Preoperative Neutrophil-Platelet Ratio as a Predictor of Acute Kidney Injury after Total Aortic Arch Replacement: A Retrospective Cohort Study

Preoperative Neutrophil-Platelet Ratio as a Predictor of Acute Kidney Injury after Total Aortic Arch Replacement: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Preoperative Neutrophil-Platelet Ratio as a Predictor of Acute Kidney Injury after Total Aortic Arch Replacement: A Retrospective Cohort Study Jing Tan, He Zhang, Qiang Chen, Xue Wang, Jing Li, Liang Zhong, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7221153/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Acute kidney injury (AKI) is a common complication following total aortic arch replacement (TAAR) in patients with acute type A aortic dissection (ATAAD), with inflammatory dysregulation contributing to its pathogenesis. This study aims to develop a predictive model that integrates neutrophil-to-lymphocyte ratio (NPR) with clinical risk factors to facilitate the early identification of high-risk patients, thereby enabling targeted interventions to mitigate AKI and enhance clinical outcomes. Methods: This research was a single-center, retrospective cohort study comprising 396 patients undergoing TAAR between July 2021 and March 2023 in China. Participants were categorized into AKI and non-AKI groups based on KDIGO criteria. ROC analysis was employed to assess the predictive capability of inflammatory markers for AKI. Significant predictors identified were incorporated into a multivariate logistic regression model, which was adjusted for potential confounding variables, to ascertain independent risk factors for AKI. Furthermore, a causal mediation analysis was performed. Results: ROC analysis identified NPR as the most robust predictor (AUC 0.660, p<0.001), surpassing both NLR and NLPR. Multivariable regression analysis confirmed that NPR (OR 1.20, 95%CI 1.10–1.30) and renal artery involvement (OR 1.68, 95% CI 1.36–2.09) were independent risk factors for AKI. Mediation models incorporate the interaction term, reveals that NPR serves as a negative moderator in the relationship between the involved renal artery (β=-0.044). Conclusions: Preoperative NPR demonstrated desirable predictive accuracy for AKI incidence following TAAR. The study further indicated that both renal artery involvement and NPR independently and significant increased AKI risk. while they negatively interact, renal artery involvement diminished the positive association between NPR and AKI, while an increase in NPR also reduced the exacerbating effect of renal artery involvement. These findings suggest potential targets for improving perioperative renal protection, though further validation and studies are needed. Clinical trial number： No. NCT05206032（Registered on December 1, 2021） Acute kidney injury Aortic dissection Neutrophil-to-platelet ratio Inflammation continuous renal replacement therapy Figures Figure 1 1. Introduction Acute Type A Aortic Dissection (ATAAD) represents a critical cardiovascular emergency necessitating prompt surgical intervention, with Total Arch Replacement (TAAR) frequently employed in cases of extensive aortic arch involvement [ 1 ]. Despite significant advancements in surgical methodologies, postoperative Acute Kidney Injury (AKI) remains a common and severe complication, affecting 20–40% of ATAAD patients undergoing TAAR, and is closely linked to heightened morbidity and mortality rates [2, 3] . The pathophysiological mechanisms underlying AKI are multifaceted, encompassing ischemia-reperfusion injury, systemic inflammatory responses and hemodynamic instability [ 4 , 5 ]. In recent years, inflammatory biomarkers, particularly ratios derived from neutrophils and platelets, have emerged as promising indicators of adverse outcomes in cardiovascular diseases, including ATAAD [ 6 ]. The neutrophil-to-platelet ratio (NPR) reflects the interaction between systemic inflammation and thrombotic pathways, both of which are implicated in the pathogenesis of AKI [ 7 , 8 ]. Preoperative NPR has demonstrated prognostic value in ATAAD, correlating with in-hospital mortality and organ dysfunction [ 9 ]. However, its specific role in predicting AKI following TAAR remains insufficiently explored. This study aims to evaluate preoperative NPR as a novel predictor of AKI in ATAAD patients undergoing TAAR. By employing machine learning algorithms and multivariate regression analyses, we seek to develop a robust predictive model that integrates NPR with established clinical risk factors [ 6 ]. Our findings could facilitate the early identification of high-risk patients, enabling targeted perioperative strategies to mitigate AKI and improve outcomes in this critically ill population. 2. Methods 2.1 Study Design and Setting This retrospective cohort study was conducted at the First Affiliated Hospital of Xi’an Jiaotong University, subsequent to obtaining approval from the institution’s Research Ethics Committee (Approval No: XJTU1AF2020LSK-266). The study population comprised patients diagnosed with ATAAD who underwent TAAR at the hospital between July 2021 and March 2023. Exclusion criteria were established as follows: (a) preoperative serum creatinine (sCr) levels exceeding 353.6 µmol/L or a confirmed diagnosis of chronic kidney disease (CKD); (b) patients who had undergone chronic dialysis or emergency dialysis within the month preceding surgery; and (c) those with missing preoperative or postoperative sCr results or incomplete clinical data. After a thorough review of electronic medical records and laboratory test results, a total of 396 ATAAD patients who met the inclusion criteria were enrolled in the study. These patients were subsequently classified according to the Stanford classification system for aortic dissection. Each participant was informed of the purpose of the study and signed a written informed consent form and registered with the ClinicalTrials.gov registry (No. NCT05206032). 2.2 Data Collection and Definitions Electronic methods were employed to systematically collect extensive demographic, clinical and laboratory data from patients. The demographic data included variables such as age, gender, hypertension, diabetes mellitus, coronary atherosclerotic heart disease (CAD), history of previous cardiovascular surgeries, duration of symptoms prior to hospital admission, and the specific renal artery involved. Procedure-related data included the duration of the operation, as well as the durations of cardiopulmonary bypass (CPB) and heart block time. Furthermore, we collected preoperative biochemical test results, including C-reactive protein (CRP), procalcitonin (PCT), and counts of neutrophils, lymphocytes, and platelets. We also recorded the duration of intensive care unit (ICU) stay and the total length of hospital stay. From these recorded values, we calculated the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte-to-platelet ratio (NLPR) and NPR [ 10 ]. Patients were categorized into AKI and non-AKI groups based on the Kidney Disease Improving Global Outcomes (KDIGO) criteria [ 11 ]. Subsequently, patients were categorized into two groups: AKI (n = 222) and non-AKI (n = 174). According to KDIGO criteria, patients with a ≥ 50% increase in sCr within 7 days or a ≥ 0.3-mg/dL (≥ 26.5-µmol/L) rise in sCr within 48 hours or oliguria were defined as patients with AKI. 2.3 Statistical Analysis The study population was divided into AKI and non-AKI groups for comparison. Missing data were addressed with multiple imputation. Continuous variables were shown as mean ± standard deviation or median (interquartile range), depending on distribution, while categorical variables were summarized with frequencies and percentages. Data normality was checked using Shapiro-Wilk tests and Q-Q plots. Normally distributed continuous variables were compared using Welch’s t-test or ANOVA, and