A Prediction Model for Post-aortic Dissection Infection Based on Rewarming Time, Drainage Output, and Hemoglobin: A Retrospective Study

A Prediction Model for Post-aortic Dissection Infection Based on Rewarming Time, Drainage Output, and Hemoglobin: A Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Prediction Model for Post-aortic Dissection Infection Based on Rewarming Time, Drainage Output, and Hemoglobin: A Retrospective Study Wanru Ma, Yuji Xiao, Yaoguang Feng, Jia Liu, Jinzhi Yin, Xiaofeng Qu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8717698/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 There is currently a lack of early and simple tools for assessing the risk of postoperative infection following aortic dissection surgery. This study aimed to develop and validate a prediction model based on early postoperative objective indicators, along with a corresponding simplified scoring system. Methods A total of 243 patients who underwent surgical treatment for aortic dissection were retrospectively enrolled. Independent predictors of postoperative infection were identified using logistic regression analysis. Based on these predictors, a nomogram prediction model was constructed. The model's performance was evaluated in terms of discrimination (area under the receiver operating characteristic curve, AUC), calibration (mean absolute error, MAE), and clinical utility (decision curve analysis, DCA). Furthermore, a simple risk score was established using the optimal cut-off values for each predictor, and its effectiveness for risk stratification was validated using a trend test. Results Rewarming time (≤ 83.5 min), postoperative day 2 drainage output (> 261.5 ml), and postoperative day 1 hemoglobin level (≤ 109.5 g/L) were identified as independent predictors of infection. The constructed nomogram demonstrated good predictive performance (AUC = 0.704, MAE = 0.036). When transformed into a 0–3 point simplified scoring system, patients were stratified into low-, medium-, and high-risk groups. The infection rates for these groups were 6.7%, 21.7%, and 38.6%, respectively, showing a significant increasing trend (P for trend = 0.004). Conclusion The simple scoring system developed in this study can effectively identify patients at high risk for infection in the early postoperative period, providing a practical tool for implementing stratified management and precise intervention. Aortic Dissection Postoperative Infection Risk Prediction Model Risk Stratification Rewarming Time Chest Tube Drainage Anemia Figures Figure 1 Figure 2 Introduction Surgical intervention for aortic dissection is a crucial life-saving treatment. With advancements in technique, postoperative survival rates have improved significantly. However, postoperative infections, such as deep sternal wound infections and mediastinitis, remain serious complications that adversely affect patient recovery, prolong hospital stays, and increase healthcare burdens [1,2]. In current clinical practice, warning signs for postoperative infection primarily rely on two approaches. The first involves comprehensive risk scores based on the patient's preoperative general condition, such as the EuroSCORE II. These scores often fail to account for the stress of surgery and dynamic postoperative physiological changes. The second approach is reactive management after typical signs of infection appear postoperatively, such as fever or leukocytosis. However, this often misses the optimal window for early intervention. Consequently, there is an urgent clinical need to develop a tool that can proactively identify high-risk patients in the early postoperative period based on objective monitoring indicators. From a pathophysiological perspective, the occurrence of postoperative infection is closely associated with surgical trauma, the intensity of the inflammatory response, and the patient's early recovery status [3-5]. Specifically, the intraoperative rewarming process may influence systemic inflammatory levels; early postoperative chest drainage output directly reflects exudation and the inflammatory state at the surgical site; and immediate postoperative hemoglobin levels are related to tissue oxygen supply and immune repair capacity. These three indicators are obtainable within 24-48 hours postoperatively, offering potential for early risk assessment. However, to date, no study has systematically integrated these three factors and translated them into a simple bedside risk stratification tool. Therefore, this study aims to develop and validate a prediction model for postoperative infection risk in aortic dissection based on "rewarming time, postoperative day 2 drainage output, and postoperative day 1 hemoglobin" through retrospective data analysis. The ultimate goal is to simplify this model into an intuitive clinical scoring system, enabling healthcare professionals to rapidly identify high-risk patients at the bedside, thereby facilitating targeted preventive strategies, optimizing perioperative management, and improving patient outcomes. Materials and Methods Study Design and Participants This was a single-center retrospective cohort study. The protocol was approved by the Ethics Review Committee of the First Affiliated Hospital of University of South China (approval number: 2024LL0410001), and the requirement for informed consent was waived. We consecutively enrolled patients with Stanford type A aortic dissection who underwent surgical treatment in the Department of Cardiothoracic and Vascular Surgery of our hospital between 2018 and 2023.Inclusion criteria were as follows: preoperative diagnosis of Stanford type A aortic dissection confirmed by CT angiography, and receipt of a standardized surgical treatment protocol involving the aortic root, ascending aorta, and/or aortic arch (specific procedures included, but were not limited to, the Bentall procedure, hemiarch/total arch replacement, Sun’s procedure, and various combined techniques).Exclusion criteria included: preoperative active infection, missing key clinical variables (e.g., early postoperative laboratory indicators), or death within 24 hours after surgery. Outcome Definition and Data Collection The primary outcome of this study was defined as deep sternal wound infection, specifically characterized by postoperative deep incision exudation, suppuration, or sternal dehiscence, as confirmed by an attending physician or higher. Patient data were retrospectively collected through the hospital electronic medical record system. Collected variables encompassed: Preoperative data, including sex, age, body mass index, comorbidities, preoperative laboratory indices, and cardiac function. Intraoperative data, such as total operative time, cardiopulmonary bypass and aortic cross-clamp durations, rewarming time, intraoperative blood loss, and transfusion volume. Postoperative data, covering intensive care unit stay duration, daily chest tube drainage output from postoperative day 1 to 3, hemoglobin, lactate, blood glucose, total protein, creatinine clearance rates, and total postoperative transfusion volume. Statistical Analysis All analyses were performed using SPSS and R. Statistical significance was set at a two-sided P value < 0.05. The analysis followed a staged approach as outlined in Fig. 1 . Data Preparation and Descriptive Analysis Descriptive statistics summarized the cohort data. Continuous variables are presented as mean ± standard deviation or median (interquartile range), and categorical variables as frequency (percentage). Univariate analyses were conducted to identify factors associated with postoperative infection: independent samples *t*-test or Mann-Whitney U test for continuous variables, and Chi-square or Fisher's exact test for categorical variables. Variables with P < 0.1 were retained for further multivariate analysis. Predictor Screening and Model Construction Variables selected from the univariate analysis were entered into a binary logistic regression model using the forward likelihood ratio method to identify independent predictors. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. A nomogram was constructed based on the final predictors to visualize individual infection risk. Internal Validation and Model Performance The nomogram’s performance was evaluated in three dimensions: Discrimination: assessed by the area under the receiver operating characteristic curve (AUC) with 95% CI. Calibration: evaluated using a calibration plot with 200 bootstrap replicates and quantified by the mean absolute error (MAE). Clinical Utility: estimated by decision curve analysis (DCA) across a range of threshold probabilities. Development and Validation of a Clinical Scoring System To enhance bedside utility, a simple scoring system was derived. For each independent continuous predictor, the optimal cut-off was determined by maximizing Youden’s index on its ROC curve. Variables were dichotomized at these cut-offs (abnormal = 1 point, normal = 0 points), creating a 0–3 point score. Patients were stratified into low- (0 points), intermediate- (1–2 points), and high-risk (3 points) groups. The Cochran-Armitage trend test assessed the significance of increasing infection rates across groups. The relative risk (RR) with 95% CI for the intermediate- and high-risk groups versus the low-risk group quantified the stratification effect. Results Patient Baseline Characteristics and Univariate Analysis Results This study included a total of 243 patients who underwent aortic dissection surgery. Based on the occurrence of postoperative infection, they were divided into two groups: a non-infection group (n = 185) and an infection group (n = 58). Clinical data from the preoperative, intraoperative, and postoperative periods were compared between the two groups using univariate analysis, with the results summarized in Table 1 .Overall, the majority of baseline characteristics showed no statistically significant differences between the two groups. Demographic profiles, including sex and age, as well as the prevalence of common comorbidities such as hypertension, diabetes, previous surgery history, and smoking history, were comparable between groups (all P > 0.05). Similarly, preoperative laboratory indices—including hemoglobin, total protein, albumin, cholesterol, creatinine clearance rate, and cardiac ejection fraction—showed no significant intergroup differences (all P > 0.05).However, the analysis identified several factors associated with postoperative infection. Among intraoperative indicators, rewarming time was significantly shorter in the infection group than in the non-infection group (78.81 ± 20.01 min vs. 85 [69.5, 101] min, P = 0.037). Early postoperative observations revealed that postoperative day 2 drainage output was significantly higher in the infection group (395 [198.75, 617.5] mL/day vs. 250 [137.5, 467.5] mL/day, P = 0.006). Furthermore, postoperative day 1 lactate was lower in the infection group (2.1 [1.475, 3.2] mmol/L vs. 2.7 [1.8, 3.9] mmol/L, P = 0.043), while postoperative day 1 hemoglobin was also significantly lower in this group (98.81 ± 14.52 g/L vs. 101 [92.5, 114] g/L, P = 0.043). Postoperative day 2 hemoglobin showed a trend toward being lower in the infection group (94.69 ± 13.41 g/L vs. 98 [88, 108] g/L, P = 0.050). All other daily postoperative measures—including drainage output, hemoglobin, lactate, total protein, creatinine clearance rate, and blood glucose control—showed no statistically significant differences between the two groups (all P > 0.05). Table 1 Comparison of Baseline Characteristics and Univariate Analysis Between the Infection and Non-infection Groups. Continuous variables are presented as mean ± standard deviation and compared using the independent samples t-test if normally distributed, or as median (interquartile range) and compared using the Mann-Whitney U test if non-normally distributed. Categorical variables are presented as number (percentage) and compared using the Chi-square test. Bold text indicates P < 0.05. Non-infection group (1), N = 185 Infection group (2), N = 58 t value / z value / χ² value P value Preoperative Data: Sex (male) 134(72.4%) 44(75.9%) 0.265 0.607 Age (years) 53(45.5, 63) 52.69 ± 10.42 -0.074 0.941 Height (m) 1.65(1.59,1.70) 1.68(1.62, 1.72) -1.782 0.075 Weight (kg) 65(57,75) 70.40 ± 15.16 -1.485 0.138 BMI (kg/m²) 23.87(21.36,27.47) 25.05 ± 4.34 -0.984 0.325 Body surface area (m²) 1.81 ± 0.25 1.88 ± 0.22 -1.791 0.075 Hypertension (yes) 102(55.1%) 28(48.3%) 0.835 0.361 Diabetes mellitus (yes) 4(2.1%) 1(1.7%) 0.042 0.838 Previous surgery history (yes) 54(29.2%) 13(22.4%) 2.956 0.086 Smoking history (yes) 31(16.8%) 10(17.2%) 0.007 0.931 Preoperative hemoglobin (g/L) 129(119,140) 123.91 ± 19.07 -1.715 0.086 Preoperative total protein (g/L) 65.91 ± 6.26 64.60 ± 6.02 1.404 0.161 Preoperative albumin (g/L) 40.33 ± 4.10 40.05(36.4, 42.55) -0.892 0.373 Preoperative cholesterol (mmol/L) 3.96 ± 0.72 3.93 ± 0.86 0.274 0.784 Preoperative creatinine clearance rate (mL/min) 86.47 ± 31.96 86.45 ± 29.72 0.006 0.995 Preoperative cardiac function (ejection fraction) (%) 61(56.0, 64.5) 62(57, 65) -0.643 0.52 Intraoperative Data : Total operation time (min) 472(417.5,529.5) 462.21 ± 87.03 -0.976 0.329 Cardiopulmonary bypass time (min) 224(195,251.5) 219.03 ± 40.75 -0.96 0.337 Aortic cross-clamp time (min) 136(119,154) 137(120.25, 149) -0.132 0.895 Rewarming time (min) 85(69.5,101) 78.81 ± 20.01 -2.086 0.037 Intraoperative blood loss (mL) 500(337.5,800) 500(400, 800) -0.192 0.847 Total intraoperative transfusion volume (mL) 800(400, 1197.5) 831.98 ± 453.70 -0.544 0.586 Anesthesia recovery time (min) 195(112.5, 307.5) 205(120, 302.5) -0.276 0.783 Duration of mechanical ventilation (min) 1150(976.5, 3090.5) 1380(978.75, 3628.75) -0.735 0.462 Endotracheal intubation time (min) 1150(976.5, 3090.5) 1447.5(987.5, 4016.25) -1.119 0.263 ICU length of stay (min) 6750(5201.5, 9662.5) 8105(5348.75, 11086.25) -1.579 0.114 Postoperative Data : Total drainage volume, Postoperative day (POD) 1 (mL/day) 330(222.5, 475) 417.5(258.75, 597.5) -1.91 0.056 Total drainage volume, POD 2 (mL/day) 250(137.5, 467.5) 395(198.75, 617.5) -2.732 0.006 Total drainage volume, POD 3 (mL/day) 120(46, 345) 172.5(73.75, 423.75) -1.647 0.1 Total postoperative transfusion volume (mL) 600(300, 1000) 525(237.5, 1100) -0.29 0.772 Blood glucose, POD 1 (mmol/L) 9.5(8.2, 11.1) 9.5(7.9, 10.625) -0.821 0.412 Blood glucose, POD 2 (mmol/L) 8.9(7.9, 10.4) 8.9(7.65, 10.1) -0.715 0.474 Blood glucose, POD 3 (mmol/L) 8.4(7.25, 10.15) -0.519 0.604 Lactate, POD 1 (mmol/L) 2.7(1.8, 3.9) 2.1(1.475, 3.2) -2.027 0.043 Lactate, POD 2 (mmol/L) 1.4(1, 1.8) 1.25(0.9, 1.725) -0.502 0.616 Lactate, POD 3 (mmol/L) 1(0.8, 1.4) 1(0.7, 1.425) -0.631 0.528 Hemoglobin, POD 1 (g/L) 101(92.5, 114) 98.81 ± 14.52 -2.021 0.043 Hemoglobin, POD 2 (g/L) 98(88, 108) 94.69 ± 13.41 -1.957 0.05 Hemoglobin, POD 3 (g/L) 93(85, 103) 92.55 ± 11.68 -0.957 0.338 Total protein, POD 1 (g/L) 58.65(55.1, 63.55) 57.44 ± 7.18 -1.677 0.093 Total protein, POD 2 (g/L) 60.29 ± 5.56 59.39 ± 6.30 1.039 0.3 Total protein, POD 3 (g/L) 60.8(57.82, 64.70) 59.77 ± 6.06 -1.85 0.064 Creatinine clearance rate, POD 1 (mL/min) 65.30 ± 32.35 62.50 ± 27.19 0.596 0.552 Creatinine clearance rate, POD 2 (mL/min) 62.9(40.15, 85.08) 64.36 ± 28.29 -0.384 0.701 Creatinine clearance rate, POD 3 (mL/min) 67.45 ± 31.91 64.56 ± 28.30 0.615 0.539 Identification of Independent Predictors for Postoperative Infection via Multivariable Logistic Regression Analysis To identify independent risk factors for postoperative infection following aortic dissection, variables with a P value less than 0.05 in the univariate analysis—namely rewarming time, postoperative day 2 drainage output, postoperative day 1 lactate, and postoperative day 1 hemoglobin—were entered into a binary logistic regression model using the forward likelihood ratio method. The results, presented in Table 2 , identified rewarming time, postoperative day 2 drainage output, and postoperative day 1 hemoglobin as independent predictors of postoperative infection. Specifically, rewarming time demonstrated a negative association with infection risk, with an odds ratio of 0.983, a 95% confidence interval of 0.969 to 0.997, and a P value of 0.012. This indicates that each additional minute of rewarming time was associated with an approximately 1.7% reduction in infection risk. Postoperative day 2 drainage output showed a positive association with infection risk, with an odds ratio of 1.002, a 95% confidence interval of 1.001 to 1.003, and a P value of 0.001. This corresponds to an approximate 0.2% increase in infection risk for each 1 mL per day increase in drainage output. Similarly, postoperative day 1 hemoglobin level was negatively associated with infection risk, with an odds ratio of 0.977, a 95% confidence interval of 0.955 to 0.999, and a P value of 0.045. This suggests that each 1 g per liter increase in hemoglobin level was associated with an approximately 2.3% decrease in infection risk. In contrast, postoperative day 1 lactate level did not reach statistical significance in the multivariable analysis, with a P value of 0.364. Table 2 Results of Multivariable Logistic Regression Analysis for Postoperative Infection in Aortic Dissection. The analysis was performed using forward stepwise logistic regression with an inclusion criterion of P < 0.05. OR: odds ratio. Variables B value B value Wald χ² value P value OR value 95% Confidence interval Rewarming time (min) -0.017 0.007 6.317 0.012 0.983 0.969–0.997 Postoperative day 2 drainage output (mL/day) 0.002 0.001 10.166 0.001 1.002 1.001–1.003 Postoperative day 1 hemoglobin (g/L) -0.023 0.011 4.019 0.045 0.977 0.955–0.999 Construction and Validation of the Prediction Model Based on the independent predictors identified through multivariable logistic regression analysis, we constructed a nomogram model for the individualized prediction of postoperative infection risk in aortic dissection (Fig. 2 A). This model integrates three early-available clinical indicators: rewarming time, postoperative day 2 drainage output, and postoperative day 1 hemoglobin. To comprehensively evaluate the model's performance, rigorous internal validation was conducted as follows. Discrimination: The model's discriminative ability was assessed using the receiver operating characteristic (ROC) curve. The area under the curve (AUC) was 0.704 (95% CI: 0.632–0.776) (Fig. 2 B). This result indicates a moderate-to-good ability of the model to distinguish between patients with and without postoperative infection. The fact that the 95% confidence interval does not include 0.5 confirms that this discriminative capability is statistically significant. Calibration: The agreement between predicted probabilities and observed outcomes was evaluated using a calibration curve. Following internal validation with 200 bootstrap resampling repetitions, the model's mean absolute error (MAE) was 0.036 (Fig. 2 C). The calibrated curve closely aligned with the ideal diagonal line, indicating good predictive accuracy and a high concordance between predicted risks and observed event rates. Clinical Utility: The clinical net benefit of the model across different decision thresholds was assessed using decision curve analysis (DCA). As shown in Fig. 2 D, within a threshold probability range of approximately 10% to 50%, the net benefit curve for using this model to guide clinical decisions—that is, to intervene in patients identified as high-risk by the model—consistently exceeded the curves for the strategies of "intervening on all patients" and "intervening on no patients." This indicates that applying this model for risk stratification can optimize clinical decision-making. It enables the effective identification and management of high-risk patients while avoiding unnecessary interventions in low-risk individuals, thereby yielding a higher net clinical benefit. Development and Validation of a Simplified Risk Scoring System To facilitate rapid clinical application, the nomogram prediction model was converted into a simplified risk scoring system. The optimal clinical cut-off values for the continuous predictors were determined by maximizing Youden’s index on their respective receiver operating characteristic (ROC) curves (see Supplementary Figure S1). The criteria were defined as follows: rewarming time ≤ 83.5 minutes, postoperative day 2 drainage output > 261.5 mL, and postoperative day 1 hemoglobin ≤ 109.5 g/L. Each criterion met was assigned 1 point, resulting in a total risk score ranging from 0 to 3. Based on the total score, patients were stratified into three risk tiers: low-risk (0 points), intermediate-risk (1–2 points), and high-risk (3 points). As shown in Table 3 , the infection rate demonstrated a significant increasing trend across the ascending risk categories. The infection rate was 6.7% (95% confidence interval, CI: 0.2–32.0%) in the low-risk group, 21.7% (95% CI: 16.1–28.4%) in the intermediate-risk group, and 38.6% (95% CI: 24.4–54.5%) in the high-risk group. This increasing trend was confirmed to be statistically significant by the Cochran–Armitage trend test (χ² = 8.166, P = 0.004). Compared with the low-risk group, the high-risk group had a relative risk of 5.76 (95% CI: 0.82–40.38, P = 0.024) for developing postoperative infection. Table 3 Risk Stratification and Incidence of Infection Based on the Simplified Scoring System. The 95% confidence intervals for infection rates were calculated using the exact binomial method (Clopper-Pearson). Relative risk values were calculated using the low-risk group as the reference, with confidence intervals derived from Koopman's asymptotic method. The P values* represent the results of two-sided Fisher's exact tests comparing each group to the low-risk group. The P value for the trend test indicates a significant increase in infection rate with ascending risk category. Risk Stratification (Score) Number of Cases, n Number of Infections Infection Rate, % (95% CI) Relative Risk, RR (95% CI) P Value Low-risk (0 points) 15 1 6.7 (0.2–32.0) 1.00 – Intermediate-risk (1–2 points) 184 40 21.7 (16.1–28.4) 3.24 (0.47–22.28) 0.315 High-risk (3 points) 44 17 38.6 (24.4–54.5) 5.76 (0.82–40.38) 0.024 Trend Test (Cochran–Armitage) χ² = 8.166, df = 1 0.004 Discussion Through retrospective analysis, this study developed and validated for the first time an early risk assessment tool specifically designed to predict postoperative infection in aortic dissection. The key finding is that three objective indicators easily obtained within 