Abstract
Background
The impact of the geriatric nutritional risk index (GNRI) on the prognosis of patients with acute Stanford type A aortic dissection (AAAD) remains unclear. This study sought to explore the predictive value of GNRI for in-hospital mortality in patients with AAAD.
Methods
We conducted a retrospective analysis of patients who underwent surgery for AAAD from January 2014 to December 2022. Based on the median GNRI score, patients were stratified into high GNRI (> 103.7) and low GNRI (≤ 103.7) groups. Multivariate logistic regression analysis was used to assess the association between GNRI and in-hospital mortality.
Results
A total of 936 patients were included, with a mean age of 52.7 ± 11.6 years. The mean GNRI was 103.8 ± 11.3, and patients were stratified into low GNRI (n = 472, 50.4%) and high GNRI (n = 464, 49.6%) groups. The low GNRI group had a significantly higher in-hospital mortality rate than the high GNRI group (23.1% vs. 17.5%, P = 0.032). Multivariate logistic regression identified GNRI ≤ 103.7 as an independent risk factor for in-hospital mortality (OR = 2.037, 95%CI:1.110–3.740, P = 0.022). CPB time (OR = 1.005, 95%CI: 1.001–1.009, P = 0.022), and lactate levels (OR = 1.221, 95%CI: 1.144–1.302, P < 0.001) were also independent risk factors for in-hospital mortality. The AUC of the logistic model was 0.811.
Conclusion
The GNRI is a validated nutritional assessment tool, and AAAD patients with a GNRI ≤ 103.7 havesignificantly higher in-hospital mortality rate. These findings highlight the importance of preoperative nutritional assessment and optimization to improve outcomes for AAAD patients.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-025-05045-4.
Keywords: Aortic dissection, Geriatric nutritional risk index, In-hospital mortality
Introduction
Aortic dissection (AD) is a life-threatening cardiovascular emergency in which blood enters the aortic wall through an intimal tear, creating true and false lumens. Its estimated incidence ranges from 4.7 to 6 cases per 100,000 person-years [1]. Acute Stanford type A aortic dissection (AAAD) accounts for approximately two-thirds of all AD cases in the International Registry of Aortic Dissection (IRAD) and carries high morbidity and mortality [2, 3]. Surgical repair is currently the only definitive treatment for AAAD. Despite recent advancements in surgical techniques, the prognosis for AAAD patients remains suboptimal, with reported in-hospital mortality rates ranging from 17–37% [4, 5]. Consequently, there is a pressing need to identify simple yet effective prognostic indicators to facilitate the development of rational and individualized treatment strategies, ultimately improving patient survival outcomes.
Malnutrition has emerged as a modifiable risk factor that significantly associated with adverse outcomes in cardiovascular diseases [6]. It is closely associated with heightened inflammatory responses, arterial calcification, and atherosclerosis progression, suggesting a potential role in cardiovascular pathogenesis [7–9]. Nutritional assessment tools, such as the Geriatric Nutritional Risk Index (GNRI), have gained attention for their ability to identify high-risk patients. The GNRI, calculated using serum albumin levels and ideal body weight [10], has been validated in predicting adverse outcomes in various cardiovascular conditions, including heart failure, coronary artery disease, and valvular heart disease [11–15]. However, its prognostic role in AAAD patients undergoing surgery remains unexplored. Given the high mortality rate and the potential of nutritional interventions to improve outcomes, we hypothesized that GNRI could serve as a valuable prognostic marker in AAAD patients. This study aimed to investigate the association between GNRI and in-hospital mortality in AAAD patients undergoing surgical repair.
Materials & methods
Study population
This retrospective observational study consecutively enrolled patients who underwent surgical repair for AAAD at our institution between January 2014 and December 2022. The diagnosis of AAAD has been confirmed by computed tomography angiography (CTA) within 14 days of symptom onset and prior to surgery. The exclusion criteria were as follows: (1) patients with neoplastic, hepatic, autoimmune, or infectious diseases, or a history of cerebral infarction or hemorrhage; (2) AAAD caused by traumatic events such as traffic accidents or esophageal foreign body impaction; (3) patients with liver cirrhosis, nephrotic syndrome, or chronic kidney disease; and (4) patients with incomplete clinical data. All patients who met the inclusion criteria were included in the study and no formal sample size calculations were performed.
