Development and Internal Validation of a Predictive Model for Postoperative Recovery Quality in Cardiovascular Surgery Patients Based on the QoR-15 Scale: A Retrospective Cohort Study

Development and Internal Validation of a Predictive Model for Postoperative Recovery Quality in Cardiovascular Surgery Patients Based on the QoR-15 Scale: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Internal Validation of a Predictive Model for Postoperative Recovery Quality in Cardiovascular Surgery Patients Based on the QoR-15 Scale: A Retrospective Cohort Study Qiaobo Zhu, Zhuan Zhang, Ning Li, Bei Ma, Luo Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7106864/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objectives A retrospective analysis was conducted to evaluate postoperative recovery quality using the Quality of Recovery-15 (QoR-15) scale in patients undergoing cardiovascular surgery. The study aimed to examine the impact of various perioperative factors on recovery and to develop a predictive model. Methods This retrospective cohort study analyzed clinical data from the medical record system for patients who underwent cardiovascular surgery at a single tertiary care center between March 2020 and September 2022. A total of 198 patients were included in the final analysis after excluding 15 patients due to incomplete data or loss to follow-up. The variables gathered encompassed demographic information (gender and age), duration of postoperative follow-up, American Society of Anesthesiologists (ASA) classification, preoperative lactate levels, emergency surgical status, and whether cardiopulmonary bypass (CPB) was implemented. The modified Frailty Index (mFI) was calculated for each patient to assess baseline frailty. In addition, detailed surgical and perioperative data were recorded. Postoperative data and QoR-15 scores were also included. Univariate and multivariate logistic regression analyses were performed to develop and validate a predictive model. Results A total of 213 patients were included in this study, with 15 patients excluded, resulting in a total of 198 postoperative QoR-15 scores. Gender, ASA classification, preoperative lactate levels, follow-up time, and mFI were identified as independent predictors of excellent postoperative recovery (QoR-15 ≥ 120). The multivariate model showed good discrimination (AUC = 0.925; 95% CI: 0.884–0.966) and internal validation (bootstrap-corrected AUC = 0.901). The Hosmer-Lemeshow test confirmed good calibration ( p = 0.394). Conclusion A simple model using five routinely available variables demonstrated strong performance in predicting recovery quality. This tool may aid clinicians in identifying patients at risk for poor postoperative outcomes, facilitating personalized perioperative strategies. Postoperative recovery Quality of Recovery-15 Cardiovascular surgery Frailty assessment Predictive mode Figures Figure 1 Figure 2 Figure 3 Background The quality of postoperative recovery (QoR) is a critical measure of patient-centered outcomes [ 1 ]. In cardiovascular and major vascular procedures, which are characterized by extensive surgical trauma and complex perioperative management, accurately predicting and enhancing postoperative recovery quality remains a significant clinical challenge. Despite the notable decline in in-hospital mortality rates for cardiac surgery in the past decade from 3.7–2.7%, many patients continue to experience suboptimal postoperative recovery, particularly in physical and emotional domains[ 2 ]. This underscores the need for robust prediction models aligned with ERAS protocols to enhance individualized care. Factors such as impaired preoperative cardiac function, prolonged operative time, frailty, and systemic hypoperfusion can contribute to poor recovery and higher complication rates[ 3 ]. As Enhanced Recovery After Surgery (ERAS) protocols become increasingly integrated into perioperative management for cardiac surgery, traditional risk assessment models have demonstrated limitations in identifying patients at risk for suboptimal recovery. Thus, there is an urgent need to develop predictive tools that are aligned with ERAS principles and that incorporate comprehensive perioperative data including patient frailty, metabolic markers, and procedural variables to support individualized care planning. The Quality of Recovery-15 (QoR-15) questionnaire is a validated, patient-reported outcome measure widely used for assessing postoperative recovery. It captures five dimensions of recovery: physical comfort, emotional state, physical independence, psychological support, and pain. Its brevity, reliability, and responsiveness make it an ideal tool for assessing recovery quality in clinical research and routine practice[ 4 ]. This retrospective study investigates the impact of perioperative factors on the quality of recovery in patients undergoing cardiovascular surgery, as measured by the QoR-15 score. We hypothesized that a combination of patient-specific factors (including frailty status, preoperative metabolic markers, and demographic characteristics) would significantly predict postoperative recovery quality. The primary objective was to develop and internally validate a predictive model to support the early identification of patients at risk for poor recovery, ultimately informing individualized perioperative care strategies and resource allocation. Materials and Methods Study design This was a single-center, retrospective observational cohort study that included patients who underwent cardiovascular surgery at the Affiliated Hospital of Yangzhou University between March 2022 and September 2024. Data were retrieved from the hospital's electronic medical record system. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting observational studies. Clinical trial number: not applicable. Inclusion criteria were as follows: (1) age ≥ 18 years; (2) completed postoperative follow-up with a recorded Quality of Recovery-15 (QoR-15) score; (3) availability of comprehensive clinical, demographic, and laboratory data; and (4) informed consent obtained from the patient or their legal representative. Exclusion criteria included: (1) preoperative diagnosis of severe cognitive impairment or psychiatric illness precluding cooperation with QoR-15 assessment; (2) in-hospital death or treatment withdrawal within 24 hours post-surgery. Anesthesia Management All patients received a standardized anesthesia protocol, including continuous intraoperative monitoring (electrocardiography, pulse oximetry, invasive arterial pressure, and depth of anesthesia). Induction agents included midazolam, etomidate, sufentanil, and rocuronium, followed by maintenance with propofol, sevoflurane, dexmedetomidine, and remifentanil. Hemodynamic stability was maintained with vasoactive agents as needed. Detailed anesthetic procedures are provided in Supplementary Appendix 1. Observational Indicators Collected data were categorized into three domains: Baseline variables : gender, age, American Society of Anesthesiologists (ASA) physical status, preoperative lactate level, comorbidities, emergency status, and cardiopulmonary bypass (CPB) use. Frailty was assessed using the Modified Frailty Index (mFI), with scores ≥ 0.27 defined as frail; Intraoperative variables : surgery type and duration, CPB time, aortic cross-clamp time, reperfusion strategy, intraoperative fluid management, and rewarming temperature; Postoperative variables : length of ICU and total hospital stay. Postoperative recovery quality was assessed using the Quality of Recovery-15 (QoR-15) questionnaire, a validated patient-reported outcome measure. It consists of 15 items spanning five domains: physical comfort (5 items), emotional state (4), physical independence (2), psychological support (2), and pain (2). Each item is rated on an 11-point scale (0–10), yielding a total score from 0 to 150. A score ≥ 120 was defined as excellent recovery; scores < 120 indicated suboptimal recovery[ 5 ]. Sample Size The sample size calculation was based on detecting a minimum clinically important difference of 10 points in the QoR-15 score, with a standard deviation of 18 points. Using a two-tailed test, with a significance level (α) of 0.05 and a power (1-β) of 90%, the required sample size was estimated at 180 patients. To account for a potential 10% dropout rate, 213 patients were ultimately enrolled. Statistical Analysis Data were analyzed using SPSS 26.0. Continuous variables were expressed as mean ± SD or median (IQR); categorical variables as frequencies and percentages. Group comparisons used t-tests, Mann-Whitney U, χ2, or Fisher’s exact tests as appropriate. Logistic regression identified predictors of recovery quality. Variables with p < 0.05 in univariate analysis entered the multivariate model. Model discrimination was evaluated with ROC curves and AUC. Calibration was assessed with the Hosmer-Lemeshow test. Internal validation used 1000 bootstrap replicates. Results General characteristics of the patients A total of 213 patients were initially enrolled in the study. After applying exclusion criteria, 198 patients were included in the final analysis (Fig. 1 ). The median follow-up time was 8.5 months (IQR: 4.2–14.7 months). Ten patients (4.7%) were lost to follow-up, and the in-hospital mortality rate was 2.5% (n = 5). Two patients (1.0%) experienced postoperative stroke, and one patient (0.5%) had a myocardial infarction. Due to the low incidence of these major adverse events, their statistical association with QoR-15 score could not be robustly assessed and were excluded from the predictive model. Table 1 was presented for baseline characteristics of patients. Table 1 Baseline characteristics of patients Excellent recovery (n = 142) Poor recovery (n = 56) p Gender male 102 (81.6%) 23 (18.4%) < 0.001 female 40 (54.8%) 33 (45.2%) Age (years) 63.9 ± 2.4 63.8 ± 3.1 < 0.001 Postoperative Recovery Time ≤ 3 months 24 (66.7%) 12 (33.3%) 12 months 47 (71.2%) 19 (28.8%) ASA Classification Grade II 11 (100%) 0 (0%) 0.036 ≥ Grade III 131 (70.1%) 56 (29.9%) Preoperative lactate Level ≤ 2 mmol/L 154 (75.6%) 21 (24.4%) 2 mmol/L 6 (26.1%) 17 (73.9%) Emergency Yes 133 (76.9%) 40 (23.1%) < 0.001 No 9 (36.0%) 16 (64.0%) Cardiopulmonary Bypass Yes 123 (69.9%) 53 (30.1%) 0.016 No 19 (86.4%) 3 (13.6%) Surgical Approach valve surgery 4 (80.0%) 1 (20.0%) 0.293 coronary artery bypass grafting 65 (75.6%) 21 (24.4%) aortic replacement 6 (50.0%) 6 (50.0%) other 67 (70.5%) 28 (29.5%) Surgery Duration (hours) 5.8 ± 1.2 6.2 ± 1.5 0.046 CPB time (min) 151.7 ± 9.1 151.0 ± 11.8 0.343 Aortic cross-clamp time (min) 112.7 ± 10.9 112.0 ± 8.7 0.418 Rebeating strategies automatic rebeating 131 (72.4%) 50 (27.6%) 0.574 non-automatic