non-normally distributed ones with the Wilcoxon rank-sum or Kruskal-Wallis tests. Categorical variables were analyzed with Fisher’s exact test or Pearson’s chi-square test. Receiver operator characteristic curve (ROC) analysis assessed the predictive ability of lab parameters for AKI. Univariate logistic regression identified potential AKI-related factors such as operation duration, cardiopulmonary bypass time, NPR, PCT, involved renal artery, NLR, and NLPR. A multivariate logistic regression analysis was subsequently performed, incorporating all significant predictors identified in the univariate analysis, while adjusting for potential confounders, to ascertain independent risk factors for AKI. Furthermore, a causal mediation analysis was conducted using structural equation modeling (SEM) to estimate the direct effect of NPR on AKI, as well as its indirect effect mediated through the "Involved renal artery." Conversely, the direct effect of the "Involved renal artery" on AKI and its indirect effect mediated through NPR were also assessed. The robustness of the mediation pathways was evaluated using a bias-corrected bootstrap method with 5,000 resamples to generate 95% confidence intervals (CIs). An E-value analysis was conducted to quantify the potential impact of unmeasured confounding effects. All statistical analyses were executed using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA), with additional analyses performed using R software (version 4.2.2) and MSTATA software ( www.mstata.com ). 3. Results 3.1 Baseline Characteristics and Surgical Parameters The study cohort consisted of 396 patients undergoing ATAAD surgery, categorized based on the occurrence of postoperative AKI as detailed in Table 1. Demographic analysis indicated no statistically significant differences in median age (53 vs. 54 years, p = 0.351) or sex distribution (p = 0.913) between the groups. The prevalence of comorbidities, such as hypertension (55.7% vs. 63.1%, p = 0.140), diabetes (2.3% vs. 3.6%, p = 0.452), coronary artery disease (5.2% vs. 5.4%, p = 0.918), and previous cardiovascular surgery (6.3% in both groups, p = 0.995), was similar across the cohorts. Notably, patients with AKI demonstrated significantly greater renal artery involvement (median 1.0 vs. 0.0, p < 0.001), longer operative times (6.5 vs. 6.0 h, p < 0.001), and extended cardiopulmonary bypass durations (155 vs. 147 min, p = 0.004). Postoperative outcomes for the AKI group included prolonged intensive care unit (ICU) stays (7 vs. 4 days, p < 0.001) and increased total hospitalization duration (19 vs. 16 days, p = 0.002). Figure 1E provides a graphical abstract. 3.2 Predictive Performance of Inflammatory Markers Inflammatory markers were significantly elevated in the AKI group, as evidenced by increased levels of PCT (0.41 vs. 0.19, p = 0.002), neutrophil count (10.1 vs. 9.0×10⁹/L, p = 0.002), platelet count ( 136 vs. 165×10⁹/L, p < 0.001), NLR (15 vs. 12, p = 0.007), NLPR (12 vs. 7, p < 0.001), and NPR (7.7 vs. 5.7, p < 0.001) (Table 1).The ROC curves of inflammatory biomarkers in AKI group is shown in Fig.1 and Table 2. The AUC values are as follows: NPR (0.660, cut-off 7.338, sensitivity 0.545, specificity 0.707), NLR (0.580, cut-off 13.297, sensitivity 0.572, specificity 0.575), NLPR (0.619, cut-off 8.458, sensitivity 0.649, specificity 0.552), PCT (0.589, cut-off 0.26, sensitivity 0.631, specificity 0.557), NEUT (0.589, cut-off 13.29, sensitivity 0.257, specificity 0.908), and PLT (0.634, cut-off 141, sensitivity 0.541, specificity 0.655). Among these, ROC analyses identified NPR as the most robust predictor of AKI, whereas NLR and NLPR exhibited comparatively lower discriminatory power. 3.3 Multivariate Regression and Mediation Analysis The binary logistic regression model was employed to investigate the impact of various predictor variables on the incidence of AKI. The findings revealed that both the NPR (OR = 1.20, 95% CI: 1.10–1.30, p < 0.001) and renal artery involvement (OR = 1.68, 95% CI: 1.36-2.09, p < 0.001) were statistically significant independent predictors of AKI, demonstrating a strong association with its occurrence (Table 3). Specifically, each unit increase in the standardized NPR value was associated with a 20% increase in the risk of AKI, while patients with renal artery involvement exhibited a 68% higher risk of developing AKI compared to those without such involvement. In contrast, other variables, including operation duration, cardiopulmonary bypass time, NLR, NLPR and PCT, did not demonstrate significant correlations (p > 0.05), as their OR confidence intervals encompassed 1, indicating that these factors were not effective predictors of AKI in this model. The model accounted for 22% of the variance in AKI risk (Nagelkerke R² = 0.22, Table S1), with operation duration, cardiopulmonary bypass time, PCT, NLR, and NLPR contributing nonsignificantly (p > 0.05). Table 4 presents the results of an analysis investigating the moderating effect of renal artery involvement on the relationship between NPR and AKI across three distinct models. In Model 1, the objective was to examine the influence of the independent variable, NPR, on the dependent variable, AKI, without considering the moderating variable, renal artery involvement. Model 2 builds upon Model 1 by incorporating the moderating variable, renal artery involvement. Model 3 further extends Model 2 by including the interaction term between the independent and moderating variables. The results from Model 1 indicate that NPR has a significant positive effect on AKI (β = 0.119, p < 0.001), accounting for 5.8% of the variance. In Model 2, upon the introduction of renal artery involvement, both NPR and renal artery involvement demonstrate significant positive effects (NPR: β= 0.107, p < 0.001; renal artery: β = 0.115, p < 0.001), enhancing the model’s explanatory power to 12%. Model 3, which incorporates the interaction term, reveals a significant moderating effect: the coefficient for the interaction term between NPR and renal artery involvement is -0.044 (p = 0.041), suggesting that renal artery involvement attenuates the positive effect of NPR on AKI. The explanatory power of the model increased to 12.9% (△R² = 0.009), and the additional explanatory power introduced by the interaction term was statistically significant (△F = 31.118, p < 0.001). All F-tests for the models indicated significance (p < 0.001), thereby affirming the validity of the model specifications. The NPR variable remained statistically significant across all three models, with its effect size increasing from 0.119 to 0.166 when accounting for the moderating effect, suggesting the presence of a suppressed indirect effect. The analysis of the moderating effect corroborated the research hypothesis, indicating that the status of renal artery involvement significantly modified the strength and direction of the relationship between NPR and AKI. The moderating effect of NPR on the relationship between renal artery involvement and AKI was evaluated across three different models (Table 5). Results from Model 1 demonstrated that renal artery involvement had a significant positive effect on AKI (β = 0.124, p < 0.001), accounting for 7.4% of the variance (adjusted R² = 0.071). In Model 2, the inclusion of NPR resulted in both variables maintaining significant positive effects (Involved renal artery: β = 0.115, p < 0.001; NPR: β = 0.107, p < 0.001), enhancing the model's explanatory power to 12% (△R² = 0.046), with a significant F-change. Model 3 introduced an interaction term, which produced a coefficient of -0.044 (p = 0.041), indicating statistical significance. This suggests that NPR serves as a negative moderator in the relationship between the Involved renal artery and AKI; as NPR levels increase, the positive effect of the Involved renal artery on AKI diminishes. The final model accounted for 12.9% of the variance (adjusted R² = 0.122), with all predictors and the interaction term achieving statistical significance (p < 0.05). The overall model fit was significant (F = 19.342, p < 0.001). The addition of the interaction term explained an extra 0.9% of the variance (△R² = 0.009), and the F-change test confirmed the presence of a moderating effect (△F = 4.211, p = 0.041). The NPR variable (β = 0.166) exhibited a stronger main effect compared to its original scale, while the negative sign of the interaction coefficient, contrary to the main effect, is consistent with the expected moderating influence. 