Assessing GERAADA Score Mortality Predictions in Type A Aortic Dissection Patients

Assessing GERAADA Score Mortality Predictions in Type A Aortic Dissection Patients | 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 Assessing GERAADA Score Mortality Predictions in Type A Aortic Dissection Patients Kan-paatib Barnabo Nampoukime, Igwenandji Adeoumi Esperance Monteiro, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3933237/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective : This study seeks to assess the predictive precision of the GERAADA score for 30-day mortality in patients with Type A aortic dissection. Methods : A retrospective study analyzed data from 382 survivors and 90 non-survivors, examining demographic, clinical, and surgical variables. GERAADA scores were calculated by a blinded cardiac surgeon using a web-based application. Results : The overall mortality is 19.06% and 18.18% for GERAADA prediction. The presence of malperfusion in more than two organs emerged as a significant risk factor for hospital mortality p=0.028. Longer surgery times were significantly associated with elevated mortality p=0.002. Moreover, postoperative ECMO, CPR, and IABP were significantly linked to increased mortality rates. Additionally, ICU stay duration, lung infection, MODS, and respiratory failure p<0.05 independently posed as risk factors for hospital mortality. Patients with Hemiparesis and peripheral malperfusion experienced no deaths, as predicted by GERAADA score. Additionally, patients with a dissection tear located at the root of the aorta exhibited a lower mortality rate of 7.14%, contrasting the higher 19.87% GERAADA prediction. Conclusion : GERAADA predictions were mostly accurate, but exceptions occurred with inotrope use, hemiparesis, peripheral malperfusion, and aortic dissection at the root. We suggest enhancing the GERAADA score by incorporating intraoperative and postoperative factors. type A aortic dissection surgery risk factors prediction score mortality Figures Figure 1 Introduction Acute Type A Aortic Dissection (ATAAD) stands as a formidable challenge in the realm of cardiovascular medicine, characterized by its life-threatening nature and high mortality rates 1 . Despite the availability of well-equipped cardiac centers capable of providing precise and timely diagnoses, along with advanced surgical techniques for aortic dissection, the mortality rate remains alarmingly high 2 . In-hospital mortality for ATAAD still varies between 5% and 25% 3 4 . Mortality risk factors in Type A aortic dissection have long been a subject of concern in the medical community. Gaining a comprehensive understanding of these factors is instrumental not only for risk assessment but also for the development of more effective and informed treatment strategies. Recently, a novel risk assessment tool for forecasting 30-day mortality in patients afflicted with ATAAD has emerged, akin to the EuroSCORE and the Society of Thoracic Surgeons score. Referred to as the German Registry of Acute Aortic Dissection Type A (GERAADA) score, this system employs straightforward and readily obtainable parameters, making it specially tailored for the prediction of 30-day mortality rates in aortic dissection patients undergoing surgical intervention 5 . Our clinical study aimed to retrospectively analyze our institutional results in the context of the newly introduced GERAADA score, to evaluate its accuracy in a consecutive series of Chinese patients diagnosed with ATAAD. Patients and methods This study constitutes a retrospective analysis encompassing consecutive patients diagnosed with Type A Aortic Dissection at the Cardiovascular Surgery Department of Tongji Hospital during the period spanning January 2021 to December 2022. Patient data were meticulously gathered through a review of medical records. Exclusion criteria comprised patients who had succumbed either before or during surgery, as well as those who did not undergo surgical intervention. In total, 472 patients met the inclusion criteria for analysis. In cases where emergency surgical procedures were not immediately performed, patients underwent intubation and sedation to mitigate the risk of aortic rupture and associated mortality before their scheduled surgeries. Data collection encompassed the 11 key preoperative parameters essential for GERAADA score computation, including age, gender, prior cardiac surgeries, inotropic support upon referral, pre-surgery resuscitation, aortic regurgitation, preoperative hemiparesis, intubation/ventilation status at referral, preoperative organ malperfusion, extent of aortic dissection, and primary entry site location. The GERAADA score calculations were meticulously conducted by a cardiac surgeon, blinded to the study, for all patients using the dedicated web-based application GERAADA_score. Surgical procedure Operative Approach The primary strategy for cases of Type A aortic dissection encompassed emergency central repair within 24 hours for over 70% of cases, addressing rupture, and potentially revascularizing unresolved malperfused areas. The procedure involved a median sternotomy combined with a conventional cardiopulmonary bypass. Cannulation utilized either a femoral, subclavian artery, or a combination of both, with a single atriocaval cannula employed for right atrial cannulation. Myocardial protection for all patients was ensured through antegrade or retrograde infusion of a cold blood cardioplegic solution. Furthermore, a left ventricular drain was inserted through the right upper pulmonary vein. Operative technique FET frozen elephant trunk was performed in most of the cases in this study.The technique consists of the implantation of the stented distal segment of the hybrid-prosthesis into the descending aorta through the opened aortic arch, while the proximal non-stented segment is used for conventional replacement of the upstream aorta.The stent that was used with the FET procedure was inserted into the descending aorta under direct vision. Then, the aortic arch was replaced by a 4-branch graft. The left carotid artery, the innominate artery and the left subclavian artery were reconstructed sequentially. Statistical analysis The mean and standard deviation were used to describe continuous variables. The difference between the two groups was compared using the Student's t-test. Categorical variables were presented as percentages or frequency distributions, and differences between the groups were assessed using the χ2 test or Fisher exact test. Multivariate logistic regression modeling was used to determine independent perioperative predictors of in-hospital death. Odds ratios (ORs) and hazard ratios (HRs) are presented with 95% confidence intervals (CIs). We defined 30-day mortality as any death occurring within the initial 30 days following surgery. Categorical variables were presented as counts and percentages, while continuous variables were represented as either median with interquartile or means with standard deviations. We conducted binomial testing to compare the GERAADA score's predicted mortality percentage to the observed mortality percentage for both the overall study cohort and its respective subgroups. Within each patient group, GERAADA scores served as the expected probability of adverse events (or predictive values), while the number of deaths and patient observations represented the count of events and trials 6 . The two-sided binomial tests were subsequently employed to rigorously test the binomial hypothesis. Additionally, receiver operating characteristic analyses were carried out for each group comparison. To assess the influence of available variables on 30-day mortality, a multivariable regression model was applied.A P-value<0.05 was considered to be statistically significant and all statistical analyses were performed using R version 4.2.3 (2023-03-15 ucrt). Results The baseline characteristics were similar between the two groups for the majority of variables considered as shown in table1. However, a noteworthy finding was the significant difference observed in cases of malperfusion involving more than two organs. In this regard, the incidence was 5.8% in the group of survivors compared to 13.3% in the non-survivors, with a p-value of 0.023. Patients in the non-survivor group had a longer surgery duration, with an average of 573.01±129.72 minutes, compared to 535.62±122.42 minutes in the survivor group (table 2). The non-survivor group had significantly higher percentages of using ECMO and IABP (6.7% vs. 0.3%, p<0.001), (6.7% vs. 0.5%,p<0.001).Most CPR (10% vs. 0%, p<0.001) and postoperative tracheotomy (18.9% vs. 9.2%, p=0.014) were performed on death group, and the same group patients stayed longer in ICU (17.31% vs. 10.39%, p<0.001). The survived group patients were extubating after surgery earlier than the death group (14.10% vs. 6.11%, p<0.001 table3). Additionally, the non-survivor group experienced higher rates of paralysis (8.9% vs. 2.4%, p=0.007), lung infection (36.7% vs. 7.3%, p<0.001), GI bleeding (14.4% vs. 2.9%, p<0.001), MODS (20% vs. 0.3%, p<0.001), respiratory failure (22.2% vs. 0.3%, p<0.001), and cardiac failure (16.7% vs. 0%, p<0.001). These differences were statistically significant and emerged as univariate predictors for 30-day mortality(table3). ICU stay (OR 1.08, 95% CI 1.02-1.14, p=0.012), lung infection (OR 15.35, 95% CI 5.49-45.26, p<0.001), MODS (OR 159.19, 95% CI 17.54-5320.35, p<0.001), and respiratory failure (OR 229.91, 95% CI 34.55-5654.37, p<0.001) were identified as independent risk factors associated with 30-day mortality (Table 4). In table 5 we presented GERAADA prediction mortality and actual mortality rates in various subgroups of patients with Type A aortic dissection (AAD). It provides valuable insights into the accuracy of the GERAADA score in predicting 30-day mortality. The GERAADA prediction for patients aged below 50 was 17.94%, with an actual mortality of 19.3%, while for patients aged 50 and above, the prediction was 18.07%, and the actual mortality was 18.92%. In both age groups, the prediction closely approximated the actual mortality, with p-values of 0.642 and 0.7163, respectively. Sex, previous surgery, inotrope use and specific aortic regurgitation levels did not show a notable difference. Patients without malperfusion had a