A Predictive Model for Postoperative Acute Kidney Injury in Patients with Acute Type A Aortic Dissection

A Predictive Model for Postoperative Acute Kidney Injury in Patients with Acute Type A Aortic Dissection | 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 A Predictive Model for Postoperative Acute Kidney Injury in Patients with Acute Type A Aortic Dissection Xuan-chen Yuan, Yi-ting Huang, Yin Huang, Xiao-fu Dai, Liang-wan Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8641396/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 prevalent and severe complication following acute type A aortic dissection (ATAAD) surgery. Although several predictive models exist, most lack rigorous validation in independent populations, limiting their clinical generalizability and readiness for implementation. Methods This single-center, retrospective cohort study enrolled 317 patients with ATAAD. The cohort was chronologically divided into training and external temporal validation sets. A predictive nomogram was developed from preoperative variables identified by multivariate logistic analysis. Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA). Sensitivity analysis confirmed the robustness of the findings. Results Higher serum creatinine (SCr) levels, elevated D-dimer (DDi) concentrations, and lower platelet counts (PLT) were identified as independent preoperative risk factors for AKI following ATAAD surgery. The nomogram demonstrated a strong and generalizable discriminatory ability, with an area under the receiver operating characteristic curve of 0.842 in the training cohort and 0.804 in the temporal validation cohort. Calibration plots and DCA confirmed the favorable calibration and clinical utility. Restricted cubic spline analysis revealed nonlinear associations of AKI with preoperative SCr and PLT levels, but a linear positive association with DDi. Conclusion We developed and temporally validated a concise nomogram using three preoperative biomarkers (PLT, SCr, DDi) for predicting AKI after ATAAD surgery, providing a practical tool for preoperative risk stratification. Acute Kidney Injury Aortic Dissection Risk Assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Acute kidney injury (AKI), which is defined as a rapid decline in renal function, manifested by an increase in serum creatinine (SCr) levels and/or a decrease in urine output [ 1 ], represents a prevalent and severe complication following surgical repaire of acute type A aortic dissection (ATAAD) [ 2 ]. Reported incidence rates range from 49.8% to 71.9% [ 3 – 5 ]. Its occurrence is associated with an increased mortality and a prolongs hospital stay, exerting a profound negative impact on prognosis [ 2 , 6 , 7 ]. Therefore, accurate perioperative assessment is paramount. Intraoperative red blood cell transfusion, prolonged surgical time, extended cardiopulmonary bypass time, prolonged circulatory arrest time, hypertension history, renal artery involvement, preoperative SCr level, preoperative neutrophil-to-lymphocyte ratio, aortic cross-clamp time, and ventilator-assisted breathing time are all closely associated with AKI development [ 8 – 10 ]. Well-known mechanisms contributing to postoperative AKI include impaired renal perfusion, ischemia-reperfusion injury, and systemic inflammatory response during surgery [ 11 – 14 ]. Although numerous studies have attempted to establish the predictive models, the vast majority have relied solely on internal validation techniques [ 15 – 17 ]. In fact, to our knowledge, only one prior model has undergone rigorous external validation using a completely independent database [ 18 ], highlighting a significant methodological gap in the field. Without robust external validation, the clinical reliability and readiness of these models for widespread implementation remain uncertain. The clinicians may face difficulties in accurately identifying high-risk patients and implementing timely preventive measures. This study aimed to develop a predictive model based on a retrospective analysis of perioperative data including clinical characters and common used serum biomarkers from patients with ATAAD at a single center. Temporal external validation will be employed to assess the model's reliability, provide new insights into the pathogenesis of AKI in patients with ATAAD, and identify valuable biomarkers for early risk assessment. The development of a clinical predictive model based on these risk factors will assist clinicians in accurately evaluating the risk of AKI during the perioperative period, enabling early identification of high-risk patients, and implementing targeted preventive strategies, ultimately improving patient prognosis. Methods Study Design and Population This single-center, retrospective cohort study was conducted at the Heart Medical Center of Fujian Medical University Union Hospital. Patients who underwent surgical repair for ATAAD from January 1st 2020 to March 31st 2023, were included in this study. The clinical characteristics were extracted from the cardiac surgery database at our hospital. Data collection encompassed demographic characteristics, comorbidities, preoperative laboratory values, and operative and postoperative outcomes. To establish internal and external validation cohorts, the study cohort was temporally divided into training and validation cohorts. The training cohort consisted of patients who underwent surgical repair for ATAAD at our institution from January 1st 2020 to December 31st 2022. Meanwhile, the validation cohort included consecutively enrolled patients from January 1st, 2023, to March 31st, 2023, ensuring chronological separation for temporal validation. The exclusion criteria were as follows: patients younger than 18 years, patients who died before surgery, patients with more than 20% missing laboratory data or incomplete clinical records, and patients with computed tomography angiography-documented renal perfusion deficits. This study was approved by the Ethics Committee of the Fujian Medical University Union Hospital (approval number: 2025KY831). The requirement for informed consent was waived due to the retrospective nature of the study. Sample Size Calculation The sample size was calculated based on events per variable metric [ 19 , 20 ], which is a widely accepted statistical analysis method. This study aimed to develop a predictive model for AKI after ATAAD. The outcome measure was the occurrence of AKI during the postoperative hospital stay, which was observed in 27.149% of the training cohort patients (Table 2 ). Initially, eight key variables were considered as candidate predictors (Table 3 ). The sample size was determined using the events-per-variable (EPV) criterion, assuming an estimated AKI incidence of 50.7% [ 2 ] and 8 candidate predictors. With an EPV of 10 and a planned training/validation split ratio of 7:3, a minimum of 158 patients in the training cohort was required. Considering a potential 10% rate of missing data or exclusion, the total sample size needed was at least 251 patients. The final cohort of 317 patients exceeded this requirement. Table 2 Characteristics of all patients included in the training and validation cohorts Variables Total (n = 317) Training (n = 221) Validation(n = 96) P-value Age, years 54.000 (46.000, 64.000) 53.000 (46.000, 63.000) 56.000 (48.000, 66.250) 0.086 Symptom duration, h 9.000 (6.000, 20.000) 9.000 (6.000, 24.000) 8.000 (6.000, 12.000) 0.020 Male 224 (70.662) 164 (70.386) 60 (71.429) 0.857 Smoking 130 (41.009) 96 (41.202) 34 (40.476) 0.908 Alcohol 67 (21.203) 52 (22.414) 15 (17.857) 0.381 Hypertension 222 (70.032) 158 (67.811) 64 (76.190) 0.151 Diabetes 10 (3.155) 8 (3.433) 2 (2.381) 0.913 Stroke 8 (2.524) 5 (2.146) 3 (3.571) 0.758 COPD 1 (0.315) 0 (0.000) 1 (1.190) 0.265 CAD 5 (1.577) 4 (1.717) 1 (1.190) 1.000 CKD 6 (1.899) 2 (0.862) 4 (4.762) 0.075 Prior cardiac surgery 6 (1.899) 6 (2.586) 0 (0.000) 0.307 WBC count, ×10⁹/L 12.630 (10.398, 15.433) 12.240 (10.110, 14.865) 13.390 (10.990, 17.435) 0.003 Hemoglobin, g/L 131.000 (119.000, 142.000) 131.000 (117.500, 142.000) 129.500 (120.000, 142.000) 0.918 PLT, ×10⁹/L 170.000 (135.500, 205.000) 179.000 (146.500, 214.000) 146.500 (117.750, 186.750) < 0.001 C-reactive protein, mg/L 5.800 (2.300, 18.570) 6.210 (2.630, 20.180) 4.175 (1.745, 15.828) 0.079 Cardiac troponin I, µg/L 0.008 (0.002, 0.043) 0.007 (0.001, 0.033) 0.011 (0.004, 0.100) 0.010 NT-proBNP, pg/mL 254.000 (129.000, 653.500) 231.000 (123.000, 630.000) 347.500 (141.750, 1033.500) 0.079 Procalcitonin, µg/L 0.101 (0.058, 0.224) 0.087 (0.051, 0.186) 0.171 (0.097, 0.388) < 0.001 Albumin, g/L 38.100 (35.100, 40.800) 38.600 (35.900, 40.900) 36.850 (34.575, 39.050) 0.006 SCr, µmol/L 84.000 (63.550, 118.000) 78.000 (60.000, 105.000) 111.000 (83.000, 142.000) < 0.001 DDi, mg/L 11.025 (3.000, 20.000) 6.075 (2.000, 20.000) 20.000 (12.000, 20.000) < 0.001 Prothrombin time, s 14.000 (13.300, 14.825) 13.800 (13.300, 14.600) 14.400 (13.800, 15.700) < 0.001 INR 1.070 (1.010, 1.160) 1.060 (1.000, 1.130) 1.110 (1.042, 1.237) < 0.001 APTT, s 37.550 (34.475, 41.225) 36.700 (34.400, 40.800) 38.200 (34.700, 41.850) 0.416 Fibrinogen, g/L 2.670 (2.020, 3.540) 2.800 (2.150, 3.845) 2.200 (1.670, 3.000) < 0.001 Thrombin time, s 17.600 (16.300, 19.125) 17.300 (16.200, 18.700) 18.500 (16.525, 20.250) 0.003 Operative time, min 315.000 (275.000, 360.000) 305.000 (265.000, 351.000) 331.000 (294.750, 390.000) < 0.001 Cardiopulmonary bypass, min 150.000 (127.000, 189.000) 146.000 (125.000, 180.000) 164.000 (133.750, 197.750) 0.015 Aortic cross-clamping, min 77.000 (55.000, 110.000) 71.000 (54.000, 106.000) 85.500 (60.000, 114.000) 0.050 Hypothermic circulatory arrest, min 75.000 (55.000, 100.000) 75.000 (55.000, 95.000) 80.000 (61.250, 105.000) 0.104 Ascending aorta replacement 281 (88.644) 207 (88.841) 74 (88.095) 0.853 Bentall procedure 26 (8.202) 19 (8.155) 7 (8.333) 0.959 Hemiarch replacement 125 (39.432) 89 (38.197) 36 (42.857) 0.454 Total arch replacement with FET 155 (48.896) 113 (48.498) 42 (50.000) 0.813 CABG 13 (4.101) 10 (4.292) 3 (3.571) 1.000 Hospital stay, days 16.000 (12.000, 21.000) 15.000 (12.000, 19.000) 19.000 (12.750, 28.250) < 0.001 ICU stay, days 4.000 (2.000, 6.000) 3.000 (2.000, 4.000) 7.000 (5.000, 14.250) < 0.001 Ventilation time, min 39.150 (19.067, 80.071) 29.817 (17.200, 53.612) 91.992 (43.342, 133.671) < 0.001 Follow-up, months 10.000 (3.200, 16.767) 10.617 (3.200, 17.258) 8.967 (3.033, 15.733) 0.500 In-hospital mortality 4 (1.262) 1 (0.429) 3 (3.571) 0.101 Reintubation 15 (4.747) 3 (1.293) 12 (14.286) < 0.001 Altered consciousness 11 (3.470) 3 (1.288) 8 (9.524) 0.001 Intracerebral hemorrhage 2 (0.631) 2 (0.858) 0 (0.000) 1.000 Quadriplegia 5 (1.577) 3 (1.288) 2 (2.381) 0.858 AKI 84 (26.498) 60 (27.149) 24 (25.000) 0.690 Intestinal ischemia 2 (0.633) 0 (0.000) 2 (2.381) 0.070 Gastrointestinal bleeding 18 (5.678) 5 (2.146) 13 (15.476) < 0.001 Infection 2 (0.631) 0 (0.000) 2 (2.381) 0.070 CRRT 46 (14.511) 4 (1.717) 42 (50.000) < 0.001 Tracheostomy 6 (1.893) 1 (0.429) 5 (5.952) 0.007 Death 31 (9.779) 12 (5.150) 19 (22.619) < 0.001 AKI: Acute kidney injury; APTT: Activated partial thromboplastin time; CABG: Coronary artery bypass grafting; CAD: Coronary artery disease; CKD: Chronic kidney disease; COPD: Chronic obstructive pulmonary disease; CRRT: Continuous renal replacement therapy; DDi: D-dimer; FET: Frozen elephant trunk; ICU: Intensive care unit; INR: International normalized ratio; NT-proBNP: N-terminal pro-B-type natriuretic peptide; PLT: Platelet count; SCr: Serum creatinine; WBC: White blood cell Table 3 Logistic regression for risk factors of postoperative acute kidney injury Variable Univariate Multivariate β S.E Z P OR (95%CI) Β S.E Z P OR (95%CI) Age 0.027 0.013 2.136 0.033 1.028 (1.002–1.054) 0.027 0.017 1.567 0.117 1.027 (0.993–1.063) Symptom duration -0.005 0.003 -1.506 0.132 0.995 (0.989–1.002) Male -0.209 0.329 -0.635 0.526 0.812 (0.426–1.547) Smoking 0.226 0.307 0.736 0.462 1.254 (0.687–2.288) Alcohol -0.176 0.385 -0.457 0.648 0.839 (0.394–1.785) Hypertension 0.497 0.341 1.460 0.144 1.644 (0.843–3.206) Diabetes -0.986 1.080 -0.913 0.361 0.373 (0.045–3.096) Stroke 0.726 0.779 0.931 0.352 2.066 (0.449–9.515) Prior cardiac surgery -14.611 727.699 -0.020 0.984 0.000 (0.000 - Inf) WBC count 0.097 0.038 2.549 0.011 1.102 (1.023–1.188) 0.069 0.050 1.384 0.167 1.072 (0.971–1.183) Hemoglobin -0.001 0.008 -0.079 0.937 0.999 (0.984–1.015) PLT -0.013 0.004 -3.664 < 0.001 0.987 (0.980–0.994) -0.013 0.005 -2.712 0.007 0.987 (0.978–0.996) C-reactive protein -0.002 0.004 -0.455 0.649 0.998 (0.991–1.006) Procalcitonin 0.253 0.216 1.168 0.243 1.287 (0.843–1.967) Cardiac troponin I -0.028 0.068 -0.417 0.676 0.972 (0.851–1.110) NT-proBNP 0.000 0.000 1.923 0.054 1.000 (1.000–1.000) 0.000 0.000 0.183 0.855 1.000 (1.000–1.000) SCr 0.012 0.004 3.293 < 0.001 1.012 (1.005–1.019) 0.009 0.004 2.056 0.040 1.009 (1.001–1.018) DDi 0.121 0.023 5.204 < 0.001 1.129 (1.079–1.182) 0.105 0.028 3.809 < 0.001 1.111 (1.052–1.172) Operative time 0.005 0.002 2.921 0.003 1.005 (1.002–1.008) 0.004 0.003 1.371 0.170 1.004 (0.998–1.010) Cardiopulmonary Bypass time 0.005 0.002 2.197 0.028 1.005 (1.001–1.010) -0.001 0.004 -0.129 0.898 0.999 (0.991–1.008) Aortic cross-clamping time 0.003 0.003 0.939 0.348 1.003 (0.997–1.008) HCA time 0.002 0.005 0.373 0.709 1.002 (0.992–1.013) CI: Confidence Interval; DDi: D-dimer; HCA: Hypothermic circulatory arrest; NT-proBNP: N-terminal pro-B-type natriuretic peptide; OR: Odds ratio; PLT: Platelet count; SCr: Serum creatinine; WBC: White blood cell Definitions All laboratory data were analyzed preoperatively. AKI was defined according to the KDIGO criteria, based on SCr levels: an increase of ≥ 0.3 mg/dL within 48 hours or ≥ 1.5 times the baseline within 7 days. Patients with AKI were diagnosed within one week of cardiac surgery [ 21 ]. Statistical Analyses All statistical analyses were conducted using the R software (version 4.4.2) and SPSS (version 26.0). Statistical significance was set at a two-tailed p-value < 0.05. Categorical variables were expressed as numbers and percentages, while continuous variables were presented as mean ± standard deviation or median (interquartile range), depending on their distribution. Univariate logistic regression analysis was performed to identify the potential risk factors for AKI in the training cohort. Variables with a p-value < 0.10 in the univariate analysis were included in the multivariate logistic regression model. Variables with a p-value < 0.05 in the multivariate analysis were considered statistically significant and were used to construct a nomogram for predicting the risk of AKI following ATAAD surgery. The discriminative ability of the nomogram was assessed using receiver operating characteristic curves, and the area under the curve (AUC) was calculated. The AUC value ranges from 0.5 to 1.0, where 0.5 indicates no discriminative ability and 1.0 indicates perfect discrimination. An AUC value greater than 0.7 was considered acceptable, while a value greater than 0.8 indicated good predictive performance. Calibration of the nomogram was assessed using the Hosmer-Lemeshow test, which graphically compares the predicted probabilities with the observed outcomes. A perfect calibration is represented by a 45-degree line. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the nomogram by quantifying the net benefit at different threshold probabilities. This analysis helps to determine the range of threshold probabilities for which the nomogram provides clinical value. This analytical framework aligned with the chronological split of the study, ensuring methodological rigor in both model derivation and validation, as outlined in Fig. 1 . Results Participants A total of 430 patients who underwent surgical repair for ATAAD between January 1st 2020, and March 31st 2023, were included in this study. After applying the exclusion criteria, the cohort was temporarily split into a training cohort (n = 221; surgeries from January 1st 2020 to December 31st 2022) and a validation cohort (n = 96; surgeries from January 1st to March 31st 2023) (Fig. 1 ). Among all the participants, 84 (26.498%) developed postoperative AKI. Baseline characteristics of the training and validation cohorts are presented in Table 2 . Model Development Univariate logistic regression analysis identified age, white blood cell count, PLT, N-terminal pro-B-type natriuretic peptide (NT-proBNP), SCr, DDi, operative time, and cardiopulmonary bypass time as potential predictors of AKI. These variables were included in the multivariate logistic regression model, which confirmed that PLT, SCr, and DDi levels were independent risk factors for AKI after ATAAD surgery (Table 3 ). Model Specification A nomogram for predicting AKI was constructed using PLT, SCr, and DDi as key predictors (Fig. 2 ). The model allowed for preoperative risk stratification by assigning weighted scores based on the values of these biomarkers before surgery. Model Performance The nomogram demonstrated a strong discriminative ability, with an AUC of 0.842 (95% CI: 0.784–0.899) in the training cohort and 0.804 (95% CI: 0.702–0.906) in the validation cohort (Fig. 3 ). To further quantify its diagnostic utility across relevant clinical thresholds, key operational characteristics such as accuracy, sensitivity, specificity, PPV, and NPV were calculated and are summarized in Table 1 . Calibration curves showed good agreement between the predicted and observed outcomes (Hosmer-Lemeshow test, p > 0.05), with p-values of 0.124 for the training cohort and 0.466 for the validation cohort (Fig. 4 A andFigure 4B). The DCA indicated that the model provided a significant net benefit across a wide range of threshold probabilities (Fig. 4Figure 4D). Table 1 Confusion matrix Data AUC (95%CI) Accuracy (95%CI) Sensitivity (95%CI) Specificity (95%CI) PPV (95%CI) NPV (95%CI) Cut-off Training 0.842 (0.784–0.899) 0.796 (0.735–0.848) 0.795 (0.732–0.858) 0.800 (0.694–0.906) 0.919 (0.872–0.965) 0.579 (0.468–0.690) 0.292 Validation 0.804 (0.702–0.906) 0.692 (0.587–0.785) 0.691 (0.581–0.801) 0.696 (0.508–0.884) 0.870 (0.781–0.960) 0.432 (0.273–0.592) 0.292 AUC: Area under the curve; CI: Confidence interval; NPV: Negative predictive value; PPV: Positive predictive value Sensitivity Analysis Preoperative SCr, DDi, and PLT levels were regrouped according to the median values and then included in the multivariate regression analysis. The results indicate that these remained independent risk factors for AKI after ATAAD surgery (Table 3 ). The nomogram still revealed strong discriminative ability, with an AUC of 0.842 (95% CI: 0.784–0.899) in the training cohort and 0.804 (95% CI: 0.702–0.906) in the validation cohort (Fig. 3 ). Calibration curves showed good agreement between the predicted and observed outcomes (Hosmer-Lemeshow test, p > 0.05) (Fig. 4 A andFigure 4B). The DCA indicated that the model provided a significant net benefit across a wide range of threshold probabilities (Fig. 4 C andFigure 4D). RCS analysis (with covariates including age, white blood cell count, NT-proBNP level, operative time, and cardiopulmonary bypass time) revealed both linear and nonlinear relationships between preoperative SCr levels and the risk of AKI after ATAAD surgery (both p < 0.001) (Fig. 5 A). After adjusting for the covariates, the p-values remained < 0.05 (specifically < 0.001 and 0.031, respectively) (Fig. 5 B). The RCS analysis also suggested that the odds ratio for the risk of AKI started to change from negative to positive when preoperative SCr exceeded 84 µmol/L. A linear relationship was observed between DDi and the risk of AKI (p < 0.001) but no significant nonlinear relationship (p = 0.168) (Fig. 5 C). The results remained the same after adjusting for covariates (p < 0.001 and p < 0.123, respectively) (Fig. 5 D). RCS analysis also indicated a significant positive correlation between preoperative DDi levels and the risk of postoperative AKI. However, when the DDi exceeded 11 mg/L, the odds ratio for postoperative AKI changed from negative to positive (Fig. 5 C). After adjusting for covariates, this value was adjusted to 10.8 mg/L, with little difference (Fig. 5 D). RCS analysis showed that preoperative PLT levels had both linear and nonlinear relationships with the risk of postoperative AKI (p < 0.001). Lower PLT counts were associated with a higher risk of postoperative AKI (Fig. 5 E). After adjusting for covariates, the results remained consistent ( p = 0.006 and p = 0.002, respectively) (Fig. 5 F). Discussion In this study, we developed and temporally validated a predictive model for postoperative AKI in patients with ATAAD using three preoperative biomarkers: PLT, SCr, and DDi. The model indicated a good predictive performance, with AUC values of 0.842 and 0.804 for the internal training and external temporal validation cohorts, respectively. The performance in the independent validation cohort was particularly noteworthy, as it demonstrated the model's robustness and generalizability beyond the data on which it was built. Calibration curves, DCA, and sensitivity analyses confirmed the model's reliability and clinical utility. These findings distinguish our study from the majority of existing AKI prediction models in this field, which lack rigorous external validation. Indeed, when compared to the single previously published model that underwent external validation using an independent database (MIMIC-III) and reported an AUC of 0.712 [ 18 ], our model achieved a superior discriminative ability (AUC 0.804) while relying on only three readily available preoperative variables. These findings provide a practical tool for preoperative risk stratification and implementation of targeted preventive strategies, ultimately improving the surgical prognosis of patients with ATAAD. Preoperative SCr is a key predictor in our model for AKI after ATAAD surgery. SCr was confirmed as an independent risk factor for postoperative AKI. The nonlinear association between preoperative creatinine levels and AKI risk indicates a threshold effect (Fig. 2 ), where the risk escalates exponentially beyond critical SCr levels. This aligns with the KDIGO 2024 concept of "renal functional reserve depletion"— preexisting kidney problems increase the risk of complications during and after surgery [ 12 , 21 ]. In addition to SCr, preoperative DDi levels were included in the model to provide a more comprehensive assessment and to enhance the predictive power for AKI. Following the exclusion of patients whose elevated DDi was attributable to preoperative CTA-confirmed renal vascular embolism, DDi retains significant predictive value for postoperative AKI in aortic dissection. This finding highlighted a strong association between elevated preoperative DDi and increased AKI risk. This association likely reflected the role of DDi as a key marker of fibrinolytic activation and the underlying thromboinflammatory burden in acute aortic dissection [ 22 – 24 ]. Elevated DDi signifies widespread intravascular coagulation and microthrombus formation, which can compromise renal microcirculation and lead to ischemic injury [ 24 – 27 ]. Previous research has linked DDi to AKI in critical illnesses like sepsis [ 28 ] This study extended that finding by demonstrating its specific and independent predictive value for AKI following ATAAD surgery [ 7 ]—a context marked by profound hemodynamic instability and systemic inflammation. A linear relationship between preoperative DDi levels and AKI risk was confirmed in sensitivity analysis using RCS (Fig. 5 C and Fig. 5 D) (p for nonlinearity > 0.15), supporting its consistent and interpretable predictive value across