The patient screening process is detailed in Fig. 1. Among the 997 AAAD patients enrolled during the study period, 15 we excluded due to neoplastic, hepatic, autoimmune, or infectious diseases; 4 due to traumatic causes (traffic accidents or esophageal foreign body impaction); and 42 due to incomplete clinical data, resulting in 936 patients for final analysis.
Fig. 1.
Patients’ flow chart of the cohort. AAAD, acute Stanford type A aortic dissection
This study was approved by the Ethics Committee of Fujian Medical University Union Hospital (No. 2019KY019) and was in accordance with the Helsinki Declaration. As a retrospective study, it waived the need for direct patient participation or informed consent.
Data collection
We extracted clinical data from the hospital’s electronic medical record system, including demographic characteristics (e.g., age, gender, body mass index, history of smoking, alcohol consumption, hypertension, diabetes mellitus), clinical data (e.g., admission vital signs, operative time, aortic clamping time, cardiopulmonary bypass (CPB) time), laboratory results (e.g., white blood cell count, albumin level, creatinine, hematocrit, platelet count, serum albumin), and outcomes (e.g., in-hospital mortality, postoperative complications, ventilation time, ICU and hospital stay). All vital signs were recorded at admission, and laboratory results were obtained from the first tests conducted within 24 h of admission. In-hospital mortality was defined as death occurring during hospitalization or within 30 days after discharge following the index surgery.
The GNRI was calculated using serum albumin levels and body weight and height obtained at admission, according to the following formula: GNRI = 14.89×serum albumin(g/L) + 41.7×(actual body weight/ideal body weight) [10, 16]. The ideal body weight was calculated as (0.75×body height in cm-62.5) for male patients, and (0.60×body height in cm-40) for female patients [10]. Patients with GNRI scores above the median value were categorized into the high GNRI group, while those with scores below the median comprised the low GNRI group.
Statistical analysis
Data were analyzed using SPSS version 25.0 and R language 4.4.3. Categorical variables were expressed as counts (percentages) and compared using the chi-square test or Fisher’s exact test, as appropriate. Continuous variables were tested for normality using Shapiro-Wilk tests; normally distributed data were presented as means ± standard deviations (SD) and compared with Student’s t-test, while non-normally distributed data were expressed as medians (interquartile range [IQR]) and analyzed using the Mann-Whitney U test. The association between GNRI and in-hospital mortality was assessed through univariable and multivariable logistic regression analysis. We selected variables for the multivariate model based on both clinical relevance and univariate statistical significance (P < 0.05). To avoid multicollinearity, highly correlated variables were excluded from the final model. Results were reported as odds ratios (ORs) with 95% confidence intervals (CIs). The predictive performance of GNRI was evaluated by calculating the area under the receiver operating characteristic curve (AUC-ROC). A restricted cubic spline was used to analyze the dose-effect relationship between GNRI and in-hospital mortality. All statistical tests were two-tailed, and P values < 0.05 were considered statistically significant.
Results
The mean age of the study population was 52.7 ± 11.6 years, with males accounting for 76.7% of the cohort. The mean GNRI was 103.8 ± 11.3. Based on the median GNRI value (103.7), patients were stratified into low GNRI (n = 472, 50.4%; GNRI ≤ 103.7) and high GNRI (n = 464, 49.6%; GNRI > 103.7) groups. The baseline characteristics of the two groups are presented in Table 1. Compared with the high GNRI group, patients in the low GNRI group were significantly older (P < 0.001), had a higher proportion of women (P < 0.001), and exhibited lower BMI values (P < 0.001). No significant differences were found between groups in other baseline variables, including history of hypertension, diabetes mellitus, smoking status, time from symptom onset to admission, symptom onset time, operative duration, or CPB time (P > 0.05).
Table 1.