GERAADA prediction of 18.01% and an actual mortality of 17.5%. Cerebral malperfusion patients had a prediction of 17.99%, with an actual mortality of 23.5%. For cardiac malperfusion, the prediction was 18.45%, and the actual mortality was 24.4%. Visceral malperfusion had a GERAADA prediction of 7.99%, and the actual mortality was 16.21%. Patients with peripheral malperfusion had a prediction of 18.45%, and there were no actual mortalities. Other malperfusion patients had a GERAADA prediction of 17.38%, while the actual mortality was 35.71%. The highest discrepancy between prediction and actual mortality was observed in the other malperfusion group, with a p-value of 0.05708. Hemiparesis patients had a GERAADA prediction of 18.59%, and there were no actual mortalities in this group. Patients with aortic entry had a GERAADA prediction of 18.07%, and the actual mortality was 19.50%. Ascending entry patients had a prediction of 17.97%, and the actual mortality was 20.9%. Patients with descending entry had a GERAADA prediction of 18.36%, and the actual mortality was 19.4%. Root entry patients had a prediction of 17.88%, with an actual mortality of 7.14%. The most significant difference between prediction and actual mortality was observed in the "root entry" group, with a p-value of 0.01095. Discussion The German Registry of Acute Aortic Dissection Type A (GERAADA) score uses very basic and easily retrievable parameters and was specifically designed for predicting the 30-day mortality rate in patients undergoing surgery for acute aortic dissection.Czerny et al. stressed that the GERAADA score should not be viewed as an absolute decision-making tool for accepting or rejecting treatment. Instead, it should be considered a valuable instrument for predicting postoperative outcomes using easily accessible and fundamental parameters 5 . Given the elevated mortality rate associated with type A aortic dissection, this predictive scoring system can be a valuable asset for surgeons. The overall mortality rate in our study stands at 19.06%, while the GERAADA score predicted a mortality of 18.18%.No significant difference was found between the mortality group and the predicted group with an Area Under Curve of 0.599 (95% CI 0.558, 0.622) (Fig. 1 ).In their study, M. Ma et al. found that the GERAADA score and EuroSCORE II predicted 30-day mortality rates of 14.7% and 3.1%, respectively, while the observed rate was 12.5% 7 . On a similar note, K. Sugiyama et al. calculated an overall 30-day mortality for their study cohort using the GERAADA score as 14.3%(8.1–77.6%),whereas the actual mortality rate was 6% 8 .GERAADA has the particularity to be focused on aortic dissection and take in consideration some aspect of complications of ATAAD like malperfusion. Malperfusion is recognized to occur in a significant portion of ATAAD cases, affecting approximately 20–30% of patients 9 . Consequently, the mortality rate for individuals experiencing this complication varies significantly, falling within the range of 17–44% 10 . It is crucial to emphasize that aortic dissection complicated by malperfusion is intimately associated with heightened mortality rates. This risk is amplified as the extent of organ involvement increases, making the management of multiple organ malperfusion an especially formidable challenge. Our study further supports this connection by revealing a noteworthy correlation between multiple organ malperfusion and in-hospital mortality. This finding is consistent with the research conducted by Kawahito and colleagues 11 . Numerous studies have examined mortality risk factors, and many of them have identified independent risk factors, as reflected in our study. Recent research conducted by Khan et al a range of independent risk factors associated with prolonged ICU stays have been identified. These factors encompass age, preoperative D-dimer levels, CPB time, the use of deep hypothermic circulatory arrest, the occurrence of postoperative stroke, postoperative acute respiratory failure, and postoperative acute renal failure 91213 . Our study corroborates these recent research findings, highlighting that the duration of ICU stay, lung infections, and respiratory failure identified as risk factors for in-hospital mortality 1415 . This association can primarily be attributed to postoperative interventions within the ICU, such as ECMO, IABP, CPR, tracheotomy, and the prolonged use of mechanical ventilation, all of which have been identified in our study as contributing factors to hospital mortality. In our actual mortality group, a notable increase in mortality is observed, particularly reaching 35.71% in cases categorized as other malperfusion, where multiple organ malperfusion is present. This contrasts with the lower prediction of 17.38% (± 1.80). Luehr et al., in their literature, reported a similar trend with more deaths in the study group, particularly in the prediction score for coronary malperfusion, where the actual mortality was 29.6% compared to the predicted 30% (± 19.4) 6 and for other malperfusion the reported 19.9% (± 19.4) for GERAADA prediction score vs 24%for the study group. No deaths were observed in patients with hemiparesis or those experiencing peripheral malperfusion, contrary to the predictions. This underscores the effectiveness of prompt aortic and organ reperfusion 16 . Patients with hemiparesis and peripheral malperfusion experienced significant post-surgery recovery, with their symptoms greatly alleviated. The GERAADA prediction score in our study, with an Area Under Curve (AUC) of 0.599 (95% CI: 0.558–0.622) as depicted in Fig. 1 , does not represent a robust model for predicting mortality in ATAAD patients.Similar to the findings of I.Zivkovi whose study concluded that EuroSCORE II demonstrates superior discriminative power for predicting operative mortality in ATAAD surgery compared to the GERAADA score. Both scoring systems demonstrate good calibration ability.In a recent assessment of popular online prediction models, Ma et al. discovered that the EuroSCORE II displayed superior predictive accuracy for surgical mortality in ATAAD patients, with the observed 30-day mortality rate validating the GERAADA score's excellent calibration 7 . Furthermore, S. Lilyanna et al. propose that the optimal utilization of the new GERAADA score should not be for patient selection or decision-making but rather for quality control and performance comparison between different hospitals, facilitating retrospective evaluation and improved resource management. Some recent studies have proposed more effective models tailored to ATAAD patients 1718 . H. Lin et al. introduced a straightforward nomogram based on six predictors, including left ventricular end-diastolic diameter < 45mm, estimated glomerular filtration rate 4 hours, to predict 30-day mortality. Meanwhile, T. Guo et al. in their literature presented a highly effective machine learning model with an AUC of 0.927 (95% CI: 0.860–0.968) 1920 . These results indicate a potential avenue for improving the predictive precision of the GERAADA score, designed for aortic dissection, by integrating intraoperative factors, given the continual advancements in surgical techniques. This enhancement gains significance, especially in the backdrop of ongoing progress in surgical methods and postoperative ICU care.K.Sugiyama suggest to add parameters such as the time from onset to arrival, family background, and hemodialysis for further accuracy. Study limitation This study has certain limitations that should be acknowledged. It is a retrospective, single-center investigation that incorporates various surgical approaches and involves multiple surgeons. Furthermore, the criteria and diagnosis of malperfusion may deviate from those established by the GERAADA score. Conclusion While the GERAADA predictions generally exhibited reliable accuracy, occasional discrepancies were identified in specific cases involving inotrope utilization, hemiparesis, peripheral malperfusion, and aortic dissection at the root. The GERAADA score, designed for acute Type A aortic dissection, can be refined through the inclusion of intraoperative and postoperative factors particularly in the face of advancing surgical techniques and improved postoperative ICU care. Abbreviations ATAAD acute Type A aortic dissection , ECMO extracorporeal membrane oxygenation, CRRT continuous renal replacement therapy, ICU intensive care unit, IABP intra-aortic balloon pump, CPR cardiopulmonary resuscitation. GERAADA German Registry of Acute Aortic Dissection type A, FET frozen elephant trunk. Declarations Ethical Approval and Consent to participate The study has been approved by the Medical Ethics Committee of Tongji Hospital TJ-IRB202402062.Written informed consent was deemed unnecessary in accordance with the local legislation and institutional requirements. Consent for publication Not applicable. Availability of supporting data The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests None Funding The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Authors' contributions Author contributions Dr Barnabo Nampoukime performed the conception and design of the paper, the patient data acquisition and analysis, and the draft of the article. Dr. Fortes Gomes, Sultani, NOUANI helped with patient data acquisition, and Dr. Pan, Dr. Wang, and Dr. Hu ,Dr Zha helped design the paper and the draft as well as the revision of the article. All authors read and approved the final manuscript. Acknowledgments We appreciate the surgeons and nurses from the Department of Cardiovascular Surgery for their advice and helpful information. References Wen M, Han Y, Ye J, et al. Peri-operative risk factors for in-hospital mortality in acute type A aortic dissection. J Thorac Dis . 2019;11(9):3887-3895. doi:10.21037/jtd.2019.09.11 Tien M, Ku A, Martinez-Acero N, Zvara J, Sun EC, Cheung AT. The Penn Classification Predicts Hospital Mortality in Acute Stanford Type A and Type B Aortic Dissections. J Cardiothorac Vasc Anesth . 2020;34(4):867-873. doi:10.1053/j.jvca.2019.08.036 An Z, Zhong K, Sun Y, Han L, Xu Z. mortality after total arch. 2023;(April):1-6. doi:10.3389/fcvm.2023.1149907 Gudbjartsson T, Ahlsson A, Geirsson A, et al. Acute type A aortic dissection–a review. Scand Cardiovasc J . 