rebeating 11 (61.5%) 6 (38.5%) Perioperative fluid therapy crystalloid fluids 16 (80.0%) 4 (20.0%) 0.000 autologous blood 34 (87.2%) 5 (12.8%) stored blood 71 (65.1%) 38 (34.9%) Rewarming temperature low 116 (69.9%) 50 (30.1%) 0.962 medium 9 (75.0%) 3 (25.0%) Duration of ICU stay ≤ 3 days 83 (80.6%) 20 (19.4%) 0.004 > 3 days 59 (62.1%) 36 (37.9%) Total hospitalization days ≤ 15 days 17 (77.3%) 5 (22.7%) 0.539 > 15 days 125 (71.0%) 51 (29.0%) mFI ≥ 0.27 3 (25%) 9 (75%) 0.004 < 0.27 126 (68.1%) 59 (31.9%) QoR-15 = Quality of Recovery-15; OR = Odds Ratio; CI = confidence interval; ASA = American Society of Anesthesiologists; CPB = Cardiopulmonary Bypass; mFI = Modified Frailty Index; ICU = Intensive Care Unit. p < 0.05 was considered statistically significant. Comparison of QoR-15 dimension scores between excellent and poor recovery groups As shown in Table 2 , patients classified as having excellent recovery (QoR-15 ≥ 120) exhibited significantly higher scores across all QoR-15 dimensions, with the most notable differences observed in emotional state, pain management, and physical comfort (all p < 0.001). Table 2 Comparison of QoR-15 dimension scores between excellent and poor recovery groups QoR-15 Domain scores (mean ± standard deviation) Excellent Recovery (n = 144) Poor Recovery (n = 54) p Physical comfort 7.8 ± 1.1 6.3 ± 1.4 < 0.001 Emotional state 8.2 ± 0.9 6.7 ± 1.2 < 0.001 Physical independence 7.5 ± 1.2 5.9 ± 1.5 0.002 Psychological support 8.0 ± 1.0 7.2 ± 1.3 0.015 Pain 7.6 ± 1.3 6.0 ± 1.7 < 0.001 Univariate analysis of factors influencing patient recovery quality As shown in Table 3 , multiple factors were identified as significantly associated with postoperative recovery quality (QoR-15) in patients. These included gender (OR = 2.121, 95% CI: 1.160–3.881, p = 0.015), ASA classification (OR = 69.143, 95% CI: 16.154-295.946, p < 0.001), preoperative lactate level (OR = 91.429, 95% CI: 20.857–400.780, p < 0.001), and the use of CPB (OR = 5.417, 95% CI: 1.576–18.618, p = 0.007). Surgical factors included valve surgery (OR = 0.409, 95% CI: 0.236–0.708, p = 0.001), aortic replacement surgery (OR = 5.587, 95% CI: 1.967–15.869, p = 0.001), duration of surgery (OR = 1.295, 95% CI: 1.068–1.570, p = 0.019), CPB time (OR = 1.020, 95% CI: 1.013–1.027, p < 0.001), and aortic cross-clamp time (OR = 1.025, 95% CI: 1.016–1.035, p < 0.001). Additionally, longer ICU stay (OR = 2.347, 95% CI: 1.286–4.285, p = 0.005) and a higher mFI (OR = 6.458, 95% CI: 1.686–24.728, p = 0.006) were also independently associated with lower recovery quality. Table 3 Univariate analysis of factors influencing patient recovery quality Variable coefficient Standard error Wald P OR 95% CI upper limit lower limit Gender 0.752 0.308 5.955 0.015 2.121 1.16 3.881 Age -0.01 0.014 0.505 0.477 0.99 0.964 1.017 Postoperative recovery time 2.408 0.352 46.899 < 0.001 11.111 5.578 22.134 ASA classification 4.236 0.742 32.608 < 0.001 69.143 16.154 295.946 Preoperative lactate level 4.516 0.754 35.865 < 0.001 91.429 20.857 400.78 Emergency 26.749 808.953 0 1.000 417.939 0 - Use of CPB 1.689 0.63 7.194 0.007 5.417 1.576 18.618 Valve surgery -0.894 0.28 10.205 0.001 0.409 0.236 0.708 Coronary artery bypass grafting 0.018 0.354 0.003 0.959 1.019 0.509 2.04 Aortic replacement surgery 1.72 0.533 10.436 0.001 5.587 1.967 15.869 Other types of surgery 0.334 0.686 0.237 0.626 1.397 0.364 5.363 Surgery time 0.258 0.098 6.899 0.019 1.295 1.068 1.570 CPB time 0.019 0.004 27.632 0.000 1.02 1.012 1.027 Cross-clamp time 0.025 0.005 30.028 0.000 1.025 1.016 1.035 Automatic rebeating 1.825 1.166 2.452 0.117 6.203 0.632 60.917 Autologous blood 0.117 0.178 0.431 0.511 1.124 0.793 1.593 Intraoperative hypothermia 2.474 1.174 4.444 0.063 11.875 1.190 118.498 Duration of ICU stay 0.853 0.307 7.732 0.005 2.347 1.286 4.282 Total hospitalization days -0.022 0.466 0.002 0.962 0.978 0.393 2.438 mFI 1.865 0.685 7.414 0.006 6.458 1.686 24.728 OR, odds ratio; CI, confidence interval; ASA, American Society of Anesthesiologists; CPB, cardiopulmonary bypass; ICU, intensive care unit; mFI, modified frailty index. p < 0.05 was considered statistically significant. Factors affecting patient recovery quality: multivariate analysis Multivariable logistic regression analysis was performed using variables that were statistically significant in the univariate analysis. As shown in Table 4 , five factors were independently associated with excellent postoperative recovery (QoR-15 ≥ 120). These included Gender (female vs. male) was independently associated with better recovery (OR = 3.402, 95% CI: 0.989–11.704, p = 0.005), suggesting that female patients were significantly more likely to achieve excellent postoperative recovery compared to male patients. ASA classification (OR = 71.333, 95% CI: 10.407-488.921, p < 0.001) and preoperative lactate level (OR = 21.859, 95% CI: 3.476-137.474, p = 0.001) were both strong predictors of recovery quality, with higher ASA grades and elevated lactate levels associated with poorer outcomes. Postoperative recovery time (OR = 16.404, 95% CI: 2.873–93.659, p = 0.002) was positively associated with higher QoR-15 scores, suggesting improved recovery quality with longer follow-up. In addition, Frailty (mFI ≥ 0.27) was strongly associated with poor recovery (OR = 92.27, 95% CI: 6.80-1252.88, p = 0.001), indicating that baseline frailty substantially impacts postoperative outcomes. In contrast, variables such as cardiopulmonary bypass, valve surgery, aortic replacement surgery, surgery duration, CPB time, aortic cross-clamp time, and ICU stay duration did not show statistically significant associations with recovery quality in the multivariate model ( p > 0.05). Table 4 Factors affecting patient recovery quality: multivariate analysis Variable coefficient Standard error Wald p OR 95%CI upper limit lower limit Gender 1.224 0.43 3.771 0.005 3.402 0.989 11.704 Postoperative recovery time 2.798 0.889 9.906 0.002 16.404 2.873 93.659 ASA classification 4.267 0.982 18.882 0.001 71.333 10.407 488.921 Preoperative lactate level 3.085 0.938 10.81 0.001 21.859 3.476 137.474 Use of CPB -0.57 1.838 0.096 0.756 0.566 0.015 20.748 Valve surgery -0.917 0.658 1.946 0.163 0.4 0.11 1.45 Aortic replacement 1.115 1.286 0.752 0.386 3.049 0.245 37.91 Surgery time 0.005 0.030 0.133 0.746 1.032 0.914 1.106 CPB time 0.007 0.029 0.068 0.794 1.008 0.953 1.066 Cross-clamp time -0.013 0.032 0.158 0.691 0.987 0.928 1.051 Duration of ICU stay -0.3 0.576 0.272 0.602 0.741 0.24 2.29 mFI 4.525 1.331 11.559 0.001 92.268 6.795 1252.877 OR, odds ratio; CI, confidence interval; ASA, American Society of Anesthesiologists; CPB, cardiopulmonary bypass; ICU, intensive care unit; mFI, modified frailty index. Diagnostic efficiency and ROC curve of factors affecting recovery quality in cardiac and major vascular surgery patients The diagnostic efficiency analysis of factors affecting recovery quality in cardiac and major vascular surgery patients showed that the area under the curve (AUC) for preoperative lactate level was 0.786 (95% CI 0.710–0.863), followed by postoperative recovery time: 0.768 (95% CI 0.695–0.840), gender: 0.589 (95% CI 0.504–0.673), mFI: 0.555 (95% CI 0.468–0.641), and ASA classification: 0.520 (95% CI 0.436–0.604). The corresponding ROC curves for each factor are shown in Fig. 2 . Higher AUC indicates better discriminatory performance. Construction of prediction model for factors affecting recovery quality in cardiac and major vascular surgery patients Based on the results of non-conditional multiple logistic stepwise regression analysis in Fig. 3 , the clinical relevant indicators including gender, ASA classification, preoperative lactate level, postoperative recovery time, and mFI were included in the prediction model: ln(p/1-p) = -5.571 + 0.862×gender + 3.844×ASA + 3.143×lactate + 2.001×postoperative recovery time + 3.712×frailty (where p represents the probability of scoring 1, and 1-p represents the probability of scoring 0). Sensitivity (TPR) and specificity (FPR) were incorporated into the ROC curve. The predictive value of the prediction model was analyzed using the ROC curve, and the AUC of the prediction model was 0.959 (95% CI: 0.764–1.022), which demonstrated strong discrimination. The reference line indicates no discrimination (AUC = 0.5). Evaluation metrics of the prediction model Logistic regression classification performance was further quantified, with the multivariate analysis results presented in Table 5 . Accuracy, defined as the proportion of correctly predicted samples among all samples, was 0.904 for the model, demonstrating strong predictive performance. The recall rate, which measures the proportion of true positives among actual positives, was 0.904, reflecting strong model sensitivity. Precision, defined as the proportion of true positives among predicted positives, was 0.903, indicating reliable predictive specificity. The F1 score, representing the harmonic mean of precision and recall, balances the trade-off between these metrics. While high precision and recall are ideal, practical applications often favor one over the other. When both are equally important, the F1 score serves as a comprehensive measure. The AUC, ranging from 0 to 1, evaluates classification quality, with higher values reflecting superior performance. An AUC of 0.959 in this study indicates excellent model performance. In this case, the AUC is 0.959, indicating a good classification performance of the model. Table 5 Evaluation metrics of the prediction model Accuracy Recall Precision F1 AUC 0.904 0.904 0.903 0.903 0.959 Discussion This study identified gender, ASA classification, preoperative lactate levels, postoperative follow-up time, and mFI as significant influencing factors affecting the recovery quality of patients undergoing cardiovascular surgery. Previous studies have found that in patients undergoing coronary artery bypass grafting, the mortality and incidence of postoperative complications were significantly higher in female patients compared to male patients[ 6 ]. Although gender per se may not directly elevate surgical risk, female patients often present with a clustering of high-risk comorbidities such as advanced age, anemia, and prior cardiovascular disease that collectively impair recovery outcomes[ 7 , 8 ], This is consistent with previous findings suggesting gender specific vulnerabilities in cardiac surgical populations[ 6 , 9 ]. In this study, gender difference was identified as a relevant factor affecting postoperative recovery quality, which may be associated with the presence of more preexisting risk factors in female patients. The results of this study indicate that as the postoperative recovery time prolonged, the postoperative recovery quality score