Baseline characteristics of patients
| Variable | Overall Population (n = 936) |
GNRI | P-value | |
|---|---|---|---|---|
| Low GNRI group (GNRI ≤ 103.7) (n = 472) |
High GNRI group (GNRI>103.7) (n = 464) |
|||
| Age (y), mean (SD) | 52.7 ± 11.6 | 55.01 ± 11.9 | 50.4 ± 10.8 | < 0.001 |
| Male, n (%) | 718 (76.7) | 326 (69.1) | 392 (84.5) | < 0.001 |
| Height (cm), mean (SD) | 168.73 ± 8.14 | 167.59 ± 8.17 | 169.98 ± 7.97 | 0.001 |
| Weight (kg), mean (SD) | 70.59 ± 13.97 | 63.03 ± 9.82 | 79.33 ± 9.82 | < 0.001 |
| BMI (kg/m2), mean (SD) | 24.82 ± 3.91 | 23.17 ± 3.28 | 26.56 ± 3.77 | < 0.001 |
| SBP (mm/Hg), mean (SD) | 140.67 ± 29.57 | 134.64 ± 29.81 | 147.06 ± 27.93 | < 0.001 |
| DBP (mm/Hg), mean (SD) | 75.55 ± 16.59 | 73.18 ± 16.39 | 78.13 ± 16.39 | < 0.001 |
| PP (mm/Hg), mean (SD) | 81.78 ± 16.60 | 61.58 ± 22.33 | 68.84 ± 21.92 | < 0.001 |
| Heart rate, mean (SD) | 65.15 ± 22.35 | 81.35 ± 16.91 | 82.40 ± 16.25 | 0.337 |
| GNRI, mean (SD) | 103.80 ± 11.31 | 95.37 ± 6.38 | 112.38 ± 8.41 | < 0.001 |
| LVEF ≤ 50% (%) | 66 (8.2) | 40 (10.1) | 26 (6.4) | 0.057 |
| History of hypertension, n (%) | 691 (73.8) | 341 (72.2) | 350 (75.4) | 0.268 |
| History of diabetes, n (%) | 33 (3.5) | 14 (3.0) | 19 (4.1) | 0.349 |
| History of smoking, n (%) | 439 (46.9) | 210 (44.5) | 229 (49.4) | 0.136 |
| History of drinking, n (%) | 334 (35.7) | 164 (34.7) | 170 (36.6) | 0.546 |
| Marfan’s syndrome, n (%) | 32 (3.4) | 20 (4.2) | 12 (2.6) | 0.165 |
| Previous cardiac surgery, n (%) | 47 (5.0) | 18 (3.8) | 29 (6.3) | 0.088 |
| Time from onset to admission | 0.576 | |||
| < 24 h | 762 (81.4) | 379 (80.3) | 383 (82.5) | |
| 1–6 d | 143 (15.3) | 75 (15.9) | 68 (14.7) | |
| 7–14 d | 31 (3.3) | 18 (3.8) | 13 (2.8) | |
| Symptom onset time | 0.596 | |||
| 00:00–06:00 | 185 (19.8) | 97 (20.6) | 88 (19.0) | |
| 06:00–12:00 | 256 (27.4) | 122 (25.8) | 134 (28.9) | |
| 12:00–18:00 | 316 (33.8) | 157 (33.3) | 159 (34.3) | |
| 18:00–00:00 | 179 (19.1) | 96 (20.3) | 83 (17.9) | |
| Dissection involving the brachiocephalic trunk | 136 (14.5) | 70 (14.8) | 66 (14.2) | 0.792 |
| Dissection involving the left common carotid arteryartery | 127 (13.6) | 69 (14.6) | 58 (12.5) | 0.344 |
| Dissection involving the left subclavian artery | 119 (12.7) | 60 (12.7) | 59 (12.7) | 0.999 |
| Glucose (mmol/L), mean (SD) | 7.20 ± 2.70 | 7.24 ± 2.84 | 7.06 ± 2.44 | 0.305 |
| WBC (× 109/L), mean (SD) | 12.48 ± 4.15 | 11.87 ± 4.01 | 13.03 ± 4.23 | < 0.001 |
