Predictive role of prognostic nutritional index and geriatric nutritional risk index for postoperative mortality risk in patients with aortic dissection: a systematic review and meta-analysis

Introduction

Aortic dissection (AD) is a catastrophic cardiovascular emergency characterized by high early mortality and rapid clinical deterioration. The estimated annual incidence ranges from 3 to 6 cases per 100,000 persons, with type A AD accounting for the majority of acute presentations requiring emergent surgical intervention (1). Despite substantial advances in surgical techniques, cerebral protection strategies, and perioperative management, operative mortality for acute type A AD remains considerable, typically ranging from 15% to 30% in contemporary series and large registries such as the International Registry of Acute AD (IRAD) (2,3). Given the fulminant nature of the disease and the complexity of surgical repair, early identification of patients at high risk of postoperative mortality remains a critical clinical challenge.

In recent years, increasing efforts have been devoted to improving risk stratification in patients undergoing surgery for AD. Established predictors include advanced age, hemodynamic instability, organ malperfusion, renal dysfunction, prolonged cardiopulmonary bypass time, and elevated inflammatory markers (4-6). Several risk models and scoring systems incorporating clinical, laboratory, and operative variables have been developed to estimate short-term mortality; however, their predictive performance varies across populations and clinical settings (3,7). More recently, systemic inflammatory indices, coagulation parameters, and organ function markers have been explored as potential prognostic indicators, reflecting the complex interplay between inflammation, immune dysregulation, and metabolic stress in AD (8). Nevertheless, easily obtainable, reproducible, and cost-effective biomarkers that can be integrated into routine perioperative evaluation are still needed.

Prognostic nutritional index (PNI) and geriatric nutritional risk index (GNRI) are composite indicators reflecting nutritional and immunological status. PNI is calculated based on serum albumin concentration and peripheral lymphocyte count, whereas GNRI incorporates serum albumin and the ratio of actual to ideal body weight (9,10). Originally developed to assess surgical risk and nutritional status, both indices have demonstrated significant prognostic value in various cardiovascular and oncological surgical settings, including cardiac surgery, coronary artery bypass grafting, and major oncologic resections, where lower PNI or GNRI has been consistently associated with increased postoperative morbidity and mortality (11-14). These findings suggest that preoperative nutritional and immune status may substantially influence surgical outcomes. However, the clinical utility of PNI and GNRI in predicting postoperative mortality among patients with AD has not been comprehensively evaluated, and available evidence remains fragmented. Therefore, we conducted this meta-analysis to systematically assess the association and predictive performance of PNI and GNRI for postoperative mortality in patients undergoing surgery for AD. We present this article in accordance with the PRISMA reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1334/rc) (15).

Methods

Literature searching

PubMed, Web of Science and Chinese National Knowledge Infrastructure (CNKI) databases were searched up to February 15, 2026 for relevant studies with following terms: prognostic nutritional index, PNI, geriatric nutritional risk index, GNRI, dissection of aorta, aortic dissection, death and mortality. Searching strategies of above three databases were shown in the Appendix 1.

Study selection

Studies meeting these criteria were included: (I) AD patients diagnosed by the computed tomography (CT) angiography; (II) patients received the surgical treatment due to AD; (III) PNI or (and) GNRI were evaluated according to the formulas: PNI =10 × serum albumin (g/dL) + 0.005 × total lymphocyte count (per mm³) (16); GNRI =14.89 × serum albumin (g/L) + 41.7 × (actual body weight/ideal body weight) (17); (IV) studies investigating the association of PNI or GNRI with mortality risk among AD patients or diagnostic performance of PNI or GNRI for mortality risk in AD patients; (V) relevant data for the mortality risk were provided and available; (VI) full texts were available; (VII) studies were published in English or Chinese.

Studies meeting these criteria were excluded: (I) letters, editorials, case reports, or conference abstract; (II) insufficient, overlapped or duplicated data.

Data collection

The following data were collected: the first author, publication year, country, sample size, number of death observational indicator (PNI or GNRI), type of AD (type A or B), cutoff value of PNI and GNRI, endpoint (in-hospital, 30-day mortality, mortality, one-year mortality), odds ratio (OR) and 95% confidence interval (CI). For diagnostic studies, number of true positive (TP) patients, false positive (FP) patients, false negative (FN) patients and true negative (TN) patients was collected, as well as the area under the curve (AUC), sensitivity and specificity.