the cohort. The present study further highlights PLT as a crucial factor influencing the risk of AKI after ATAAD repair. Within the systemic hypercoagulable state, PLT are instrumental in forming fibrin-rich microthrombi that can embolize the renal microvasculature, directly inducing ischemic injury [ 9 , 29 ]. Platelet consumption during this process is clinically reflected by thrombocytopenia, which has been consistently identified as an independent risk factor for postoperative AKI and mortality [ 9 ]. Beyond this microthrombotic pathway, a low PLT count predisposes patients to bleeding, which contributes to AKI through a secondary mechanism. The resultant coagulopathic hemorrhage frequently necessitates massive transfusion, an intervention independently associated with an increased risk of AKI, potentially due to proinflammatory responses and hemolysis-mediated nephrotoxicity [ 30 , 31 ]. Overall, these findings show that changes in PLT levels around surgery affect AKI development through multiple mechanisms. More research is needed to understand these pathways and improve patient outcomes. This study had several limitations. First, it was a single-center retrospective cohort study, which may have limited the generalizability of the findings. However, the use of temporal external validation within the same center partially mitigated this concern by demonstrating reproducibility across different time frames. Second, although a temporal external validation was performed, the sample size of the validation cohort was relatively small, which may have affected the robustness of the model's performance. Third, some potential confounders, such as intraoperative hemodynamic fluctuations and postoperative complications, were not fully accounted for in the analysis. Future multi-center prospective studies with larger sample sizes are required to validate and refine this model. Additionally, the mechanisms underlying the association between identified biomarkers and AKI require further investigation through basic and translational studies. Conclusions A temporal external validation study confirmed the performance of a three-biomarker nomogram (PLT, SCr, DDi) for predicting postoperative AKI in ATAAD patients. This concise, validated tool facilitates reliable preoperative risk stratification to guide preventive care. Abbreviations AKI Acute kidney injury APTT Activated partial thromboplastin time ATAAD Acute type A aortic dissection CABG Coronary artery bypass grafting-associated CAD Coronary artery disease CKD Chronic kidney disease COPD Chronic obstructive pulmonary disease CRRT Continuous renal replacement therapy DCA Decision curve analysis DDi D-dimer FET Frozen elephant trunk ICU Intensive care unit INR International normalized ratio KDIGO Kidney Disease: Improving Global Outcomes NT-proBNP N-terminal pro–B-type natriuretic peptide PLT Platelet counts SCr Serum creatinine WBC White blood cell Declarations Acknowledgements Not applicable Fujian Provincial Center for Cardiovascular Medicine and Union Hospital of Fujian Medical University are acknowledged for providing this opportunity to conduct this study. Appreciation is also expressed to all staff involved in data collection and management for their contributions. Authors’ Contributions X-cY and Y-tH conceived and designed the study, performed the formal analysis, and wrote the original draft. YH and X-fD conducted the investigation and contributed to data curation. L-wC provided resources and assisted with software and validation. YL supervised the project, administered its execution, and contributed to manuscript review. Y-tH and M-fC acquired funding, contributed to validation, and reviewed and edited the manuscript. All authors read and approved the final manuscript. Funding This research was funded by Xiamen Health Guidance Program, grant number 3502Z20224ZD1192. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committees of Union Hospital, Fujian Medical University (2025KY831). Consent for publication Not applicable. Competing interests The authors declare no competing interests. Clinical trial number Not applicable. References Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):c179–84. 10.1159/000339789 . Wang L, Zhong G, Lv X, Dong Y, Hou Y, Dai X, et al. Risk factors for acute kidney injury after Stanford type A aortic dissection repair surgery: a systematic review and meta-analysis. Ren Fail. 2022;44(1):1462–76. 10.1080/0886022x.2022.2113795 . Li L, Zhou J, Hao X, Zhang W, Yu D, Xie Y, et al. The Incidence, Risk Factors and In-Hospital Mortality of Acute Kidney Injury in Patients After Surgery for Acute Type A Aortic Dissection: A Single-Center Retrospective Analysis of 335 Patients. Front Med. 2020;7:557044. 10.3389/fmed.2020.557044 . Zhao W, Wang YP, Tang X, Jiang Y, Xue Y, Wang Y, et al. Development and validation of LCMM prediction algorithms to estimate recovery pattern of postoperative AKI in type A aortic dissection: a retrospective study. Front Cardiovasc Med. 2024;11:1364332. 10.3389/fcvm.2024.1364332 . Chang CH, Chen SW, Chen JJ, Chan YH, Yen CL, Lee TH, et al. Incidence and Transition of Acute Kidney Injury, Acute Kidney Disease to Chronic Kidney Disease after Acute Type A Aortic Dissection Surgery. J Clin Med. 2021;10(20). 10.3390/jcm10204769 . Meng W, Li R, Zha EL. Postoperative acute kidney injury and early and long-term mortality in acute aortic dissection patients: A meta-analysis. Medicine. 2021;100(2):e23426. 10.1097/md.0000000000023426 . Helgason D, Helgadottir S, Ahlsson A, Gunn J, Hjortdal V, Hansson EC, et al. Acute Kidney Injury After Acute Repair of Type A Aortic Dissection. Ann Thorac Surg. 2021;111(4):1292–8. 10.1016/j.athoracsur.2020.07.019 . Sheng W, Xia W, Niu Z, Yang H. Incidence of Acute Kidney Injury and Risk Factors of Prognosis in Patients with Acute Stanford Type A Aortic Dissection. Annals of thoracic and cardiovascular surgery: official. J Association Thorac Cardiovasc Surg Asia. 2023;29(5):249–55. 10.5761/atcs.oa.22-00242 . Tang Z, Shao Y. Postoperative thrombocytopenia and subsequent consequences in acute type A aortic dissection. Ann Med. 2023;55(2):2281653. 10.1080/07853890.2023.2281653 . Xu J, Wang Z, Zhang Q, Wang D, Jiang C, Wang H. Toll-Like Receptor 4 Is an Early and Sensitive Biomarker to Detect Acute Kidney Injury after Surgery for Type A Aortic Dissection. Rev Cardiovasc Med. 2022;23(11):363. 10.31083/j.rcm2311363 . Cheruku SR, Raphael J, Neyra JA, Fox AA. Acute Kidney Injury after Cardiac Surgery: Prediction, Prevention, and Management. Anesthesiology. 2023;139(6):880–98. 10.1097/aln.0000000000004734 . Yoon S-Y, Kim J-S, Jeong K-H, Kim S-K. Acute Kidney Injury: Biomarker-Guided Diagnosis and Management. Medicina. 2022;58(3):340. 10.3390/medicina58030340 . Chen Y, Dong K, Fang C, Shi H, Luo W, Tang CE, et al. The predictive values of monocyte-lymphocyte ratio in postoperative acute kidney injury and prognosis of patients with Stanford type A aortic dissection. Front Immunol. 2023;14:1195421. 10.3389/fimmu.2023.1195421 . Wang Z, Xu J, Zhang Y, Chen C, Kong C, Tang L, et al. Prediction of acute kidney injury incidence following acute type A aortic dissection surgery with novel biomarkers: a prospective observational study. BMC Med. 2023;21(1):503. 10.1186/s12916-023-03215-9 . Li W, Yu W, Chen Y, Tan W, Zhang F, Zhang Y. Development and validation of a nomogram for predicting acute kidney injury risks in patients undergoing acute stanford type A aortic dissection repair surgery. BMC Nephrol. 2025;26(1):257. 10.1186/s12882-025-04150-y . Luo CC, Zhong YL, Qiao ZY, Li CN, Liu YM, Zheng J, et al. Development and validation of a nomogram for postoperative severe acute kidney injury in acute type A aortic dissection. J geriatric cardiology: JGC. 2022;19(10):734–42. 10.11909/j.issn.1671-5411.2022.10.003 . Du R, Wang L, Wang Y, Zhao Z, Zhang D, Zuo S. AKI prediction model in acute aortic dissection surgery: nomogram development and validation. Front Med. 2025;12:1562956. 10.3389/fmed.2025.1562956 . Wei Z, Liu S, Chen Y, Liu H, Liu G, Hu Y, et al. Machine Learning Model-Based Prediction of In-Hospital Acute Kidney Injury Risk in Acute Aortic Dissection Patients. Rev Cardiovasc Med. 2025;26(2):25768. 10.31083/rcm25768 . van Smeden M, Moons KG, de Groot JA, Collins GS, Altman DG, Eijkemans MJ, et al. Sample size for binary logistic prediction models: Beyond events per variable criteria. Stat Methods Med Res. 2019;28(8):2455–74. 10.1177/0962280218784726 . Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol. 2007;165(6):710–8. 10.1093/aje/kwk052 . Kidney Disease. Improving Global Outcomes CKDWG. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024;105(4S):S117–314. 10.1016/j.kint.2023.10.018 . Han L, Dai L, Li H-Y, Lan F, Jiang W-J, Zhang H-J. Elevated D-dimer increases the risk of dialysis after surgery in patients with Stanford A aortic dissection through the impact of the coagulation system. J Thorac Dis. 2018;10(12):6783–93. 10.21037/jtd.2018.11.138 . Xin Q, Xie T, Chen R, Zhang X, Tong Y, Wang H, et al. A Predictive Model Based on Inflammatory and Coagulation Indicators for Sepsis-Induced Acute Kidney Injury. JIR. 2022;15:4561–71. 10.2147/jir.s372246 . Wauthier L, Favresse J, Hardy M, Douxfils J, Le Gal G, Roy PM, et al. D-dimer testing: A narrative review. In: Makowski GS, editor. Advances in Clinical Chemistry. Advances in Clinical Chemistry. Elsevier; 2023. pp. 151–223. Wang Z, Chen T, Ge P, Ge M, Lu L, Zhang L, et al. Risk factors for 30-day mortality in patients who received DeBakey type I aortic dissection repair surgery. J Cardiothorac Surg. 2021;16(1):320. 10.1186/s13019-021-01702-9 . Zhang Y, Lan Y, Chen T, Chen Q, Guo Z, Jiang N. Prediction of Acute Kidney Injury for Acute Type A Aortic Dissection Patients Who Underwent Sun’s Procedure by a Perioperative Nomogram. Cardiorenal Med. 2022;12(3):117–30. 10.1159/000524907 . Park J, Kim SU, Choi HJ, Hong SH, Chae MS. Predictive Role of the D-Dimer Level in Acute Kidney Injury in Living Donor Liver Transplantation: A Retrospective Observational Cohort Study. J Clin Med. 2022;11(2):450. 10.3390/jcm11020450 . Vijayan AL, Vanimaya, Ravindran S, Saikant R, Lakshmi S, Kartik R, et al. Procalcitonin: a promising diagnostic marker for sepsis and antibiotic therapy. j intensive care. 2017;5(1). 10.1186/s40560-017-0246-8 . Guan X-L, Li L, Li H-Y, Gong M, Zhang H-J, Wang X-L. Risk factor prediction of severe postoperative acute kidney injury at stage 3 in patients with acute type A aortic dissection using thromboelastography. Front Cardiovasc Med. 2023;10:1109620. 10.3389/fcvm.2023.1109620 . Li C-N, Ge Y-P, Liu H, Zhang C-H, Zhong Y-L, Chen S-W, et al. Blood Transfusion and Acute Kidney Injury After Total Aortic Arch Replacement for Acute Stanford Type A Aortic Dissection. Heart Lung Circ. 2022;31(1):136–43. 10.1016/j.hlc.2021.05.087 . Naeem SS, Sodha NR, Sellke FW, Ehsan A. Impact of Packed Red Blood Cell and Platelet Transfusions in Patients Undergoing Dissection¦Repair. J Surg Res. 2018;232:338–45. 10.1016/j.jss.2018.06.048 . 