24–48 hours after surgery—intraoperative rewarming time, postoperative day 2 drainage output, and postoperative day 1 hemoglobin level—served as independent predictors of infection. The nomogram model and the simplified 0–3 point scoring system established based on these predictors demonstrated favorable discrimination (AUC = 0.704), calibration (MAE = 0.036), and clinical utility in internal validation, providing a direct basis for early identification of high-risk patients and implementation of stratified management. This study confirms the classic role of increased postoperative drainage—reflecting tissue exudation and inflammatory response intensity—and early anemia—affecting tissue oxygen supply and immune repair—as risk factors for infection. By integrating relevant literature, their specific mechanisms and clinical relevance can be explored in depth from the following two perspectives. First, increased postoperative drainage is an important manifestation of both local and systemic inflammatory responses. In patients with aortic dissection, elevated preoperative systemic inflammatory response index (SIRI) and systemic immune-inflammatory index (SII) have been shown to be closely associated with postoperative adverse events [ 6 , 7 ]. This suggests that a pronounced preoperative inflammatory state may persist into the postoperative period, exacerbating tissue exudation and thereby leading to increased drainage output. Broader surgical patient studies further support this association, indicating that levels of early postoperative inflammatory cytokines such as IL-6 and C-reactive protein (CRP) are significantly correlated with both drainage volume and surgical site infection risk [ 3 , 8 ]. Therefore, increased drainage is not an isolated phenomenon but rather a key intermediate link connecting preoperative inflammatory status, surgical trauma stress, and postoperative infection risk. Second, early postoperative anemia increases infection risk by impairing tissue oxygen supply and immune function. Studies indicate that increased preoperative transfusion of red blood cells and plasma in aortic dissection is associated with higher postoperative complication rates [ 9 ], indirectly reflecting the negative impact of anemia or coagulation dysfunction on outcomes. In models such as abdominal surgery, low preoperative albumin levels—often linked to nutritional status and anemia—also significantly elevate the risk of postoperative infection [ 10 ]. Anemia reduces the oxygen-carrying capacity of blood, directly affecting tissue oxygenation, thereby weakening the bactericidal function of immune cells and tissue repair capacity, creating a microenvironment conducive to infection. In summary, increased postoperative drainage and early anemia in aortic dissection—classic risk factors for infection—are underpinned by a dual pathophysiological process involving inflammatory response and depletion of physiological reserves. These findings not only emphasize the importance of monitoring and managing these early postoperative objective indicators but also provide a multidimensional perspective for understanding the mechanisms of infection development. In clinical practice, targeted interventions such as meticulous fluid management, proactive anemia correction, and inflammation modulation may hold potential value in reducing postoperative infection risk. The most challenging and noteworthy finding is the negative correlation between rewarming time and infection risk, suggesting that longer rewarming time corresponds to a lower risk of infection (OR = 0.983). This appears to contradict the intuitive notion that prolonged operative time may increase complications. We speculate that this may reveal an underrecognized compensatory strategy in clinical practice: during aortic dissection surgery, particularly in the management of high-risk patients, clinical teams often favor a rapid rewarming strategy to shorten cardiopulmonary bypass time and promptly restore physiological homeostasis. However, this approach may carry significant risks of physiological fluctuation, and its clinical benefit and safety are highly dependent on the patient's specific pathological status and surgical context. On one hand, rapid rewarming has proven beneficial in certain clinical scenarios. For instance, in patients with post-cardiac arrest syndrome, rapid rewarming is associated with improved neurological outcomes, suggesting a potential protective effect in specific ischemia-reperfusion contexts [ 11 ]. On the other hand, this strategy is not universally applicable. In septic animal models, rapid rewarming conversely leads to shortened survival and exacerbated acute lung injury, possibly related to aggravated systemic inflammatory response [ 12 ]. In the treatment of severe frostbite, although rapid rewarming is considered standard care, its effect on tissue survival remains unclear [ 13 ]. These disparities indicate that the benefits and drawbacks of rapid rewarming largely depend on the nature of the underlying disease and the systemic inflammatory state. Returning to the specific setting of aortic dissection surgery, temperature management is far from a simple choice between "fast" and "slow"; rather, it involves a comprehensive decision-making process encompassing the depth of hypothermia, its duration, and the rewarming rate. Studies have shown that moderate hypothermic circulatory arrest, compared to deep hypothermic circulatory arrest, can reduce the incidence of postoperative acute kidney injury and delirium [ 14 ]. Further delineating this, in patients with acute DeBakey type I aortic dissection, high-moderate hypothermia (24.1–28.0°C) is associated with lower in-hospital mortality and renal injury risk for those without perfusion deficits, while for patients with pre-existing distal perfusion impairment, low-moderate hypothermia (20.1–24.0°C) may help reduce the incidence of paraplegia [ 5 ]. Collectively, this evidence suggests that an optimal rewarming strategy should be based on an individualized hypothermia protocol, rather than pursued in isolation solely for speed. Conversely, for hemodynamically stable aortic dissection patients, adopting a slower and more controlled rewarming strategy is key to achieving a smooth transition and improving prognosis. The core advantage of this approach lies in allowing the body to optimize peripheral perfusion with better physiological compensatory capacity during weaning from cardiopulmonary bypass, thereby potentially mitigating systemic inflammatory response syndrome and ischemia-reperfusion injury. The physiological basis for this is supported by evidence from multiple research areas. In the field of organ transplantation, compared to rapid rewarming, a gradual pressure-increase rewarming protocol for isolated kidneys significantly improves functional parameters, reduces renal tubular damage, and enhances endothelial protection, clearly demonstrating the advantage of slow rewarming in alleviating ischemia-reperfusion injury [ 15 ]. In experimental shock models, animals subjected to slow rewarming also showed significantly better survival rates than those in the rapid rewarming group, suggesting the latter may prematurely forfeit the protective effects of hypothermia [ 16 ]. From a microcirculatory perspective, under conditions of hemodynamically stable systemic hypothermia, the reductions in cardiac output, renal blood flow, and oxygen consumption represent a modifiable physiological adaptation [ 17 ]. Together, these mechanisms indicate that slow rewarming facilitates a more stable hemodynamic transition, creating a safe recovery window for tissues transitioning from "protective hypothermia" to "normal metabolism." In the specific context of aortic dissection surgery, the value of this strategy is concretely reflected in the choice of temperature management protocols. Numerous clinical studies have confirmed that compared to deep hypothermic circulatory arrest, the strategy of moderate hypothermic circulatory arrest combined with selective cerebral perfusion not only provides sufficient cerebral and distal organ protection but also effectively reduces postoperative bleeding risk, the incidence of neurological complications, in-hospital mortality, and improves long-term survival [ 18 – 20 ]. This provides indirect clinical outcome-based evidence for the benefits of "avoiding excessive deep hypothermia and allowing gradual rewarming." Furthermore, in acute aortic dissection surgery, moderate hypothermia combined with a slow rewarming strategy may not only aid in cerebral protection but also potentially reduce the severity of systemic inflammatory response syndrome by optimizing peripheral perfusion [ 21 ]. Therefore, the seemingly paradoxical observation in this study—that rewarming time is negatively correlated with infection risk—holds a deeper implication: longer rewarming time likely serves as a surrogate marker reflecting both relatively stable intraoperative patient condition and more meticulous perioperative temperature management. For such patients, the surgical team is afforded the opportunity to implement a slower, more controlled rewarming strategy, thereby promoting a more stable hemodynamic transition, optimizing tissue perfusion, and mitigating systemic inflammation and ischemia-reperfusion injury. Ultimately, this enhances the patient's postoperative defensive capacity and reduces infection risk. Consequently, rewarming time should not be viewed in isolation as merely a technical parameter, but rather understood as an indicator that comprehensively reflects both the patient's inherent physiological stability and the quality of perioperative management. This finding further underscores that temperature management in aortic dissection surgery should be regarded as a dynamic, individualized, and holistic strategy. Its core objective is to achieve an optimal physiological transition for the patient while ensuring surgical safety. limitations This study has several limitations. First, as a single-center retrospective study, the generalizability of its conclusions may be influenced by the specific diagnosis and treatment protocols of the center, and retrospective data collection carries the potential for information bias. Second, although the sample size meets preliminary modeling requirements, the relatively limited number of infection events may affect the precision of effect estimates for predictors in the multivariable model, warranting cautious interpretation of the high-risk subgroup assessments. Finally, the proposed prediction model and scoring system have only undergone internal validation; their true discriminatory performance and clinical applicability require external validation in independent, multicenter prospective cohorts. Conclusion This study developed and validated a risk prediction model for postoperative infection in aortic dissection, based on early postoperative objective indicators: rewarming time, postoperative day 2 drainage output, and postoperative day 1 hemoglobin. This model can be transformed into a convenient bedside scoring system, facilitating the rapid identification of high-risk patients and enabling effective stratification of infection risk in the early postoperative period. This