In this meta-analysis, the postoperative mortality risk was the primary outcome and we aimed to assess the association of PNI and GNRI with postoperative mortality risk among surgical AD patients.

Quality assessment

All included studies were retrospective cohort studies, therefore Newcastle-Ottawa Scale (NOS) was applied for the quality assessment and studies with NOS scores ≥6 were defined as high-quality studies (18).

Two investigators performed the literature search, selection, data collection, quality assessment and data analyses independently. Any disagreement was resolved by discussion.

Statistical analysis

About the association of PNI and GNRI with mortality risk in operated AD, all analyses were performed through STATA 17.0 software. Heterogeneity among studies was calculated by Q test and I² statistic. If significant heterogeneity was observed, represented as P<0.10 and/or I²>50%, the random effects model was applied; otherwise, fixed effects model was applied (19). OR and 95% CI were combined to assess prognostic role of PNI and GNRI for mortality risk.

Subsequently, we evaluated the ability of PNI to discriminate postoperative mortality risk in patients with AD. The evidence from eligible studies was first descriptively summarized and then quantitatively synthesized. For each study, the corresponding 2×2 contingency tables were either directly extracted or reconstructed from the reported diagnostic indices to ensure consistency of effect estimates. A diagnostic meta-analysis was conducted to calculate pooled sensitivity and specificity, and to construct the summary receiver operating characteristic (SROC) curve. Forest plots were generated to visualize study-level diagnostic performance. Considering potential between-study variability, a random-effects model was adopted. The diagnostic OR (DOR) was derived from the reconstructed contingency data to reflect the overall discriminatory capacity of PNI. All diagnostic analyses were performed using Meta-DiSc 2.0, an online tool specifically designed for diagnostic test meta-analysis (20,21).

Leave-one-out sensitivity analysis was performed to detect sources of heterogeneity and assess the stability of the pooled results (22,23).

Results

Literature selection

Thirty records were searched from three databases and seven available studies were included after reviewing titles, abstracts and full texts (24-30) (Figure 1).

Figure 1 PRISMA flow diagram for this meta-analysis. CNKI, Chinese National Knowledge Infrastructure.

Basic characteristics

All seven studies were retrospective and 2,967 patients were enrolled with the mortality rate of 15.77% (468/2,967). Most studies were from China (6/7), focused on type A AD (6/7) and PNI (5/7). All studies were with high-quality. Specific information was presented in Table 1.

Association of PNI with postoperative mortality risk in type A AD patients

Five studies investigated the relationship between PNI and mortality risk among type A AD patients (24-28). Pooled results manifested the association between lower PNI and higher risk of mortality in type A AD patients (OR =0.77, 95% CI: 0.69–0.87, P<0.001; I²=52.7%, P=0.08) (Figure 2). Then subgroup analysis stratified by the country was performed, which indicated consistent findings (non-China: OR =0.80, 95% CI: 0.68–0.93, P=0.005; China: OR =0.75, 95% CI: 0.63–0.88, P=0.001) (Figure S1, Table 2).

Figure 2 Association of PNI for postoperative mortality risk in AD patients. AD, aortic dissection; CI, confidence interval; OR, odds ratio; PNI, prognostic nutritional index.

Association of GNRI with postoperative mortality risk in AD patients

Only two studies explored the association of GNRI with postoperative mortality risk in AD patients (29,30). Pooled results indicated that a lower GNRI was related to significantly increased risk of mortality in AD patients (OR =0.42, 95% CI: 0.24–0.74, P=0.002; I²=17.3%, P=0.27) (Figure 3). Then subgroup analysis based on the type of AD revealed similar results (type A: OR =0.49, 95% CI: 0.27–0.90, P=0.02; type B: OR =0.22, 95% CI: 0.06–0.80, P=0.02) (Figure S2, Table 2).

Figure 3 Association of GNRI for postoperative mortality risk in AD patients. AD, aortic dissection; CI, confidence interval; GNRI, geriatric nutritional risk index; OR, odds ratio.