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-8641396","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599910176,"identity":"0cfa827d-3f23-45c5-ba29-2970fef0914d","order_by":0,"name":"Xuan-chen Yuan","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuan-chen","middleName":"","lastName":"Yuan","suffix":""},{"id":599910177,"identity":"0190811f-b442-4269-aef2-a853a8e6a889","order_by":1,"name":"Yi-ting Huang","email":"","orcid":"","institution":"Xiamen Cardiovasular Hospital Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Yi-ting","middleName":"","lastName":"Huang","suffix":""},{"id":599910178,"identity":"fd404ae3-0ff5-46c3-8fec-b2a71a463470","order_by":2,"name":"Yin Huang","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yin","middleName":"","lastName":"Huang","suffix":""},{"id":599910180,"identity":"82ac3038-f0d3-4d85-9530-36617de8aecc","order_by":3,"name":"Xiao-fu Dai","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao-fu","middleName":"","lastName":"Dai","suffix":""},{"id":599910182,"identity":"be51b5bc-6e57-4a08-bd19-98ec3c51752a","order_by":4,"name":"Liang-wan Chen","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liang-wan","middleName":"","lastName":"Chen","suffix":""},{"id":599910184,"identity":"6f2a79d4-d556-4d21-a8aa-8a4cffd6842d","order_by":5,"name":"Yong Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACA2YGBgkgzcPP3n7wQUJFDfFaZCR7ziQbPDhzjAgtDBAtNgY3EswkH7YwE9Zizs778NaNmjs8BmcOpFUkNrAx8Ld3J+DVYtnMbmydc+wZj+TxxmM3EnfIMEicObsBv8MOs7FJ57Ad5uED2nIj8Qwbg4FELjFa/h3mYQD6pSCxjZlILblth3kEgFoYiNJi2czGbJ3bd5gHFMgSCWeO8RD0izn/McbbOd8O24Oi8uOPiho5/vZe/FowAA9pykfBKBgFo2AUYAUATWFJJa0tDEAAAAAASUVORK5CYII=","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yong","middleName":"","lastName":"Lin","suffix":""},{"id":599910185,"identity":"059d1fad-86a3-4f1b-be44-133465fb9bc3","order_by":6,"name":"Mei-fang Chen","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mei-fang","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-01-19 16:22:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8641396/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8641396/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104177305,"identity":"9396e7d7-5a1c-41af-a2a0-e3a62d62d1c9","added_by":"auto","created_at":"2026-03-08 16:45:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":506493,"visible":true,"origin":"","legend":"\u003cp\u003ePatient selection flowchart. ATAAD: acute type A aortic dissection; CTA: computed tomography angiography\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8641396/v1/99d4d80ea57752c393091979.png"},{"id":104177306,"identity":"37bf09a0-8265-4327-884f-25f5ce594445","added_by":"auto","created_at":"2026-03-08 16:45:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67420,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting postoperative AKI in ATAAD. SCR: serum creatinine; DDi: D-dimer; PLT: platelet count.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8641396/v1/4a7988861ef681946b25ba99.png"},{"id":104177308,"identity":"a01ba878-5b57-45a3-9586-6c88b33ccc56","added_by":"auto","created_at":"2026-03-08 16:45:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":314591,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve. Patients treated between January 1st, 2020, and December 31st, 2022, were assigned to the training cohort for model establishment, and those treated between January, 1st, 2023, and March, 31st, 2023, were assigned to the validation cohort for temporal external validation. The areas under the curve with 95% confidence intervals were 0.842 (0.784-0.899) for the training cohort and 0.804 (0.702-0.906) for the validation cohort. The optimal cut-off value was set at 0.292.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8641396/v1/6d8a65abb2f99459af839462.png"},{"id":104177307,"identity":"975d58fb-c102-4a9f-9e74-7b1b23a03e66","added_by":"auto","created_at":"2026-03-08 16:45:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":649460,"visible":true,"origin":"","legend":"\u003cp\u003eModel performance. The Hosmer-Lemeshow test indicated that the P-values for the calibration curves of the training cohort (A) and validation cohort (B) were 0.124 and 0.466, respectively (\u0026gt;0.05), indicating good agreement between predicted and observed probabilities. Decision curve analysis for the training cohort (C) demonstrated a maximum net benefit of 0.27 at threshold probabilities between 0.06 and 0.69. In the validation cohort (D), the maximum net benefit was 0.23 at thresholds between 0.04 and 0.58, indicating the model's clinical utility across a wide range of risk thresholds.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8641396/v1/29fd749e44b2a215d4cf96df.png"},{"id":104403907,"identity":"eea1aa31-da02-4fec-b5b9-7027d663d80f","added_by":"auto","created_at":"2026-03-11 12:19:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":717437,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline (RCS) curves depicting the associations between various preoperative biomarkers and AKI risk. The y-axis is presented on a log scale. Solid lines represent odds ratios, and shaded areas indicate 95% confidence intervals. Dotted horizontal lines denote the reference line at an odds ratio of 1.0. Panels (A, C, E) illustrate unadjusted associations for serum creatinine (SCr), D-dimer (DDi), and platelet count (PLT), respectively. Panels (B, D, F) show associations adjusted for covariates. Significant nonlinear relationships were observed for creatinine (P for nonlinearity \u0026lt; 0.001 in both models, A and B) and platelet count (P for nonlinearity \u0026lt; 0.001 in both models, E and F), whereas the association between D-dimer and AKI risk was linear (P for nonlinearity \u0026gt; 0.15 in both unadjusted and adjusted models, C and D). Red arrows highlight potential threshold values for SCr, DDi, and PLT.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8641396/v1/0d09c494a3a61e1fe3634bae.png"},{"id":108804114,"identity":"1141f3ee-632d-4757-898a-117fd08d5a5f","added_by":"auto","created_at":"2026-05-08 15:16:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2734623,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8641396/v1/fac3256b-f306-489b-a2f4-8f9e4d6d3fc1.pdf"},{"id":106723502,"identity":"0b032ec8-57c7-43cd-a595-5aaa859215ed","added_by":"auto","created_at":"2026-04-12 17:52:49","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":948194,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphic abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8641396/v1/c8c9a9c7f10def456e697787.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Predictive Model for Postoperative Acute Kidney Injury in Patients with Acute Type A Aortic Dissection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute kidney injury (AKI), which is defined as a rapid decline in renal function, manifested by an increase in serum creatinine (SCr) levels and/or a decrease in urine output [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], represents a prevalent and severe complication following surgical repaire of acute type A aortic dissection (ATAAD) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Reported incidence rates range from 49.8% to 71.9% [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Its occurrence is associated with an increased mortality and a prolongs hospital stay, exerting a profound negative impact on prognosis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, accurate perioperative assessment is paramount.\u003c/p\u003e \u003cp\u003eIntraoperative red blood cell transfusion, prolonged surgical time, extended cardiopulmonary bypass time, prolonged circulatory arrest time, hypertension history, renal artery involvement, preoperative SCr level, preoperative neutrophil-to-lymphocyte ratio, aortic cross-clamp time, and ventilator-assisted breathing time are all closely associated with AKI development [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Well-known mechanisms contributing to postoperative AKI include impaired renal perfusion, ischemia-reperfusion injury, and systemic inflammatory response during surgery [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough numerous studies have attempted to establish the predictive models, the vast majority have relied solely on internal validation techniques [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In fact, to our knowledge, only one prior model has undergone rigorous external validation using a completely independent database [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], highlighting a significant methodological gap in the field. Without robust external validation, the clinical reliability and readiness of these models for widespread implementation remain uncertain. The clinicians may face difficulties in accurately identifying high-risk patients and implementing timely preventive measures.\u003c/p\u003e \u003cp\u003eThis study aimed to develop a predictive model based on a retrospective analysis of perioperative data including clinical characters and common used serum biomarkers from patients with ATAAD at a single center. Temporal external validation will be employed to assess the model's reliability, provide new insights into the pathogenesis of AKI in patients with ATAAD, and identify valuable biomarkers for early risk assessment. The development of a clinical predictive model based on these risk factors will assist clinicians in accurately evaluating the risk of AKI during the perioperative period, enabling early identification of high-risk patients, and implementing targeted preventive strategies, ultimately improving patient prognosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis single-center, retrospective cohort study was conducted at the Heart Medical Center of Fujian Medical University Union Hospital. Patients who underwent surgical repair for ATAAD from January 1st 2020 to March 31st 2023, were included in this study. The clinical characteristics were extracted from the cardiac surgery database at our hospital. Data collection encompassed demographic characteristics, comorbidities, preoperative laboratory values, and operative and postoperative outcomes. To establish internal and external validation cohorts, the study cohort was temporally divided into training and validation cohorts. The training cohort consisted of patients who underwent surgical repair for ATAAD at our institution from January 1st 2020 to December 31st 2022. Meanwhile, the validation cohort included consecutively enrolled patients from January 1st, 2023, to March 31st, 2023, ensuring chronological separation for temporal validation. The exclusion criteria were as follows: patients younger than 18 years, patients who died before surgery, patients with more than 20% missing laboratory data or incomplete clinical records, and patients with computed tomography angiography-documented renal perfusion deficits. This study was approved by the Ethics Committee of the Fujian Medical University Union Hospital (approval number: 2025KY831). The requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample Size Calculation\u003c/h3\u003e\n\u003cp\u003eThe sample size was calculated based on events per variable metric [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which is a widely accepted statistical analysis method. This study aimed to develop a predictive model for AKI after ATAAD. The outcome measure was the occurrence of AKI during the postoperative hospital stay, which was observed in 27.149% of the training cohort patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Initially, eight key variables were considered as candidate predictors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The sample size was determined using the events-per-variable (EPV) criterion, assuming an estimated AKI incidence of 50.7% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and 8 candidate predictors. With an EPV of 10 and a planned training/validation split ratio of 7:3, a minimum of 158 patients in the training cohort was required. Considering a potential 10% rate of missing data or exclusion, the total sample size needed was at least 251 patients. The final cohort of 317 patients exceeded this requirement.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of all patients included in the training and validation cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;317)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining (n\u0026thinsp;=\u0026thinsp;221)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation(n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.000 (46.000, 64.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.000 (46.000, 63.