2020;54(1):1-13. doi:10.1080/14017431.2019.1660401 Czerny M, Siepe M, Beyersdorf F, et al. Prediction of mortality rate in acute type A dissection : the German Registry for Acute Type A Aortic Dissection score. 2020;58(June):700-706. doi:10.1093/ejcts/ezaa156 Luehr M, Merkle-storms J, Gerfer S, Li Y, Krasivskyi I. 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Machine Learning Models for Predicting In-Hospital Mortality in Acute Aortic Dissection Patients. 2021;8(September):1-14. doi:10.3389/fcvm.2021.727773 Tables Table 1:preoperative characteristics for 30 days hospital mortality Variables Survived Death P-value n 382 90 Sex,male,mean±SD 287 (75.1) 73 (81.1) 0.288 Age,y,mean±SD 51.92 ±11.30 52.01 ±12.65 0.944 BMI 25.38 ±3.96 25.89 ±4.72 0.290 Ejection fraction,,mean±SD 57.97 ±6.72 56.92 ±7.83 0.198 Lactate dehydrogenase(135-214U/L),mean±SD 296.15 ±226.77 271.20 ±131.70 0.316 Creatinine(59-104umol/l),mean±SD 107.02 ±125.86 133.75 ±108.86 0.064 Cardiac troponin<34.2pg/ml,mean±SD 1635.11 ±7386.28 574.61 ±2215.64 0.179 Myoglobin(<154.9ng/ml),mean±SD 169.04 ±277.81 216.11 ±344.80 0.169 Creatinine kinase(<7.2ng/ml ),mean±SD 5.99 ±20.95 7.71 ±26.91 0.507 Smoker 94 (24.6) 24 (26.7) 0.787 Diabetes 37 (9.7) 6 (6.7) 0.489 Coronary Artery Disease 21 (5.5) 10 (11.1) 0.090 Hypertension 228 (59.7) 62 (68.9) 0.135 Pericardial effusion 107 (28.0) 30 (33.3) 0.383 History of cardiac surgery 28 (7.3) 8 (8.9) 0.779 Marfan Syndrome 5 (1.3) 4 (4.4) 0.126 Intubate before surgery 172 (45.0) 42 (46.7) 0.870 Cerebral malperfusion 18 (4.7) 5 (5.6) 0.950 Visceral malperfusion 47 (12.3) 11 (12.2) 1.000 Kidneys malperfusion 24 (6.3) 2 (2.2) 0.207 Limbs malperfusion 11 (2.9) 1 (1.1) 0.557 Cardiac malperfusion 34 (8.9) 13 (14.4) 0.166 Malperfusion in more than two organs 22 (5.8) 12 (13.3) 0.023 Data are presented as mean ±SD or as number (%) Table 2: intraoperative characteristics for 30 days hospital mortality Variables Survived Death P-value n 382 90 Surgery time(min) 535.62 ±122.42 573.01 ±129.72 0.010 Aorta cannulation 8 (2.1) 0 (0.0) 0.352 Femoral cannulation 47 (12.3) 14 (15.6) 0.514 Axillary cannulation 98 (25.7) 26 (28.9) 0.621 Axillary and femoral cannulation 138 (36.1) 31 (34.4) 0.859 Subclavian artery cannulation 26 (6.8) 5 (5.6) 0.846 Subclavian and femoral artery cannulation 42 (11.0) 12 (13.3) 0.658 CPB time (min),mean±SD 239.66± 113.87 264.83± 86.03 0.050 Aorta clamping time (min),mean±SD 123.29 ±46.44 132.51 ±36.00 0.079 Lowest temperature(C),mean±SD 26.34 ±2.42 26.18 ±2.36 0.565 HCA (min) 9.56 ±13.92 12.13 ±14.05 0.116 Data are presented as mean SD or as number (%), HCA hypothermic circulatory arrest, CPB cardiopulmonary bypass. Table 3: postoperative risks factors for 30 days hospital mortality Variables Survived Death p n 382 90 ECMO 1 (0.3) 6 (6.7) <0.001 CRRT 31 (8.1) 14 (15.6) 0.050 CPR 0 (0.0) 9 (10.0) <0.001 Tracheotomy 35 (9.2) 17 (18.9) 0.014 IABP 2 (0.5) 6 (6.7) <0.001 ICU Stay(days) 11.24 ±10.39 16.34 ±17.31 <0.001 Time of extubation (days ) 3.97 ±6.11 8.45 ±14.10 <0.001 Reexploration 21 (5.5) 8 (8.9) 0.336 Coma 19 (5.0) 7 (7.8) 0.428 Paralysis 9 (2.4) 8 (8.9) 0.007 Stroke 18 (4.7) 3 (3.3) 0.774 Lung infection 28 (7.3) 33 (36.7) <0.001 GI bleeding 11 (2.9) 13 (14.4) <0.001 TND 9 (2.4) 4 (4.4) 0.465 MODS 1 (0.3) 18 (20.0) <0.001 Respiratory failure 1 (0.3) 20 (22.2) <0.001 Cardiac failure 0 (0.0) 15 (16.7) <0.001 Kidney failure 12 (3.1) 5 ( 5.6) 0.429 Data are presented as mean SD or as number (%), ECMO extracorporeal membrane oxygenation, CRRT continuous renal replacement therapy, ICU intensive care unit, IABP intra-aortic balloon pump, CPR cardiopulmonary resuscitation.TND transient Neurologic dysfunction Table 4: multivariate analysis of risks factors for 30 days hospital mortality Variables Odds CI P-value Surgery time 1.00 1.00-1.00 0.121 ECMO 2.86 0.05-213.00 0.640 Tracheotomy 1.31 0.39-4.33 0.658 IABP 4.59 0.31-70.31 0.267 ICU stay(days) 1.08 1.02-1.14 0.012 Time of extubation 1.00 0.96-1.05 0.863 Paralysis 3.07 0.49-15.85 0.200 Lung infection 15.35 5.49-45.26 <0.001 GI bleeding 3.61 0.80-15.36 0.088 MODS 159.19 17.54-5320.35 <0.001 Respiratory failure 229.91 34.55-5654.37 <0.001 Malperfusion in more than two organs 2.19 0.95-4.89 0.060 Data are presented as mean SD or as number (%), ECMO extracorporeal membrane oxygenation, CRRT continuous renal replacement therapy, ICU intensive care unit, IABP intra-aortic balloon pump, CPR cardiopulmonary resuscitation. Table 5: GERAADA prediction score versus actual study group Variables GERAADA prediction mortality Actual Mortality Study group p-value 95%CI Lower Upper AUC AGE <50 17.94%（±1.93） 19.3% 0.642 0.4063 0.618 0.5121 AGE ≥50 18.07%（±2.04） 18.92% 0.7163 0.5761 0.7403 0.6582 Sex female 18.08%（±2.32） 15.17% 0.3973 0.5883 0.8297 0.709 Sex male 18.10%（±1.89） 20.3% 0.4955 0.6484 0.793 0.572 History of cardiac surgery 18.31%（±1.96） 22.22% 0.5816 0.3078 0.8172 0.5625 Resuscitation before surgery 18.08%（±2.09） 19.62% 0.1228 0.4823 0.4823 0.5815 Inotrope at referral 18.31%（±1.97） 15.27% 0.3161 0.5411 0.783 0.6621 Aortic regurgitation AR I-II 17.96%（±1.83） 11.32% 0.1376 0.4927 0.4927 0.727 ARIII-IV 24.21%（±2.08） 18.02% 0.1656 0.5417 0.5417 0.6685 No regurgitation 18.03%（±2.00） 18.82% 0.7137 0.482 0.647 0.5645 Malperfusion No malperfusion 18.01%（±2.03） 17.5% 0.8087 0.5323 0.6924 0.6124 Cerebral malperfusion 17.99%（±2.06） 23.5% 0.6092 0.3287 0.902 0.6154 Cardiac malperfusion 18.45%（±2.16） 24.4% 0.3607 0.3539 0.7798 0.5668 Visceral malperfusion 17.99%（±1.54） 16.21% 0.775 0.3693 0.9533 0.6613 Peripheral malperfusion 18.45%（±1.96） 0% <0.00001 - - - Other malperfusion 17.38%（±1.80） 35.71% 0.05708 0.3561 0.8217 0.5889 Hemiparesis 18.59%（±2.07） 0% <0.00001 - - - Extension of the tear Aortic 18.12%（±2.09） 19.20% 0.76 0.4758 0.7436 0.6097 Supra aortic 18.00%（±2.09） 18.01% 0.9729 0.4952 0.6844 0.5898 Descending 17.96%（±1.75） 20.43% 0.4746 0.4647 0.728 0.5963 Entry of the tear Aortic 18.07% （±2.12） 19.50% 0.6426 0.5297 0.7359 0.6328 Ascending 17.97%（±1.97） 20.9% 0.2793 0.4804 0.6692 0.5748 Descending 18.36%（±2.01） 19.4% 0.8911 0.2493 0.8707 0.56 Root 17.88%（±1.62） 7.14% 0.01095 0.7433 0.9832 0.8632 Additional Declarations No competing interests reported. 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Esperance Monteiro","email":"","orcid":"","institution":"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Igwenandji","middleName":"Adeoumi Esperance","lastName":"Monteiro","suffix":""},{"id":273333715,"identity":"99b7cdca-5181-42af-8c42-5bf09d7eb786","order_by":2,"name":"Libing Hu","email":"","orcid":"","institution":"Wuhan Tongji Aerospace City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Libing","middleName":"","lastName":"Hu","suffix":""},{"id":273333716,"identity":"1c39d043-b160-48d4-90c3-d00d90f7a9ee","order_by":3,"name":"Youmin Pan","email":"","orcid":"","institution":"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Youmin","middleName":"","lastName":"Pan","suffix":""},{"id":273333717,"identity":"1424dfc6-4f48-4372-9688-c8c88599ee18","order_by":4,"name":"Zhengbiao Zha","email":"","orcid":"","institution":"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhengbiao","middleName":"","lastName":"Zha","suffix":""},{"id":273333718,"identity":"69701385-594b-4733-af0a-d775d509f3aa","order_by":5,"name":"Lud Merveil Nouani","email":"","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":false,"prefix":"","firstName":"Lud","middleName":"Merveil","lastName":"Nouani","suffix":""},{"id":273333719,"identity":"a2aea41f-524b-466a-835d-aed65474546d","order_by":6,"name":"Djessica Fortes Gomes","email":"","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":false,"prefix":"","firstName":"Djessica","middleName":"Fortes","lastName":"Gomes","suffix":""},{"id":273333720,"identity":"1a32b051-ef91-45eb-8751-ba729f1a5057","order_by":7,"name":"Mustafa Abbas Farhood Sultani","email":"","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"Abbas Farhood","lastName":"Sultani","suffix":""},{"id":273333721,"identity":"a3f97503-c453-41c7-b0e7-032b88264fe0","order_by":8,"name":"Haihao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACAwkwZcPAOANIQTgJRGlJkwBqYWwgRcthCaANjA0MxGgxl+4x/Fzw63wd8+wG9geWOYcZ+NlzDBh+7sCtxXLOGWPpmX23JRjnHGBskNx2mEGy540BY+8ZPA67kbtBmrcHqGVGAkSLwY0cA2bGNrxaNv/m7TmH0GJPhJZt0jw/DiDZIkFQS/43a96GZMnGGYmNMyS3pfNInHlWcLAXr5a05Ns8f+z4DWckH/gsuc1ajr89eeODn3i0gAHIGYYNjA3MwEjiAQkcIKABCP4wMMiDtH4grHQUjIJRMApGIAAA/yhTBUXwG3EAAAAASUVORK5CYII=","orcid":"","institution":"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Haihao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-02-06 08:01:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3933237/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3933237/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51333215,"identity":"da9acb1c-7c9e-4ef2-9fa1-4b9e468980ff","added_by":"auto","created_at":"2024-02-19 18:03:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1622315,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis for comparison of the GERAADA score prediction of 30 day mortality versus actual mortality of the study group.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3933237/v1/39136b438ea5ca617d2f441e.png"},{"id":58507454,"identity":"d10a7d01-20eb-4fe0-b2f3-11c65d732344","added_by":"auto","created_at":"2024-06-17 15:01:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":665609,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3933237/v1/02b0e971-0e1a-401e-89b3-92f102429f3c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing GERAADA Score Mortality Predictions in Type A Aortic Dissection Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute Type A Aortic Dissection (ATAAD) stands as a formidable challenge in the realm of cardiovascular medicine, characterized by its life-threatening nature and high mortality rates\u003csup\u003e1\u003c/sup\u003e. Despite the availability of well-equipped cardiac centers capable of providing precise and timely diagnoses, along with advanced surgical techniques for aortic dissection, the mortality rate remains alarmingly high\u003csup\u003e2\u003c/sup\u003e. In-hospital mortality for ATAAD still varies between 5% and 25% \u003csup\u003e3\u003c/sup\u003e\u003csup\u003e4\u003c/sup\u003e. Mortality risk factors in Type A aortic dissection have long been a subject of concern in the medical community. Gaining a comprehensive understanding of these factors is instrumental not only for risk assessment but also for the development of more effective and informed treatment strategies.