tool provides a basis for implementing targeted monitoring and preventive interventions, with the potential to improve perioperative management for patients undergoing aortic dissection surgery. Declarations Availability of Data and Materials The datasets used in this study are available from the corresponding author upon reasonable request. Author Contributions This study was conceptualized and designed by Wanru Ma and Juan Luo, with Wanru Ma drafting the initial manuscript. Data curation was performed by Jinzhi Yin and Xiaofeng Qu. Data analysis and interpretation were conducted by Yuji Xiao and Jia Liu. Yaoguang Feng, as the departmental director, provided overarching supervision, resources, and critical guidance. Juan Luo, as the corresponding author, supervised the entire research process and finalized the manuscript. All authors have read and approved the final version. Ethics Approval and Consent to Participate This work was approved by the hospital’s ethics committee (ethics approval number 2024LL1016001) and preoperative patients and their families. Acknowledgments We gratefully acknowledge the colleagues from the Department of Cardiothoracic Surgery and the Medical Records Department of the First Affiliated Hospital of University of South China for their support and invaluable contributions to the implementation of this study. Funding This work was supported by a grant from the Guidance Project of Hengyang Municipal Science and Technology Bureau (202121034632). Conflict of Interest The authors declare no conflict of interest. References DiazCastrillon CE, SernaGallegos D, Arnaoutakis G, et al. The Burden of Major Complications on Failure-to-Rescue After Surgery for Acute Type A Aortic Dissections: An analysis of over 19,000 patients. J Thorac Cardiovasc Surg. 2025;169(5):1415–e142611. 10.1016/j.jtcvs.2024.07.015 . Bai L, Ge L, Zhang Y, et al. Experience of the Postoperative Intensive Care Treatment of Stanford Type A Aortic Dissection. Int J Clin Pract. 2023;2023:4191277. 10.1155/2023/4191277 . Yu Q, Cen C, Gao M, et al. Combination of early Interleukin-6 and – 18 levels predicts postoperative nosocomial infection. Front Endocrinol (Lausanne). 2022;13:1019667. 10.3389/fendo.2022.1019667 . Wang Z, Li C, Quan Q, et al. Study on Risk Factors and Nutritional Status of Postoperative Infection in Patients Undergoing Abdominal Surgery. Contrast Media Mol Imaging. 2022;2022:8063851. 10.1155/2022/8063851 . Chai FY, Jiffre D. Preoperative hypoalbuminemia is an independent risk factor for the development of surgical site infection following gastrointestinal surgery. Ann Surg. 2011;254(4):665. 10.1097/SLA.0b013e31823062f3 . Zhao Y, Hong X, Xie X, et al. Preoperative systemic inflammatory response index predicts long-term outcomes in type B aortic dissection after endovascular repair. Front Immunol. 2022;13:992463. 10.3389/fimmu.2022.992463 . Zhao Y, Jiang J, Yuan Y, et al. Prognostic Value of the Systemic Immune Inflammation Index after Thoracic Endovascular Aortic Repair in Patients with Type B Aortic Dissection. Dis Markers. 2023;2023:2126882. 10.1155/2023/2126882 . Bi X, Li Y, Lin J, et al. Concentration standardization improves the capacity of drainage CRP and IL-6 to predict surgical site infections. Exp Biol Med (Maywood). 2020;245(16):1513–7. 10.1177/1535370220945290 . Cai H, Shourav FM, Shao Y, et al. Impact of Pan-Immune Inflammation Value on Short-Term Outcomes and Long-Term Prognosis in Patients with Type A Aortic Dissection. J Inflamm Res. 2025;18:7855–66. 10.2147/JIR.S522998 . Griffin FS, Stead TS, Zeyl VG, et al. Low Preoperative Albumin Levels Significantly Associated with Increased Risk of Wound Infection and Bleeding After Panniculectomy. Plast Surg (Oakv). Published online Oct. 2024;25. 10.1177/22925503241292350 . Shin M, Fujita M, Hifumi T, et al. Rapid rewarming rate associated with favorable neurological outcomes in patients with post-cardiac arrest syndrome patients treated with targeted temperature management. Acute Med Surg. 2023;10(1):e897. 10.1002/ams2.897 . Jo YH, Kim K, Lee JH, et al. Rapid rewarming after therapeutic hypothermia worsens outcome in sepsis. Clin Exp Emerg Med. 2014;1(2):120–5. 10.15441/ceem.14.015 . Rogers C, Lacey AM, Endorf FW, et al. The Effects Of Rapid Rewarming On Tissue Salvage In Severe Frostbite Injury. J Burn Care Res. 2022;43(4):906–11. 10.1093/jbcr/irab218 . Abdulwahab HAM, Kolashov A, Haneya A, et al. Temperature management in acute type A aortic dissection treatment: deep vs. moderate hypothermic circulatory arrest. Is colder better? Front Cardiovasc Med. 2024;11:1447007. 10.3389/fcvm.2024.1447007 . Mahboub P, Ottens P, Seelen M, et al. Gradual Rewarming with Gradual Increase in Pressure during Machine Perfusion after Cold Static Preservation Reduces Kidney Ischemia Reperfusion Injury. PLoS ONE. 2015;10(12):e0143859. 10.1371/journal.pone.0143859 . Burggraf M, Lendemans S, Waack IN, et al. Slow as Compared to Rapid Rewarming After Mild Hypothermia Improves Survival in Experimental Shock. J Surg Res. 2019;236:300–10. 10.1016/j.jss.2018.11.057 . Caminos Eguillor JF, Ferrara G, Kanoore Edul VS, et al. Effects of Systemic Hypothermia on Microcirculation in Conditions of Hemodynamic Stability and in Hemorrhagic Shock. Shock. 2021;55(5):686–92. 10.1097/SHK.0000000000001616 . Belyaev AM, Boldyrev SY, Myalyuk PA, et al. Optimizing Therapeutic Hypothermia Depths in Acute Type A Aortic Dissection Repair. J Surg Res. 2024;303:636–44. 10.1016/j.jss.2024.09.023 . Numata S, Tsutsumi Y, Monta O, et al. Acute type A aortic dissection repair with mild-to-moderate hypothermic circulatory arrest and selective cerebral perfusion. J Cardiovasc Surg (Torino). 2015;56(4):525–30. Shen K, Zhou X, Tan L, et al. An innovative arch-first surgical procedure under moderate hypothermia for acute type A aortic dissection. J Cardiovasc Surg (Torino). 2020;61(2):214–9. 10.23736/S0021-9509.18.10180-7 . Borojevic M, Safradin I, Vrljic D, et al. Rewarming strategy and neuromonitoring are significant details in neurological outcome after surgical repair of type A aortic dissection. Eur J Cardiothorac Surg. 2013;44(2):402. 10.1093/ejcts/ezt047 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8717698","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592199593,"identity":"d6a231a6-e937-453f-8248-2bff4195e38b","order_by":0,"name":"Wanru Ma","email":"","orcid":"","institution":"The First Affiliated Hospital of Hengyang Medical School, University of South China","correspondingAuthor":false,"prefix":"","firstName":"Wanru","middleName":"","lastName":"Ma","suffix":""},{"id":592199594,"identity":"ec8368c5-01aa-4343-baa2-b78a199730a3","order_by":1,"name":"Yuji Xiao","email":"","orcid":"","institution":"The First Affiliated Hospital of Hengyang Medical School, University of South China","correspondingAuthor":false,"prefix":"","firstName":"Yuji","middleName":"","lastName":"Xiao","suffix":""},{"id":592199595,"identity":"7fe8fe1d-c73a-419c-a325-7304234b0210","order_by":2,"name":"Yaoguang Feng","email":"","orcid":"","institution":"The First Affiliated Hospital of Hengyang Medical School, University of South China","correspondingAuthor":false,"prefix":"","firstName":"Yaoguang","middleName":"","lastName":"Feng","suffix":""},{"id":592199596,"identity":"76a67eac-936d-4f2c-870d-d2545a371062","order_by":3,"name":"Jia Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Hengyang Medical School, University of South China","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Liu","suffix":""},{"id":592199597,"identity":"b2b39b15-624a-4332-98ca-6736a476e301","order_by":4,"name":"Jinzhi Yin","email":"","orcid":"","institution":"The First Affiliated Hospital of Hengyang Medical School, University of South China","correspondingAuthor":false,"prefix":"","firstName":"Jinzhi","middleName":"","lastName":"Yin","suffix":""},{"id":592199598,"identity":"5f002586-64b3-4b19-8140-9c528d180f14","order_by":5,"name":"Xiaofeng Qu","email":"","orcid":"","institution":"The First Affiliated Hospital of Hengyang Medical School, University of South China","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Qu","suffix":""},{"id":592199599,"identity":"28280f87-c32e-402d-9e56-6fbd86bbfc2a","order_by":6,"name":"Juan Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACNvnzHw58qGDjsT/eQKQWPgkGw4MzzvDJMZw5QKQWOQkG48O8bXLGDDcSiHWYdEMCUItZYuPMxxtvMNTYRBPWInPgwME559ISm6XTii0YjqXlNhDUwpDYcOBN2bHENukcMwnGhsPEaElmOMDD9j+xR/IMsVok0hgO8rSxGUtI8BCrhecMAzCQ2eQMeIB+SSDGL/LtPcwfQFFpwH54440PNTaEtSADA4kEUpRDtJCqYxSMglEwCkYGAADhSkCqaLPumQAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Hengyang Medical School, University of South China","correspondingAuthor":true,"prefix":"","firstName":"Juan","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2026-01-28 07:53:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8717698/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8717698/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102941621,"identity":"eb55b219-ecc5-402c-9b11-4834d0fbd5b0","added_by":"auto","created_at":"2026-02-18 17:20:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86910,"visible":true,"origin":"","legend":"\u003cp\u003eTechnical Roadmap\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8717698/v1/83ac19df817750e27cb49eae.png"},{"id":102941620,"identity":"c4b7ab60-260a-4892-8b72-0d567e3a3770","added_by":"auto","created_at":"2026-02-18 17:20:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53255,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting postoperative infection risk in aortic dissection and its validation: (a) The predictive nomogram; (b) The receiver operating characteristic (ROC) curve; (c) The calibration curve; (d) The decision curve analysis (DCA）\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8717698/v1/e866d3b89dbd348e709f8675.jpeg"},{"id":106953884,"identity":"17f4e024-a8d5-427f-8635-2181a045fe12","added_by":"auto","created_at":"2026-04-15 07:58:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1223905,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8717698/v1/671640a4-4a74-41a7-b697-af587553ffb9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Prediction Model for Post-aortic Dissection Infection Based on Rewarming Time, Drainage Output, and Hemoglobin: A Retrospective Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSurgical intervention for aortic dissection is a crucial life-saving treatment. With advancements in technique, postoperative survival rates have improved significantly. However, postoperative infections, such as deep sternal wound infections and mediastinitis, remain serious complications that adversely affect patient recovery, prolong hospital stays, and increase healthcare burdens [1,2].\u003c/p\u003e\n\u003cp\u003eIn current clinical practice, warning signs for postoperative infection primarily rely on two approaches. The first involves comprehensive risk scores based on the patient\u0026apos;s preoperative general condition, such as the EuroSCORE II. These scores often fail to account for the stress of surgery and dynamic postoperative physiological changes. The second approach is reactive management after typical signs of infection appear postoperatively, such as fever or leukocytosis. However, this often misses the optimal window for early intervention. Consequently, there is an urgent clinical need to develop a tool that can proactively identify high-risk patients in the early postoperative period based on objective monitoring indicators.