4. Discussion This study offers novel insights into the utility of the preoperative NPR as a predictor of AKI risk following TAAR in patients with ATAAD. The findings indicate that NPR, serving as a composite biomarker encompassing systemic inflammation and thrombosis pathways, demonstrates superior predictive capability when compared to conventional inflammatory markers such as the NLR and NLPR. This enhanced predictive performance is attributed to NPR's ability to concurrently reflect neutrophil-driven inflammatory processes and platelet activation-induced microcirculatory dysfunction [ 12 ]. Notably, an increase of one unit in NPR is associated with a 20% elevation in AKI risk, implying that preoperative systemic inflammation may substantially heighten renal vulnerability through mechanisms such as ischemia-reperfusion injury and endothelial dysfunction [ 13 , 14 ]. These findings are consistent with contemporary research trends that underscore the prognostic significance of inflammatory biomarkers in cardiovascular emergencies [ 15 ]. Neutrophils inflict direct damage on renal tubular epithelial cells through the action of myeloperoxidase (MPO) and reactive oxygen species (ROS), while platelet α-granule-derived P-selectin and platelet factor 4 (PF4) facilitate microthrombosis [ 16 – 18 ]. This dual pathophysiological mechanism demonstrates synergistic effects in the development of AKI [ 19 ]. Importantly, NPR accounts for 90% of the direct effects on AKI risk, with only 10.08% mediated through renal artery involvement, thereby highlighting systemic inflammation as the primary driver of AKI. This observation is in line with the "dual-hit theory," which posits that systemic inflammation superimposed on local ischemia contributes to cardiac surgery-associated AKI [ 20 , 21 ]. Anatomical risk analysis indicates that renal artery involvement elevates AKI risk by 68%, corroborating previous studies on the impact of visceral ischemia in ATAAD. Imaging-confirmed renal artery obstruction primarily induces renal hypoperfusion through direct mechanical occlusion, while the NPR-mediated effects, comprising 7.8%, suggest an inflammatory exacerbation of hypoxia-induced injury [ 6 ]. This interaction between systemic inflammation and local ischemic factors underscores the complexity of AKI pathogenesis in this context [ 15 ]. The interaction between anatomical and functional risk factors provides a basis for stratified clinical interventions. In cases involving renal artery involvement, it is recommended that anti-inflammatory therapies and microcirculatory protection be used in conjunction with anatomical revascularization [ 22 , 23 ]. The predictive model that incorporates the NPR and renal artery status accounts for 22% of the variability in AKI risk, surpassing the explanatory power of conventional clinical models [ 23 , 24 ]. For patients identified as high-risk, we propose the following interventions: 1) Optimization of cardiopulmonary bypass management to ensure renal venous pressure remains below 10 mmHg[ 25 ]; 2) Early postoperative administration of broad-spectrum anti-inflammatory agents, such as ulinastatin; [ 26 ]; 3) Implementation of prophylactic continuous renal replacement therapy (CRRT) [ 6 , 27 ]. Notably, preoperative NPR levels exhibit a dose-dependent correlation with postoperative CRRT requirements (p < 0.001), offering quantitative guidance for the timing of CRRT in high-risk populations. The observed mutual attenuating effect between renal artery involvement and NPR implies that these factors may operate through partially overlapping pathological pathways. In the context of AKI pathogenesis, vascular ischemic factors and systemic inflammatory responses exert independent effects while also demonstrating complex antagonistic interactions. Ischemia, primarily driven by renal artery involvement, may partially mitigate NPR-related inflammatory damage by activating protective mechanisms, such as BNIP3-mediated mitophagy [ 28 ]. Conversely, NPR-associated inflammation may mitigate ischemia-induced direct cell death by activating other compensatory pathways (e.g., upregulation of Netrin-1) [ 29 , 30 ]. 5. Limitations and Future Directions This study has several limitations. Firstly, its retrospective design may introduce selection bias, particularly concerning unmeasured confounders such as baseline renal function or variations in intraoperative management. Secondly, the mediation analysis presumes a linear relationship between variables, which may not adequately capture the complexity of inflammatory interactions. Future prospective studies are necessary to validate these findings and to explore dynamic changes in inflammatory markers over time. Additionally, mechanistic investigations into the role of NPR in AKI-such as cytokine profiling or microvascular imaging—could elucidate its dual systemic and local effects. Finally, the modest predictive accuracy of our model indicates that additional biomarkers, such as urinary injury markers or genetic polymorphisms, may enhance risk prediction. 6. Conclusion NPR and renal artery involvement have been identified as independent predictors of AKI following TAAR for ATAAD. The impact of NPR is mediated in part by compromised renal perfusion and predominantly through systemic inflammation. These findings highlight the dual pathological mechanisms contributing to AKI in this context and suggest potential targets for enhancing perioperative renal protection. Prospective validation and further mechanistic studies are necessary to translate these insights into customized therapeutic strategies. Abbreviations AKI Acute kidney injury TAAR Total aortic arch replacement ATAAD Acute type A aortic dissection NPR Neutrophil-to-platelet ratio NLR Neutrophil-to-lymphocyte ratio CKD Chronic kidney disease sCr Serum creatinine CRP C-reactive protein PCT Procalcitonin ROC Receiver operating characteristic AUC Area under the curve CI Confidence interval Declarations Ethics approval and consent to participate Declarations Ethics approval and consent to participate. This study protocol was reviewed and approved by the Ethics Review Committee of First Affiliated Hospital of Xi'an Jiaotong University, approval number No: XJTU1AF2020LSK-266 . Each participant was informed of the purpose of the study and signed a written informed consent form. Also, we confirmed all the methods included in this study are in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Data availability Not applicable. Due to ethical restrictions imposed by the institutional review board (IRB approval number : No. XJTU1AF2020LSK-266), individual-level data cannot be publicly shared. Competing interests The authors declare no competing interests. Funding This work was supported by Science and Technology Development Plan Project of Shaanxi Province (2025SF-YBXM-183). Author contributions Jing Tan : Writing - original draft; Formal analysis, Data curation. He Zhang : Methodology, Formal analysis. Qiang Chen: Project administration, Software. Xue Wang : Formal analysis, Data curation. Jing Li: Investigation, Data curation. Liang Zhong: Investigation, Data curation. Yongjian Zhang : Investigation. Xinglong Zheng : Supervision, Methodology. Ruini Du: Writing – review & editing, Visualization, Validation, Resources, Methodology. Zhanqin Zhang: Writing - review & editing, Writing - original draft, Supervision. Acknowledgements Not applicable Author details 1 Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China. 