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.000 (48.000, 66.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom duration, h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.000 (6.000, 20.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.000 (6.000, 24.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.000 (6.000, 12.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e224 (70.662)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e164 (70.386)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60 (71.429)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130 (41.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96 (41.202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (40.476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67 (21.203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (22.414)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (17.857)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e222 (70.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158 (67.811)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64 (76.190)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (3.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (3.433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (2.381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (2.524)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (2.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (3.571)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.315)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (1.190)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (1.577)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (1.717)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (1.190)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (1.899)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (0.862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (4.762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior cardiac surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (1.899)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (2.586)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC count, \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.630 (10.398, 15.433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.240 (10.110, 14.865)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.390 (10.990, 17.435)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131.000 (119.000, 142.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131.000 (117.500, 142.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129.500 (120.000, 142.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT, \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170.000 (135.500, 205.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e179.000 (146.500, 214.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146.500 (117.750, 186.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.800 (2.300, 18.570)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.210 (2.630, 20.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.175 (1.745, 15.828)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac troponin I, \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008 (0.002, 0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007 (0.001, 0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011 (0.004, 0.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP, pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e254.000 (129.000, 653.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e231.000 (123.000, 630.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e347.500 (141.750, 1033.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin, \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.101 (0.058, 0.224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.087 (0.051, 0.186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.171 (0.097, 0.388)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.100 (35.100, 40.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.600 (35.900, 40.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.850 (34.575, 39.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCr, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.000 (63.550, 118.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.000 (60.000, 105.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111.000 (83.000, 142.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDDi, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.025 (3.000, 20.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.075 (2.000, 20.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.000 (12.000, 20.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProthrombin time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.000 (13.300, 14.825)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.800 (13.300, 14.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.400 (13.800, 15.700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.070 (1.010, 1.160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.060 (1.000, 1.130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.110 (1.042, 1.237)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.550 (34.475, 41.225)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.700 (34.400, 40.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.200 (34.700, 41.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrinogen, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.670 (2.020, 3.540)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.800 (2.150, 3.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.200 (1.670, 3.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrombin time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.600 (16.300, 19.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.300 (16.200, 18.700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.500 (16.525, 20.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperative time, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e315.000 (275.000, 360.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e305.000 (265.000, 351.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331.000 (294.750, 390.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiopulmonary bypass, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150.000 (127.000, 189.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e146.000 (125.000, 180.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e164.000 (133.750, 197.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic cross-clamping, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77.000 (55.000, 110.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.000 (54.000, 106.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.500 (60.000, 114.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothermic circulatory arrest, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.000 (55.000, 100.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.000 (55.000, 95.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.000 (61.250, 105.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscending aorta replacement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281 (88.644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207 (88.841)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74 (88.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBentall procedure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (8.202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (8.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (8.333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemiarch replacement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125 (39.432)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89 (38.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36 (42.857)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal arch replacement with FET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155 (48.896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113 (48.498)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42 (50.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCABG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (4.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (4.292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (3.571)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital stay, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.000 (12.000, 21.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.000 (12.000, 19.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.000 (12.750, 28.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU stay, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.000 (2.000, 6.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.000 (2.000, 4.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.000 (5.000, 14.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentilation time, min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.150 (19.067, 80.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.817 (17.200, 53.612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.992 (43.342, 133.671)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up, months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.000 (3.200, 16.767)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.617 (3.200, 17.258)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.967 (3.033, 15.733)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (1.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.429)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (3.571)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReintubation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (4.747)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (1.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (14.286)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAltered consciousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (3.470)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (1.288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (9.524)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntracerebral hemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (0.631)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (0.858)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuadriplegia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (1.577)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (1.288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (2.381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84 (26.498)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60 (27.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (25.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntestinal ischemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (0.633)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (2.381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal bleeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (5.678)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (2.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (15.476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (0.631)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (2.381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (14.511)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (1.717)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42 (50.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTracheostomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (1.893)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.429)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (5.952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (9.779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (5.