\u003c/p\u003e\n\u003cp\u003eRecently, a novel risk assessment tool for forecasting 30-day mortality in patients afflicted with ATAAD has emerged, akin to the EuroSCORE and the Society of Thoracic Surgeons score. Referred to as the German Registry of Acute Aortic Dissection Type A (GERAADA) score, this system employs straightforward and readily obtainable parameters, making it specially tailored for the prediction of 30-day mortality rates in aortic dissection patients undergoing surgical intervention\u003csup\u003e5\u003c/sup\u003e. Our clinical study aimed to retrospectively analyze our institutional results in the context of the newly introduced GERAADA score, to evaluate its accuracy in a consecutive series of Chinese patients diagnosed with ATAAD.\u003c/p\u003e"},{"header":"Patients and methods","content":"\u003cp\u003eThis study constitutes a retrospective analysis encompassing consecutive patients diagnosed with Type A Aortic Dissection at the Cardiovascular Surgery Department of Tongji Hospital during the period spanning January 2021 to December 2022. Patient data were meticulously gathered through a review of medical records. Exclusion criteria comprised patients who had succumbed either before or during surgery, as well as those who did not undergo surgical intervention. In total, 472 patients met the inclusion criteria for analysis. In cases where emergency surgical procedures were not immediately performed, patients underwent intubation and sedation to mitigate the risk of aortic rupture and associated mortality before their scheduled surgeries. Data collection encompassed the 11 key preoperative parameters essential for GERAADA score computation, including age, gender, prior cardiac surgeries, inotropic support upon referral, pre-surgery resuscitation, aortic regurgitation, preoperative hemiparesis, intubation/ventilation status at referral, preoperative organ malperfusion, extent of aortic dissection, and primary entry site location. The GERAADA score calculations were meticulously conducted by a cardiac surgeon, blinded to the study, for all patients using the dedicated web-based application GERAADA_score.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSurgical procedure\u003c/p\u003e\n\u003cp\u003eOperative Approach\u003c/p\u003e\n\u003cp\u003eThe primary strategy for cases of Type A aortic dissection encompassed emergency central repair within 24 hours for over 70% of cases, addressing rupture, and potentially revascularizing unresolved malperfused areas. The procedure involved a median sternotomy combined with a conventional cardiopulmonary bypass. Cannulation utilized either a femoral, subclavian artery, or a combination of both, with a single atriocaval cannula employed for right atrial cannulation. Myocardial protection for all patients was ensured through antegrade or retrograde infusion of a cold blood cardioplegic solution. Furthermore, a left ventricular drain was inserted through the right upper pulmonary vein.\u003c/p\u003e\n\u003cp\u003eOperative technique\u003c/p\u003e\n\u003cp\u003eFET frozen elephant trunk was performed in most of the cases in this study.The technique consists of the implantation of the stented distal segment of the hybrid-prosthesis into the descending aorta through the opened aortic arch, while the proximal non-stented segment is used for conventional replacement of the upstream aorta.The stent that was used with the FET procedure was inserted into the descending aorta under direct vision. Then, the aortic arch was replaced by a 4-branch graft. The left carotid artery, the innominate artery and the left subclavian artery were reconstructed sequentially.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eThe mean and standard deviation were used to describe continuous variables. The difference between the two groups was compared using the Student\u0026apos;s t-test. Categorical variables were presented as percentages or frequency distributions, and differences between the groups were assessed using the \u0026chi;2 test or Fisher exact test. Multivariate logistic regression modeling was used to determine independent perioperative predictors of in-hospital death. Odds ratios (ORs) and hazard ratios (HRs) are presented with 95% confidence intervals (CIs).\u003c/p\u003e\n\u003cp\u003eWe defined 30-day mortality as any death occurring within the initial 30 days following surgery. Categorical variables were presented as counts and percentages, while continuous variables were represented as either median with interquartile or means with standard deviations. We conducted binomial testing to compare the GERAADA score\u0026apos;s predicted mortality percentage to the observed mortality percentage for both the overall study cohort and its respective subgroups. Within each patient group, GERAADA scores served as the expected probability of adverse events (or predictive values), while the number of deaths and patient observations represented the count of events and trials\u003csup\u003e6\u003c/sup\u003e. The two-sided binomial tests were subsequently employed to rigorously test the binomial hypothesis. Additionally, receiver operating characteristic analyses were carried out for each group comparison. To assess the influence of available variables on 30-day mortality, a multivariable regression model was applied.A P-value\u0026lt;0.05 was considered to be statistically significant and all statistical analyses were performed using R version 4.2.3 (2023-03-15 ucrt).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe baseline characteristics were similar between the two groups for the majority of variables considered as shown in table1. However, a noteworthy finding was the significant difference observed in cases of malperfusion involving more than two organs. In this regard, the incidence was 5.8% in the group of survivors compared to 13.3% in the non-survivors, with a p-value of 0.023.\u003c/p\u003e\n\u003cp\u003ePatients in the non-survivor group had a longer surgery duration, with an average of 573.01\u0026plusmn;129.72 minutes, compared to 535.62\u0026plusmn;122.42 minutes in the survivor group (table 2).\u003c/p\u003e\n\u003cp\u003eThe non-survivor group had significantly higher percentages of using ECMO and IABP (6.7% vs. 0.3%, p\u0026lt;0.001), (6.7% vs. 0.5%,p\u0026lt;0.001).Most CPR (10% vs. 0%, p\u0026lt;0.001) and postoperative tracheotomy (18.9% vs. 9.2%, p=0.014) were performed on death group, and the same group patients stayed longer in ICU (17.31% vs. 10.39%, p\u0026lt;0.001). The survived group patients were extubating after surgery earlier than the death group (14.10% vs. 6.11%, p\u0026lt;0.001 table3).\u003c/p\u003e\n\u003cp\u003eAdditionally, the non-survivor group experienced higher rates of paralysis (8.9% vs. 2.4%, p=0.007), lung infection (36.7% vs. 7.3%, p\u0026lt;0.001), GI bleeding (14.4% vs. 2.9%, p\u0026lt;0.001), MODS (20% vs. 0.3%, p\u0026lt;0.001), respiratory failure (22.2% vs. 0.3%, p\u0026lt;0.001), and cardiac failure (16.7% vs. 0%, p\u0026lt;0.001). These differences were statistically significant and emerged as univariate predictors for 30-day mortality(table3).\u003c/p\u003e\n\u003cp\u003eICU stay (OR 1.08, 95% CI 1.02-1.14, p=0.012), lung infection (OR 15.35, 95% CI 5.49-45.26, p\u0026lt;0.001), MODS (OR 159.19, 95% CI 17.54-5320.35, p\u0026lt;0.001), and respiratory failure (OR 229.91, 95% CI 34.55-5654.37, p\u0026lt;0.001) were identified as independent risk factors associated with 30-day mortality (Table 4).\u003c/p\u003e\n\u003cp\u003eIn table 5 we presented GERAADA prediction mortality and actual mortality rates in various subgroups of patients with Type A aortic dissection (AAD). It provides valuable insights into the accuracy of the GERAADA score in predicting 30-day mortality. The GERAADA prediction for patients aged below 50 was 17.94%, with an actual mortality of 19.3%, while for patients aged 50 and above, the prediction was 18.07%, and the actual mortality was 18.92%. In both age groups, the prediction closely approximated the actual mortality, with p-values of 0.642 and 0.7163, respectively.\u003c/p\u003e\n\u003cp\u003eSex, previous surgery, inotrope use and specific aortic regurgitation levels did not show a notable difference. Patients without malperfusion had a GERAADA prediction of 18.01% and an actual mortality of 17.5%. Cerebral malperfusion patients had a prediction of 17.99%, with an actual mortality of 23.5%. For cardiac malperfusion, the prediction was 18.45%, and the actual mortality was 24.4%. Visceral malperfusion had a GERAADA prediction of 7.99%, and the actual mortality was 16.21%. Patients with peripheral malperfusion had a prediction of 18.45%, and there were no actual mortalities. Other malperfusion patients had a GERAADA prediction of 17.38%, while the actual mortality was 35.71%. The highest discrepancy between prediction and actual mortality was observed in the other malperfusion group, with a p-value of 0.05708. Hemiparesis patients had a GERAADA prediction of 18.59%, and there were no actual mortalities in this group. Patients with aortic entry had a GERAADA prediction of 18.07%, and the actual mortality was 19.50%. Ascending entry patients had a prediction of 17.97%, and the actual mortality was 20.9%. Patients with descending entry had a GERAADA prediction of 18.36%, and the actual mortality was 19.4%. Root entry patients had a prediction of 17.88%, with an actual mortality of 7.14%. The most significant difference between prediction and actual mortality was observed in the \u0026quot;root entry\u0026quot; group, with a p-value of 0.01095.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe German Registry of Acute Aortic Dissection Type A (GERAADA) score uses very basic and easily retrievable parameters and was specifically designed for predicting the 30-day mortality rate in patients undergoing surgery for acute aortic dissection.Czerny et al. stressed that the GERAADA score should not be viewed as an absolute decision-making tool for accepting or rejecting treatment. Instead, it should be considered a valuable instrument for predicting postoperative outcomes using easily accessible and fundamental parameters\u003csup\u003e5\u003c/sup\u003e. Given the elevated mortality rate associated with type A aortic dissection, this predictive scoring system can be a valuable asset for surgeons.