Diagnostic accuracy of PNI for postoperative mortality risk in type A AD patients

Four studies explored the diagnostic role of PNI for postoperative mortality risk in type A AD patients (24,26-28). After combining these four diagnostic studies, pooled sensitivity and specificity was 0.808 (95% CI: 0.704–0.882) and 0.767 (95% CI: 0.647–0.856), with the positive likelihood ratio (LR+), negative likelihood ratio (LR−) and false positive rate (FPR) of 3.475 (95% CI: 2.251–5.364), 0.25 (95% CI: 0.16–0.391) and 0.233 (95% CI: 0.144–0.353). Detailed data for the sensitivity (Figure 4A) and specificity (Figure 4B) and SROC curve (Figure 4C) were shown in Figure 4. The DOR was 13.908 (95% CI: 6.892–28.07). Besides, information of heterogeneity analysis was shown in Table 3. In overall, it was indicated that PNI had a high diagnostic accuracy for postoperative mortality risk in type A AD patients.

Figure 4 Forest plots of sensitivity (A) and specificity (B) and SROC curve (C) for the predictive role of PNI for postoperative mortality risk in AD patients. AD, aortic dissection; CI, confidence interval; FN, false negative; FP, false positive; PNI, prognostic nutritional index; ROC, receiver operating characteristic; SROC, summary receiver operating characteristic; TN, true negative; TP, true positive.

Sensitivity analysis for the association of PNI with postoperative mortality risk in type A AD patients

In this meta-analysis, leave-one-out sensitivity analysis about the association of PNI with postoperative mortality risk in type A AD patients was performed, which indicated that the pooled results were stable and reliable (Figure 5).

Figure 5 Sensitivity analysis about the association of GNRI for postoperative mortality risk in AD patients. AD, aortic dissection; CI, confidence interval; GNRI, geriatric nutritional risk index.

Discussion

In this meta-analysis, we systematically evaluated the predictive ability of PNI and GNRI for postoperative mortality in patients with AD. The pooled results demonstrated that lower preoperative PNI was significantly associated with increased mortality risk in patients with type A AD. Furthermore, PNI exhibited favorable discriminatory ability, with relatively high pooled sensitivity and specificity, as well as a considerable DOR indicating its potential utility as a simple prognostic indicator. In addition, although based on limited evidence, lower GNRI was also found to be significantly associated with higher postoperative mortality risk in AD patients. These findings suggest that preoperative nutritional and immunological status, as reflected by PNI and GNRI, may play an important role in risk stratification of patients undergoing surgery for AD. Given that both indices are derived from routinely available clinical parameters (serum albumin, lymphocyte count, and body weight), they are inexpensive, easily accessible, and reproducible in daily clinical practice. Therefore, PNI and GNRI may serve as practical tools for early identification of high-risk patients, facilitating individualized perioperative management and potentially improving clinical outcomes.

The underlying mechanisms by which PNI and GNRI predict postoperative mortality in patients with AD are likely multifactorial, reflecting the complex interplay between nutritional status, systemic inflammation, immune competence, and metabolic reserve. First, malnutrition is closely linked with impaired immune function and increased susceptibility to infection and organ dysfunction. Serum albumin, a fundamental component of both PNI and GNRI, is not only a marker of nutritional status but also a negative acute-phase reactant that decreases in systemic inflammation and stress states (31,32). Hypoalbuminemia has been independently associated with increased postoperative complications and mortality in various surgical populations, including cardiac surgery, due to its role in maintaining oncotic pressure, endothelial stability, and antioxidant capacity (33,34). In the context of AD, surgical repair induces profound inflammatory and catabolic stress, and low preoperative albumin may reflect an inadequate reserve to withstand such physiological insults (35,36). Second, lymphopenia, another element of PNI, represents impaired cellular immunity. Lymphocytes play a critical role in modulating inflammatory responses and infection control (37,38). Reduced lymphocyte count has been associated with adverse outcomes in cardiovascular diseases and postoperative settings, as it may indicate chronic stress, immunosenescence, and a diminished ability to counteract perioperative infections or systemic inflammation (39,40). In AD patients, greater systemic inflammation and immune dysregulation have been linked to poorer outcomes, and lymphocyte-driven indices such as PNI capture this dimension better than isolated markers (41,42). Third, the body weight component in GNRI (actual/ideal body weight) reflects both muscle mass and energy reserves, which are essential for recovery after major surgery. Sarcopenia and weight loss are increasingly recognized as independent predictors of adverse outcomes in surgical and cardiovascular cohorts, as they are associated with reduced physiological reserve and impaired wound healing (43-45). GNRI integrates this anthropometric information with albumin levels, providing a more comprehensive assessment of nutritional risk, especially in elderly or frail patients. Finally, chronic inflammation and malnutrition are synergistic in promoting catabolism, endothelial dysfunction, and impaired organ perfusion, all of which can contribute to postoperative complications such as acute kidney injury, respiratory failure, and multiorgan dysfunction (46,47). Given the acute systemic inflammatory response triggered by aortic cross-clamping, cardiopulmonary bypass, and ischemia-reperfusion injury in AD repair, patients with lower PNI and GNRI are biologically predisposed to poorer resilience and recovery (48,49). Therefore, both indices may serve as integrated surrogates of physiological vulnerability that extend beyond traditional clinical risk factors. It should also be noted that albumin is a negative acute-phase reactant, and acute inflammatory responses associated with AD may contribute to reduced albumin levels. Therefore, nutritional indices incorporating albumin may partly reflect inflammatory status rather than pure nutritional reserve.