2 School of Nursing and Rehabilitation, Xi'an FANYI University, Xi'an, China. 3 Department of Cardiovascular Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China. 4 Shaanxi Provincial Center for Regenerative Medicine and Surgical Engineering, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China. Electronic address: [email protected] . Electronic address: [email protected] . References Zhu Y, Lingala B, Baiocchi M, Tao JJ, Toro Arana V, Khoo JW, Williams KM, Traboulsi AA-R, Hammond HC, Lee AM et al : Type A Aortic Dissection-Experience Over 5 Decades: JACC Historical Breakthroughs in Perspective. J Am Coll Cardiol 2020, 76(14):1703-1713. 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Tables Table 1 Patient demographics and baseline characteristics 1 Median (IQR); n (%) 2 Wilcoxon rank sum test; Pearson's Chi-squared test Table 2 Prognostic values of inflammation markers and blood cell counts in predicting AKI , ROC analysis Table3 Results of Univariate and Multivariate Logistic regression Table 4 The moderating effect of involved renal artery between NPR and AKI. Model 1 Model 2 Model 3 Coef SE t P Coef SE t P Coef SE t P const 0.561 0.024 23.095 0.000 *** 0.445 0.032 13.808 0.000 *** 0.449 0.032 13.971 0.000 *** NPR 0.119 0.024 4.905 0.000 *** 0.107 0.024 4.525 0.000 *** 0.166 0.037 4.464 0.000 *** Involved renal artery 0.115 0.022 5.262 0.000 *** 0.115 0.022 5.294 0.000 *** NPR *Involved renal artery -0.044 0.021 -2.052 0.041 ** R² 0.058 0.12 0.129 Adjusted R² 0.055 0.115 0.122 F F(396, 1)=24.061， P =0.000 *** F(2, 393)=26.689， P =0.000 *** F(3, 392)=19.342， P =0.000 *** △R² 0.058 0.12 0.129 △F △F(1, 396)=24.061， P =0.000 *** △F(1, 393)=27.687， P =0.000 *** △F(1, 392)=31.118， P =0.000 *** Note: ***, **, and * represent significance levels of 1%, 5%, and 10% respectively. Coef, Coefficient; SE: Standard error. Table 5 The moderating effect of NPR between involved renal artery and AKI. Model 1 Model 2 Model 3 Coef SE t P Coef SE t P Coef SE t P const 0.435 0.033 13.211 0.000 *** 0.445 0.032 13.808 0.000 *** 0.449 0.032 13.971 0.000 *** Involved renal artery 0.124 0.022 5.599 0.000 *** 0.115 0.022 5.262 0.000 *** 0.115 0.022 5.294 0.000 *** NPR 0.107 0.024 4.525 0.000 *** 0.166 0.037 4.464 0.000 *** Involved renal artery*NPR -0.044 0.021 -2.052 0.041 ** R² 0.074 0.12 0.129 Adjusted R² 0.071 0.115 0.122 F F(396, 1)=31.353，P=0.000 *** F(2, 393)=26.689，P=0.000 *** F(3, 392)=19.342，P=0.000 *** △R² 0.074 0.12 0.129 △F △F(1, 396)=31.353，P=0.000 *** △F(1, 393)=20.475，P=0.000 *** △F(1, 392)=31.118，P=0.000 *** Note: ***, **, and * represent significance levels of 1%, 5%, and 10% respectively. Coef, Coefficient; SE: Standard error. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7221153","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515650840,"identity":"d0f2700f-63f5-4d59-a46a-0eaea797176c","order_by":0,"name":"Jing Tan","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Tan","suffix":""},{"id":515650841,"identity":"9bbcc6e3-9390-4344-b262-8ab80d21e51d","order_by":1,"name":"He Zhang","email":"","orcid":"","institution":"Xi'an FANYI University","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Zhang","suffix":""},{"id":515650842,"identity":"703c3623-3404-48a0-9c0c-05fc0a7878ff","order_by":2,"name":"Qiang Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Chen","suffix":""},{"id":515650843,"identity":"cf7907a0-e8f3-4a91-a7f4-d7acf73b7011","order_by":3,"name":"Xue Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Wang","suffix":""},{"id":515650844,"identity":"c8df797f-9af5-400f-a5bb-91f2b96bf2b6","order_by":4,"name":"Jing Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi'an Jiaotong 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09:21:02","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":139470,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7221153/v1/90740062e8f05393104afccf.html"},{"id":91832501,"identity":"f23eece4-8993-4165-8401-1c2e9b7bea3d","added_by":"auto","created_at":"2025-09-22 09:21:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69670,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of Inflammatory biomarkers in AKI group.\u003c/p\u003e\n\u003cp\u003ePredictive values of Inflammatory biomarkers were evaluated by AUC curve in AKI.\u003c/p\u003e\n\u003cp\u003e6 potential biomarkers include NPR (AUC = 0.660)，NLR (AUC = 0.580)，NLPR(AUC = 0.619)，PCT(AUC = 0.589), NEUT (AUC = 0.589), PLT (AUC = 634).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7221153/v1/68d9f1601f8f674464c2dc52.png"},{"id":92233262,"identity":"8a4afa6f-e958-45ff-9d35-9f4244276ada","added_by":"auto","created_at":"2025-09-26 06:54:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1207262,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7221153/v1/69b497e3-b991-4373-ab28-a532925eb0e1.pdf"},{"id":91832502,"identity":"bcfa8d30-aa8b-49bb-89b9-0706574da504","added_by":"auto","created_at":"2025-09-22 09:21:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14678,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7221153/v1/f81898c73198b342ea81a6c2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preoperative Neutrophil-Platelet Ratio as a Predictor of Acute Kidney Injury after Total Aortic Arch Replacement: A Retrospective Cohort Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcute Type A Aortic Dissection (ATAAD) represents a critical cardiovascular emergency necessitating prompt surgical intervention, with Total Arch Replacement (TAAR) frequently employed in cases of extensive aortic arch involvement [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite significant advancements in surgical methodologies, postoperative Acute Kidney Injury (AKI) remains a common and severe complication, affecting 20\u0026ndash;40% of ATAAD patients undergoing TAAR, and is closely linked to heightened morbidity and mortality rates \u003csup\u003e[2, 3]\u003c/sup\u003e. The pathophysiological mechanisms underlying AKI are multifaceted, encompassing ischemia-reperfusion injury, systemic inflammatory responses and hemodynamic instability [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn recent years, inflammatory biomarkers, particularly ratios derived from neutrophils and platelets, have emerged as promising indicators of adverse outcomes in cardiovascular diseases, including ATAAD [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The neutrophil-to-platelet ratio (NPR) reflects the interaction between systemic inflammation and thrombotic pathways, both of which are implicated in the pathogenesis of AKI [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Preoperative NPR has demonstrated prognostic value in ATAAD, correlating with in-hospital mortality and organ dysfunction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, its specific role in predicting AKI following TAAR remains insufficiently explored.\u003c/p\u003e\u003cp\u003eThis study aims to evaluate preoperative NPR as a novel predictor of AKI in ATAAD patients undergoing TAAR. By employing machine learning algorithms and multivariate regression analyses, we seek to develop a robust predictive model that integrates NPR with established clinical risk factors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Our findings could facilitate the early identification of high-risk patients, enabling targeted perioperative strategies to mitigate AKI and improve outcomes in this critically ill population.