion 32 (2017): 321–327. [DOI] [PubMed] [Google Scholar]
- 24. Liu H., Li D., Jia Y., and Zeng R., “Predictive Value of White Blood Cells, Neutrophils, Platelets, Platelet to Lymphocyte and Neutrophil to Lymphocyte Ratios in Patients With Acute Aortic Dissection,” Brazilian Journal of Cardiovascular Surgery 35 (2020): 1031–1033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Gollmann‐Tepeköylü C., Graber M., Hirsch J., et al., “Toll‐Like Receptor 3 Mediates Aortic Stenosis through a Conserved Mechanism of Calcification,” Circulation 147, no. 20 (2023): 1518–1533. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Zhao Y. F., Zuo Z. A., Li Z. Y., et al., “Integrated Multi‐Omics Profiling Reveals Neutrophil Extracellular Traps Potentiate Aortic Dissection Progression,” Nature Communications 15, no. 1 (2024): 10736. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Dreher L., Kuehl M. B., Wenzel U. O., and Kylies D., “Aortic Aneurysm and Dissection: Complement and Precision Medicine in Aortic Disease,” American Journal of Physiology. Heart and Circulatory Physiology 328, no. 4 (2025): H814–829. [DOI] [PubMed] [Google Scholar]
- 28. Guvenc T. S., Güzelburc O., Ekmekci A., et al., “The Effect of Left Ventricular Assist Device Implantation on Serum Albumin, Total Protein and Body Mass: A Short‐Term, Longitudinal Follow‐Up Study,” Heart, Lung & Circulation 26 (2017): 702–708. [DOI] [PubMed] [Google Scholar]
- 29. Gao Y., Li D., Cao Y., Zhu X., Zeng Z., and Tang L., “Prognostic Value of Serum Albumin for Patients With Acute Aortic Dissection: A Retrospective Cohort Study,” Medicine 98 (2019): e14486. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Xia M., Zhang C., Gu J., et al., “Impact of Serum Albumin Levels on Long‐Term All‐Cause, Cardiovascular, and Cardiac Mortality in Patients With First‐Onset Acute Myocardial Infarction,” Clinica Chimica Acta; International Journal of Clinical Chemistry 477 (2018): 89–93. [DOI] [PubMed] [Google Scholar]
- 31. Zhu L., Chen M., and Lin X., “Serum Albumin Level for Prediction of All‐Cause Mortality in Acute Coronary Syndrome Patients: A Meta‐Analysis,” Bioscience Reports 40 (2020): BSR20190881. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Zeng J. L., Chen J. H., Zhang L. W., et al., “Lactate Dehydrogenase‐to‐Albumin Ratio: A Superior Inflammatory Marker for Predicting Contrast‐Associated Acute Kidney Injury After Percutaneous Coronary Intervention,” Clinical Cardiology 47, no. 2 (2024): e24219. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Lessomo F. Y. N., Fan Q., Wang Z. Q., and Mukuka C., “The Relationship Between Leukocyte to Albumin Ratio and Atrial Fibrillation Severity,” BMC Cardiovascular Disorders 23, no. 1 (2023): 67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Wu Q., Zheng J., Lin J., et al., “Preoperative Blood Urea Nitrogen‐to‐Serum Albumin Ratio for Prediction of In‐Hospital Mortality in Patients Who Underwent Emergency Surgery for Acute Type A Aortic Dissection,” Hypertension Research 47, no. 7 (2024): 1934–1942. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Hülshoff A., Schricker T., Elgendy H., Hatzakorzian R., and Lattermann R., “Albumin Synthesis in Surgical Patients,” Nutrition 29 (2013): 703–707. [DOI] [PubMed] [Google Scholar]
- 36. Henry B. M., Borasino S., Ortmann L., et al., “Perioperative Serum Albumin and Its Influence on Clinical Outcomes in Neonates and Infants Undergoing Cardiac Surgery With Cardiopulmonary Bypass: A Multi‐Centre Retrospective Study,” Cardiology in the Young 29 (2019): 761–767. [DOI] [PubMed] [Google Scholar]
- 37. Simsek B., Cinar T., Inan D., et al., “C‐Reactive Protein/Albumin Ratio Predicts Acute Kidney Injury in Patients with Moderate to Severe Chronic Kidney Disease and Non‐ST‐Segment Elevation Myocardial Infarction,” Angiology 73 (2022): 132–138. [DOI] [PubMed] [Google Scholar]
- 38. Karabağ Y., Çağdaş M., Rencuzogullari I., et al., “The C‐Reactive Protein to Albumin Ratio Predicts Acute Kidney Injury in Patients With ST‐Segment Elevation Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention,” Heart, Lung & Circulation 28 (2019): 1638–1645. [DOI] [PubMed] [Google Scholar]
- 39. Luo M. H., Luo J. C., Zhang Y. J., et al., “Early Postoperative Organ Dysfunction Is Highly Associated With the Mortality Risk of Patients With Type A Aortic Dissection,” Interactive Cardiovascular and Thoracic Surgery 35, no. 6 (2022): ivac266. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Xuan W., Wu X., Zheng L., et al., “Gut Microbiota‐Derived Acetic Acids Promoted Sepsis‐Induced Acute Respiratory Distress Syndrome by Delaying Neutrophil Apoptosis Through FABP4,” Cellular and Molecular Life Sciences 81, no. 1 (2024): 438. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Bhamidipati C. M., LaPar D. J., Mehta G. S., et al., “Albumin Is a Better Predictor of Outcomes Than Body Mass Index Following Coronary Artery Bypass Grafting,” Surgery 150 (2011): 626–634. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Lin Y. J., Lin J. L., Peng Y. C., Li S. L., and Chen L. W., “TG/HDL‐C Ratio Predicts In‐Hospital Mortality in Patients With Acute Type A Aortic Dissection,” BMC Cardiovascular Disorders 22 (2022): 346. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Fadayomi A. B., Ibala R., Bilotta F., Westover M. B., and Akeju O., “A Systematic Review and Meta‐Analysis Examining the Impact of Sleep Disturbance on Postoperative Delirium,” Critical Care Medicine 46 (2018): e1204–e1212. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Berbel‐Franco D., Lopez‐Delgado J. C., Putzu A., et al., “The Influence of Postoperative Albumin Levels on the Outcome of Cardiac Surgery,” Journal of Cardiothoracic Surgery 15 (2020): 78. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Vrsalović M. and Vrsalović Presečki A., “Admission C‐Reactive Protein and Outcomes in Acute Aortic Dissection: A Systematic Review,” Croatian Medical Journal 60 (2019): 309–315. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Full data set available from the corresponding author. However, reanalysis of the full data needs to be approved by Fujian Medical University Union Hospital.