significantly increased in patients undergoing cardiac and major vascular surgery. Apart from the direct effect of recovery time, this could also be attributed to the fact that with longer recovery time, some patients were able to return to work, achieve self-worth, and experience physical and psychological well-being, which significantly improved their quality of life[ 10 ]. A study conducted at the Santa Marta Hospital in Lisbon, Portugal[ 11 ] showed that active follow up, identification of intervention targets, and implementation of interventions significantly improved postoperative quality of life in elderly patients (average age of 74 years of 430 cases) undergoing cardiac surgery. Conversely, post-discharge depression in the family and social environment could hinder patients' ability to perform activities of daily living and reintegrate into society, thereby reducing their quality of life[ 12 ]. These findings suggest that during the perioperative period, in addition to focusing on preoperative physical function regulation, attention should also be given to the postoperative recovery process. Encouraging early mobilization and activity, providing health education to patients and their families, adopting a scientific approach to the disease without excessive protection, and organizing collective activities that benefit patients' physical and mental well-being in collaboration with the community can broaden patients' social interactions and improve their symptoms. This study found that ASA grading was also one of the factors that affect patients' postoperative recovery quality. ASA grading is a simple assessment of patients' physiological status, with higher grades indicating poorer organ function or more complex comorbidities. Preoperative assessment of ASA grading helps in the initial prediction of surgical risks. In non-cardiac and cardiac and major vascular surgeries, higher ASA grades are significantly correlated with poorer postoperative recovery quality[ 13 ]. However, a study on abdominal surgery[ 14 ] retrospectively analyzed the medical records of 241 patients undergoing abdominal surgery and found that ASA grading had limited predictive ability for postoperative pulmonary complications in abdominal surgery, possibly because it only assessed the patients' physiological status and not their respiratory risk index. From this study, it can be concluded that preoperative ASA scoring can be used as one of the criteria for predicting postoperative recovery quality in patients undergoing cardiac vascular surgery. Elevated preoperative lactate levels can serve as indicators of systemic hypoperfusion or poor perfusion. Lactic acidosis is an independent criterion for assessing disease severity and has become a prognostic factor for survival in critical care and trauma settings[ 15 ]. In this study, arterial blood was extracted from the radial artery of all patients before surgery and preoperative lactate levels were measured through blood gas analysis. Retrospective analysis revealed that elevated preoperative lactate levels were associated with lower postoperative recovery quality in patients[ 16 ]. Bennett et al.[ 16 ] found that elevated lactate levels have a stronger impact on recovery quality than any other factor. Another study[ 17 ] showed that dynamic lactate levels in septic patients were more indicative of the severity of poor organ perfusion compared to a single lactate level. It is worth noting that factors such as the primary disease, cardiac arrest during CPB, and the CPB itself can lead to systemic poor organ perfusion, especially in the form of low perfusion in the intestinal circulation and delayed renal perfusion. Monitoring lactate levels is a sensitive indicator in clinical practice for evaluating microcirculation and early organ failure, as it is easily accessible and relatively inexpensive. However, there is still a significant degree of uncertainty regarding the relationship between lactate levels and patient prognosis[ 18 ]. Previous studies have shown that hyperlactatemia within 24 hours after surgery (often within 4 hours) has adverse effects on patient prognosis, leading to prolonged hospital stay, increased postoperative complications, and mortality, which is detrimental to patient recovery[ 19 ]. There is no consensus on the optimal timing of lactate level assessment to evaluate postoperative recovery quality. Therefore, dynamically monitoring changes in perioperative lactate levels can more accurately predict postoperative recovery quality. Frailty is a state of reduced physiological reserves that exceeds the expected decline associated with normal aging, resulting from the cumulative effects of various physiological changes over time[ 20 ]. The incidence of frailty in cardiac surgery patients is as high as 62%[ 21 ]. The frailty index (mFI) used in this study is considered the "gold standard" for assessing frailty[ 22 ]. We found that mFI is one of the factors that influence postoperative recovery quality in patients. This suggests that in clinical practice, mFI can be used to assess patients preoperatively. To identify those who are frail before surgery and to intervene early can improve postoperative recovery quality to some extent. The developed predictive model has several practical applications in clinical practice. First, it can be used during preoperative assessment to identify high-risk patients who may benefit from enhanced perioperative care protocols, including intensive monitoring, early mobilization programs, and targeted psychological support. Second, the model can inform resource allocation decisions, helping hospitals optimize staffing and bed management for patients at different risk levels.The model's variables are routinely collected in clinical practice, making it feasible for implementation without additional costs or complex procedures. Healthcare providers can easily calculate the risk score using standard preoperative data, facilitating its integration into electronic health record systems and clinical decision support tools. This study has several important limitations that should be considered when interpreting the results. First, as a single-center, retrospective study, the generalizability of our findings to other populations and healthcare settings may be limited. The patient population at our tertiary care center may not be representative of all cardiovascular surgery patients, potentially limiting external validity. Second, the retrospective design introduces potential selection bias and unmeasured confounding. Important variables such as socioeconomic status, social support systems, and detailed comorbidity profiles were not systematically collected and may influence recovery outcomes. Third, the study lacks external validation in an independent cohort, which is essential for confirming the model's performance before clinical implementation. The apparent high performance (AUC = 0.925) may be optimistic due to model overfitting, despite internal validation efforts. Fourth, the QoR-15 scale, while validated, may not capture all aspects of recovery quality that are important to patients, particularly in the context of Chinese healthcare culture. Cultural adaptation and validation of patient-reported outcome measures remain important considerations. Finally, the relatively small sample size, particularly for certain surgical subgroups, may limit the precision of effect estimates and the ability to detect important clinical associations. Future multicenter studies with larger sample sizes are needed to confirm these findings. This study successfully developed and internally validated a predictive model for postoperative recovery quality in cardiovascular surgery patients using the QoR-15 scale. The model, incorporating gender, ASA classification, preoperative lactate levels, recovery duration, and frailty status, demonstrated good discriminative ability with an optimism-corrected AUC of 0.901. While these findings provide valuable insights for perioperative risk stratification and individualized care planning, external validation in diverse populations is essential before clinical implementation. The model's practical utility lies in its use of readily available clinical variables, making it feasible for integration into routine perioperative care. Future research should focus on prospective validation, model refinement, and evaluation of clinical impact when implemented in practice. Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of the Affiliated Hospital of Yangzhou University[2023-YKL01-(09)]. Consent for publication All authors have read and approved the final manuscript and consent to its publication. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Funding This work was supported by the Traditional Chinese Medicine Science and Technology Development Program of Jiangsu Province (MS2022151). Authors' contributions Qiaobo Zhu and Zhuan Zhang designed the experiments, and wrote the initial draft of the manuscript. Bei Ma, Ning Li and Luo Zhang conducted the data collection and analysis. Luo Zhang and Zhuan Zhang contributed to the revision of the manuscript. All authors read and approved the final manuscript. Declaration interest The authors report no conflict of interest. References Gregory, A.J., R.C. Arora, S. Chatterjee, et al. Enhanced Recovery After Surgery (ERAS) cardiac turnkey order set for perioperative pain management in cardiac surgery: Proceedings from the American Association for Thoracic Surgery (AATS) ERAS Conclave 2023. JTCVS Open, 2024. 22: p. 14-24. Guo, K., F. Xu, Y. Li, et al. Mortality and cardiac arrest rates of emergency surgery in developed and developing countries: a systematic review and meta-analysis. BMC Anesthesiology, 2024. 24(1): p. 178. Diz-Ferreira, E., P. Diaz-Vidal, U. Fernandez-Vazquez, et al. 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Interact Cardiovasc Thorac Surg, 2019. 28(2): p. 199-205. Martínez-Ortega, J.M., P. Nogueras, J.E. Muñoz-Negro, et al. Quality of life, anxiety and depressive symptoms in patients with psoriasis: A case-control study. J Psychosom Res, 2019. 124: p. 109780. Birlik, A.B., H. Tozan, and K.B. Kose, Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis. PLOS Digit Health, 2025. 4(6): p. e0000889. Kara, S., E. Küpeli, H.E.B. Yılmaz, et al. Predicting Pulmonary Complications Following Upper and Lower Abdominal Surgery: ASA vs. ARISCAT Risk Index. Turk J Anaesthesiol Reanim, 2020. 48(2): p. 96-101. Yucel, N., T. Ozturk Demir, S. Derya, et al. Potential Risk Factors for In-Hospital Mortality in Patients with Moderate-to-Severe Blunt Multiple Trauma Who Survive Initial Resuscitation. Emerg Med Int, 2018. 