\u003c/p\u003e\n\u003cp\u003eFrom a pathophysiological perspective, the occurrence of postoperative infection is closely associated with surgical trauma, the intensity of the inflammatory response, and the patient\u0026apos;s early recovery status [3-5]. Specifically, the intraoperative rewarming process may influence systemic inflammatory levels; early postoperative chest drainage output directly reflects exudation and the inflammatory state at the surgical site; and immediate postoperative hemoglobin levels are related to tissue oxygen supply and immune repair capacity. These three indicators are obtainable within 24-48 hours postoperatively, offering potential for early risk assessment. However, to date, no study has systematically integrated these three factors and translated them into a simple bedside risk stratification tool.\u003c/p\u003e\n\u003cp\u003eTherefore, this study aims to develop and validate a prediction model for postoperative infection risk in aortic dissection based on \u0026quot;rewarming time, postoperative day 2 drainage output, and postoperative day 1 hemoglobin\u0026quot; through retrospective data analysis. The ultimate goal is to simplify this model into an intuitive clinical scoring system, enabling healthcare professionals to rapidly identify high-risk patients at the bedside, thereby facilitating targeted preventive strategies, optimizing perioperative management, and improving patient outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eThis was a single-center retrospective cohort study. The protocol was approved by the Ethics Review Committee of the First Affiliated Hospital of University of South China (approval number: 2024LL0410001), and the requirement for informed consent was waived. We consecutively enrolled patients with Stanford type A aortic dissection who underwent surgical treatment in the Department of Cardiothoracic and Vascular Surgery of our hospital between 2018 and 2023.Inclusion criteria were as follows: preoperative diagnosis of Stanford type A aortic dissection confirmed by CT angiography, and receipt of a standardized surgical treatment protocol involving the aortic root, ascending aorta, and/or aortic arch (specific procedures included, but were not limited to, the Bentall procedure, hemiarch/total arch replacement, Sun\u0026rsquo;s procedure, and various combined techniques).Exclusion criteria included: preoperative active infection, missing key clinical variables (e.g., early postoperative laboratory indicators), or death within 24 hours after surgery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Definition and Data Collection\u003c/h2\u003e \u003cp\u003eThe primary outcome of this study was defined as deep sternal wound infection, specifically characterized by postoperative deep incision exudation, suppuration, or sternal dehiscence, as confirmed by an attending physician or higher. Patient data were retrospectively collected through the hospital electronic medical record system. Collected variables encompassed: Preoperative data, including sex, age, body mass index, comorbidities, preoperative laboratory indices, and cardiac function. Intraoperative data, such as total operative time, cardiopulmonary bypass and aortic cross-clamp durations, rewarming time, intraoperative blood loss, and transfusion volume. Postoperative data, covering intensive care unit stay duration, daily chest tube drainage output from postoperative day 1 to 3, hemoglobin, lactate, blood glucose, total protein, creatinine clearance rates, and total postoperative transfusion volume.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed using SPSS and R. Statistical significance was set at a two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The analysis followed a staged approach as outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Preparation and Descriptive Analysis\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics summarized the cohort data. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range), and categorical variables as frequency (percentage). Univariate analyses were conducted to identify factors associated with postoperative infection: independent samples *t*-test or Mann-Whitney U test for continuous variables, and Chi-square or Fisher's exact test for categorical variables. Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were retained for further multivariate analysis.\u003c/p\u003e\n\u003ch3\u003ePredictor Screening and Model Construction\u003c/h3\u003e\n\u003cp\u003eVariables selected from the univariate analysis were entered into a binary logistic regression model using the forward likelihood ratio method to identify independent predictors. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. A nomogram was constructed based on the final predictors to visualize individual infection risk.\u003c/p\u003e\n\u003ch3\u003eInternal Validation and Model Performance\u003c/h3\u003e\n\u003cp\u003eThe nomogram\u0026rsquo;s performance was evaluated in three dimensions: Discrimination: assessed by the area under the receiver operating characteristic curve (AUC) with 95% CI. Calibration: evaluated using a calibration plot with 200 bootstrap replicates and quantified by the mean absolute error (MAE). Clinical Utility: estimated by decision curve analysis (DCA) across a range of threshold probabilities.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and Validation of a Clinical Scoring System\u003c/h2\u003e \u003cp\u003eTo enhance bedside utility, a simple scoring system was derived. For each independent continuous predictor, the optimal cut-off was determined by maximizing Youden\u0026rsquo;s index on its ROC curve. Variables were dichotomized at these cut-offs (abnormal\u0026thinsp;=\u0026thinsp;1 point, normal\u0026thinsp;=\u0026thinsp;0 points), creating a 0\u0026ndash;3 point score.\u003c/p\u003e \u003cp\u003ePatients were stratified into low- (0 points), intermediate- (1\u0026ndash;2 points), and high-risk (3 points) groups. The Cochran-Armitage trend test assessed the significance of increasing infection rates across groups. The relative risk (RR) with 95% CI for the intermediate- and high-risk groups versus the low-risk group quantified the stratification effect.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient Baseline Characteristics and Univariate Analysis Results\u003c/h2\u003e \u003cp\u003eThis study included a total of 243 patients who underwent aortic dissection surgery. Based on the occurrence of postoperative infection, they were divided into two groups: a non-infection group (n\u0026thinsp;=\u0026thinsp;185) and an infection group (n\u0026thinsp;=\u0026thinsp;58). Clinical data from the preoperative, intraoperative, and postoperative periods were compared between the two groups using univariate analysis, with the results summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.Overall, the majority of baseline characteristics showed no statistically significant differences between the two groups. Demographic profiles, including sex and age, as well as the prevalence of common comorbidities such as hypertension, diabetes, previous surgery history, and smoking history, were comparable between groups (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similarly, preoperative laboratory indices\u0026mdash;including hemoglobin, total protein, albumin, cholesterol, creatinine clearance rate, and cardiac ejection fraction\u0026mdash;showed no significant intergroup differences (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).However, the analysis identified several factors associated with postoperative infection. Among intraoperative indicators, rewarming time was significantly shorter in the infection group than in the non-infection group (78.81\u0026thinsp;\u0026plusmn;\u0026thinsp;20.01 min vs. 85 [69.5, 101] min, P\u0026thinsp;=\u0026thinsp;0.037). Early postoperative observations revealed that postoperative day 2 drainage output was significantly higher in the infection group (395 [198.75, 617.5] mL/day vs. 250 [137.5, 467.5] mL/day, P\u0026thinsp;=\u0026thinsp;0.006). Furthermore, postoperative day 1 lactate was lower in the infection group (2.1 [1.475, 3.2] mmol/L vs. 2.7 [1.8, 3.9] mmol/L, P\u0026thinsp;=\u0026thinsp;0.043), while postoperative day 1 hemoglobin was also significantly lower in this group (98.81\u0026thinsp;\u0026plusmn;\u0026thinsp;14.52 g/L vs. 101 [92.5, 114] g/L, P\u0026thinsp;=\u0026thinsp;0.043). Postoperative day 2 hemoglobin showed a trend toward being lower in the infection group (94.69\u0026thinsp;\u0026plusmn;\u0026thinsp;13.41 g/L vs. 98 [88, 108] g/L, P\u0026thinsp;=\u0026thinsp;0.050). All other daily postoperative measures\u0026mdash;including drainage output, hemoglobin, lactate, total protein, creatinine clearance rate, and blood glucose control\u0026mdash;showed no statistically significant differences between the two groups (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Baseline Characteristics and Univariate Analysis Between the Infection and Non-infection Groups. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared using the independent samples t-test if normally distributed, or as median (interquartile range) and compared using the Mann-Whitney U test if non-normally distributed. Categorical variables are presented as number (percentage) and compared using the Chi-square test. Bold text indicates P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-infection group (1), N\u0026thinsp;=\u0026thinsp;185\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInfection group (2), N\u0026thinsp;=\u0026thinsp;58\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et value / z value / χ\u0026sup2; value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative Data:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134(72.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(75.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53(45.5, 63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.69\u0026thinsp;\u0026plusmn;\u0026thinsp;10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65(1.59,1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68(1.62, 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.782\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\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65(57,75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.40\u0026thinsp;\u0026plusmn;\u0026thinsp;15.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.87(21.36,27.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody surface area (m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.791\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\u003eHypertension (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102(55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious surgery history (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.956\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\u003eSmoking history (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative hemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129(119,140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.91\u0026thinsp;\u0026plusmn;\u0026thinsp;19.