\u003c/p\u003e \u003cp\u003eThe overall mortality rate in our study stands at 19.06%, while the GERAADA score predicted a mortality of 18.18%.No significant difference was found between the mortality group and the predicted group with an Area Under Curve of 0.599 (95% CI 0.558, 0.622) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).In their study, M. Ma et al. found that the GERAADA score and EuroSCORE II predicted 30-day mortality rates of 14.7% and 3.1%, respectively, while the observed rate was 12.5%\u003csup\u003e7\u003c/sup\u003e. On a similar note, K. Sugiyama et al. calculated an overall 30-day mortality for their study cohort using the GERAADA score as 14.3%(8.1\u0026ndash;77.6%),whereas the actual mortality rate was 6%\u003csup\u003e8\u003c/sup\u003e.GERAADA has the particularity to be focused on aortic dissection and take in consideration some aspect of complications of ATAAD like malperfusion.\u003c/p\u003e \u003cp\u003eMalperfusion is recognized to occur in a significant portion of ATAAD cases, affecting approximately 20\u0026ndash;30% of patients\u003csup\u003e9\u003c/sup\u003e. Consequently, the mortality rate for individuals experiencing this complication varies significantly, falling within the range of 17\u0026ndash;44%\u003csup\u003e10\u003c/sup\u003e. It is crucial to emphasize that aortic dissection complicated by malperfusion is intimately associated with heightened mortality rates. This risk is amplified as the extent of organ involvement increases, making the management of multiple organ malperfusion an especially formidable challenge. Our study further supports this connection by revealing a noteworthy correlation between multiple organ malperfusion and in-hospital mortality. This finding is consistent with the research conducted by Kawahito and colleagues\u003csup\u003e11\u003c/sup\u003e. Numerous studies have examined mortality risk factors, and many of them have identified independent risk factors, as reflected in our study. Recent research conducted by Khan et al a range of independent risk factors associated with prolonged ICU stays have been identified. These factors encompass age, preoperative D-dimer levels, CPB time, the use of deep hypothermic circulatory arrest, the occurrence of postoperative stroke, postoperative acute respiratory failure, and postoperative acute renal failure\u003csup\u003e91213\u003c/sup\u003e. Our study corroborates these recent research findings, highlighting that the duration of ICU stay, lung infections, and respiratory failure identified as risk factors for in-hospital mortality\u003csup\u003e1415\u003c/sup\u003e. This association can primarily be attributed to postoperative interventions within the ICU, such as ECMO, IABP, CPR, tracheotomy, and the prolonged use of mechanical ventilation, all of which have been identified in our study as contributing factors to hospital mortality.\u003c/p\u003e \u003cp\u003eIn our actual mortality group, a notable increase in mortality is observed, particularly reaching 35.71% in cases categorized as other malperfusion, where multiple organ malperfusion is present. This contrasts with the lower prediction of 17.38% (\u0026plusmn;\u0026thinsp;1.80). Luehr et al., in their literature, reported a similar trend with more deaths in the study group, particularly in the prediction score for coronary malperfusion, where the actual mortality was 29.6% compared to the predicted 30% (\u0026plusmn;\u0026thinsp;19.4)\u003csup\u003e6\u003c/sup\u003e and for other malperfusion the reported 19.9% (\u0026plusmn;\u0026thinsp;19.4) for GERAADA prediction score vs 24%for the study group. No deaths were observed in patients with hemiparesis or those experiencing peripheral malperfusion, contrary to the predictions. This underscores the effectiveness of prompt aortic and organ reperfusion\u003csup\u003e16\u003c/sup\u003e. Patients with hemiparesis and peripheral malperfusion experienced significant post-surgery recovery, with their symptoms greatly alleviated.\u003c/p\u003e \u003cp\u003eThe GERAADA prediction score in our study, with an Area Under Curve (AUC) of 0.599 (95% CI: 0.558\u0026ndash;0.622) as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, does not represent a robust model for predicting mortality in ATAAD patients.Similar to the findings of I.Zivkovi whose study concluded that EuroSCORE II demonstrates superior discriminative power for predicting operative mortality in ATAAD surgery compared to the GERAADA score. Both scoring systems demonstrate good calibration ability.In a recent assessment of popular online prediction models, Ma et al. discovered that the EuroSCORE II displayed superior predictive accuracy for surgical mortality in ATAAD patients, with the observed 30-day mortality rate validating the GERAADA score's excellent calibration\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, S. Lilyanna et al. propose that the optimal utilization of the new GERAADA score should not be for patient selection or decision-making but rather for quality control and performance comparison between different hospitals, facilitating retrospective evaluation and improved resource management.\u003c/p\u003e \u003cp\u003eSome recent studies have proposed more effective models tailored to ATAAD patients\u003csup\u003e1718\u003c/sup\u003e. H. Lin et al. introduced a straightforward nomogram based on six predictors, including left ventricular end-diastolic diameter\u0026thinsp;\u0026lt;\u0026thinsp;45mm, estimated glomerular filtration rate\u0026thinsp;\u0026lt;\u0026thinsp;50 ml/min/1.73 m\u0026sup2;, persistent abdominal pain, radiological celiac trunk malperfusion, concomitant coronary artery bypass grafting, and cardiopulmonary bypass time\u0026thinsp;\u0026gt;\u0026thinsp;4 hours, to predict 30-day mortality. Meanwhile, T. Guo et al. in their literature presented a highly effective machine learning model with an AUC of 0.927 (95% CI: 0.860\u0026ndash;0.968) \u003csup\u003e1920\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese results indicate a potential avenue for improving the predictive precision of the GERAADA score, designed for aortic dissection, by integrating intraoperative factors, given the continual advancements in surgical techniques. This enhancement gains significance, especially in the backdrop of ongoing progress in surgical methods and postoperative ICU care.K.Sugiyama suggest to add parameters such as the time from onset to arrival, family background, and hemodialysis for further accuracy.\u003c/p\u003e \u003cp\u003eStudy limitation\u003c/p\u003e \u003cp\u003eThis study has certain limitations that should be acknowledged. It is a retrospective, single-center investigation that incorporates various surgical approaches and involves multiple surgeons. Furthermore, the criteria and diagnosis of malperfusion may deviate from those established by the GERAADA score.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWhile the GERAADA predictions generally exhibited reliable accuracy, occasional discrepancies were identified in specific cases involving inotrope utilization, hemiparesis, peripheral malperfusion, and aortic dissection at the root. The GERAADA score, designed for acute Type A aortic dissection, can be refined through the inclusion of intraoperative and postoperative factors particularly in the face of advancing surgical techniques and improved postoperative ICU care.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eATAAD acute Type A aortic dissection , ECMO extracorporeal membrane oxygenation, CRRT continuous renal replacement therapy, ICU intensive care unit, IABP intra-aortic balloon pump, CPR cardiopulmonary resuscitation. GERAADA German Registry of Acute Aortic Dissection type A, FET frozen elephant trunk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical Approval and Consent to participate\u003c/p\u003e\n\u003cp\u003eThe study has been approved by the Medical Ethics Committee of Tongji Hospital TJ-IRB202402062.Written informed consent was deemed unnecessary in accordance with the local legislation and institutional requirements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of supporting data\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eAuthor contributions Dr Barnabo Nampoukime performed the conception and design of the paper, the patient data acquisition and analysis, and the draft of the article. Dr. Fortes Gomes, Sultani, NOUANI helped with patient data acquisition, and Dr. Pan, Dr. Wang, and Dr. Hu ,Dr Zha helped design the paper and the draft as well as the revision of the article. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe appreciate the surgeons and nurses from the Department of Cardiovascular Surgery for their advice and helpful information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWen M, Han Y, Ye J, et al. Peri-operative risk factors for in-hospital mortality in acute type A aortic dissection. \u003cem\u003eJ Thorac Dis\u003c/em\u003e. 2019;11(9):3887-3895. doi:10.21037/jtd.2019.09.11\u003c/li\u003e\n\u003cli\u003eTien M, Ku A, Martinez-Acero N, Zvara J, Sun EC, Cheung AT. The Penn Classification Predicts Hospital Mortality in Acute Stanford Type A and Type B Aortic Dissections. \u003cem\u003eJ Cardiothorac Vasc Anesth\u003c/em\u003e. 2020;34(4):867-873. doi:10.1053/j.jvca.2019.08.036\u003c/li\u003e\n\u003cli\u003eAn Z, Zhong K, Sun Y, Han L, Xu Z. mortality after total arch. 2023;(April):1-6. doi:10.3389/fcvm.2023.1149907\u003c/li\u003e\n\u003cli\u003eGudbjartsson T, Ahlsson A, Geirsson A, et al. Acute type A aortic dissection\u0026ndash;a review. \u003cem\u003eScand Cardiovasc J\u003c/em\u003e. 2020;54(1):1-13. doi:10.1080/14017431.2019.1660401\u003c/li\u003e\n\u003cli\u003eCzerny M, Siepe M, Beyersdorf F, et al. Prediction of mortality rate in acute type A dissection : the German Registry for Acute Type A Aortic Dissection score. 2020;58(June):700-706. doi:10.1093/ejcts/ezaa156\u003c/li\u003e\n\u003cli\u003eLuehr M, Merkle-storms J, Gerfer S, Li Y, Krasivskyi I. Evaluation of the GERAADA score for prediction of 30-day mortality in patients with acute type A aortic dissection. 