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Design and Setting\u003c/h2\u003e\n \u003cp\u003eThis retrospective cohort study was conducted at the First Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University, subsequent to obtaining approval from the institution\u0026rsquo;s Research Ethics Committee (Approval No: XJTU1AF2020LSK-266). The study population comprised patients diagnosed with ATAAD who underwent TAAR at the hospital between July 2021 and March 2023. Exclusion criteria were established as follows: (a) preoperative serum creatinine (sCr) levels exceeding 353.6 \u0026micro;mol/L or a confirmed diagnosis of chronic kidney disease (CKD); (b) patients who had undergone chronic dialysis or emergency dialysis within the month preceding surgery; and (c) those with missing preoperative or postoperative sCr results or incomplete clinical data. After a thorough review of electronic medical records and laboratory test results, a total of 396 ATAAD patients who met the inclusion criteria were enrolled in the study. These patients were subsequently classified according to the Stanford classification system for aortic dissection. Each participant was informed of the purpose of the study and signed a written informed consent form and registered with the ClinicalTrials.gov registry (No. NCT05206032).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data Collection and Definitions\u003c/h2\u003e\n \u003cp\u003eElectronic methods were employed to systematically collect extensive demographic, clinical and laboratory data from patients. The demographic data included variables such as age, gender, hypertension, diabetes mellitus, coronary atherosclerotic heart disease (CAD), history of previous cardiovascular surgeries, duration of symptoms prior to hospital admission, and the specific renal artery involved. Procedure-related data included the duration of the operation, as well as the durations of cardiopulmonary bypass (CPB) and heart block time. Furthermore, we collected preoperative biochemical test results, including C-reactive protein (CRP), procalcitonin (PCT), and counts of neutrophils, lymphocytes, and platelets. We also recorded the duration of intensive care unit (ICU) stay and the total length of hospital stay.\u003c/p\u003e\n \u003cp\u003eFrom these recorded values, we calculated the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte-to-platelet ratio (NLPR) and NPR [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Patients were categorized into AKI and non-AKI groups based on the Kidney Disease Improving Global Outcomes (KDIGO) criteria [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Subsequently, patients were categorized into two groups: AKI (n\u0026thinsp;=\u0026thinsp;222) and non-AKI (n\u0026thinsp;=\u0026thinsp;174). According to KDIGO criteria, patients with a\u0026thinsp;\u0026ge;\u0026thinsp;50% increase in sCr within 7 days or a\u0026thinsp;\u0026ge;\u0026thinsp;0.3-mg/dL (\u0026ge;\u0026thinsp;26.5-\u0026micro;mol/L) rise in sCr within 48 hours or oliguria were defined as patients with AKI.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eThe study population was divided into AKI and non-AKI groups for comparison. Missing data were addressed with multiple imputation. Continuous variables were shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range), depending on distribution, while categorical variables were summarized with frequencies and percentages. Data normality was checked using Shapiro-Wilk tests and Q-Q plots. Normally distributed continuous variables were compared using Welch\u0026rsquo;s t-test or ANOVA, and non-normally distributed ones with the Wilcoxon rank-sum or Kruskal-Wallis tests. Categorical variables were analyzed with Fisher\u0026rsquo;s exact test or Pearson\u0026rsquo;s chi-square test. Receiver operator characteristic curve (ROC) analysis assessed the predictive ability of lab parameters for AKI. Univariate logistic regression identified potential AKI-related factors such as operation duration, cardiopulmonary bypass time, NPR, PCT, involved renal artery, NLR, and NLPR. A multivariate logistic regression analysis was subsequently performed, incorporating all significant predictors identified in the univariate analysis, while adjusting for potential confounders, to ascertain independent risk factors for AKI. Furthermore, a causal mediation analysis was conducted using structural equation modeling (SEM) to estimate the direct effect of NPR on AKI, as well as its indirect effect mediated through the \u0026quot;Involved renal artery.\u0026quot; Conversely, the direct effect of the \u0026quot;Involved renal artery\u0026quot; on AKI and its indirect effect mediated through NPR were also assessed. The robustness of the mediation pathways was evaluated using a bias-corrected bootstrap method with 5,000 resamples to generate 95% confidence intervals (CIs). An E-value analysis was conducted to quantify the potential impact of unmeasured confounding effects. All statistical analyses were executed using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA), with additional analyses performed using R software (version 4.2.2) and MSTATA software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.mstata.com\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Baseline Characteristics and Surgical Parameters\u003c/h2\u003e\n\u003cp\u003eThe study cohort consisted of 396 patients undergoing ATAAD surgery, categorized based on the occurrence of postoperative AKI as detailed in Table 1. Demographic analysis indicated no statistically significant differences in median age (53 vs. 54 years, p = 0.351) or sex distribution (p = 0.913) between the groups. The prevalence of comorbidities, such as hypertension (55.7% vs. 63.1%, p = 0.140), diabetes (2.3% vs. 3.6%, p = 0.452), coronary artery disease (5.2% vs. 5.4%, p = 0.918), and previous cardiovascular surgery (6.3% in both groups, p = 0.995), was similar across the cohorts. Notably, patients with AKI demonstrated significantly greater renal artery involvement (median 1.0 vs. 0.0, p \u0026lt; 0.001), longer operative times (6.5 vs. 6.0 h, p \u0026lt; 0.001), and extended cardiopulmonary bypass durations (155 vs. 147 min, p = 0.004). Postoperative outcomes for the AKI group included prolonged intensive care unit (ICU) stays (7 vs. 4 days, p \u0026lt; 0.001) and increased total hospitalization duration (19 vs. 16 days, p = 0.002). Figure 1E provides a graphical abstract.\u003c/p\u003e\n\u003ch2\u003e3.2 Predictive Performance of Inflammatory Markers\u003c/h2\u003e\n\u003cp\u003eInflammatory markers were significantly elevated in the AKI group, as evidenced by increased levels of PCT (0.41 vs. 0.19, p = 0.002), neutrophil count (10.1 vs. 9.0\u0026times;10⁹/L, p = 0.002), platelet count ( 136 vs. 165\u0026times;10⁹/L, p \u0026lt; 0.001), NLR (15 vs. 12, p = 0.007), NLPR (12 vs. 7, p \u0026lt; 0.001), and NPR (7.7 vs. 5.7, p \u0026lt; 0.001) (Table 1).The\u0026nbsp;ROC curves of inflammatory biomarkers in AKI group\u0026nbsp;is shown in Fig.1 and Table 2. The AUC values are as follows: NPR (0.660, cut-off 7.338, sensitivity 0.545, specificity 0.707), NLR (0.580, cut-off 13.297, sensitivity 0.572, specificity 0.575), NLPR (0.619, cut-off 8.458, sensitivity 0.649, specificity 0.552), PCT (0.589, cut-off 0.26, sensitivity 0.631, specificity 0.557), NEUT (0.589, cut-off 13.29, sensitivity 0.257, specificity 0.908), and PLT (0.634, cut-off 141, sensitivity 0.541, specificity 0.655). Among these, ROC analyses identified NPR as the most robust predictor of AKI, whereas NLR and NLPR exhibited comparatively lower discriminatory power.