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.715\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\u003ePreoperative total protein (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.91\u0026thinsp;\u0026plusmn;\u0026thinsp;6.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.60\u0026thinsp;\u0026plusmn;\u0026thinsp;6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative albumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.05(36.4, 42.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative creatinine clearance rate (mL/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.47\u0026thinsp;\u0026plusmn;\u0026thinsp;31.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.45\u0026thinsp;\u0026plusmn;\u0026thinsp;29.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative cardiac function (ejection fraction) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61(56.0, 64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(57, 65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntraoperative Data\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal operation time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e472(417.5,529.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462.21\u0026thinsp;\u0026plusmn;\u0026thinsp;87.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiopulmonary bypass time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224(195,251.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219.03\u0026thinsp;\u0026plusmn;\u0026thinsp;40.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic cross-clamp time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136(119,154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137(120.25, 149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRewarming time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85(69.5,101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.81\u0026thinsp;\u0026plusmn;\u0026thinsp;20.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative blood loss (mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500(337.5,800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500(400, 800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal intraoperative transfusion volume (mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800(400, 1197.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e831.98\u0026thinsp;\u0026plusmn;\u0026thinsp;453.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnesthesia recovery time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195(112.5, 307.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205(120, 302.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of mechanical ventilation (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1150(976.5, 3090.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1380(978.75, 3628.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndotracheal intubation time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1150(976.5, 3090.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1447.5(987.5, 4016.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU length of stay (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6750(5201.5, 9662.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8105(5348.75, 11086.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePostoperative Data\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal drainage volume, Postoperative day (POD) 1 (mL/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330(222.5, 475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e417.5(258.75, 597.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal drainage volume, POD 2 (mL/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250(137.5, 467.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e395(198.75, 617.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal drainage volume, POD 3 (mL/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120(46, 345)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172.5(73.75, 423.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal postoperative transfusion volume (mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600(300, 1000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e525(237.5, 1100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood glucose, POD 1 (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.5(8.2, 11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5(7.9, 10.625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood glucose, POD 2 (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.9(7.9, 10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9(7.65, 10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood glucose, POD 3 (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.4(7.25, 10.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate, POD 1 (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7(1.8, 3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1(1.475, 3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate, POD 2 (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4(1, 1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25(0.9, 1.725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate, POD 3 (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.8, 1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.7, 1.425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, POD 1 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101(92.5, 114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.81\u0026thinsp;\u0026plusmn;\u0026thinsp;14.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, POD 2 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98(88, 108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.69\u0026thinsp;\u0026plusmn;\u0026thinsp;13.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, POD 3 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93(85, 103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.55\u0026thinsp;\u0026plusmn;\u0026thinsp;11.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein, POD 1 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.65(55.1, 63.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.44\u0026thinsp;\u0026plusmn;\u0026thinsp;7.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein, POD 2 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.29\u0026thinsp;\u0026plusmn;\u0026thinsp;5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.39\u0026thinsp;\u0026plusmn;\u0026thinsp;6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein, POD 3 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.8(57.82, 64.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.77\u0026thinsp;\u0026plusmn;\u0026thinsp;6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine clearance rate, POD 1 (mL/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.30\u0026thinsp;\u0026plusmn;\u0026thinsp;32.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.50\u0026thinsp;\u0026plusmn;\u0026thinsp;27.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine clearance rate, POD 2 (mL/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.9(40.15, 85.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.36\u0026thinsp;\u0026plusmn;\u0026thinsp;28.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine clearance rate, POD 3 (mL/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.45\u0026thinsp;\u0026plusmn;\u0026thinsp;31.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.56\u0026thinsp;\u0026plusmn;\u0026thinsp;28.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Independent Predictors for Postoperative Infection via Multivariable Logistic Regression Analysis\u003c/h2\u003e \u003cp\u003eTo identify independent risk factors for postoperative infection following aortic dissection, variables with a P value less than 0.05 in the univariate analysis\u0026mdash;namely rewarming time, postoperative day 2 drainage output, postoperative day 1 lactate, and postoperative day 1 hemoglobin\u0026mdash;were entered into a binary logistic regression model using the forward likelihood ratio method. The results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, identified rewarming time, postoperative day 2 drainage output, and postoperative day 1 hemoglobin as independent predictors of postoperative infection. Specifically, rewarming time demonstrated a negative association with infection risk, with an odds ratio of 0.983, a 95% confidence interval of 0.969 to 0.997, and a P value of 0.012. This indicates that each additional minute of rewarming time was associated with an approximately 1.7% reduction in infection risk. Postoperative day 2 drainage output showed a positive association with infection risk, with an odds ratio of 1.002, a 95% confidence interval of 1.001 to 1.003, and a P value of 0.001. This corresponds to an approximate 0.2% increase in infection risk for each 1 mL per day increase in drainage output. Similarly, postoperative day 1 hemoglobin level was negatively associated with infection risk, with an odds ratio of 0.977, a 95% confidence interval of 0.955 to 0.999, and a P value of 0.045. This suggests that each 1 g per liter increase in hemoglobin level was associated with an approximately 2.3% decrease in infection risk. In contrast, postoperative day 1 lactate level did not reach statistical significance in the multivariable analysis, with a P value of 0.364.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Multivariable Logistic Regression Analysis for Postoperative Infection in Aortic Dissection. The analysis was performed using forward stepwise logistic regression with an inclusion criterion of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. OR: odds ratio.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \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\u003eB value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald χ\u0026sup2; value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% Confidence interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRewarming time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.969\u0026ndash;0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative day 2 drainage output (mL/day)\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\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.001\u0026ndash;1.