2021;59(December 2020):1109-1114. doi:10.1093/ejcts/ezaa455\u003c/li\u003e\n\u003cli\u003eMa M, Cao H, Li K, et al. Evaluation of Two Online Risk Prediction Models for the Mortality Rate of Acute Type A Aortic Dissection Surgery: The German Registry of Acute Aortic Dissection Type A Score and the European System for Cardiac Operative Risk Evaluation II. \u003cem\u003eJ Clin Med\u003c/em\u003e. 2023;12(14). doi:10.3390/jcm12144728\u003c/li\u003e\n\u003cli\u003eSugiyama K, Watanuki H, Tochii M, Futamura Y, Kitagawa Y, Makino S. Impact of GERAADA score in patients with acute type A aortic dissection. \u003cem\u003eJ Cardiothorac Surg\u003c/em\u003e. Published online 2022:1-9. doi:10.1186/s13019-022-01858-y\u003c/li\u003e\n\u003cli\u003eMohammed, Firoj, Khan., Xian, en, Fa., Hai, Bin, Yu. (2017). Factors of Prolonged Intensive Care Unit Stay After Surgery in Patients with Type A Acute Aortic Dissection. Biology and medicine 9(1):1-5. doi: 10.4172/0974-8369.1000367. No Title.\u003c/li\u003e\n\u003cli\u003eKawahito K, Kimura N, Yamaguchi A, Aizawa K. Malperfusion in type A aortic dissection : results of emergency central aortic repair. \u003cem\u003eGen Thorac Cardiovasc Surg\u003c/em\u003e. 2019;67(7):594-601. doi:10.1007/s11748-019-01072-z\u003c/li\u003e\n\u003cli\u003eKawahito K, Adachi H, Yamaguchi A, Ino T. Preoperative Risk Factors for Hospital Mortality in. 2001;4975(00).\u003c/li\u003e\n\u003cli\u003eHoefer D, Ruttmann E, Riha M, et al. Factors influencing intensive care unit length of stay after surgery for acute aortic dissection type A. \u003cem\u003eAnn Thorac Surg\u003c/em\u003e. 2002;73(3):714-718. doi:10.1016/S0003-4975(01)03572-X\u003c/li\u003e\n\u003cli\u003eLi B, Li B. Risk Factors for Postoperative Mortality in Patients with Acute Stanford Type A Aortic Dissection. Published online 2021:7007-7015.\u003c/li\u003e\n\u003cli\u003eChen Q, Zhang B, Yang J, Mo X, Zhang L, Li M. Predicting Intensive Care Unit Length of Stay After Acute Type A Aortic Dissection Surgery Using Machine Learning. 2021;8(July):1-7. doi:10.3389/fcvm.2021.675431\u003c/li\u003e\n\u003cli\u003eKimura N, Tanaka M, Kawahito K, et al. Risk factors for prolonged mechanical ventilation following surgery for acute type A aortic dissection. \u003cem\u003eCirc J\u003c/em\u003e. 2008;72(11):1751-1757. doi:10.1253/circj.CJ-08-0306\u003c/li\u003e\n\u003cli\u003eUchida K, Karube N, Kasama K, et al. Early reperfusion strategy improves the outcomes of surgery for type A acute aortic dissection with malperfusion. \u003cem\u003eJ Thorac Cardiovasc Surg\u003c/em\u003e. 2018;156(2):483-489. doi:10.1016/j.jtcvs.2018.02.007\u003c/li\u003e\n\u003cli\u003eKuang J, Yang J, Wang Q, Yu C, Li Y, Fan R. A preoperative mortality risk assessment model for Stanford type A acute aortic dissection. \u003cem\u003eBMC Cardiovasc Disord\u003c/em\u003e. Published online 2020:1-9. doi:10.1186/s12872-020-01802-9\u003c/li\u003e\n\u003cli\u003eRen Y, Huang S, Li Q, Liu C, Li L, Tan J. Prognostic factors and prediction models for acute aortic dissection : a systematic review. Published online 2021:1-12. doi:10.1136/bmjopen-2020-042435\u003c/li\u003e\n\u003cli\u003eChen J, Bai Y, Liu H, Qin M. Prediction of in-hospital death following acute type A aortic dissection. (1).\u003c/li\u003e\n\u003cli\u003eGuo T, Fang Z, Yang G, Zhou Y, Ding N, Peng W. Machine Learning Models for Predicting In-Hospital Mortality in Acute Aortic Dissection Patients. 2021;8(September):1-14. doi:10.3389/fcvm.2021.727773\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1:preoperative characteristics for 30 days hospital mortality\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"596\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003eSurvived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eSex,male,mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e287 (75.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e73 (81.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eAge,y,mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e51.92 \u0026plusmn;11.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e52.01 \u0026plusmn;12.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e25.38 \u0026plusmn;3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e25.89 \u0026plusmn;4.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eEjection fraction,,mean\u0026plusmn;SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e57.97 \u0026plusmn;6.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e56.92 \u0026plusmn;7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eLactate dehydrogenase(135-214U/L),mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e296.15 \u0026plusmn;226.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e271.20 \u0026plusmn;131.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eCreatinine(59-104umol/l),mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e107.02 \u0026plusmn;125.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e133.75 \u0026plusmn;108.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eCardiac troponin\u0026lt;34.2pg/ml,mean\u0026plusmn;SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e1635.11 \u0026plusmn;7386.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e574.61 \u0026plusmn;2215.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eMyoglobin(\u0026lt;154.9ng/ml),mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e169.04 \u0026plusmn;277.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e216.11 \u0026plusmn;344.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eCreatinine kinase(\u0026lt;7.2ng/ml ),mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e5.99 \u0026plusmn;20.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e7.71 \u0026plusmn;26.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eSmoker\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e94 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e24 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e37 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e6 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eCoronary Artery Disease\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e21 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e10 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e228 (59.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e62 (68.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003ePericardial effusion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e107 (28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e30 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eHistory of cardiac surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e28 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e8 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eMarfan Syndrome\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e5 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e4 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eIntubate before surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e172 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e42 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eCerebral malperfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e18 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e5 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eVisceral malperfusion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e47 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e11 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eKidneys malperfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e24 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eLimbs malperfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e11 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eCardiac malperfusion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e34 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e13 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.34228187919463%\" valign=\"top\"\u003e\n \u003cp\u003eMalperfusion in more than two organs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.798657718120804%\" valign=\"top\"\u003e\n \u003cp\u003e22 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953020134228186%\" valign=\"top\"\u003e\n \u003cp\u003e12 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906040268456376%\" valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn;SD or as number (%)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: intraoperative characteristics for 30 days hospital mortality\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.28593508500773%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802163833075735%\" valign=\"top\"\u003e\n \u003cp\u003eSurvived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.256568778979908%\" valign=\"top\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.65533230293663%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.28593508500773%\" valign=\"top\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802163833075735%\" valign=\"top\"\u003e\n \u003cp\u003e382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.256568778979908%\" valign=\"top\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.65533230293663%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.28593508500773%\" valign=\"top\"\u003e\n \u003cp\u003eSurgery time(min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802163833075735%\" valign=\"top\"\u003e\n \u003cp\u003e535.62 \u0026plusmn;122.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.256568778979908%\" valign=\"top\"\u003e\n \u003cp\u003e573.01 \u0026plusmn;129.