| Neutrophil (× 109/L), mean (SD) | 11.30 ± 8.11 | 11.02 ± 9.25 | 11.53 ± 6.93 | 0.344 |
| Lymphocyte (× 109/L), mean (SD) | 1.46 ± 2.70 | 1.69 ± 3.53 | 1.25 ± 1.49 | 0.013 |
| Monocyte (× 109/L), mean (SD) | 1.05 ± 1.59 | 1.19 ± 1.96 | 0.92 ± 1.09 | 0.011 |
| Platelet (× 109/L), median (IQR) | 174.0 (139.00-209.00) | 174.67 ± 64.90 | 182.84 ± 54.77 | 0.038 |
| RBC (× 109/L), mean (SD) | 4.32 ± 0.62 | 4.10 ± 0.61 | 4.54 ± 0.55 | < 0.001 |
| Total serum protein (g/L), mean (SD) | 64.06 ± 6.82 | 60.99 ± 6.20 | 67.19 ± 5.95 | < 0.001 |
| Serum albumin (g/L), mean (SD) | 37.03 ± 5.31 | 33.86 ± 3.94 | 40.26 ± 4.52 | < 0.001 |
| Serum globulin (g/L), mean (SD) | 21.16 ± 4.68 | 27.07 ± 5.10 | 27.24 ± 4.20 | 0.554 |
| Creatinine (umol/L), median (IQR) | 87.00 (68.90, 121.00) | 86.00 (66.00, 130.00) | 88.55 (71.65, 118.00) | 0.462 |
| Hemoglobin(g/L), mean (SD) | 128.47 ± 19.17 | 121.58 ± 18.79 | 135.30 ± 17.06 | < 0.001 |
| hs-TNT (pg/mL), median (IQR) | 0.013 (0.004, 0.067) | 0.019 (0.005, 0.107) | 0.011 (0.003, 0.038) | 0.001 |
| Lactate (mmol/L), mean (SD) | 5.97 ± 4.40 | 5.61 ± 4.30 | 6.29 ± 4.44 | 0.108 |
| Surgical type | 0.347 | |||
| Simple aortic surgeries | 858 (91.7) | 429 (90.9) | 429 (92.5) | |
| Combined CABG | 48 (5.1) | 26 (5.5) | 22 (4.7) | |
| Combined valve surgery | 27 (2.9) | 14 (3.0) | 13 (2.8) | |
| Combined CABG and valve surgery | 3 (0.6) | 3 (0.6) | 0 | |
| Operating time (min), mean (SD) | 318.79 ± 89.29 | 321.89 ± 90.44 | 315.67 ± 88.77 | 0.289 |
| CPB time (min), mean (SD) | 164.14 ± 66.93 | 165.83 ± 67.03 | 162.60 ± 67.29 | 0.463 |
| Aortic cross-clamping time (min), mean (SD) | 79.51 ± 50.24 | 80.70 ± 52.11 | 77.93 ± 47.64 | 0.401 |
GNRI, Geriatric Nutritional Risk Index; SD, standard deviation; IQR, interquartile range; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure; LVEF, left ventricular ejection fraction; WBC, white blood cell; RBC, red blood cell; hs-TNT, high-sensitivity troponin T; CPB, cardiopulmonary bypass
Clinical outcomes for the two groups are presented in Table 2. The overall in-hospital mortality rate was 20.3%, with patients in the low GNRI group showing significantly higher mortality compared with the high GNRI group (23.1% vs. 17.5%, P = 0.032). Notably, while the high GNRI group exhibited longer mechanical ventilation times, prolonged ICU stays, and higher complication rates, these differences did not reach statistical significance (P > 0.05).
Table 2.