Several limitations of this meta-analysis should be acknowledged. First, the number of included studies was relatively small, and most of them were conducted in China. This may limit the generalizability of our findings to other ethnic populations and healthcare settings. In addition, the limited sample size may reduce the statistical power and increase the risk of potential selection bias. Second, only two studies evaluated the association between GNRI and postoperative mortality in patients with AD. Therefore, the pooled estimate for GNRI should be interpreted with caution. Moreover, due to the limited number of eligible studies, we were unable to further assess the diagnostic performance of GNRI, such as pooled sensitivity, specificity, or SROC analysis. Third, owing to the small number of available studies and incomplete reporting of relevant variables, more detailed subgroup analyses (e.g., stratified by cutoff values, follow-up duration, diabetes or age) could not be performed. This may have hindered a more comprehensive exploration of potential sources of heterogeneity. Future large-scale, multicenter prospective studies from diverse populations are warranted to validate the predictive value of PNI and GNRI in patients with AD. Fourth, given the limited number of eligible studies, several methodological limitations should be acknowledged. First, heterogeneity existed in the modeling of nutritional indices: PNI was analyzed as either a continuous or dichotomized variable, and GNRI was categorized using different approaches (e.g., quantiles or median-based thresholds). In addition, different effect measures were reported, including ORs and HRs. Importantly, there was also heterogeneity in outcome assessment timing, as some studies reported in-hospital or 30-day mortality, whereas others evaluated longer-term outcomes such as 1-year or follow-up mortality. These endpoints may reflect different pathophysiological processes and risk determinants. Therefore, variations in variable modeling, effect measures, and outcome definitions may have introduced clinical and statistical heterogeneity. The pooled estimates should thus be interpreted with caution. Future studies using standardized definitions and consistent follow-up time points are warranted to provide more robust and comparable evidence. In addition, although major databases were searched, other sources such as Embase and Cochrane Library were not included, which may have led to the omission of potentially relevant studies.

Given the limited number of available studies and predominance of single-center retrospective data in the current meta-analysis, future research is warranted to strengthen the evidence regarding PNI and GNRI in predicting postoperative mortality in AD. First, large-scale, prospective, multicenter cohorts with diverse ethnic and geographic representation are needed to confirm the prognostic value and generalizability of these nutritional indices. Second, standardized cutoff values for PNI and GNRI should be established through receiver-operating characteristic analysis in larger populations to facilitate clinical implementation and reduce heterogeneity across studies. Third, integration of PNI and GNRI with established risk models (e.g., IRAD risk score) and novel biomarkers such as inflammatory cytokines or frailty indices may improve risk stratification beyond single markers (50,51). Additionally, mechanistic studies exploring the interaction between nutritional status, immune competence, and postoperative organ dysfunction could elucidate biological pathways and identify targets for perioperative optimization. Finally, randomized interventional trials evaluating whether preoperative nutritional or immunomodulatory support can improve outcomes in high-risk AD patients would provide critical evidence for clinical application.