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.65533230293663%\" valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.28593508500773%\" valign=\"top\"\u003e\n \u003cp\u003eAorta cannulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802163833075735%\" valign=\"top\"\u003e\n \u003cp\u003e8 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.256568778979908%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.65533230293663%\" valign=\"top\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.28593508500773%\" valign=\"top\"\u003e\n \u003cp\u003eFemoral cannulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802163833075735%\" valign=\"top\"\u003e\n \u003cp\u003e47 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.256568778979908%\" valign=\"top\"\u003e\n \u003cp\u003e14 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.65533230293663%\" valign=\"top\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.28593508500773%\" valign=\"top\"\u003e\n \u003cp\u003eAxillary cannulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802163833075735%\" valign=\"top\"\u003e\n \u003cp\u003e98 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.256568778979908%\" valign=\"top\"\u003e\n \u003cp\u003e26 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.65533230293663%\" valign=\"top\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.28593508500773%\" valign=\"top\"\u003e\n \u003cp\u003eAxillary and femoral cannulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802163833075735%\" valign=\"top\"\u003e\n \u003cp\u003e138 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.256568778979908%\" valign=\"top\"\u003e\n \u003cp\u003e31 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.65533230293663%\" valign=\"top\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.28593508500773%\" valign=\"top\"\u003e\n \u003cp\u003eSubclavian artery cannulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802163833075735%\" valign=\"top\"\u003e\n \u003cp\u003e26 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.256568778979908%\" valign=\"top\"\u003e\n \u003cp\u003e5 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.65533230293663%\" valign=\"top\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.28593508500773%\" valign=\"top\"\u003e\n \u003cp\u003eSubclavian and femoral artery cannulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802163833075735%\" valign=\"top\"\u003e\n \u003cp\u003e42 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.256568778979908%\" valign=\"top\"\u003e\n \u003cp\u003e12 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.65533230293663%\" valign=\"top\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.28593508500773%\" valign=\"top\"\u003e\n \u003cp\u003eCPB time (min),mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802163833075735%\" valign=\"top\"\u003e\n \u003cp\u003e239.66\u0026plusmn; 113.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.256568778979908%\" valign=\"top\"\u003e\n \u003cp\u003e264.83\u0026plusmn; 86.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.65533230293663%\" valign=\"top\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.28593508500773%\" valign=\"top\"\u003e\n \u003cp\u003eAorta clamping time (min),mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802163833075735%\" valign=\"top\"\u003e\n \u003cp\u003e123.29 \u0026plusmn;46.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.256568778979908%\" valign=\"top\"\u003e\n \u003cp\u003e132.51 \u0026plusmn;36.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.65533230293663%\" valign=\"top\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.28593508500773%\" valign=\"top\"\u003e\n \u003cp\u003eLowest temperature(C),mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802163833075735%\" valign=\"top\"\u003e\n \u003cp\u003e26.34 \u0026plusmn;2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.256568778979908%\" valign=\"top\"\u003e\n \u003cp\u003e26.18 \u0026plusmn;2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.65533230293663%\" valign=\"top\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.28593508500773%\" valign=\"top\"\u003e\n \u003cp\u003eHCA (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.802163833075735%\" valign=\"top\"\u003e\n \u003cp\u003e9.56 \u0026plusmn;13.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.256568778979908%\" valign=\"top\"\u003e\n \u003cp\u003e12.13 \u0026plusmn;14.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.65533230293663%\" valign=\"top\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean SD or as number (%), HCA hypothermic circulatory arrest, CPB cardiopulmonary bypass.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: postoperative risks factors for 30 days hospital mortality\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003eSurvived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eECMO\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e6 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eCRRT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e31 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e14 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eCPR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e9 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eTracheotomy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e35 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e17 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eIABP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e2 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e6 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eICU Stay(days)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e11.24 \u0026plusmn;10.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e16.34 \u0026plusmn;17.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eTime of extubation (days )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e3.97 \u0026plusmn;6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e8.45 \u0026plusmn;14.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eReexploration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e21 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e8 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eComa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e19 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e7 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eParalysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e9 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e8 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eStroke\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e18 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e3 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eLung infection\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e28 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e33 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eGI bleeding\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e11 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e13 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eTND\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e9 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e4 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eMODS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e18 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e20 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eCardiac failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e15 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.346007604562736%\" valign=\"top\"\u003e\n \u003cp\u003eKidney failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e12 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.482889733840302%\" valign=\"top\"\u003e\n \u003cp\u003e5 ( 5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.688212927756654%\" valign=\"top\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean SD or as number (%), ECMO extracorporeal membrane oxygenation, CRRT continuous renal replacement therapy, ICU intensive care unit, IABP intra-aortic balloon pump, CPR cardiopulmonary resuscitation.TND transient Neurologic dysfunction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4: multivariate analysis of risks factors for 30 days hospital mortality\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.62279293739968%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.23756019261637%\" valign=\"top\"\u003e\n \u003cp\u003eOdds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.545746388443018%\" valign=\"top\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.62279293739968%\" valign=\"top\"\u003e\n \u003cp\u003eSurgery time\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.23756019261637%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.545746388443018%\" valign=\"top\"\u003e\n \u003cp\u003e1.00-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.62279293739968%\" valign=\"top\"\u003e\n \u003cp\u003eECMO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.23756019261637%\" valign=\"top\"\u003e\n \u003cp\u003e2.86\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.545746388443018%\" valign=\"top\"\u003e\n \u003cp\u003e0.05-213.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.62279293739968%\" valign=\"top\"\u003e\n \u003cp\u003eTracheotomy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.23756019261637%\" valign=\"top\"\u003e\n \u003cp\u003e1.31\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.545746388443018%\" valign=\"top\"\u003e\n \u003cp\u003e0.39-4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.62279293739968%\" valign=\"top\"\u003e\n \u003cp\u003eIABP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.23756019261637%\" valign=\"top\"\u003e\n \u003cp\u003e4.59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.545746388443018%\" valign=\"top\"\u003e\n \u003cp\u003e0.31-70.31\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.62279293739968%\" valign=\"top\"\u003e\n \u003cp\u003eICU stay(days)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.23756019261637%\" valign=\"top\"\u003e\n \u003cp\u003e1.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.545746388443018%\" valign=\"top\"\u003e\n \u003cp\u003e1.02-1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.62279293739968%\" valign=\"top\"\u003e\n \u003cp\u003eTime of extubation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.23756019261637%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.545746388443018%\" valign=\"top\"\u003e\n \u003cp\u003e0.96-1.05\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.62279293739968%\" valign=\"top\"\u003e\n \u003cp\u003eParalysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.23756019261637%\" valign=\"top\"\u003e\n \u003cp\u003e3.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.545746388443018%\" valign=\"top\"\u003e\n \u003cp\u003e0.49-15.85\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.62279293739968%\" valign=\"top\"\u003e\n \u003cp\u003eLung infection\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.23756019261637%\" valign=\"top\"\u003e\n \u003cp\u003e15.35 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.545746388443018%\" valign=\"top\"\u003e\n \u003cp\u003e5.49-45.