2018: p. 6461072. Bennett, J.M., E.S. Wise, K.M. Hocking, et al. Hyperlactemia Predicts Surgical Mortality in Patients Presenting With Acute Stanford Type-A Aortic Dissection. J Cardiothorac Vasc Anesth, 2017. 31(1): p. 54-60. Wang, S., D. Wang, X. Huang, H. Wang, et al. Risk factors and in-hospital mortality of postoperative hyperlactatemia in patients after acute type A aortic dissection surgery. BMC Cardiovasc Disord, 2021. 21(1): p. 431. Park, I.H., H.K. Cho, J.H. Oh, et al. Clinical Significance of Serum Lactate in Acute Myocardial Infarction: A Cardiac Magnetic Resonance Imaging Study. J Clin Med, 2021. 10(22). Tuzun, B., S. Ergun, S. Ozalp, M. Akif Onalan, et al. Effect of cardiopulmonary bypass on late-onset hyperlactatemia after pediatric cardiac surgery. Turk Gogus Kalp Damar Cerrahisi Derg, 2025. 33(1): p. 27-35. Santhirapala, R., J. Partridge, and C.J. MacEwen, The older surgical patient - to operate or not? A state of the art review. Anaesthesia, 2020. 75 Suppl 1: p. e46-e53. Darvall, J.N., S. Braat, D.A. Story, et al. Protocol for a prospective observational study to develop a frailty index for use in perioperative and critical care. BMJ Open, 2019. 9(1): p. e024682. Mojica-Márquez, A.E., J.L. Rodríguez-López, et al. External validation of life expectancy prognostic models in patients evaluated for palliative radiotherapy at the end-of-life. Cancer Med, 2020. 9(16): p. 5781-5787. Additional Declarations No competing interests reported. Supplementary Files SupplementaryAppendix1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 Nov, 2025 Reviewers invited by journal 10 Nov, 2025 Editor invited by journal 30 Sep, 2025 Editor assigned by journal 17 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 12 Jul, 2025 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. 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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-7106864","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":546497127,"identity":"306db518-e76c-4620-8ecd-e5c98179f014","order_by":0,"name":"Qiaobo Zhu","email":"","orcid":"","institution":"the Affiliated Hospital of Yangzhou University, Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Qiaobo","middleName":"","lastName":"Zhu","suffix":""},{"id":546497128,"identity":"f032ae51-8120-4833-8e38-4bfaa04c85aa","order_by":1,"name":"Zhuan Zhang","email":"","orcid":"","institution":"the Affiliated Hospital of Yangzhou University, Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhuan","middleName":"","lastName":"Zhang","suffix":""},{"id":546497129,"identity":"bbf0c8eb-c960-478f-9983-10d2c91c71c4","order_by":2,"name":"Ning Li","email":"","orcid":"","institution":"the Affiliated Hospital of Yangzhou University, Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Li","suffix":""},{"id":546497130,"identity":"1356abb5-6d04-4213-a25f-2e02278f53bd","order_by":3,"name":"Bei Ma","email":"","orcid":"","institution":"the Affiliated Hospital of Yangzhou University, Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Bei","middleName":"","lastName":"Ma","suffix":""},{"id":546497131,"identity":"5d748dc9-2520-417d-b2c6-413ff0456af4","order_by":4,"name":"Luo Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACCRBRwMDAxt588MEHBhCDKC0GDAz8PMeSDWeAtDATq0Vyho+ZNA+IQ0iL/OzmZw+/GByWM7jBYGxs82ubPB8zA+OHjzm4tTDOOWZuLGNw2NjgdkPi49y+24ZtzAzMkjO34dbCLJFgJi1hcDhxw50Dh41ze24zArWwMfPi0cImkf4NouVGYpu0Zc9te4JaeCRyzCQ/ALXMnJHMJs3w43YiQS0SEjll0gwG6cbAQGY27G24ndzGzNiM1y/yM9K3Sf6osJZjY+//+ODHn9u289ubD374iEcLOAh4YCzGNjDZgF89SMkPOPMPQcWjYBSMglEwAgEAXX1PQbSfedUAAAAASUVORK5CYII=","orcid":"","institution":"the Affiliated Hospital of Yangzhou University, Yangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Luo","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-07-12 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12:28:27","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113742,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7106864/v1/e0cb9d53c0ae15909f04dd63.html"},{"id":96365203,"identity":"f3b98453-1702-4b80-a231-216a1fab2b78","added_by":"auto","created_at":"2025-11-20 10:10:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":183620,"visible":true,"origin":"","legend":"\u003cp\u003eSTROBE flowchart\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7106864/v1/00db3139edf496dc76d58fb5.png"},{"id":96290277,"identity":"6e106539-2bad-4a6e-8d8a-e367920272e1","added_by":"auto","created_at":"2025-11-19 12:28:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112245,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) Curves for Individual Predictors of Postoperative Recovery Quality\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7106864/v1/17f875d87cc7e2d7fc93a007.png"},{"id":96290274,"identity":"058ddba8-b4d0-4564-8668-082ef84e1513","added_by":"auto","created_at":"2025-11-19 12:28:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41185,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve of the Multivariate Prediction Model for Excellent Postoperative Recovery\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7106864/v1/89534649a97446a97da2cd89.png"},{"id":96452983,"identity":"6c9b9e14-105d-44e0-bc77-97a916c8f587","added_by":"auto","created_at":"2025-11-21 09:56:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1534684,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7106864/v1/cfa7347b-9f02-4a78-83e2-94a04bc27758.pdf"},{"id":96290272,"identity":"39a23995-9b76-40e6-9bd5-74850bc4fe37","added_by":"auto","created_at":"2025-11-19 12:28:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20103,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7106864/v1/5e45ef6e5e2c13d333ee965b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Internal Validation of a Predictive Model for Postoperative Recovery Quality in Cardiovascular Surgery Patients Based on the QoR-15 Scale: A Retrospective Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe quality of postoperative recovery (QoR) is a critical measure of patient-centered outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In cardiovascular and major vascular procedures, which are characterized by extensive surgical trauma and complex perioperative management, accurately predicting and enhancing postoperative recovery quality remains a significant clinical challenge. Despite the notable decline in in-hospital mortality rates for cardiac surgery in the past decade from 3.7\u0026ndash;2.7%, many patients continue to experience suboptimal postoperative recovery, particularly in physical and emotional domains[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This underscores the need for robust prediction models aligned with ERAS protocols to enhance individualized care.\u003c/p\u003e\u003cp\u003eFactors such as impaired preoperative cardiac function, prolonged operative time, frailty, and systemic hypoperfusion can contribute to poor recovery and higher complication rates[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As Enhanced Recovery After Surgery (ERAS) protocols become increasingly integrated into perioperative management for cardiac surgery, traditional risk assessment models have demonstrated limitations in identifying patients at risk for suboptimal recovery. Thus, there is an urgent need to develop predictive tools that are aligned with ERAS principles and that incorporate comprehensive perioperative data including patient frailty, metabolic markers, and procedural variables to support individualized care planning.\u003c/p\u003e\u003cp\u003eThe Quality of Recovery-15 (QoR-15) questionnaire is a validated, patient-reported outcome measure widely used for assessing postoperative recovery. It captures five dimensions of recovery: physical comfort, emotional state, physical independence, psychological support, and pain. Its brevity, reliability, and responsiveness make it an ideal tool for assessing recovery quality in clinical research and routine practice[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis retrospective study investigates the impact of perioperative factors on the quality of recovery in patients undergoing cardiovascular surgery, as measured by the QoR-15 score. We hypothesized that a combination of patient-specific factors (including frailty status, preoperative metabolic markers, and demographic characteristics) would significantly predict postoperative recovery quality. The primary objective was to develop and internally validate a predictive model to support the early identification of patients at risk for poor recovery, ultimately informing individualized perioperative care strategies and resource allocation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eStudy design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis was a single-center, retrospective observational cohort study that included patients who underwent cardiovascular surgery at the Affiliated Hospital of Yangzhou University between March 2022 and September 2024. Data were retrieved from the hospital's electronic medical record system. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting observational studies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical trial number: not applicable.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInclusion criteria were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) completed postoperative follow-up with a recorded Quality of Recovery-15 (QoR-15) score; (3) availability of comprehensive clinical, demographic, and laboratory data; and (4) informed consent obtained from the patient or their legal representative.\u003c/p\u003e\u003cp\u003eExclusion criteria included: (1) preoperative diagnosis of severe cognitive impairment or psychiatric illness precluding cooperation with QoR-15 assessment; (2) in-hospital death or treatment withdrawal within 24 hours post-surgery.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnesthesia Management\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll patients received a standardized anesthesia protocol, including continuous intraoperative monitoring (electrocardiography, pulse oximetry, invasive arterial pressure, and depth of anesthesia). Induction agents included midazolam, etomidate, sufentanil, and rocuronium, followed by maintenance with propofol, sevoflurane, dexmedetomidine, and remifentanil. Hemodynamic stability was maintained with vasoactive agents as needed. Detailed anesthetic procedures are provided in \u003cb\u003eSupplementary Appendix 1.