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative day 1 hemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.955\u0026ndash;0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and Validation of the Prediction Model\u003c/h2\u003e \u003cp\u003eBased on the independent predictors identified through multivariable logistic regression analysis, we constructed a nomogram model for the individualized prediction of postoperative infection risk in aortic dissection (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). This model integrates three early-available clinical indicators: rewarming time, postoperative day 2 drainage output, and postoperative day 1 hemoglobin. To comprehensively evaluate the model's performance, rigorous internal validation was conducted as follows. Discrimination: The model's discriminative ability was assessed using the receiver operating characteristic (ROC) curve. The area under the curve (AUC) was 0.704 (95% CI: 0.632\u0026ndash;0.776) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). This result indicates a moderate-to-good ability of the model to distinguish between patients with and without postoperative infection. The fact that the 95% confidence interval does not include 0.5 confirms that this discriminative capability is statistically significant. Calibration: The agreement between predicted probabilities and observed outcomes was evaluated using a calibration curve. Following internal validation with 200 bootstrap resampling repetitions, the model's mean absolute error (MAE) was 0.036 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The calibrated curve closely aligned with the ideal diagonal line, indicating good predictive accuracy and a high concordance between predicted risks and observed event rates. Clinical Utility: The clinical net benefit of the model across different decision thresholds was assessed using decision curve analysis (DCA). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, within a threshold probability range of approximately 10% to 50%, the net benefit curve for using this model to guide clinical decisions\u0026mdash;that is, to intervene in patients identified as high-risk by the model\u0026mdash;consistently exceeded the curves for the strategies of \"intervening on all patients\" and \"intervening on no patients.\" This indicates that applying this model for risk stratification can optimize clinical decision-making. It enables the effective identification and management of high-risk patients while avoiding unnecessary interventions in low-risk individuals, thereby yielding a higher net clinical benefit.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and Validation of a Simplified Risk Scoring System\u003c/h2\u003e \u003cp\u003eTo facilitate rapid clinical application, the nomogram prediction model was converted into a simplified risk scoring system. The optimal clinical cut-off values for the continuous predictors were determined by maximizing Youden\u0026rsquo;s index on their respective receiver operating characteristic (ROC) curves (see Supplementary Figure S1). The criteria were defined as follows: rewarming time\u0026thinsp;\u0026le;\u0026thinsp;83.5 minutes, postoperative day 2 drainage output\u0026thinsp;\u0026gt;\u0026thinsp;261.5 mL, and postoperative day 1 hemoglobin\u0026thinsp;\u0026le;\u0026thinsp;109.5 g/L. Each criterion met was assigned 1 point, resulting in a total risk score ranging from 0 to 3. Based on the total score, patients were stratified into three risk tiers: low-risk (0 points), intermediate-risk (1\u0026ndash;2 points), and high-risk (3 points). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the infection rate demonstrated a significant increasing trend across the ascending risk categories. The infection rate was 6.7% (95% confidence interval, CI: 0.2\u0026ndash;32.0%) in the low-risk group, 21.7% (95% CI: 16.1\u0026ndash;28.4%) in the intermediate-risk group, and 38.6% (95% CI: 24.4\u0026ndash;54.5%) in the high-risk group. This increasing trend was confirmed to be statistically significant by the Cochran\u0026ndash;Armitage trend test (χ\u0026sup2; = 8.166, P\u0026thinsp;=\u0026thinsp;0.004). Compared with the low-risk group, the high-risk group had a relative risk of 5.76 (95% CI: 0.82\u0026ndash;40.38, P\u0026thinsp;=\u0026thinsp;0.024) for developing postoperative infection.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk Stratification and Incidence of Infection Based on the Simplified Scoring System. The 95% confidence intervals for infection rates were calculated using the exact binomial method (Clopper-Pearson). Relative risk values were calculated using the low-risk group as the reference, with confidence intervals derived from Koopman's asymptotic method. The P values* represent the results of two-sided Fisher's exact tests comparing each group to the low-risk group. The P value for the trend test indicates a significant increase in infection rate with ascending risk category.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Stratification (Score)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Cases, n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Infections\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInfection Rate, % (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelative Risk, RR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003e\u003cb\u003eLow-risk (0 points)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.7 (0.2\u0026ndash;32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntermediate-risk (1\u0026ndash;2 points)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.7 (16.1\u0026ndash;28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.24 (0.47\u0026ndash;22.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh-risk (3 points)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.6 (24.4\u0026ndash;54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.76 (0.82\u0026ndash;40.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrend Test (Cochran\u0026ndash;Armitage)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2; = 8.166, df\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough retrospective analysis, this study developed and validated for the first time an early risk assessment tool specifically designed to predict postoperative infection in aortic dissection. The key finding is that three objective indicators easily obtained within 24\u0026ndash;48 hours after surgery\u0026mdash;intraoperative rewarming time, postoperative day 2 drainage output, and postoperative day 1 hemoglobin level\u0026mdash;served as independent predictors of infection. The nomogram model and the simplified 0\u0026ndash;3 point scoring system established based on these predictors demonstrated favorable discrimination (AUC\u0026thinsp;=\u0026thinsp;0.704), calibration (MAE\u0026thinsp;=\u0026thinsp;0.036), and clinical utility in internal validation, providing a direct basis for early identification of high-risk patients and implementation of stratified management.\u003c/p\u003e \u003cp\u003eThis study confirms the classic role of increased postoperative drainage\u0026mdash;reflecting tissue exudation and inflammatory response intensity\u0026mdash;and early anemia\u0026mdash;affecting tissue oxygen supply and immune repair\u0026mdash;as risk factors for infection. By integrating relevant literature, their specific mechanisms and clinical relevance can be explored in depth from the following two perspectives. First, increased postoperative drainage is an important manifestation of both local and systemic inflammatory responses. In patients with aortic dissection, elevated preoperative systemic inflammatory response index (SIRI) and systemic immune-inflammatory index (SII) have been shown to be closely associated with postoperative adverse events [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This suggests that a pronounced preoperative inflammatory state may persist into the postoperative period, exacerbating tissue exudation and thereby leading to increased drainage output. Broader surgical patient studies further support this association, indicating that levels of early postoperative inflammatory cytokines such as IL-6 and C-reactive protein (CRP) are significantly correlated with both drainage volume and surgical site infection risk [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, increased drainage is not an isolated phenomenon but rather a key intermediate link connecting preoperative inflammatory status, surgical trauma stress, and postoperative infection risk.\u003c/p\u003e \u003cp\u003eSecond, early postoperative anemia increases infection risk by impairing tissue oxygen supply and immune function. Studies indicate that increased preoperative transfusion of red blood cells and plasma in aortic dissection is associated with higher postoperative complication rates [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], indirectly reflecting the negative impact of anemia or coagulation dysfunction on outcomes. In models such as abdominal surgery, low preoperative albumin levels\u0026mdash;often linked to nutritional status and anemia\u0026mdash;also significantly elevate the risk of postoperative infection [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Anemia reduces the oxygen-carrying capacity of blood, directly affecting tissue oxygenation, thereby weakening the bactericidal function of immune cells and tissue repair capacity, creating a microenvironment conducive to infection. In summary, increased postoperative drainage and early anemia in aortic dissection\u0026mdash;classic risk factors for infection\u0026mdash;are underpinned by a dual pathophysiological process involving inflammatory response and depletion of physiological reserves. These findings not only emphasize the importance of monitoring and managing these early postoperative objective indicators but also provide a multidimensional perspective for understanding the mechanisms of infection development. In clinical practice, targeted interventions such as meticulous fluid management, proactive anemia correction, and inflammation modulation may hold potential value in reducing postoperative infection risk.\u003c/p\u003e \u003cp\u003eThe most challenging and noteworthy finding is the negative correlation between rewarming time and infection risk, suggesting that longer rewarming time corresponds to a lower risk of infection (OR\u0026thinsp;=\u0026thinsp;0.983). This appears to contradict the intuitive notion that prolonged operative time may increase complications. We speculate that this may reveal an underrecognized compensatory strategy in clinical practice: during aortic dissection surgery, particularly in the management of high-risk patients, clinical teams often favor a rapid rewarming strategy to shorten cardiopulmonary bypass time and promptly restore physiological homeostasis. However, this approach may carry significant risks of physiological fluctuation, and its clinical benefit and safety are highly dependent on the patient's specific pathological status and surgical context. On one hand, rapid rewarming has proven beneficial in certain clinical scenarios. For instance, in patients with post-cardiac arrest syndrome, rapid rewarming is associated with improved neurological outcomes, suggesting a potential protective effect in specific ischemia-reperfusion contexts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. On the other hand, this strategy is not universally applicable. In septic animal models, rapid rewarming conversely leads to shortened survival and exacerbated acute lung injury, possibly related to aggravated systemic inflammatory response [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In the treatment of severe frostbite, although rapid rewarming is considered standard care, its effect on tissue survival remains unclear [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These disparities indicate that the benefits and drawbacks of rapid rewarming largely depend on the nature of the underlying disease and the systemic inflammatory state. Returning to the specific setting of aortic dissection surgery, temperature management is far from a simple choice