Conclusions

In overall, based on available evidence, it was indicated that PNI played a well predictive role for postoperative mortality risk in type A AD patients and GNRI was associated with mortality risk of AD patients.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1334/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1334/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1334/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Evangelista A, Isselbacher EM, Bossone E, et al. Insights From the International Registry of Acute Aortic Dissection: A 20-Year Experience of Collaborative Clinical Research. Circulation 2018;137:1846-60. [Crossref] [PubMed]
2. Yang G, Zhou Y, He H, et al. A nomogram for predicting in-hospital mortality in acute type A aortic dissection patients. J Thorac Dis 2020;12:264-75. [Crossref] [PubMed]
3. Harris KM, Nienaber CA, Peterson MD, et al. Early Mortality in Type A Acute Aortic Dissection: Insights From the International Registry of Acute Aortic Dissection. JAMA Cardiol 2022;7:1009-15. [Crossref] [PubMed]
4. Rampoldi V, Trimarchi S, Eagle KA, et al. Simple risk models to predict surgical mortality in acute type A aortic dissection: the International Registry of Acute Aortic Dissection score. Ann Thorac Surg 2007;83:55-61. [Crossref] [PubMed]
5. Kuang J, Yang J, Wang Q, et al. A preoperative mortality risk assessment model for Stanford type A acute aortic dissection. BMC Cardiovasc Disord 2020;20:508. [Crossref] [PubMed]
6. Xing L, Zhou Y, Han Y, et al. Simple Death Risk Models to Predict In-hospital Outcomes in Acute Aortic Dissection in Emergency Department. Front Med (Lausanne) 2022;9:890567. [Crossref] [PubMed]
7. Frankel WC, Green SY, Orozco-Sevilla V, et al. Contemporary Surgical Strategies for Acute Type A Aortic Dissection. Semin Thorac Cardiovasc Surg 2020;32:617-29. [Crossref] [PubMed]
8. Xie LF, Xie QG, Gao WP, et al. The prognostic value of preoperative systemic inflammatory response index in predicting outcomes of acute type A aortic dissection patients underwent surgical treatment. Front Immunol 2024;15:1388109. [Crossref] [PubMed]
9. Ishiguro T, Aoyama T, Ju M, et al. Prognostic Nutritional Index as a Predictor of Prognosis in Postoperative Patients With Gastric Cancer. In Vivo 2023;37:1290-6. [Crossref] [PubMed]
10. Xie S, Wu Q. Geriatric nutritional risk index predicts postoperative delirium in elderly: A meta-analysis. Saudi Med J 2024;45:869-75. [Crossref] [PubMed]
11. Kahraman S, Zencirkıran Aguş H, Kalkan AK, et al. Prognostic nutritional index predicts mortality in infective endocarditis. Turk Kardiyol Dern Ars 2020;48:392-402. [Crossref] [PubMed]
12. Gu X, Li J, Huang X, et al. Prognostic nutritional index as a predictor of in-hospital mortality in neonates and infants undergoing cardiac surgery. Front Nutr 2025;12:1686989. [Crossref] [PubMed]
13. Dong CH, Chen SY, Zeng HL, et al. Geriatric nutritional risk index predicts all-cause mortality in patients with heart failure: A systematic review and meta-analysis. Clinics (Sao Paulo) 2021;76:e2258. [Crossref] [PubMed]
14. Massussi M, Adamo M, Rosato S, et al. Functional and metabolic frailty predicts mortality in patients undergoing TAVI: Insights from the OBSERVANT II study. Eur J Intern Med 2022;106:90-6. [Crossref] [PubMed]
15. Zhang X, Tan R, Lam WC, et al. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Extension for Chinese Herbal Medicines 2020 (PRISMA-CHM 2020). Am J Chin Med 2020;48:1279-313. [Crossref] [PubMed]
16. Li P, Wang X, Lai Y, et al. The prognostic value of pre-treatment prognostic nutritional index in esophageal squamous cell carcinoma: A meta-analysis. Medicine (Baltimore) 2019;98:e15280. [Crossref] [PubMed]