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (22.619)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAKI: Acute kidney injury; APTT: Activated partial thromboplastin time; CABG: Coronary artery bypass grafting; CAD: Coronary artery disease; CKD: Chronic kidney disease; COPD: Chronic obstructive pulmonary disease; CRRT: Continuous renal replacement therapy; DDi: D-dimer; FET: Frozen elephant trunk; ICU: Intensive care unit; INR: International normalized ratio; NT-proBNP: N-terminal pro-B-type natriuretic peptide; PLT: Platelet count; SCr: Serum creatinine; WBC: White blood cell\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression for risk factors of postoperative acute kidney injury\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eΒ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.028 (1.002\u0026ndash;1.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.027 (0.993\u0026ndash;1.063)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.995 (0.989\u0026ndash;1.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.812 (0.426\u0026ndash;1.547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.254 (0.687\u0026ndash;2.288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.839 (0.394\u0026ndash;1.785)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.644 (0.843\u0026ndash;3.206)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.373 (0.045\u0026ndash;3.096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.066 (0.449\u0026ndash;9.515)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior cardiac surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-14.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e727.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000 (0.000 - Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.102 (1.023\u0026ndash;1.188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.072 (0.971\u0026ndash;1.183)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.999 (0.984\u0026ndash;1.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.987 (0.980\u0026ndash;0.994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.987 (0.978\u0026ndash;0.996)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.998 (0.991\u0026ndash;1.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.287 (0.843\u0026ndash;1.967)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac troponin I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.972 (0.851\u0026ndash;1.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (1.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.000 (1.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.012 (1.005\u0026ndash;1.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.009 (1.001\u0026ndash;1.018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDDi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.129 (1.079\u0026ndash;1.182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.111 (1.052\u0026ndash;1.172)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperative time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.005 (1.002\u0026ndash;1.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.004 (0.998\u0026ndash;1.010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiopulmonary Bypass time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.005 (1.001\u0026ndash;1.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.999 (0.991\u0026ndash;1.008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic cross-clamping time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.003 (0.997\u0026ndash;1.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCA time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.002 (0.992\u0026ndash;1.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eCI: Confidence Interval; DDi: D-dimer; HCA: Hypothermic circulatory arrest; NT-proBNP: N-terminal pro-B-type natriuretic peptide; OR: Odds ratio; PLT: Platelet count; SCr: Serum creatinine; WBC: White blood cell\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDefinitions\u003c/h3\u003e\n\u003cp\u003eAll laboratory data were analyzed preoperatively. AKI was defined according to the KDIGO criteria, based on SCr levels: an increase of \u0026ge;\u0026thinsp;0.3 mg/dL within 48 hours or \u0026ge;\u0026thinsp;1.5 times the baseline within 7 days. Patients with AKI were diagnosed within one week of cardiac surgery [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were conducted using the R software (version 4.4.2) and SPSS (version 26.0). Statistical significance was set at a two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Categorical variables were expressed as numbers and percentages, while continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range), depending on their distribution. Univariate logistic regression analysis was performed to identify the potential risk factors for AKI in the training cohort. Variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in the univariate analysis were included in the multivariate logistic regression model. Variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the multivariate analysis were considered statistically significant and were used to construct a nomogram for predicting the risk of AKI following ATAAD surgery. The discriminative ability of the nomogram was assessed using receiver operating characteristic curves, and the area under the curve (AUC) was calculated. The AUC value ranges from 0.5 to 1.0, where 0.5 indicates no discriminative ability and 1.0 indicates perfect discrimination. An AUC value greater than 0.7 was considered acceptable, while a value greater than 0.8 indicated good predictive performance. Calibration of the nomogram was assessed using the Hosmer-Lemeshow test, which graphically compares the predicted probabilities with the observed outcomes. A perfect calibration is represented by a 45-degree line. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the nomogram by quantifying the net benefit at different threshold probabilities. This analysis helps to determine the range of threshold probabilities for which the nomogram provides clinical value. This analytical framework aligned with the chronological split of the study, ensuring methodological rigor in both model derivation and validation, as outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 430 patients who underwent surgical repair for ATAAD between January 1st 2020, and March 31st 2023, were included in this study. After applying the exclusion criteria, the cohort was temporarily split into a training cohort (n\u0026thinsp;=\u0026thinsp;221; surgeries from January 1st 2020 to December 31st 2022) and a validation cohort (n\u0026thinsp;=\u0026thinsp;96; surgeries from January 1st to March 31st 2023) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among all the participants, 84 (26.498%) developed postoperative AKI. Baseline characteristics of the training and validation cohorts are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eModel Development\u003c/h3\u003e\n\u003cp\u003eUnivariate logistic regression analysis identified age, white blood cell count, PLT, N-terminal pro-B-type natriuretic peptide (NT-proBNP), SCr, DDi, operative time, and cardiopulmonary bypass time as potential predictors of AKI. These variables were included in the multivariate logistic regression model, which confirmed that PLT, SCr, and DDi levels were independent risk factors for AKI after ATAAD surgery (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eModel Specification\u003c/h3\u003e\n\u003cp\u003eA nomogram for predicting AKI was constructed using PLT, SCr, and DDi as key predictors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The model allowed for preoperative risk stratification by assigning weighted scores based on the values of these biomarkers before surgery.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eModel Performance\u003c/h2\u003e \u003cp\u003eThe nomogram demonstrated a strong discriminative ability, with an AUC of 0.842 (95% CI: 0.784\u0026ndash;0.899) in the training cohort and 0.804 (95% CI: 0.702\u0026ndash;0.906) in the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). To further quantify its diagnostic utility across relevant clinical thresholds, key operational characteristics such as accuracy, sensitivity, specificity, PPV, and NPV were calculated and are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Calibration curves showed good agreement between the predicted and observed outcomes (Hosmer-Lemeshow test, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), with p-values of 0.124 for the training cohort and 0.466 for the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA andFigure 4B). The DCA indicated that the model provided a significant net benefit across a wide range of threshold probabilities (Fig.\u0026nbsp;4Figure 4D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfusion matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccuracy (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.842 (0.784\u0026ndash;0.899)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.796 (0.735\u0026ndash;0.848)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.795 (0.732\u0026ndash;0.858)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.800 (0.694\u0026ndash;0.906)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.919 (0.872\u0026ndash;0.965)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.579 (0.468\u0026ndash;0.690)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValidation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.804 (0.702\u0026ndash;0.906)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.692 (0.587\u0026ndash;0.785)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.691 (0.581\u0026ndash;0.801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.696 (0.508\u0026ndash;0.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.870 (0.781\u0026ndash;0.960)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.432 (0.273\u0026ndash;0.592)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAUC: Area under the curve; CI: Confidence interval; NPV: Negative predictive value; PPV: Positive predictive value\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis\u003c/h2\u003e \u003cp\u003ePreoperative SCr, DDi, and PLT levels were regrouped according to the median values and then included in the multivariate regression analysis. The results indicate that these remained independent risk factors for AKI after ATAAD surgery (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The nomogram still revealed strong discriminative ability, with an AUC of 0.842 (95% CI: 0.784\u0026ndash;0.899) in the training cohort and 0.804 (95% CI: 0.702\u0026ndash;0.906) in the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Calibration curves showed good agreement between the predicted and observed outcomes (Hosmer-Lemeshow test, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA andFigure 4B). The DCA indicated that the model provided a significant net benefit across a wide range of threshold probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC andFigure 4D). RCS analysis (with covariates including age, white blood cell count, NT-proBNP level, operative time, and cardiopulmonary bypass time) revealed both linear and nonlinear relationships between preoperative SCr levels and the risk of AKI after ATAAD surgery (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). After adjusting for the covariates, the p-values remained\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (specifically\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and 0.031, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The RCS analysis also suggested that the odds ratio for the risk of AKI started to change from negative to positive when preoperative SCr exceeded 84 \u0026micro;mol/L. A linear relationship was observed