\u003c/p\u003e\n\u003ch2\u003e3.3 Multivariate Regression and Mediation Analysis\u003c/h2\u003e\n\u003cp\u003eThe binary logistic regression model was employed to investigate the impact of various predictor variables on the incidence of AKI. The findings revealed that both the NPR (OR = 1.20, 95% CI: 1.10\u0026ndash;1.30, p \u0026lt; 0.001) and renal artery involvement (OR = 1.68, 95% CI: 1.36-2.09, p \u0026lt; 0.001) were statistically significant independent predictors of AKI, demonstrating a strong association with its occurrence (Table 3). Specifically, each unit increase in the standardized NPR value was associated with a 20% increase in the risk of AKI, while patients with renal artery involvement exhibited a 68% higher risk of developing AKI compared to those without such involvement. In contrast, other variables, including operation duration, cardiopulmonary bypass time, NLR, NLPR and PCT, did not demonstrate significant correlations (p \u0026gt; 0.05), as their OR confidence intervals encompassed 1, indicating that these factors were not effective predictors of AKI in this model. The model accounted for 22% of the variance in AKI risk (Nagelkerke R\u0026sup2; = 0.22, Table S1), with operation duration, cardiopulmonary bypass time, PCT, NLR, and NLPR contributing nonsignificantly (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eTable 4 presents the results of an analysis investigating the moderating effect of renal artery involvement on the relationship between NPR and AKI across three distinct models. In Model 1, the objective was to examine the influence of the independent variable, NPR, on the dependent variable, AKI, without considering the moderating variable, renal artery involvement. Model 2 builds upon Model 1 by incorporating the moderating variable, renal artery involvement. Model 3 further extends Model 2 by including the interaction term between the independent and moderating variables. The results from Model 1 indicate that NPR has a significant positive effect on AKI (\u0026beta; = 0.119, p \u0026lt; 0.001), accounting for 5.8% of the variance. In Model 2, upon the introduction of renal artery involvement, both NPR and renal artery involvement demonstrate significant positive effects (NPR: \u0026beta;= 0.107, p \u0026lt; 0.001; renal artery: \u0026beta; = 0.115, p \u0026lt; 0.001), enhancing the model\u0026rsquo;s explanatory power to 12%. Model 3, which incorporates the interaction term, reveals a significant moderating effect: the coefficient for the interaction term between NPR and renal artery involvement is -0.044 (p = 0.041), suggesting that renal artery involvement attenuates the positive effect of NPR on AKI. The explanatory power of the model increased to 12.9% (△R\u0026sup2; = 0.009), and the additional explanatory power introduced by the interaction term was statistically significant (△F = 31.118, p \u0026lt; 0.001). All F-tests for the models indicated significance (p \u0026lt; 0.001), thereby affirming the validity of the model specifications. The NPR variable remained statistically significant across all three models, with its effect size increasing from 0.119 to 0.166 when accounting for the moderating effect, suggesting the presence of a suppressed indirect effect. The analysis of the moderating effect corroborated the research hypothesis, indicating that the status of renal artery involvement significantly modified the strength and direction of the relationship between NPR and AKI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe moderating effect of NPR on the relationship between renal artery involvement and AKI was evaluated across three different models (Table 5). Results from Model 1 demonstrated that renal artery involvement had a significant positive effect on AKI (\u0026beta; = 0.124, p \u0026lt; 0.001), accounting for 7.4% of the variance (adjusted R\u0026sup2; = 0.071). In Model 2, the inclusion of NPR resulted in both variables maintaining significant positive effects (Involved renal artery: \u0026beta; = 0.115, p \u0026lt; 0.001; NPR: \u0026beta; = 0.107, p \u0026lt; 0.001), enhancing the model\u0026apos;s explanatory power to 12% (△R\u0026sup2; = 0.046), with a significant F-change. Model 3 introduced an interaction term, which produced a coefficient of -0.044 (p = 0.041), indicating statistical significance. This suggests that NPR serves as a negative moderator in the relationship between the Involved renal artery and AKI; as NPR levels increase, the positive effect of the Involved renal artery on AKI diminishes. The final model accounted for 12.9% of the variance (adjusted R\u0026sup2; = 0.122), with all predictors and the interaction term achieving statistical significance (p \u0026lt; 0.05). The overall model fit was significant (F = 19.342, p \u0026lt; 0.001). The addition of the interaction term explained an extra 0.9% of the variance (△R\u0026sup2; = 0.009), and the F-change test confirmed the presence of a moderating effect (△F = 4.211, p = 0.041). The NPR variable (\u0026beta; = 0.166) exhibited a stronger main effect compared to its original scale, while the negative sign of the interaction coefficient, contrary to the main effect, is consistent with the expected moderating influence.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study offers novel insights into the utility of the preoperative NPR as a predictor of AKI risk following TAAR in patients with ATAAD. The findings indicate that NPR, serving as a composite biomarker encompassing systemic inflammation and thrombosis pathways, demonstrates superior predictive capability when compared to conventional inflammatory markers such as the NLR and NLPR. This enhanced predictive performance is attributed to NPR's ability to concurrently reflect neutrophil-driven inflammatory processes and platelet activation-induced microcirculatory dysfunction [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Notably, an increase of one unit in NPR is associated with a 20% elevation in AKI risk, implying that preoperative systemic inflammation may substantially heighten renal vulnerability through mechanisms such as ischemia-reperfusion injury and endothelial dysfunction [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese findings are consistent with contemporary research trends that underscore the prognostic significance of inflammatory biomarkers in cardiovascular emergencies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Neutrophils inflict direct damage on renal tubular epithelial cells through the action of myeloperoxidase (MPO) and reactive oxygen species (ROS), while platelet α-granule-derived P-selectin and platelet factor 4 (PF4) facilitate microthrombosis [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This dual pathophysiological mechanism demonstrates synergistic effects in the development of AKI [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Importantly, NPR accounts for 90% of the direct effects on AKI risk, with only 10.08% mediated through renal artery involvement, thereby highlighting systemic inflammation as the primary driver of AKI. This observation is in line with the \"dual-hit theory,\" which posits that systemic inflammation superimposed on local ischemia contributes to cardiac surgery-associated AKI [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnatomical risk analysis indicates that renal artery involvement elevates AKI risk by 68%, corroborating previous studies on the impact of visceral ischemia in ATAAD. Imaging-confirmed renal artery obstruction primarily induces renal hypoperfusion through direct mechanical occlusion, while the NPR-mediated effects, comprising 7.8%, suggest an inflammatory exacerbation of hypoxia-induced injury [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This interaction between systemic inflammation and local ischemic factors underscores the complexity of AKI pathogenesis in this context [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The interaction between anatomical and functional risk factors provides a basis for stratified clinical interventions. In cases involving renal artery involvement, it is recommended that anti-inflammatory therapies and microcirculatory protection be used in conjunction with anatomical revascularization [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe predictive model that incorporates the NPR and renal artery status accounts for 22% of the variability