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eObservational Indicators\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCollected data were categorized into three domains: \u003cb\u003eBaseline variables\u003c/b\u003e: gender, age, American Society of Anesthesiologists (ASA) physical status, preoperative lactate level, comorbidities, emergency status, and cardiopulmonary bypass (CPB) use. Frailty was assessed using the Modified Frailty Index (mFI), with scores\u0026thinsp;\u0026ge;\u0026thinsp;0.27 defined as frail; \u003cb\u003eIntraoperative variables\u003c/b\u003e: surgery type and duration, CPB time, aortic cross-clamp time, reperfusion strategy, intraoperative fluid management, and rewarming temperature; \u003cb\u003ePostoperative variables\u003c/b\u003e: length of ICU and total hospital stay. Postoperative recovery quality was assessed using the Quality of Recovery-15 (QoR-15) questionnaire, a validated patient-reported outcome measure. It consists of 15 items spanning five domains: physical comfort (5 items), emotional state (4), physical independence (2), psychological support (2), and pain (2). Each item is rated on an 11-point scale (0\u0026ndash;10), yielding a total score from 0 to 150. A score\u0026thinsp;\u0026ge;\u0026thinsp;120 was defined as excellent recovery; scores\u0026thinsp;\u0026lt;\u0026thinsp;120 indicated suboptimal recovery[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample Size\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe sample size calculation was based on detecting a minimum clinically important difference of 10 points in the QoR-15 score, with a standard deviation of 18 points. Using a two-tailed test, with a significance level (α) of 0.05 and a power (1-β) of 90%, the required sample size was estimated at 180 patients. To account for a potential 10% dropout rate, 213 patients were ultimately enrolled.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData were analyzed using SPSS 26.0. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR); categorical variables as frequencies and percentages. Group comparisons used t-tests, Mann-Whitney U, χ2, or Fisher\u0026rsquo;s exact tests as appropriate. Logistic regression identified predictors of recovery quality. Variables with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis entered the multivariate model. Model discrimination was evaluated with ROC curves and AUC. Calibration was assessed with the Hosmer-Lemeshow test. Internal validation used 1000 bootstrap replicates.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eGeneral characteristics of the patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 213 patients were initially enrolled in the study. After applying exclusion criteria, 198 patients were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median follow-up time was 8.5 months (IQR: 4.2\u0026ndash;14.7 months). Ten patients (4.7%) were lost to follow-up, and the in-hospital mortality rate was 2.5% (n\u0026thinsp;=\u0026thinsp;5). Two patients (1.0%) experienced postoperative stroke, and one patient (0.5%) had a myocardial infarction. Due to the low incidence of these major adverse events, their statistical association with QoR-15 score could not be robustly assessed and were excluded from the predictive model. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e was presented for baseline characteristics of patients.\u003c/p\u003e\u003cp\u003e\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\u003eBaseline characteristics of patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eExcellent recovery (n\u0026thinsp;=\u0026thinsp;142)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoor recovery (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102 (81.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (18.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (54.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (45.2%)\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\u003e63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003ePostoperative Recovery Time\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;3 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3 months-12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (74.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (26.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47 (71.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (28.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eASA Classification\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge; Grade III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e131 (70.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (29.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003ePreoperative lactate Level\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;2 mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e154 (75.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (24.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2 mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (26.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (73.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eEmergency\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133 (76.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (36.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (64.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eCardiopulmonary Bypass\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e123 (69.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (30.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (86.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (13.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eSurgical Approach\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003evalve surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (80.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecoronary artery bypass grafting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (75.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (24.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaortic replacement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eother\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (70.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (29.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery Duration (hours)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCPB time (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e151.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e151.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.343\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\u003e112.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eRebeating strategies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eautomatic rebeating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e131 (72.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (27.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.574\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enon-automatic rebeating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (61.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (38.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003ePerioperative fluid therapy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecrystalloid fluids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (80.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eautologous blood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (87.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (12.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003estored blood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (65.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (34.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eRewarming temperature\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (69.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (30.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (25.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eDuration of ICU stay\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;3 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (80.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;3 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (62.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (37.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eTotal hospitalization days\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;15 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (77.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (22.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.539\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;15 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125 (71.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (29.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003emFI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126 (68.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (31.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eQoR-15\u0026thinsp;=\u0026thinsp;Quality of Recovery-15; OR\u0026thinsp;=\u0026thinsp;Odds Ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval; ASA\u0026thinsp;=\u0026thinsp;American Society of Anesthesiologists; CPB\u0026thinsp;=\u0026thinsp;Cardiopulmonary Bypass; mFI\u0026thinsp;=\u0026thinsp;Modified Frailty Index; ICU\u0026thinsp;=\u0026thinsp;Intensive Care Unit. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of QoR-15 dimension scores between excellent and poor recovery groups\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, patients classified as having excellent recovery (QoR-15\u0026thinsp;\u0026ge;\u0026thinsp;120) exhibited significantly higher scores across all QoR-15 dimensions, with the most notable differences observed in emotional state, pain management, and physical comfort (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eComparison of QoR-15 dimension scores between excellent and poor recovery groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQoR-15 Domain scores\u003c/p\u003e\u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExcellent Recovery (n\u0026thinsp;=\u0026thinsp;144)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoor Recovery (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical comfort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmotional state\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical independence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychological support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eUnivariate analysis of factors influencing patient recovery quality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, multiple factors were identified as significantly associated with postoperative recovery quality (QoR-15) in patients. These included gender (OR\u0026thinsp;=\u0026thinsp;2.121, 95% CI: 1.160\u0026ndash;3.881, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015), ASA classification (OR\u0026thinsp;=\u0026thinsp;69.143, 95% CI: 16.154-295.946, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), preoperative lactate level (OR\u0026thinsp;=\u0026thinsp;91.429, 95% CI: 20.857\u0026ndash;400.780, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the use of CPB (OR\u0026thinsp;=\u0026thinsp;5.417, 95% CI: 1.576\u0026ndash;18.618, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). Surgical factors included valve surgery (OR\u0026thinsp;=\u0026thinsp;0.409, 95% CI: 0.236\u0026ndash;0.708, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), aortic replacement surgery (OR\u0026thinsp;=\u0026thinsp;5.587, 95% CI: 1.967\u0026ndash;15.869, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), duration of surgery (OR\u0026thinsp;=\u0026thinsp;1.295, 95% CI: 1.068\u0026ndash;1.570, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), CPB time (OR\u0026thinsp;=\u0026thinsp;1.020, 95% CI: 1.013\u0026ndash;1.027, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and aortic cross-clamp time (OR\u0026thinsp;=\u0026thinsp;1.025, 95% CI: 1.016\u0026ndash;1.035, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, longer ICU stay (OR\u0026thinsp;=\u0026thinsp;2.347, 95% CI: 1.286\u0026ndash;4.285, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and a higher mFI (OR\u0026thinsp;=\u0026thinsp;6.458, 95% CI: 1.686\u0026ndash;24.728, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) were also independently associated with lower recovery quality.