between \"fast\" and \"slow\"; rather, it involves a comprehensive decision-making process encompassing the depth of hypothermia, its duration, and the rewarming rate. Studies have shown that moderate hypothermic circulatory arrest, compared to deep hypothermic circulatory arrest, can reduce the incidence of postoperative acute kidney injury and delirium [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Further delineating this, in patients with acute DeBakey type I aortic dissection, high-moderate hypothermia (24.1\u0026ndash;28.0\u0026deg;C) is associated with lower in-hospital mortality and renal injury risk for those without perfusion deficits, while for patients with pre-existing distal perfusion impairment, low-moderate hypothermia (20.1\u0026ndash;24.0\u0026deg;C) may help reduce the incidence of paraplegia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Collectively, this evidence suggests that an optimal rewarming strategy should be based on an individualized hypothermia protocol, rather than pursued in isolation solely for speed. Conversely, for hemodynamically stable aortic dissection patients, adopting a slower and more controlled rewarming strategy is key to achieving a smooth transition and improving prognosis. The core advantage of this approach lies in allowing the body to optimize peripheral perfusion with better physiological compensatory capacity during weaning from cardiopulmonary bypass, thereby potentially mitigating systemic inflammatory response syndrome and ischemia-reperfusion injury. The physiological basis for this is supported by evidence from multiple research areas. In the field of organ transplantation, compared to rapid rewarming, a gradual pressure-increase rewarming protocol for isolated kidneys significantly improves functional parameters, reduces renal tubular damage, and enhances endothelial protection, clearly demonstrating the advantage of slow rewarming in alleviating ischemia-reperfusion injury [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In experimental shock models, animals subjected to slow rewarming also showed significantly better survival rates than those in the rapid rewarming group, suggesting the latter may prematurely forfeit the protective effects of hypothermia [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. From a microcirculatory perspective, under conditions of hemodynamically stable systemic hypothermia, the reductions in cardiac output, renal blood flow, and oxygen consumption represent a modifiable physiological adaptation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Together, these mechanisms indicate that slow rewarming facilitates a more stable hemodynamic transition, creating a safe recovery window for tissues transitioning from \"protective hypothermia\" to \"normal metabolism.\" In the specific context of aortic dissection surgery, the value of this strategy is concretely reflected in the choice of temperature management protocols. Numerous clinical studies have confirmed that compared to deep hypothermic circulatory arrest, the strategy of moderate hypothermic circulatory arrest combined with selective cerebral perfusion not only provides sufficient cerebral and distal organ protection but also effectively reduces postoperative bleeding risk, the incidence of neurological complications, in-hospital mortality, and improves long-term survival [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This provides indirect clinical outcome-based evidence for the benefits of \"avoiding excessive deep hypothermia and allowing gradual rewarming.\" Furthermore, in acute aortic dissection surgery, moderate hypothermia combined with a slow rewarming strategy may not only aid in cerebral protection but also potentially reduce the severity of systemic inflammatory response syndrome by optimizing peripheral perfusion [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, the seemingly paradoxical observation in this study\u0026mdash;that rewarming time is negatively correlated with infection risk\u0026mdash;holds a deeper implication: longer rewarming time likely serves as a surrogate marker reflecting both relatively stable intraoperative patient condition and more meticulous perioperative temperature management. For such patients, the surgical team is afforded the opportunity to implement a slower, more controlled rewarming strategy, thereby promoting a more stable hemodynamic transition, optimizing tissue perfusion, and mitigating systemic inflammation and ischemia-reperfusion injury. Ultimately, this enhances the patient's postoperative defensive capacity and reduces infection risk. Consequently, rewarming time should not be viewed in isolation as merely a technical parameter, but rather understood as an indicator that comprehensively reflects both the patient's inherent physiological stability and the quality of perioperative management. This finding further underscores that temperature management in aortic dissection surgery should be regarded as a dynamic, individualized, and holistic strategy. Its core objective is to achieve an optimal physiological transition for the patient while ensuring surgical safety.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003elimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, as a single-center retrospective study, the generalizability of its conclusions may be influenced by the specific diagnosis and treatment protocols of the center, and retrospective data collection carries the potential for information bias. Second, although the sample size meets preliminary modeling requirements, the relatively limited number of infection events may affect the precision of effect estimates for predictors in the multivariable model, warranting cautious interpretation of the high-risk subgroup assessments. Finally, the proposed prediction model and scoring system have only undergone internal validation; their true discriminatory performance and clinical applicability require external validation in independent, multicenter prospective cohorts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study developed and validated a risk prediction model for postoperative infection in aortic dissection, based on early postoperative objective indicators: rewarming time, postoperative day 2 drainage output, and postoperative day 1 hemoglobin. This model can be transformed into a convenient bedside scoring system, facilitating the rapid identification of high-risk patients and enabling effective stratification of infection risk in the early postoperative period. This tool provides a basis for implementing targeted monitoring and preventive interventions, with the potential to improve perioperative management for patients undergoing aortic dissection surgery.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conceptualized and designed by Wanru Ma and Juan Luo, with Wanru Ma drafting the initial manuscript. Data curation was performed by\u0026nbsp;Jinzhi Yin and Xiaofeng Qu. Data analysis and interpretation were conducted by\u0026nbsp;Yuji Xiao and Jia Liu. Yaoguang Feng, as the departmental director, provided overarching supervision, resources, and critical guidance. Juan Luo, as the corresponding author, supervised the entire research process and finalized the manuscript. All authors have read and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was approved by the hospital\u0026rsquo;s ethics committee (ethics approval number 2024LL1016001) and preoperative patients and their families.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the colleagues from the Department of Cardiothoracic Surgery and the Medical Records Department of the First Affiliated Hospital of University of South China for their support and invaluable contributions to the implementation of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from the Guidance Project of Hengyang Municipal Science and Technology Bureau (202121034632).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDiazCastrillon CE, SernaGallegos D, Arnaoutakis G, et al. 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Eur J Cardiothorac Surg. 2013;44(2):402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ejcts/ezt047\u003c/span\u003e\u003cspan address=\"10.1093/ejcts/ezt047\" 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":"Aortic Dissection, Postoperative Infection, Risk Prediction Model, Risk Stratification, Rewarming Time, Chest Tube Drainage, Anemia","lastPublishedDoi":"10.21203/rs.3.rs-8717698/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8717698/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThere is currently a lack of early and simple tools for assessing the risk of postoperative infection following aortic dissection surgery. This study aimed to develop and validate a prediction model based on early postoperative objective indicators, along with a corresponding simplified scoring system.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 243 patients who underwent surgical treatment for aortic dissection were retrospectively enrolled. Independent predictors of postoperative infection were identified using logistic regression analysis. Based on these predictors, a nomogram prediction model was constructed. The model's performance was evaluated in terms of discrimination (area under the receiver operating characteristic curve, AUC), calibration (mean absolute error, MAE), and clinical utility (decision curve analysis, DCA). Furthermore, a simple risk score was established using the optimal cut-off values for each predictor, and its effectiveness for risk stratification was validated using a trend test.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eRewarming time (\u0026le;\u0026thinsp;83.5 min), postoperative day 2 drainage output (\u0026gt;\u0026thinsp;261.5 ml), and postoperative day 1 hemoglobin level (\u0026le;\u0026thinsp;109.5 g/L) were identified as independent predictors of infection. The constructed nomogram demonstrated good predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.704, MAE\u0026thinsp;=\u0026thinsp;0.036). When transformed into a 0\u0026ndash;3 point simplified scoring system, patients were stratified into low-, medium-, and high-risk groups. The infection rates for these groups were 6.7%, 21.7%, and 38.6%, respectively, showing a significant increasing trend (P for trend\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe simple scoring system developed in this study can effectively identify patients at high risk for infection in the early postoperative period, providing a practical tool for implementing stratified management and precise intervention.\u003c/p\u003e","manuscriptTitle":"A Prediction Model for Post-aortic Dissection Infection Based on Rewarming Time, Drainage Output, and Hemoglobin: A Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 17:20:02","doi":"10.21203/rs.3.rs-8717698/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":"55bea21c-3713-413d-945b-617e539d3c5c","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T07:57:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 17:20:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8717698","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8717698","identity":"rs-8717698","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}