in AKI risk, surpassing the explanatory power of conventional clinical models [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For patients identified as high-risk, we propose the following interventions: 1) Optimization of cardiopulmonary bypass management to ensure renal venous pressure remains below 10 mmHg[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; 2) Early postoperative administration of broad-spectrum anti-inflammatory agents, such as ulinastatin; [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]; 3) Implementation of prophylactic continuous renal replacement therapy (CRRT) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Notably, preoperative NPR levels exhibit a dose-dependent correlation with postoperative CRRT requirements (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), offering quantitative guidance for the timing of CRRT in high-risk populations. The observed mutual attenuating effect between renal artery involvement and NPR implies that these factors may operate through partially overlapping pathological pathways. In the context of AKI pathogenesis, vascular ischemic factors and systemic inflammatory responses exert independent effects while also demonstrating complex antagonistic interactions. Ischemia, primarily driven by renal artery involvement, may partially mitigate NPR-related inflammatory damage by activating protective mechanisms, such as BNIP3-mediated mitophagy [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Conversely, NPR-associated inflammation may mitigate ischemia-induced direct cell death by activating other compensatory pathways (e.g., upregulation of Netrin-1) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Limitations and Future Directions","content":"\u003cp\u003eThis study has several limitations. Firstly, its retrospective design may introduce selection bias, particularly concerning unmeasured confounders such as baseline renal function or variations in intraoperative management. Secondly, the mediation analysis presumes a linear relationship between variables, which may not adequately capture the complexity of inflammatory interactions. Future prospective studies are necessary to validate these findings and to explore dynamic changes in inflammatory markers over time. Additionally, mechanistic investigations into the role of NPR in AKI-such as cytokine profiling or microvascular imaging\u0026mdash;could elucidate its dual systemic and local effects. Finally, the modest predictive accuracy of our model indicates that additional biomarkers, such as urinary injury markers or genetic polymorphisms, may enhance risk prediction.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eNPR and renal artery involvement have been identified as independent predictors of AKI following TAAR for ATAAD. The impact of NPR is mediated in part by compromised renal perfusion and predominantly through systemic inflammation. These findings highlight the dual pathological mechanisms contributing to AKI in this context and suggest potential targets for enhancing perioperative renal protection. Prospective validation and further mechanistic studies are necessary to translate these insights into customized therapeutic strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAKI Acute kidney injury \u003c/p\u003e\n\u003cp\u003eTAAR Total aortic arch replacement \u003c/p\u003e\n\u003cp\u003eATAAD Acute type A aortic dissection \u003c/p\u003e\n\u003cp\u003eNPR Neutrophil-to-platelet ratio \u003c/p\u003e\n\u003cp\u003eNLR Neutrophil-to-lymphocyte ratio \u003c/p\u003e\n\u003cp\u003eCKD Chronic kidney disease\u003c/p\u003e\n\u003cp\u003esCr Serum creatinine\u003c/p\u003e\n\u003cp\u003eCRP C-reactive protein\u003c/p\u003e\n\u003cp\u003ePCT Procalcitonin\u003c/p\u003e\n\u003cp\u003eROC Receiver operating characteristic \u003c/p\u003e\n\u003cp\u003eAUC Area under the curve \u003c/p\u003e\n\u003cp\u003eCI Confidence interval \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeclarations Ethics approval and consent to participate. This study protocol was reviewed and approved by\u0026nbsp;the Ethics Review Committee of First Affiliated Hospital of Xi\u0026apos;an Jiaotong University, approval number\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNo:\u0026nbsp;XJTU1AF2020LSK-266\u003cstrong\u003e.\u003c/strong\u003e Each participant was informed of the purpose of the study and signed a written informed consent form. Also, we confirmed all the methods included in this study are in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003eNot applicable. Due to ethical restrictions imposed by the institutional review board (IRB approval number\u003cstrong\u003e: \u003c/strong\u003eNo. XJTU1AF2020LSK-266), individual-level data cannot be publicly shared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Science and Technology Development Plan Project of Shaanxi Province (2025SF-YBXM-183).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJing Tan\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Writing - original draft;\u0026nbsp;Formal analysis, Data curation. He Zhang\u003cstrong\u003e:\u003c/strong\u003e Methodology, Formal analysis.\u0026nbsp;\u003cstrong\u003eQiang Chen:\u003c/strong\u003e Project administration,\u0026nbsp;Software.\u0026nbsp;Xue Wang\u003cstrong\u003e:\u003c/strong\u003e Formal analysis, Data curation.\u0026nbsp;\u003cstrong\u003eJing Li:\u003c/strong\u003e Investigation, Data curation.\u0026nbsp;\u003cstrong\u003eLiang Zhong:\u003c/strong\u003e Investigation, Data curation. Yongjian Zhang\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eInvestigation. Xinglong Zheng\u003cstrong\u003e:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSupervision, Methodology. Ruini Du: Writing \u0026ndash; review \u0026amp; editing, Visualization, Validation, Resources, Methodology. Zhanqin Zhang: Writing - review \u0026amp; editing, Writing - original draft, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Anesthesiology, The First Affiliated Hospital of Xi\u0026apos;an Jiaotong University, Xi\u0026apos;an, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eSchool of Nursing and Rehabilitation, Xi\u0026apos;an FANYI University, Xi\u0026apos;an, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDepartment of Cardiovascular Surgery, The First Affiliated Hospital of Xi\u0026apos;an Jiaotong University, Xi\u0026apos;an, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eShaanxi Provincial Center for Regenerative Medicine and Surgical Engineering, The First Affiliated Hospital of Xi\u0026apos;an Jiaotong University, Xi\u0026apos;an, China; Department of Anesthesiology, The First Affiliated Hospital of Xi\u0026apos;an Jiaotong University, Xi\u0026apos;an, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eElectronic address: [email protected].\u003c/p\u003e\n\u003cp\u003eElectronic address: [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhu Y, Lingala B, Baiocchi M, Tao JJ, Toro Arana V, Khoo JW, Williams KM, Traboulsi AA-R, Hammond HC, Lee AM\u003cem\u003e et al\u003c/em\u003e: Type A Aortic Dissection-Experience Over 5 Decades: JACC Historical Breakthroughs in Perspective. \u003cem\u003eJ Am Coll Cardiol \u003c/em\u003e2020, 76(14):1703-1713.\u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rez-Fern\u0026aacute;ndez X, Ulsamer A, C\u0026aacute;mara-Rosell M, Sbraga F, Boza-Hern\u0026aacute;ndez E, Moret-Ru\u0026iacute;z E, Plata-Menchaca E, Santiago-Bautista D, Boronat-Garc\u0026iacute;a P, Gumucio-Sanguino 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X, Feng Z, Hong Q: Mechanisms Underlying the Impact of Interleukin Family on Acute Kidney Injury: Pathogenesis, Progression, and Therapy. \u003cem\u003eResearch (Wash D C) \u003c/em\u003e2025, 8:0738.