\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\u003eUnivariate analysis of factors influencing patient recovery quality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"left\" 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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStandard error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eupper limit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003elower limit\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.881\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostoperative recovery time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.134\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASA classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e295.946\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative lactate level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e91.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e400.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmergency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e808.953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e417.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse of CPB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.618\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValve surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.708\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary artery bypass grafting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAortic replacement surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.869\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther types of surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.570\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCPB time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-clamp time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutomatic rebeating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e60.917\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutologous blood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.593\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative hypothermia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e118.498\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of ICU stay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.282\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal hospitalization days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.438\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24.728\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eOR, odds ratio; CI, confidence interval; ASA, American Society of Anesthesiologists; CPB, cardiopulmonary bypass; ICU, intensive care unit; mFI, modified frailty index. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFactors affecting patient recovery quality: multivariate analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eMultivariable logistic regression analysis was performed using variables that were statistically significant in the univariate analysis. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, five factors were independently associated with excellent postoperative recovery (QoR-15\u0026thinsp;\u0026ge;\u0026thinsp;120). These included Gender (female vs. male) was independently associated with better recovery (OR\u0026thinsp;=\u0026thinsp;3.402, 95% CI: 0.989\u0026ndash;11.704, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), suggesting that female patients were significantly more likely to achieve excellent postoperative recovery compared to male patients. ASA classification (OR\u0026thinsp;=\u0026thinsp;71.333, 95% CI: 10.407-488.921, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and preoperative lactate level (OR\u0026thinsp;=\u0026thinsp;21.859, 95% CI: 3.476-137.474, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) were both strong predictors of recovery quality, with higher ASA grades and elevated lactate levels associated with poorer outcomes. Postoperative recovery time (OR\u0026thinsp;=\u0026thinsp;16.404, 95% CI: 2.873\u0026ndash;93.659, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) was positively associated with higher QoR-15 scores, suggesting improved recovery quality with longer follow-up. In addition, Frailty (mFI\u0026thinsp;\u0026ge;\u0026thinsp;0.27) was strongly associated with poor recovery (OR\u0026thinsp;=\u0026thinsp;92.27, 95% CI: 6.80-1252.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), indicating that baseline frailty substantially impacts postoperative outcomes.\u003c/p\u003e\u003cp\u003eIn contrast, variables such as cardiopulmonary bypass, valve surgery, aortic replacement surgery, surgery duration, CPB time, aortic cross-clamp time, and ICU stay duration did not show statistically significant associations with recovery quality in the multivariate model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactors affecting patient recovery quality: multivariate analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"left\" 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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStandard error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eupper limit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003elower\u003c/p\u003e\u003cp\u003elimit\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.704\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostoperative recovery time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e93.659\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASA classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e488.921\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative lactate level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e137.474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse of CPB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.748\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValve surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAortic replacement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCPB time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross-clamp time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of ICU stay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e92.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1252.877\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eOR, odds ratio; CI, confidence interval; ASA, American Society of Anesthesiologists; CPB, cardiopulmonary bypass; ICU, intensive care unit; mFI, modified frailty index.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiagnostic efficiency and ROC curve of factors affecting recovery quality in cardiac and major vascular surgery patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe diagnostic efficiency analysis of factors affecting recovery quality in cardiac and major vascular surgery patients showed that the area under the curve (AUC) for preoperative lactate level was 0.786 (95% CI 0.710\u0026ndash;0.863), followed by postoperative recovery time: 0.768 (95% CI 0.695\u0026ndash;0.840), gender: 0.589 (95% CI 0.504\u0026ndash;0.673), mFI: 0.555 (95% CI 0.468\u0026ndash;0.641), and ASA classification: 0.520 (95% CI 0.436\u0026ndash;0.604). The corresponding ROC curves for each factor are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Higher AUC indicates better discriminatory performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction of prediction model for factors affecting recovery quality in cardiac and major vascular surgery patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the results of non-conditional multiple logistic stepwise regression analysis in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the clinical relevant indicators including gender, ASA classification, preoperative lactate level, postoperative recovery time, and mFI were included in the prediction model: ln(p/1-p) = -5.571\u0026thinsp;+\u0026thinsp;0.862\u0026times;gender\u0026thinsp;+\u0026thinsp;3.844\u0026times;ASA\u0026thinsp;+\u0026thinsp;3.143\u0026times;lactate\u0026thinsp;+\u0026thinsp;2.001\u0026times;postoperative recovery time\u0026thinsp;+\u0026thinsp;3.712\u0026times;frailty (where p represents the probability of scoring 1, and 1-p represents the probability of scoring 0). Sensitivity (TPR) and specificity (FPR) were incorporated into the ROC curve. The predictive value of the prediction model was analyzed using the ROC curve, and the AUC of the prediction model was 0.959 (95% CI: 0.764\u0026ndash;1.022), which demonstrated strong discrimination. The reference line indicates no discrimination (AUC\u0026thinsp;=\u0026thinsp;0.5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvaluation metrics of the prediction model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLogistic regression classification performance was further quantified, with the multivariate analysis results presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Accuracy, defined as the proportion of correctly predicted samples among all samples, was 0.904 for the model, demonstrating strong predictive performance. The recall rate, which measures the proportion of true positives among actual positives, was 0.904, reflecting strong model sensitivity. Precision, defined as the proportion of true positives among predicted positives, was 0.903, indicating reliable predictive specificity. The F1 score, representing the harmonic mean of precision and recall, balances the trade-off between these metrics. While high precision and recall are ideal, practical applications often favor one over the other. When both are equally important, the F1 score serves as a comprehensive measure. The AUC, ranging from 0 to 1, evaluates classification quality, with higher values reflecting superior performance. An AUC of 0.959 in this study indicates excellent model performance. In this case, the AUC is 0.959, indicating a good classification performance of the model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEvaluation metrics of the prediction model\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRecall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrecision\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified gender, ASA classification, preoperative lactate levels, postoperative follow-up time, and mFI as significant influencing factors affecting the recovery quality of patients undergoing cardiovascular surgery.