between DDi and the risk of AKI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but no significant nonlinear relationship (p\u0026thinsp;=\u0026thinsp;0.168) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The results remained the same after adjusting for covariates (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.123, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). RCS analysis also indicated a significant positive correlation between preoperative DDi levels and the risk of postoperative AKI. However, when the DDi exceeded 11 mg/L, the odds ratio for postoperative AKI changed from negative to positive (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). After adjusting for covariates, this value was adjusted to 10.8 mg/L, with little difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). RCS analysis showed that preoperative PLT levels had both linear and nonlinear relationships with the risk of postoperative AKI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Lower PLT counts were associated with a higher risk of postoperative AKI (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). After adjusting for covariates, the results remained consistent ( p\u0026thinsp;=\u0026thinsp;0.006 and p\u0026thinsp;=\u0026thinsp;0.002, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed and temporally validated a predictive model for postoperative AKI in patients with ATAAD using three preoperative biomarkers: PLT, SCr, and DDi. The model indicated a good predictive performance, with AUC values of 0.842 and 0.804 for the internal training and external temporal validation cohorts, respectively. The performance in the independent validation cohort was particularly noteworthy, as it demonstrated the model's robustness and generalizability beyond the data on which it was built. Calibration curves, DCA, and sensitivity analyses confirmed the model's reliability and clinical utility. These findings distinguish our study from the majority of existing AKI prediction models in this field, which lack rigorous external validation. Indeed, when compared to the single previously published model that underwent external validation using an independent database (MIMIC-III) and reported an AUC of 0.712 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], our model achieved a superior discriminative ability (AUC 0.804) while relying on only three readily available preoperative variables. These findings provide a practical tool for preoperative risk stratification and implementation of targeted preventive strategies, ultimately improving the surgical prognosis of patients with ATAAD.\u003c/p\u003e \u003cp\u003ePreoperative SCr is a key predictor in our model for AKI after ATAAD surgery. SCr was confirmed as an independent risk factor for postoperative AKI. The nonlinear association between preoperative creatinine levels and AKI risk indicates a threshold effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), where the risk escalates exponentially beyond critical SCr levels. This aligns with the KDIGO 2024 concept of \"renal functional reserve depletion\"\u0026mdash; preexisting kidney problems increase the risk of complications during and after surgery [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to SCr, preoperative DDi levels were included in the model to provide a more comprehensive assessment and to enhance the predictive power for AKI. Following the exclusion of patients whose elevated DDi was attributable to preoperative CTA-confirmed renal vascular embolism, DDi retains significant predictive value for postoperative AKI in aortic dissection. This finding highlighted a strong association between elevated preoperative DDi and increased AKI risk. This association likely reflected the role of DDi as a key marker of fibrinolytic activation and the underlying thromboinflammatory burden in acute aortic dissection [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Elevated DDi signifies widespread intravascular coagulation and microthrombus formation, which can compromise renal microcirculation and lead to ischemic injury [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Previous research has linked DDi to AKI in critical illnesses like sepsis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] This study extended that finding by demonstrating its specific and independent predictive value for AKI following ATAAD surgery [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u0026mdash;a context marked by profound hemodynamic instability and systemic inflammation. A linear relationship between preoperative DDi levels and AKI risk was confirmed in sensitivity analysis using RCS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) (p for nonlinearity\u0026thinsp;\u0026gt;\u0026thinsp;0.15), supporting its consistent and interpretable predictive value across the cohort.\u003c/p\u003e \u003cp\u003eThe present study further highlights PLT as a crucial factor influencing the risk of AKI after ATAAD repair. Within the systemic hypercoagulable state, PLT are instrumental in forming fibrin-rich microthrombi that can embolize the renal microvasculature, directly inducing ischemic injury [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Platelet consumption during this process is clinically reflected by thrombocytopenia, which has been consistently identified as an independent risk factor for postoperative AKI and mortality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Beyond this microthrombotic pathway, a low PLT count predisposes patients to bleeding, which contributes to AKI through a secondary mechanism. The resultant coagulopathic hemorrhage frequently necessitates massive transfusion, an intervention independently associated with an increased risk of AKI, potentially due to proinflammatory responses and hemolysis-mediated nephrotoxicity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Overall, these findings show that changes in PLT levels around surgery affect AKI development through multiple mechanisms. More research is needed to understand these pathways and improve patient outcomes.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, it was a single-center retrospective cohort study, which may have limited the generalizability of the findings. However, the use of temporal external validation within the same center partially mitigated this concern by demonstrating reproducibility across different time frames. Second, although a temporal external validation was performed, the sample size of the validation cohort was relatively small, which may have affected the robustness of the model's performance. Third, some potential confounders, such as intraoperative hemodynamic fluctuations and postoperative complications, were not fully accounted for in the analysis. Future multi-center prospective studies with larger sample sizes are required to validate and refine this model. Additionally, the mechanisms underlying the association between identified biomarkers and AKI require further investigation through basic and translational studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eA temporal external validation study confirmed the performance of a three-biomarker nomogram (PLT, SCr, DDi) for predicting postoperative AKI in ATAAD patients. This concise, validated tool facilitates reliable preoperative risk stratification to guide preventive care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAKI Acute kidney injury\u003c/p\u003e\n\u003cp\u003eAPTT Activated partial thromboplastin time\u003c/p\u003e\n\u003cp\u003eATAAD Acute type A aortic dissection\u003c/p\u003e\n\u003cp\u003eCABG Coronary artery bypass grafting-associated\u003c/p\u003e\n\u003cp\u003eCAD Coronary artery disease\u003c/p\u003e\n\u003cp\u003eCKD Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eCOPD Chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003eCRRT Continuous renal replacement therapy\u003c/p\u003e\n\u003cp\u003eDCA Decision curve analysis\u003c/p\u003e\n\u003cp\u003eDDi D-dimer\u003c/p\u003e\n\u003cp\u003eFET Frozen elephant trunk\u003c/p\u003e\n\u003cp\u003eICU Intensive care unit\u003c/p\u003e\n\u003cp\u003eINR International normalized ratio\u003c/p\u003e\n\u003cp\u003eKDIGO Kidney Disease: Improving Global Outcomes\u003c/p\u003e\n\u003cp\u003eNT-proBNP N-terminal pro\u0026ndash;B-type natriuretic peptide\u003c/p\u003e\n\u003cp\u003ePLT Platelet counts\u003c/p\u003e\n\u003cp\u003eSCr Serum creatinine\u003c/p\u003e\n\u003cp\u003eWBC White blood cell\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eFujian Provincial Center for Cardiovascular Medicine and Union Hospital of Fujian Medical University are acknowledged for providing this opportunity to conduct this study. Appreciation is also expressed to all staff involved in data collection and management for their contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX-cY and Y-tH conceived and designed the study, performed the formal analysis, and wrote the original draft. YH and X-fD conducted the investigation and contributed to data curation. L-wC provided resources and assisted with software and validation. YL supervised the project, administered its execution, and contributed to manuscript review. Y-tH and M-fC acquired funding, contributed to validation, and reviewed and edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Xiamen Health Guidance Program, grant number 3502Z20224ZD1192.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committees of Union Hospital, Fujian Medical University (2025KY831).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKhwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):c179\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000339789\u003c/span\u003e\u003cspan address=\"10.1159/000339789\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Zhong G, Lv X, Dong Y, Hou Y, Dai X, et al. Risk factors for acute kidney injury after Stanford type A aortic dissection repair surgery: a systematic review and meta-analysis. Ren Fail. 2022;44(1):1462\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/0886022x.2022.2113795\u003c/span\u003e\u003cspan address=\"10.1080/0886022x.2022.2113795\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Zhou J, Hao X, Zhang W, Yu D, Xie Y, et al. The Incidence, Risk Factors and In-Hospital Mortality of Acute Kidney Injury in Patients After Surgery for Acute Type A Aortic Dissection: A Single-Center Retrospective Analysis of 335 Patients. Front Med. 2020;7:557044. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2020.557044\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2020.557044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao W, Wang YP, Tang X, Jiang Y, Xue Y, Wang Y, et al. Development and validation of LCMM prediction algorithms to estimate recovery pattern of postoperative AKI in type A aortic dissection: a retrospective study. Front Cardiovasc Med. 2024;11:1364332. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2024.1364332\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2024.1364332\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang CH, Chen SW, Chen JJ, Chan YH, Yen CL, Lee TH, et al. Incidence and Transition of Acute Kidney Injury, Acute Kidney Disease to Chronic Kidney Disease after Acute Type A Aortic Dissection Surgery. J Clin Med. 2021;10(20). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm10204769\u003c/span\u003e\u003cspan address=\"10.3390/jcm10204769\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng W, Li R, Zha EL. Postoperative acute kidney injury and early and long-term mortality in acute aortic dissection patients: A meta-analysis. Medicine. 2021;100(2):e23426. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/md.0000000000023426\u003c/span\u003e\u003cspan address=\"10.1097/md.0000000000023426\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelgason D, Helgadottir S, Ahlsson A, Gunn J, Hjortdal V, Hansson EC, et al. Acute Kidney Injury After Acute Repair of Type A Aortic Dissection. Ann Thorac Surg. 2021;111(4):1292\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.athoracsur.2020.07.019\u003c/span\u003e\u003cspan address=\"10.1016/j.athoracsur.2020.07.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheng W, Xia W, Niu Z, Yang H. Incidence of Acute Kidney Injury and Risk Factors of Prognosis in Patients with Acute Stanford Type A Aortic Dissection. Annals of thoracic and cardiovascular surgery: official. J Association Thorac Cardiovasc Surg Asia. 2023;29(5):249\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5761/atcs.oa.22-00242\u003c/span\u003e\u003cspan address=\"10.5761/atcs.oa.22-00242\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang Z, Shao Y. Postoperative thrombocytopenia and subsequent consequences in acute type A aortic dissection. Ann Med. 2023;55(2):2281653. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/07853890.2023.2281653\u003c/span\u003e\u003cspan address=\"10.1080/07853890.2023.2281653\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu J, Wang Z, Zhang Q, Wang D, Jiang C, Wang H. Toll-Like Receptor 4 Is an Early and Sensitive Biomarker to Detect Acute Kidney Injury after Surgery for Type A Aortic Dissection. Rev Cardiovasc Med. 2022;23(11):363. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31083/j.rcm2311363\u003c/span\u003e\u003cspan address=\"10.31083/j.rcm2311363\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheruku SR, Raphael J, Neyra JA, Fox AA. Acute Kidney Injury after Cardiac Surgery: Prediction, Prevention, and Management. Anesthesiology. 2023;139(6):880\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/aln.0000000000004734\u003c/span\u003e\u003cspan address=\"10.1097/aln.0000000000004734\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoon S-Y, Kim J-S, Jeong K-H, Kim S-K. Acute Kidney Injury: Biomarker-Guided Diagnosis and Management. Medicina. 2022;58(3):340. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/medicina58030340\u003c/span\u003e\u003cspan address=\"10.3390/medicina58030340\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Dong K, Fang C, Shi H, Luo W, Tang CE, et al. The predictive values of monocyte-lymphocyte ratio in postoperative acute kidney injury and prognosis of patients with Stanford type A aortic dissection. Front Immunol. 2023;14:1195421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2023.1195421\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1195421\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Xu J, Zhang Y, Chen C, Kong C, Tang L, et al. Prediction of acute kidney injury incidence following acute type A aortic dissection surgery with novel biomarkers: a prospective observational study. BMC Med. 2023;21(1):503. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12916-023-03215-9\u003c/span\u003e\u003cspan address=\"10.1186/s12916-023-03215-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi W, Yu W, Chen Y, Tan W, Zhang F, Zhang Y. Development and validation of a nomogram for predicting acute kidney injury risks in patients undergoing acute stanford type A aortic dissection repair surgery. BMC Nephrol. 2025;26(1):257. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12882-025-04150-y\u003c/span\u003e\u003cspan address=\"10.1186/s12882-025-04150-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo CC, Zhong YL, Qiao ZY, Li CN, Liu YM, Zheng J, et al. Development and validation of a nomogram for postoperative severe acute kidney injury in acute type A aortic dissection. J geriatric cardiology: JGC. 2022;19(10):734\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11909/j.issn.1671-5411.2022.10.003\u003c/span\u003e\u003cspan address=\"10.11909/j.issn.1671-5411.2022.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu R, Wang L, Wang Y, Zhao Z, Zhang D, Zuo S. AKI prediction model in acute aortic dissection surgery: nomogram development and validation. Front Med. 2025;12:1562956. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2025.1562956\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2025.1562956\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei Z, Liu S, Chen Y, Liu H, Liu G, Hu Y, et al. Machine Learning Model-Based Prediction of In-Hospital Acute Kidney Injury Risk in Acute Aortic Dissection Patients. Rev Cardiovasc Med. 2025;26(2):25768. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31083/rcm25768\u003c/span\u003e\u003cspan address=\"10.31083/rcm25768\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Smeden M, Moons KG, de Groot JA, Collins GS, Altman DG, Eijkemans MJ, et al. Sample size for binary logistic prediction models: Beyond events per variable criteria. Stat Methods Med Res. 2019;28(8):2455\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0962280218784726\u003c/span\u003e\u003cspan address=\"10.1177/0962280218784726\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol. 2007;165(6):710\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/aje/kwk052\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwk052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKidney Disease. Improving Global Outcomes CKDWG. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024;105(4S):S117\u0026ndash;314. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.kint.2023.10.018\u003c/span\u003e\u003cspan address=\"10.1016/j.kint.2023.10.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan L, Dai L, Li H-Y, Lan F, Jiang W-J, Zhang H-J. Elevated D-dimer increases the risk of dialysis after surgery in patients with Stanford A aortic dissection through the impact of the coagulation system. J Thorac Dis. 2018;10(12):6783\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/jtd.2018.11.138\u003c/span\u003e\u003cspan address=\"10.21037/jtd.2018.11.138\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXin Q, Xie T, Chen R, Zhang X, Tong Y, Wang H, et al. A Predictive Model Based on Inflammatory and Coagulation Indicators for Sepsis-Induced Acute Kidney Injury. JIR. 2022;15:4561\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/jir.s372246\u003c/span\u003e\u003cspan address=\"10.2147/jir.s372246\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWauthier L, Favresse J, Hardy M, Douxfils J, Le Gal G, Roy PM, et al. D-dimer testing: A narrative review. In: Makowski GS, editor. Advances in Clinical Chemistry. Advances in Clinical Chemistry. Elsevier; 2023. pp. 151\u0026ndash;223.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Chen T, Ge P, Ge M, Lu L, Zhang L, et al. Risk factors for 30-day mortality in patients who received DeBakey type I aortic dissection repair surgery. J Cardiothorac Surg. 2021;16(1):320. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13019-021-01702-9\u003c/span\u003e\u003cspan address=\"10.1186/s13019-021-01702-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Lan Y, Chen T, Chen Q, Guo Z, Jiang N. Prediction of Acute Kidney Injury for Acute Type A Aortic Dissection Patients Who Underwent Sun\u0026rsquo;s Procedure by a Perioperative Nomogram. Cardiorenal Med. 2022;12(3):117\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000524907\u003c/span\u003e\u003cspan address=\"10.1159/000524907\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark J, Kim SU, Choi HJ, Hong SH, Chae MS. Predictive Role of the D-Dimer Level in Acute Kidney Injury in Living Donor Liver Transplantation: A Retrospective Observational Cohort Study. J Clin Med. 2022;11(2):450. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm11020450\u003c/span\u003e\u003cspan address=\"10.3390/jcm11020450\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVijayan AL, Vanimaya, Ravindran S, Saikant R, Lakshmi S, Kartik R, et al. Procalcitonin: a promising diagnostic marker for sepsis and antibiotic therapy. j intensive care. 2017;5(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40560-017-0246-8\u003c/span\u003e\u003cspan address=\"10.1186/s40560-017-0246-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuan X-L, Li L, Li H-Y, Gong M, Zhang H-J, Wang X-L. Risk factor prediction of severe postoperative acute kidney injury at stage 3 in patients with acute type A aortic dissection using thromboelastography. Front Cardiovasc Med. 2023;10:1109620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2023.1109620\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2023.1109620\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C-N, Ge Y-P, Liu H, Zhang C-H, Zhong Y-L, Chen S-W, et al. Blood Transfusion and Acute Kidney Injury After Total Aortic Arch Replacement for Acute Stanford Type A Aortic Dissection. Heart Lung Circ. 2022;31(1):136\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.hlc.2021.05.087\u003c/span\u003e\u003cspan address=\"10.1016/j.hlc.2021.05.087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaeem SS, Sodha NR, Sellke FW, Ehsan A. Impact of Packed Red Blood Cell and Platelet Transfusions in Patients Undergoing Dissection\u0026brvbar;Repair. J Surg Res. 2018;232:338\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jss.2018.06.048\u003c/span\u003e\u003cspan address=\"10.1016/j.jss.2018.06.048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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, Risk Assessment","lastPublishedDoi":"10.21203/rs.3.rs-8641396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8641396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcute kidney injury (AKI) is a prevalent and severe complication following acute type A aortic dissection (ATAAD) surgery. Although several predictive models exist, most lack rigorous validation in independent populations, limiting their clinical generalizability and readiness for implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis single-center, retrospective cohort study enrolled 317 patients with ATAAD. The cohort was chronologically divided into training and external temporal validation sets. A predictive nomogram was developed from preoperative variables identified by multivariate logistic analysis. Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA). Sensitivity analysis confirmed the robustness of the findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigher serum creatinine (SCr) levels, elevated D-dimer (DDi) concentrations, and lower platelet counts (PLT) were identified as independent preoperative risk factors for AKI following ATAAD surgery. The nomogram demonstrated a strong and generalizable discriminatory ability, with an area under the receiver operating characteristic curve of 0.842 in the training cohort and 0.804 in the temporal validation cohort. Calibration plots and DCA confirmed the favorable calibration and clinical utility. Restricted cubic spline analysis revealed nonlinear associations of AKI with preoperative SCr and PLT levels, but a linear positive association with DDi.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed and temporally validated a concise nomogram using three preoperative biomarkers (PLT, SCr, DDi) for predicting AKI after ATAAD surgery, providing a practical tool for preoperative risk stratification.\u003c/p\u003e","manuscriptTitle":"A Predictive Model for Postoperative Acute Kidney Injury in Patients with Acute Type A Aortic Dissection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 16:45:08","doi":"10.21203/rs.3.rs-8641396/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":"8f9b0952-edf7-4e0c-bfda-5ba923804246","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-05T10:44:53+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T10:56:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 16:45:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8641396","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8641396","identity":"rs-8641396","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}