17. Tian L, Bu Y, Wang Y, et al. Predictive role of geriatric nutritional risk index for postoperative complications in operated esophageal cancer patients: a meta-analysis. J Cardiothorac Surg 2025;20:440. [Crossref] [PubMed]
18. Wang Y, Li J, Chang S, et al. Risk and Influencing Factors for Subsequent Primary Lung Cancer After Treatment of Breast Cancer: A Systematic Review and Two Meta-Analyses Based on Four Million Cases. J Thorac Oncol 2021;16:1893-908. [Crossref] [PubMed]
19. Li J, Wang Y, Li J, et al. Meta-analysis of Lobectomy and Sublobar Resection for Stage I Non-small Cell Lung Cancer With Spread Through Air Spaces. Clin Lung Cancer 2022;23:208-13. [Crossref] [PubMed]
20. Plana MN, Arevalo-Rodriguez I, Fernández-García S, et al. Meta-DiSc 2.0: a web application for meta-analysis of diagnostic test accuracy data. BMC Med Res Methodol 2022;22:306. [Crossref] [PubMed]
21. Reitsma JB, Glas AS, Rutjes AW, et al. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 2005;58:982-90. [Crossref] [PubMed]
22. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics 1994;50:1088-101.
23. Egger M, Davey Smith G, Schneider M, et al. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997;315:629-34. [Crossref] [PubMed]
24. Keskin HA, Kurtul A, Esenboğa K, et al. Prognostic nutritional index predicts in-hospital mortality in patients with acute Stanford type A aortic dissection. Perfusion 2021;36:710-6. [Crossref] [PubMed]
25. Lin Y, Chen Q, Peng Y, et al. Prognostic nutritional index predicts in-hospital mortality in patients with acute type A aortic dissection. Heart Lung 2021;50:159-64. [Crossref] [PubMed]
26. Chang W, Wang Y, He J, et al. Correlation between prognostic nutrition index and postoperative in-hospital death in patients with acute aortic dissection. China Medicine 2024;19:25-9.
27. Hu JW, Shi T. Postoperative Prognostic Nutritional Index and Fibrinogen Could Well Predict Poor Prognosis of Acute Type A Aortic Dissection Patients After Surgery. Braz J Cardiovasc Surg 2024;39:e20220185. [Crossref] [PubMed]
28. Ru W. The Value of the Systemic Immunoinflammatory Index Combined with the Prognostic Nutritional Index in Assessing the Postoperative Prognosis of Patients with Acute Stanford Type A Aortic Dissection. Translational Medicine Journal 2024;13:340-3+349.
29. Lin L, Li S, Peng Y, et al. Association between geriatric nutritional risk index and In-Hospital mortality in acute aortic dissection patients: a retrospective cohort study. BMC Cardiovasc Disord 2025;25:572. [Crossref] [PubMed]
30. Zhao K, Niu J, He Y, et al. The role of geriatric nutritional risk index in predicting survival of type B aortic dissection patients after thoracic endovascular aortic repair. J Nutr Health Aging 2025;29:100572. [Crossref] [PubMed]
31. Eckart A, Struja T, Kutz A, et al. Relationship of Nutritional Status, Inflammation, and Serum Albumin Levels During Acute Illness: A Prospective Study. Am J Med 2020;133:713-722.e7. [Crossref] [PubMed]
32. Li H, Zhou Y, Zhang X, et al. The relationship between hemoglobin, albumin, lymphocyte, and platelet (HALP) score and 28-day mortality in patients with sepsis: a retrospective analysis of the MIMIC-IV database. BMC Infect Dis 2025;25:333. [Crossref] [PubMed]
33. Wohlauer M, Brier C, Kuramochi Y, et al. Preoperative Hypoalbuminemia is a Risk Factor for Early and Late Mortality in Patients Undergoing Endovascular Juxtarenal and Thoracoabdominal Aortic Aneurysm Repair. Ann Vasc Surg 2017;42:198-204. [Crossref] [PubMed]
34. Xu R, Hao M, Zhou W, et al. Preoperative hypoalbuminemia in patients undergoing cardiac surgery: a meta-analysis. Surg Today 2023;53:861-72. [Crossref] [PubMed]