In-hospital outcomes of the two groups
| Variable | Overall Population (n = 936) |
Low group (GNRI ≤ 103.7) (n = 472) |
High group (GNRI>103.7) (n = 464) |
P-value |
|---|---|---|---|---|
| In-hospital mortality, n (%) | 190 (20.3) | 109 (23.1) | 81 (17.5) | 0.032 |
| Gastrointestinal hemorrhage, n (%) | 77 (8.2) | 41 (8.7) | 36 (7.8) | 0.605 |
| Acute renal failure, n (%) | 218 (23.3) | 119 (25.2) | 99 (21.3) | 0.161 |
| MODS, n (%) | 26 (2.8) | 17 (3.6) | 9 (1.9) | 0.122 |
| Tracheotomy, n (%) | 95 (10.2) | 45 (9.6) | 50 (10.8) | 0.536 |
| Cerebral infarction, n (%) | 58 (6.2) | 26 (5.5) | 31 (6.7) | 0.453 |
| Pericardial effusion, n (%) | 118 (12.6) | 56 (11.9) | 62 (13.4) | 0.490 |
| Pleural effusion, n (%) | 189 (20.2) | 104 (22.0) | 85 (18.3) | 0.157 |
| Readmission to ICU, n (%) | 77 (8.3) | 37 (7.9) | 40 (8.6) | 0.677 |
| Duration of MV (h), median (IQR) | 49.4 (26.5-108.5) | 50.0 (23.5–109.0) | 48.8 (30.0-108.0) | 0.749 |
| Duration of ICU stays (d), median (IQR) | 5.7 (3.6–10.0) | 5.7 (3.5–10.2) | 5.7 (3.6–9.6) | 0.465 |
| Duration of hospital stays (d), median (IQR) | 20.0 (15.0–27.0) | 20.0 (15.0–26.0) | 20.0 (14.0–27.0) | 0.988 |
GNRI, Geriatric Nutritional Risk Index; MODS, multiple organ dysfunction syndrome; IQR, interquartile range; MV, mechanical ventilation; ICU, intensive care unit
Univariate analysis identified several factors significantly associated with in-hospital mortality, including age, GNRI ≤ 103.7, history of hypertension, time from onset to admission, WBC, platelet count, creatinine level, lactate level, hs-TNT, LVEF ≤ 50%, and CPB time (P < 0.05). Variables with P < 0.05 were subsequently included in a multivariate logistic regression model. The analysis revealed that GNRI ≤ 103.7 emerged as an independent predictor of in-hospital mortality (OR = 2.037, 95%CI:1.110–3.740, P = 0.022), along with CPB time (OR = 1.005, 95%CI: 1.001–1.009, P = 0.022), and lactate levels (OR = 1.221, 95%CI: 1.144–1.302, P < 0.001), as detailed in Table 3. The AUC of the logistic model was 0.811, indicating good predictive performance (Fig. 2). Restricted cubic spline analysis indicated a linear association between GNRI and in-hospital mortality (P = 0.007, Fig. 3).
Table 3.
Predictors of mortality in univariate and multivariate regression analysis
| Variable | Univariate analysis | Multivariate analysis | ||
|---|---|---|---|---|
| OR (95% CI) | P value | OR (95% CI) | P value | |
| Age (y) | 1.028(1.014, 1.043) | < 0.001 | 1.024 (0.998, 1.050) | 0.077 |
| Male | 1.105 (0.763, 1.602) | 0.597 | ||
| BMI (kg/m2) | 1.008 (0.959, 1.059) | 0.764 | ||
| SBP (mm/Hg) | 0.999 (0.994, 1.005) | 0.793 | ||
| DBP (mm/Hg) | 0.998 (0.989, 1.008) | 0.725 | ||
| PP (mm/Hg) | 0.998 (0.991, 1.005) | 0.631 | ||
| Heart rate | 1.004 (0.994, 1.013) | 0.452 | ||
| History of hypertension | 1.665 (1.120, 2.475) | 0.012 | 1.461 (0.747, 2.857) | 0.267 |
| History of diabetes | 0.868 (0.353, 2.134) | 0.758 | ||
| History of smoking | 0.784 (0.568, 1.082) | 0.138 | ||
| History of drinking | 1.261 (0.909, 1.749) | 0.165 | ||
| Marfan’s syndrome | 1.563 (0.711, 3.436) | 0.266 | ||
| Previous cardiac surgery | 0.926 (0.440, 1.951) | 0.841 | ||
| Time from onset to admission | ||||
| < 24 h | Reference | Reference | ||
| 1–6 d | 0.210 (0.136, 0.323) | < 0.001 | 0.592 (0.221, 1.584) | 0.296 |
| 7–14 d | 0.094 (0.029, 0.306) | < 0.001 | 0.187 (0.021, 1.706) | 0.137 |
| Symptom onset time | ||||
| 00:00–06:00 | Reference | |||