\u003c/p\u003e\u003cp\u003ePrevious studies have found that in patients undergoing coronary artery bypass grafting, the mortality and incidence of postoperative complications were significantly higher in female patients compared to male patients[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although gender per se may not directly elevate surgical risk, female patients often present with a clustering of high-risk comorbidities such as advanced age, anemia, and prior cardiovascular disease that collectively impair recovery outcomes[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], This is consistent with previous findings suggesting gender specific vulnerabilities in cardiac surgical populations[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In this study, gender difference was identified as a relevant factor affecting postoperative recovery quality, which may be associated with the presence of more preexisting risk factors in female patients.\u003c/p\u003e\u003cp\u003eThe results of this study indicate that as the postoperative recovery time prolonged, the postoperative recovery quality score significantly increased in patients undergoing cardiac and major vascular surgery. Apart from the direct effect of recovery time, this could also be attributed to the fact that with longer recovery time, some patients were able to return to work, achieve self-worth, and experience physical and psychological well-being, which significantly improved their quality of life[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A study conducted at the Santa Marta Hospital in Lisbon, Portugal[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] showed that active follow up, identification of intervention targets, and implementation of interventions significantly improved postoperative quality of life in elderly patients (average age of 74 years of 430 cases) undergoing cardiac surgery. Conversely, post-discharge depression in the family and social environment could hinder patients' ability to perform activities of daily living and reintegrate into society, thereby reducing their quality of life[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These findings suggest that during the perioperative period, in addition to focusing on preoperative physical function regulation, attention should also be given to the postoperative recovery process. Encouraging early mobilization and activity, providing health education to patients and their families, adopting a scientific approach to the disease without excessive protection, and organizing collective activities that benefit patients' physical and mental well-being in collaboration with the community can broaden patients' social interactions and improve their symptoms.\u003c/p\u003e\u003cp\u003eThis study found that ASA grading was also one of the factors that affect patients' postoperative recovery quality. ASA grading is a simple assessment of patients' physiological status, with higher grades indicating poorer organ function or more complex comorbidities. Preoperative assessment of ASA grading helps in the initial prediction of surgical risks. In non-cardiac and cardiac and major vascular surgeries, higher ASA grades are significantly correlated with poorer postoperative recovery quality[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, a study on abdominal surgery[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] retrospectively analyzed the medical records of 241 patients undergoing abdominal surgery and found that ASA grading had limited predictive ability for postoperative pulmonary complications in abdominal surgery, possibly because it only assessed the patients' physiological status and not their respiratory risk index. From this study, it can be concluded that preoperative ASA scoring can be used as one of the criteria for predicting postoperative recovery quality in patients undergoing cardiac vascular surgery.\u003c/p\u003e\u003cp\u003eElevated preoperative lactate levels can serve as indicators of systemic hypoperfusion or poor perfusion. Lactic acidosis is an independent criterion for assessing disease severity and has become a prognostic factor for survival in critical care and trauma settings[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this study, arterial blood was extracted from the radial artery of all patients before surgery and preoperative lactate levels were measured through blood gas analysis. Retrospective analysis revealed that elevated preoperative lactate levels were associated with lower postoperative recovery quality in patients[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Bennett et al.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] found that elevated lactate levels have a stronger impact on recovery quality than any other factor. Another study[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] showed that dynamic lactate levels in septic patients were more indicative of the severity of poor organ perfusion compared to a single lactate level. It is worth noting that factors such as the primary disease, cardiac arrest during CPB, and the CPB itself can lead to systemic poor organ perfusion, especially in the form of low perfusion in the intestinal circulation and delayed renal perfusion. Monitoring lactate levels is a sensitive indicator in clinical practice for evaluating microcirculation and early organ failure, as it is easily accessible and relatively inexpensive. However, there is still a significant degree of uncertainty regarding the relationship between lactate levels and patient prognosis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Previous studies have shown that hyperlactatemia within 24 hours after surgery (often within 4 hours) has adverse effects on patient prognosis, leading to prolonged hospital stay, increased postoperative complications, and mortality, which is detrimental to patient recovery[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. There is no consensus on the optimal timing of lactate level assessment to evaluate postoperative recovery quality. Therefore, dynamically monitoring changes in perioperative lactate levels can more accurately predict postoperative recovery quality.\u003c/p\u003e\u003cp\u003eFrailty is a state of reduced physiological reserves that exceeds the expected decline associated with normal aging, resulting from the cumulative effects of various physiological changes over time[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The incidence of frailty in cardiac surgery patients is as high as 62%[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The frailty index (mFI) used in this study is considered the \"gold standard\" for assessing frailty[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. We found that mFI is one of the factors that influence postoperative recovery quality in patients. This suggests that in clinical practice, mFI can be used to assess patients preoperatively. To identify those who are frail before surgery and to intervene early can improve postoperative recovery quality to some extent.\u003c/p\u003e\u003cp\u003eThe developed predictive model has several practical applications in clinical practice. First, it can be used during preoperative assessment to identify high-risk patients who may benefit from enhanced perioperative care protocols, including intensive monitoring, early mobilization programs, and targeted psychological support. Second, the model can inform resource allocation decisions, helping hospitals optimize staffing and bed management for patients at different risk levels.The model's variables are routinely collected in clinical practice, making it feasible for implementation without additional costs or complex procedures. Healthcare providers can easily calculate the risk score using standard preoperative data, facilitating its integration into electronic health record systems and clinical decision support tools.\u003c/p\u003e\u003cp\u003eThis study has several important limitations that should be considered when interpreting the results. First, as a single-center, retrospective study, the generalizability of our findings to other populations and healthcare settings may be limited. The patient population at our tertiary care center may not be representative of all cardiovascular surgery patients, potentially limiting external validity. Second, the retrospective design introduces potential selection bias and unmeasured confounding. Important variables such as socioeconomic status, social support systems, and detailed comorbidity profiles were not systematically collected and may influence recovery outcomes. Third, the study lacks external validation in an independent cohort, which is essential for confirming the model's performance before clinical implementation. The apparent high performance (AUC\u0026thinsp;=\u0026thinsp;0.925) may be optimistic due to model overfitting, despite internal validation efforts. Fourth, the QoR-15 scale, while validated, may not capture all aspects of recovery quality that are important to patients, particularly in the context of Chinese healthcare culture. Cultural adaptation and validation of patient-reported outcome measures remain important considerations. Finally, the relatively small sample size, particularly for certain surgical subgroups, may limit the precision of effect estimates and the ability to detect important clinical associations. Future multicenter studies with larger sample sizes are needed to confirm these findings.