35. Chen Y, Ling C, Song Y, et al. Neutrophil-to-albumin ratio predicts 30-day mortality in acute aortic dissection: a retrospective cohort study. Front Cardiovasc Med 2026;13:1791503. [Crossref] [PubMed]
36. Şahin C, Şahin Y. Prognostic Value of Hemoglobin, Albumin, Lymphocyte, and Platelet Score in Predicting Mortality in Patients With Aortic Dissection: A Retrospective Single-Center Study Based on ROC Curve Analysis. Emerg Med Int 2026;2026:6996031. [Crossref] [PubMed]
37. De B, Ludmir EB, Messick CA, et al. Prognostic impact of lymphopenia and neutrophil-lymphocyte ratio for patients with anal squamous cell carcinoma. J Gastrointest Oncol 2021;12:2412-22. [Crossref] [PubMed]
38. Kim J, Choi H, Jeun SS, et al. From lymphopenia to restoration: IL-7 immunotherapy for lymphocyte recovery in glioblastoma. Cancer Lett 2024;588:216714. [Crossref] [PubMed]
39. Perry LA, Liu Z, Loth J, et al. Perioperative Neutrophil-Lymphocyte Ratio Predicts Mortality After Cardiac Surgery: Systematic Review and Meta-Analysis. J Cardiothorac Vasc Anesth 2022;36:1296-303. [Crossref] [PubMed]
40. Wheatley J, Liu Z, Loth J, et al. The prognostic value of elevated neutrophil-lymphocyte ratio for cardiac surgery-associated acute kidney injury: A systematic review and meta-analysis. Acta Anaesthesiol Scand 2023;67:131-41. [Crossref] [PubMed]
41. Hsieh WC, Henry BM, Hsieh CC, et al. Prognostic Role of Admission C-Reactive Protein Level as a Predictor of In-Hospital Mortality in Type-A Acute Aortic Dissection: A Meta-Analysis. Vasc Endovascular Surg 2019;53:547-57. [Crossref] [PubMed]
42. Toprak B, Bilgiç A. The role of the modified Glasgow prognostic score in predicting postoperative mortality following open-heart surgery. J Cardiothorac Surg 2025;20:310. [Crossref] [PubMed]
43. Bhasin S, Travison TG, Manini TM, et al. Sarcopenia Definition: The Position Statements of the Sarcopenia Definition and Outcomes Consortium. J Am Geriatr Soc 2020;68:1410-8. [Crossref] [PubMed]
44. Damluji AA, Alfaraidhy M, AlHajri N, et al. Sarcopenia and Cardiovascular Diseases. Circulation 2023;147:1534-53. [Crossref] [PubMed]
45. Yun WG, Han Y, Lee I, et al. Association of computed tomography-derived body composition with surgical and oncologic outcomes in periampullary adenocarcinoma. Surg Endosc 2026;40:3330-40. [Crossref] [PubMed]
46. Sugita J, Fujiu K. Systemic Inflammatory Stress Response During Cardiac Surgery. Int Heart J 2018;59:457-9. [Crossref] [PubMed]
47. Habes QLM, Kant N, Beunders R, et al. Relationships Between Systemic Inflammation, Intestinal Damage and Postoperative Organ Dysfunction in Adults Undergoing Low-Risk Cardiac Surgery. Heart Lung Circ 2023;32:395-404. [Crossref] [PubMed]
48. Cai X, Li X, Shi J, et al. S100A8/A9 high-expression macrophages mediate renal tubular epithelial cell damage in acute kidney injury following acute type A aortic dissection surgery. Front Mol Biosci 2025;12:1530741. [Crossref] [PubMed]
49. Jin M, Cheng Y, Yang Y, et al. Protection of xenon against postoperative oxygen impairment in adults undergoing Stanford Type-A acute aortic dissection surgery: Study protocol for a prospective, randomized controlled clinical trial. Medicine (Baltimore) 2017;96:e7857. [Crossref] [PubMed]
50. McMillan DC. The systemic inflammation-based Glasgow Prognostic Score: a decade of experience in patients with cancer. Cancer Treat Rev 2013;39:534-40. [Crossref] [PubMed]
51. Tolenaar JL, Froehlich W, Jonker FH, et al. Predicting in-hospital mortality in acute type B aortic dissection: evidence from International Registry of Acute Aortic Dissection. Circulation 2014;130:S45-50. [Crossref] [PubMed]