| 06:00–12:00 | 1.001 (0.636, 1.575) | 0.998 | ||
| 12:00–18:00 | 0.724 (0.460, 1.141) | 0.164 | ||
| 18:00–00:00 | 1.042 (0.638, 1.701) | 0.869 | ||
| Glucose (mmol/L) | 1.021 (0.962, 1.084) | 0.494 | ||
| WBC (× 109/L) | 1.056 (1.018, 1.096) | 0.004 | 1.002 (0.926,1.083) | 0.969 |
| Neutrophil (× 109/L) | 1.007 (0.989, 1.025) | 0.476 | ||
| Lymphocyte (× 109/L) | 0.968 (0.900, 1.042) | 0.388 | ||
| Monocyte (× 109/L) | 1.007 (0.913, 1.111) | 0.892 | ||
| Platelet (× 109/L) | 0.994 (0.991, 0.997) | < 0.001 | 0.996 (0.990, 1.002) | 0.176 |
| RBC (× 109/L) | 0.814 (0.628, 1.055) | 0.120 | ||
| Creatinine (umol/L) | 1.002 (1.001, 1.004) | 0.002 | 1.001 (0.997, 1.006) | 0.572 |
| Hemoglobin(g/L) | 0.993 (0.985, 1.001) | 0.092 | ||
| hs-TNT (pg/mL) | 1.106 (1.029, 1.188) | 0.006 | 1.163 (1.000, 1.354) | 0.051 |
| Lactate (mmol/L) | 1.227 (1.164, 1.294) | < 0.001 | 1.221(1.144, 1.302) | < 0.001 |
| LVEF ≤ 50% (%) | 0.495 (0.285, 0.857) | 0.012 | 2.317(0.845, 6.352) | 0.103 |
| Surgical type | ||||
| Simple aortic surgeries | Reference | |||
| Combined CABG | 0.469 (0.042, 5.204) | 0.538 | ||
| Combined valve surgery | 1.152 (0.098, 13.558) | 0.911 | ||
| Combined CABG and valve surgery | 0.875 (0.068, 11.313) | 0.919 | ||
| CPB time (min) | 1.007 (1.004, 1.009) | < 0.001 | 1.005 (1.001, 1.009) | 0.022 |
| GNRI (≤ 103.7 vs. > 103.7) |
1.420 (1.030, 1.958) 56, 2.084) |
0.033 | 2.037 (1.110, 3.740) | 0.022 |
OR, odds ratio; CI, confidence interval; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure; WBC, white blood cell; RBC, red blood cell; hs-TNT, high-sensitivity troponin T; LVEF, left ventricular ejection fraction; CPB, cardiopulmonary bypass
Fig. 2.
The ROC curve for logistic regression model
Fig. 3.
Restricted cubic spline analysis of the association between GNRI and mortality
Discussion
In this study, we investigated the association between the GNRI and in-hospital mortality in patients undergoing surgical repair for AAAD. The key findings were as follows: (1) The mean GNRI score was 103.8 ± 11.3, with 472 patients (50.4%) classified into the low GNRI group (GNRI ≤ 103.7). These patients were significantly older, had a higher proportion of females, and had lower BMI compared to the high GNRI group. (2) The overall in-hospital mortality rate was 20.3%, with significantly higher mortality observed in the low GNRI group versus the high GNRI group. (3) After adjusting for potential confounders, GNRI ≤ 103.7 was independently associated with increased in-hospital mortality risk. Additionally, CPB time and lactate levels also emerged as independent risk factors for in-hospital mortality.
Various nutritional screening tools, including the Mini-Nutritional Assessment (MNA-SF), Subjective Global Assessment (SGA), and Nutritional Risk Screening 2002 (NRS-2002), were developed to detect malnutrition in hospitalized patients. However, these tools frequently depend on subjective health evaluations, which may lead to inaccurate assessments in AAAD patients experiencing acute pain and requiring emergency surgery. The GNRI, proposed by Bouillance et al. in 2002 [10], modified the NRI by replacing actual body weight with the ideal body weight derived from height measurements. This adaptation reduces recall bias and provides a more objective nutritional risk assessment. While Bouillanne’s initial study categorized GNRI scores into four risk groups, subsequent studies have not established to establish a standardized cutoff value.