\u003c/li\u003e\n\u003cli\u003eLiu L, Xie S, Liao X, Zhang L, Zhong L: Netrin-1 pretreatment protects rat kidney against ischemia/reperfusion injury via suppression of oxidative stress and neuropeptide Y expression. \u003cem\u003eJ Biochem Mol Toxicol \u003c/em\u003e2013, 27(4):231-236.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Patient demographics and baseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg 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\" width=\"888\" height=\"930\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e1\u003c/sup\u003eMedian (IQR); n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003eWilcoxon rank sum test; Pearson\u0026apos;s Chi-squared test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eTable 2 Prognostic values of inflammation markers and blood cell counts in predicting\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;AKI\u003c/strong\u003e\u003cstrong\u003e, ROC analysis\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cimg 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width=\"927\" height=\"312\"\u003e\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eTable3 Results of Univariate and Multivariate Logistic regression\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cimg 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\" width=\"944\" height=\"417\"\u003e\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cstrong\u003eTable 4 The moderating effect of involved renal artery between NPR and AKI.\u003c/strong\u003e\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003econst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e23.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e13.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e13.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eNPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eInvolved renal artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eNPR *Involved renal artery\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e-0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-2.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.041\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003eF(396, 1)=24.061，\u003cem\u003eP\u003c/em\u003e=0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003eF(2, 393)=26.689，\u003cem\u003eP\u003c/em\u003e=0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003eF(3, 392)=19.342，\u003cem\u003eP\u003c/em\u003e=0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e△R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e△F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e△F(1, 396)=24.061，\u003cem\u003eP\u003c/em\u003e=0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e△F(1, 393)=27.687，\u003cem\u003eP\u003c/em\u003e=0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e△F(1, 392)=31.118，\u003cem\u003eP\u003c/em\u003e=0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u0026nbsp;Note: ***, **, and * represent significance levels of 1%, 5%, and 10% respectively.\u0026nbsp;\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eCoef, Coefficient; SE: Standard error.\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 The moderating effect of \u0026nbsp;NPR between involved renal artery and AKI.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003econst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e13.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e13.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e13.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eInvolved renal artery\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eNPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eInvolved renal artery*NPR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e-0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e-2.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.041\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003eF(396, 1)=31.353，P=0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003eF(2, 393)=26.689，P=0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003eF(3, 392)=19.342，P=0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e△R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e△F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e△F(1, 396)=31.353，P=0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e△F(1, 393)=20.475，P=0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 28px;\"\u003e\n \u003cp\u003e△F(1, 392)=31.118，P=0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;Note: ***, **, and * represent significance levels of 1%, 5%, and 10% respectively.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCoef, Coefficient; SE: Standard error.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute kidney injury, Aortic dissection, Neutrophil-to-platelet ratio, Inflammation, continuous renal replacement therapy","lastPublishedDoi":"10.21203/rs.3.rs-7221153/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7221153/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Acute kidney injury (AKI) is a common complication following total aortic arch replacement (TAAR) in patients with acute type A aortic dissection (ATAAD), with inflammatory dysregulation contributing to its pathogenesis. This study aims to develop a predictive model that integrates neutrophil-to-lymphocyte ratio (NPR) with clinical risk factors to facilitate the early identification of high-risk patients, thereby enabling targeted interventions to mitigate AKI and enhance clinical outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis research was a single-center, retrospective cohort study comprising 396 patients undergoing TAAR between July 2021 and March 2023 in China. Participants were categorized into AKI and non-AKI groups based on KDIGO criteria. ROC analysis was employed to assess the predictive capability of inflammatory markers for AKI. Significant predictors identified were incorporated into a multivariate logistic regression model, which was adjusted for potential confounding variables, to ascertain independent risk factors for AKI. Furthermore, a causal mediation analysis was performed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eROC analysis identified NPR as the most robust predictor (AUC 0.660, p\u0026lt;0.001), surpassing both NLR and NLPR. Multivariable regression analysis confirmed that NPR (OR 1.20, 95%CI 1.10–1.30) and renal artery involvement (OR 1.68, 95% CI 1.36–2.09) were independent risk factors for AKI. Mediation models incorporate the interaction term, reveals that NPR serves as a negative moderator in the relationship between the involved renal artery (β=-0.044).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003ePreoperative NPR demonstrated desirable predictive accuracy for AKI incidence following TAAR. The study further indicated that both renal artery involvement and NPR independently and significant increased AKI risk. while they negatively interact, renal artery involvement diminished the positive association between NPR and AKI, while an increase in NPR also reduced the exacerbating effect of renal artery involvement. These findings suggest potential targets for improving perioperative renal protection, though further validation and studies are needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number：\u003c/strong\u003eNo. NCT05206032（Registered on December 1, 2021）\u003c/p\u003e","manuscriptTitle":"Preoperative Neutrophil-Platelet Ratio as a Predictor of Acute Kidney Injury after Total Aortic Arch Replacement: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 09:20:57","doi":"10.21203/rs.3.rs-7221153/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28d12b5c-796f-4391-889b-2a57e391ae1d","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-26T06:54:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 09:20:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7221153","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7221153","identity":"rs-7221153","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}