\u003c/p\u003e\u003cp\u003eThis study successfully developed and internally validated a predictive model for postoperative recovery quality in cardiovascular surgery patients using the QoR-15 scale. The model, incorporating gender, ASA classification, preoperative lactate levels, recovery duration, and frailty status, demonstrated good discriminative ability with an optimism-corrected AUC of 0.901. While these findings provide valuable insights for perioperative risk stratification and individualized care planning, external validation in diverse populations is essential before clinical implementation. The model's practical utility lies in its use of readily available clinical variables, making it feasible for integration into routine perioperative care. Future research should focus on prospective validation, model refinement, and evaluation of clinical impact when implemented in practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e The study was approved by the Ethics Committee of the Affiliated Hospital of Yangzhou University[2023-YKL01-(09)].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e All authors have read and approved the final manuscript and consent to its publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was supported by the Traditional Chinese Medicine Science and Technology Development Program of Jiangsu Province (MS2022151).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e Qiaobo Zhu and\u0026nbsp;Zhuan Zhang designed the experiments, and wrote the initial draft of the manuscript. Bei Ma, Ning Li and Luo Zhang conducted the data collection and analysis. Luo Zhang and Zhuan Zhang contributed to the revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration interest\u003c/strong\u003e The authors report no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGregory, A.J., R.C. Arora, S. Chatterjee, et al. Enhanced Recovery After Surgery (ERAS) cardiac turnkey order set for perioperative pain management in cardiac surgery: Proceedings from the American Association for Thoracic Surgery (AATS) ERAS Conclave 2023. JTCVS Open, 2024. 22: p. 14-24.\u003c/li\u003e\n\u003cli\u003eGuo, K., F. Xu, Y. Li, et al. Mortality and cardiac arrest rates of emergency surgery in developed and developing countries: a systematic review and meta-analysis. BMC Anesthesiology, 2024. 24(1): p. 178.\u003c/li\u003e\n\u003cli\u003eDiz-Ferreira, E., P. Diaz-Vidal, U. Fernandez-Vazquez, et al. Effect of Enhanced Recovery After Surgery (ERAS) Programs on Perioperative Outcomes in Patients Undergoing Cardiac Surgery: A Systematic Review and Meta-analysis. JOURNAL OF CARDIOTHORACIC AND VASCULAR ANESTHESIA, 2025. 39(5): p. 1325-1334.\u003c/li\u003e\n\u003cli\u003eLe Gac, G., A. Mansour, M. Labory, et al. Patient-reported outcomes: validation of the French Quality of Recovery-15 score in cardiac surgery. BRITISH JOURNAL OF ANAESTHESIA, 2024. 133(2): p. 450-452.\u003c/li\u003e\n\u003cli\u003eEssafti, M., M. Bahi, K. Haji, et al. Validation of the Arabic version of the postoperative Quality of Recovery-15 score. BRITISH JOURNAL OF ANAESTHESIA, 2023. 131(6): p. e187-e190.\u003c/li\u003e\n\u003cli\u003eAtiya, M., E. Schorr, L.K. Stein, et al. Sex Differences in Ischemic Stroke Readmission Rates and Subsequent Outcomes After Coronary Artery Bypass Graft Surgery. J Stroke Cerebrovasc Dis, 2021. 30(5): p. 105659.\u003c/li\u003e\n\u003cli\u003eGaudino, M., C.N. Bairey Merz, S. Sandner, et al. Randomized Comparison of the Outcome of Single Versus Multiple Arterial Grafts trial (ROMA):Women-a trial dedicated to women to improve coronary bypass outcomes. J Thorac Cardiovasc Surg, 2023.\u003c/li\u003e\n\u003cli\u003eAlamri, H.M., T.O. Alotaibi, A.A. Alghatani, et al. Effect of Gender on Postoperative Outcome and Duration of Ventilation After Coronary Artery Bypass Grafting (CABG). Cureus, 2023. 15(4): p. e37717.\u003c/li\u003e\n\u003cli\u003eJabagi, H., D.T. Tran, R. Hessian, et al. Impact of Gender on Arterial Revascularization Strategies for Coronary Artery Bypass Grafting. Ann Thorac Surg, 2018. 105(1): p. 62-68.\u003c/li\u003e\n\u003cli\u003eGuddeti, R.R., V.S. Pajjuru, R.W. Walters, et al. Impact of gender on in-hospital mortality and 90-day readmissions in patients undergoing transcatheter edge-to-edge mitral valve repair: Analysis from the National Readmission Database. CATHETERIZATION AND CARDIOVASCULAR INTERVENTIONS, 2021. 98(6): p. E954-E962.\u003c/li\u003e\n\u003cli\u003eCoelho, P., L. Miranda, P.M.P. Barros, et al. Quality of life after elective cardiac surgery in elderly patients. Interact Cardiovasc Thorac Surg, 2019. 28(2): p. 199-205.\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Ortega, J.M., P. Nogueras, J.E. Mu\u0026ntilde;oz-Negro, et al. Quality of life, anxiety and depressive symptoms in patients with psoriasis: A case-control study. J Psychosom Res, 2019. 124: p. 109780.\u003c/li\u003e\n\u003cli\u003eBirlik, A.B., H. Tozan, and K.B. Kose, Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis. PLOS Digit Health, 2025. 4(6): p. e0000889.\u003c/li\u003e\n\u003cli\u003eKara, S., E. K\u0026uuml;peli, H.E.B. Yılmaz, et al. Predicting Pulmonary Complications Following Upper and Lower Abdominal Surgery: ASA vs. ARISCAT Risk Index. Turk J Anaesthesiol Reanim, 2020. 48(2): p. 96-101.\u003c/li\u003e\n\u003cli\u003eYucel, N., T. Ozturk Demir, S. Derya, et al. Potential Risk Factors for In-Hospital Mortality in Patients with Moderate-to-Severe Blunt Multiple Trauma Who Survive Initial Resuscitation. Emerg Med Int, 2018. 2018: p. 6461072.\u003c/li\u003e\n\u003cli\u003eBennett, J.M., E.S. Wise, K.M. Hocking, et al. Hyperlactemia Predicts Surgical Mortality in Patients Presenting With Acute Stanford Type-A Aortic Dissection. J Cardiothorac Vasc Anesth, 2017. 31(1): p. 54-60.\u003c/li\u003e\n\u003cli\u003eWang, S., D. Wang, X. Huang, H. Wang, et al. Risk factors and in-hospital mortality of postoperative hyperlactatemia in patients after acute type A aortic dissection surgery. BMC Cardiovasc Disord, 2021. 21(1): p. 431.\u003c/li\u003e\n\u003cli\u003ePark, I.H., H.K. Cho, J.H. Oh, et al. Clinical Significance of Serum Lactate in Acute Myocardial Infarction: A Cardiac Magnetic Resonance Imaging Study. J Clin Med, 2021. 10(22).\u003c/li\u003e\n\u003cli\u003eTuzun, B., S. Ergun, S. Ozalp, M. Akif Onalan, et al. Effect of cardiopulmonary bypass on late-onset hyperlactatemia after pediatric cardiac surgery. Turk Gogus Kalp Damar Cerrahisi Derg, 2025. 33(1): p. 27-35.\u003c/li\u003e\n\u003cli\u003eSanthirapala, R., J. Partridge, and C.J. MacEwen, The older surgical patient - to operate or not? A state of the art review. Anaesthesia, 2020. 75 Suppl 1: p. e46-e53.\u003c/li\u003e\n\u003cli\u003eDarvall, J.N., S. Braat, D.A. Story, et al. Protocol for a prospective observational study to develop a frailty index for use in perioperative and critical care. BMJ Open, 2019. 9(1): p. e024682.\u003c/li\u003e\n\u003cli\u003eMojica-M\u0026aacute;rquez, A.E., J.L. Rodr\u0026iacute;guez-L\u0026oacute;pez, et al. External validation of life expectancy prognostic models in patients evaluated for palliative radiotherapy at the end-of-life. Cancer Med, 2020. 9(16): p. 5781-5787.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Postoperative recovery, Quality of Recovery-15, Cardiovascular surgery, Frailty assessment, Predictive mode","lastPublishedDoi":"10.21203/rs.3.rs-7106864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7106864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eA retrospective analysis was conducted to evaluate postoperative recovery quality using the Quality of Recovery-15 (QoR-15) scale in patients undergoing cardiovascular surgery. The study aimed to examine the impact of various perioperative factors on recovery and to develop a predictive model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e This retrospective cohort study analyzed clinical data from the medical record system for patients who underwent cardiovascular surgery at a single tertiary care center between March 2020 and September 2022. A total of 198 patients were included in the final analysis after excluding 15 patients due to incomplete data or loss to follow-up. The variables gathered encompassed demographic information (gender and age), duration of postoperative follow-up, American Society of Anesthesiologists (ASA) classification, preoperative lactate levels, emergency surgical status, and whether cardiopulmonary bypass (CPB) was implemented. The modified Frailty Index (mFI) was calculated for each patient to assess baseline frailty. In addition, detailed surgical and perioperative data were recorded. Postoperative data and QoR-15 scores were also included. Univariate and multivariate logistic regression analyses were performed to develop and validate a predictive model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 213 patients were included in this study, with 15 patients excluded, resulting in a total of 198 postoperative QoR-15 scores. Gender, ASA classification, preoperative lactate levels, follow-up time, and mFI were identified as independent predictors of excellent postoperative recovery (QoR-15\u0026thinsp;\u0026ge;\u0026thinsp;120). The multivariate model showed good discrimination (AUC\u0026thinsp;=\u0026thinsp;0.925; 95% CI: 0.884\u0026ndash;0.966) and internal validation (bootstrap-corrected AUC\u0026thinsp;=\u0026thinsp;0.901). The Hosmer-Lemeshow test confirmed good calibration (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.394).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eA simple model using five routinely available variables demonstrated strong performance in predicting recovery quality. This tool may aid clinicians in identifying patients at risk for poor postoperative outcomes, facilitating personalized perioperative strategies.\u003c/p\u003e","manuscriptTitle":"Development and Internal Validation of a Predictive Model for Postoperative Recovery Quality in Cardiovascular Surgery Patients Based on the QoR-15 Scale: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 12:28:22","doi":"10.21203/rs.3.rs-7106864/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"325929321230471204093829492973236886710","date":"2025-11-12T11:05:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-10T10:51:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-30T07:19:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-18T01:42:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-18T01:42:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-07-12T08:33:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6cf00c43-4fb2-4f77-930e-6e6fd8a0dc02","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T12:28:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 12:28:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7106864","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7106864","identity":"rs-7106864","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}