In this study, we stratified AAAD patients according to the median GNRI value (103.7) observed in our cohort, dividing them into high GNRI (> 103.7) and low GNRI (≤ 103.7) groups. This approach provides a more patient-specific and clinically meaningful approach for this distinct population. Compared to other nutritional assessment tools such as MNA and NRS-2002, GNRI demonstrated unique advantages for AAAD patients. Its dependence on objective parameters (serum albumin and ideal body weight) eliminates subjective interpretation, making it particularly suitable for emergency settings where rapid and accurate assessment is critical.
Malnutrition is prevalent among surgical patients and has been consistently associated with adverse outcomes, including prolonged mechanical ventilation, extended ICU stays, increased infection risk, and higher mortality rates [17–19]. The impact of nutritional status on cardiovascular disease management has gained increasing recognition in recent years. Masato Ogawa et al. [20] demonstrated that preoperative malnutrition (GNRI < 92) may impair postoperative recovery in elderly patients undergoing elective cardiac surgery. Similarly, Chen et al. [14, 21] established the preoperative GNRI as a strong predictor of postoperative delirium in cardiac surgery patients. A meta-analysis by Sina Kazemian et al. [22], which included 13 studies, further revealed that lower GNRI scores were associated with increased 1-year mortality and 2-year cardiovascular mortality in patients undergoing transcatheter aortic valve implantation (TAVI). Notably, GNRI has shown superior predictive value for short-term mortality compared to other nutritional indices in TAVI patients [23]. To our knowledge, this is the first study to evaluate the prognostic value of GNRI in AAAD patients. Consistent with findings in other cardiovascular populations, we found AAAD patients with GNRI ≤ 103.7 had a 1.874-fold higher risk of in-hospital mortality compared to those with higher GNRI scores.
The mechanisms underlying the association between GNRI ≤ 103.7 and postoperative in-hospital mortality in AAAD patients remain incompletely understood. However, evidence from other cardiovascular conditions suggests that malnutrition may contribute to poor outcomes through exacerbated systemic inflammation and diminished physiological reserve. Kaneko et al. [24] found that low GNRI predicted increased all-cause mortality in heart failure patients, likely reflecting the combined effects of hypoproteinemia and reduced BMI. We propose that GNRI’s prognostic value in AAAD patients stems primarily from its two key components: serum albumin levels and BMI. In our study, patients with low GNRI showed significantly lower serum albumin levels compared to those in the high GNRI group, a finding consistent with the role of hypoalbuminemia as a marker of systemic inflammation and malnutrition.
Hypoalbuminemia and low BMI synergistically contribute to adverse outcomes in AAAD through distinct yet interconnected pathological pathways [25]. Hypoalbuminemia directly exacerbated vascular inflammation by impairing NF-κB-mediated suppression of pro-inflammatory cytokines (e.g., IL-6) while amplifying endothelial oxidative stress, thereby accelerating aortic wall degradation [26, 27]. This hypoalbuminemia state reflected severe malnutrition [28], which has been consistently associated with adverse outcomes in cardiac surgery patients, including increased cardiovascular complications, prolonged hospitalization, and higher all-cause mortality [29–32]. Our findings demonstrate that patients with a low GNRI exhibited a significantly lower BMI than those with a high GNRI. This observation corresponds to reported “obesity paradox” in AAAD patients, where a lower BMI is linked to increased all-cause mortality [34]. Mechanistically, low BMI may induce immunometabolic dysfunction through myokine dysregulation (e.g., elevated myostatin), promoting pro-inflammatory macrophage polarization and impairing tissue repair capacity [33]. Furthermore, low BMI indicates reduced physiological reserves due to decreased muscle mass, potentially diminishing patients’ resilience to surgical stress and recovery [35]. We hypothesize that acute nutritional imbalance in AAAD (reflected by low GNRI) drives immunometabolic dysregulation through hypoalbuminemia and low BMI, thereby promoting inflammatory injury of the aortic wall and end-organ failure. Consistent with previous research, our study confirmed lactate levels and CPB time as important mortality predictors in AAAD patients [36, 37]. The integration of GNRI with these acute markers provides a more comprehensive risk assessment model for clinical decision-making.
In our cohort, the majority of in-hospital deaths were primarily attributed to cardiovascular causes, followed by complications such as multiple organ dysfunction syndrome (MODS). We speculate that MODS is a critical driver of postoperative mortality in AAAD patients. Malnutrition, reflected by hypoalbuminemia and reduced organ reserve capacity, may exacerbate systemic inflammation and increase susceptibility to refractory shock and multi-organ failure, ultimately contributing to mortality. Collectively, these findings highlight the importance of understanding the interplay between malnutrition, inflammation, and cardiovascular outcomes in AAAD. Further exploration of these relationships may uncover novel therapeutic targets to mitigate postoperative mortality and morbidity, potentially improving patient outcomes. Interestingly, although the differences did not reach statistical significance, patients in the high GNRI group tended to have longer mechanical ventilation times, longer ICU stays, and higher complication rates compared to those in the low GNRI group.This phenomenon may result from the fact that AAAD is a life-threatening condition requiring emergent surgical intervention, where standardized postoperative care protocols may mitigate the impact of nutritional status on outcomes such as ICU stay, hospital stay, and MV duration.
Our findings suggest that AAAD patients with GNRI ≤ 103.7 may benefit from preoperative nutritional optimization and prophylactic measures to enhance their physiological reserve, potentially improving postoperative outcomes. For high-risk patients (GNRI ≤ 103.7), we propose implementing preoperative nutritional optimization, including protein supplementation within 72 h before surgery when feasible, combined with ERAS-guided intraoperative strategies (e.g., reduced fasting periods) and postoperative stepwise nutritional rehabilitation tailored to recovery progress. To facilitate clinical implementation, we recommend integrating GNRI into electronic health records and establishing multidisciplinary nutrition support teams to enable comprehensive risk-to-intervention management.
This study has several limitations. First, although we adjusted for potential confounders in multivariable analyses, residual confounding from unmeasured variables (such as C-reactive protein and interleukin-6) may exist. Second, due to the retrospective design constraints, specific mortality causes could not be systematically classified; this gap should be addressed in future prospective studies. Third, while preoperative GNRI was assessed, changes in nutritional status during hospitalization and their long-term effects were not evaluated. Further studies should validate GNRI’s prognostic value and investigate whether targeted nutritional interventions can improve surgical outcomes in AAAD patients.
Conclusion
In conclusion, our study demonstrates that GNRI represents as a simple and objective tool for assessing nutritional risk in AAAD patients, with GNRI ≤ 103.7 independently predicting in-hospital mortality. These findings highlight the clinical importance of preoperative nutritional optimization for outcome improvement. Future studies should prospectively evaluate the impact of targeted nutritional interventions on long-term outcomes in AAAD patients.
Supplementary Information
Acknowledgements
None.
Abbreviations
- GNRI
geriatric nutritional risk index
- AAAD
acute Stanford type A aortic dissection
- IRAD
International Registry of Aortic Dissection
- NRI
nutritional risk index
- BMI
body mass index
- CTA
computed tomography angiography
- CPB
cardiopulmonary bypass
- SD
standard deviation
- IQR
interquartile ranges
- OR
odds ratio
- CI
confidence interval
- MNA-SF
Mini-Nutritional Assessment
- MODS
multiple organ dysfunction syndrome
- SGA
subjective global assessment
- NRS-2002
nutritional risk screening 2002
- TAVI
transcatheter aortic valve implantation
Author contributions
Conceptualization: LLY, and LYJ. Methodology: LLY, and LSL. Formal Analysis, and Data Curation: All authors. Writing– Original Draft Preparation: LLY. Writing– Review & Editing: All authors. Project Administration: LYJ, CLW. Funding Acquisition: LYJ, CLW. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the Joint Funds for the Innovation of Science and Technology, Fujian Province (No.2017Y9052); Guiding Project of Fujian Science and Technology (No.2021Y0023); and Fujian Key Laboratory of Cardio-Thoracic Surgery (Union Hospital, Fujian Medical University).
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Fujian Medical University Union Hospital (No. 2019KY019). Informed consent was obtained from all subjects involved in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Lingyu Lin and Sailan Li are Co-first authors.
Contributor Information
Liangwan Chen, Email: fjxhlwc@163.com.
Yanjuan Lin, Email: fjxhyjl@163.com.
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Supplementary Materials
Data Availability Statement
No datasets were generated or analysed during the current study.