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.62279293739968%\" valign=\"top\"\u003e\n \u003cp\u003eGI bleeding\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.23756019261637%\" valign=\"top\"\u003e\n \u003cp\u003e3.61\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.545746388443018%\" valign=\"top\"\u003e\n \u003cp\u003e0.80-15.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.62279293739968%\" valign=\"top\"\u003e\n \u003cp\u003eMODS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.23756019261637%\" valign=\"top\"\u003e\n \u003cp\u003e159.19\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.545746388443018%\" valign=\"top\"\u003e\n \u003cp\u003e17.54-5320.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.62279293739968%\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.23756019261637%\" valign=\"top\"\u003e\n \u003cp\u003e229.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.545746388443018%\" valign=\"top\"\u003e\n \u003cp\u003e34.55-5654.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.62279293739968%\" valign=\"top\"\u003e\n \u003cp\u003eMalperfusion in more than two organs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.23756019261637%\" valign=\"top\"\u003e\n \u003cp\u003e2.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.545746388443018%\" valign=\"top\"\u003e\n \u003cp\u003e0.95-4.89\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean SD or as number (%), ECMO extracorporeal membrane oxygenation, CRRT continuous renal replacement therapy, ICU intensive care unit, IABP intra-aortic balloon pump, CPR cardiopulmonary resuscitation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 5: GERAADA prediction score versus actual study group\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.321888412017167%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.244635193133046%\" valign=\"top\"\u003e\n \u003cp\u003eGERAADA prediction\u003c/p\u003e\n \u003cp\u003emortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.236051502145923%\" valign=\"top\"\u003e\n \u003cp\u003eActual\u003c/p\u003e\n \u003cp\u003eMortality\u003c/p\u003e\n \u003cp\u003eStudy group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.261802575107296%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.781115879828327%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLower \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Upper \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\" valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eAGE \u0026lt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e17.94%（\u0026plusmn;1.93）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e19.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\" valign=\"top\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.4063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.5121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eAGE\u0026nbsp;\u0026ge;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.07%（\u0026plusmn;2.04）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e18.92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\" valign=\"top\"\u003e\n \u003cp\u003e0.7163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.5761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.7403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.6582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eSex female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.08%（\u0026plusmn;2.32）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\" valign=\"top\"\u003e\n \u003cp\u003e15.17%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\" valign=\"top\"\u003e\n \u003cp\u003e0.3973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.5883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.8297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eSex male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.10%（\u0026plusmn;1.89）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\" valign=\"top\"\u003e\n \u003cp\u003e20.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\" valign=\"top\"\u003e\n \u003cp\u003e0.4955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.6484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eHistory of cardiac surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.31%（\u0026plusmn;1.96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e22.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\" valign=\"top\"\u003e\n \u003cp\u003e0.5816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.3078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.8172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.5625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eResuscitation before surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.08%（\u0026plusmn;2.09）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e19.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.1228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.4823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.4823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.5815\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eInotrope at referral\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.31%（\u0026plusmn;1.97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e15.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.3161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.5411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.6621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eAortic regurgitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eAR I-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e17.96%（\u0026plusmn;1.83）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e11.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.1376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.4927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.4927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eARIII-IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e24.21%（\u0026plusmn;2.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e18.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.1656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.5417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.5417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.6685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eNo regurgitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.03%（\u0026plusmn;2.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e18.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.7137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.5645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eMalperfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eNo malperfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.01%（\u0026plusmn;2.03）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e17.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.8087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.5323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.6924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.6124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eCerebral malperfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e17.99%（\u0026plusmn;2.06）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e23.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.6092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.3287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.6154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eCardiac malperfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.45%（\u0026plusmn;2.16）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e24.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.3607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.3539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.7798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.5668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eVisceral malperfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e17.99%（\u0026plusmn;1.54）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e16.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.3693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.9533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.6613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003ePeripheral malperfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.45%（\u0026plusmn;1.96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e\u0026lt;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eOther malperfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e17.38%（\u0026plusmn;1.80）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e35.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.05708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.3561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.8217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.5889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eHemiparesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.59%（\u0026plusmn;2.07）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e\u0026lt;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eExtension of the tear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eAortic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.12%（\u0026plusmn;2.09）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e19.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.4758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.7436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.6097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eSupra aortic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.00%（\u0026plusmn;2.09）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e18.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.9729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.4952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.6844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.5898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eDescending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e17.96%（\u0026plusmn;1.75）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e20.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.4746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.4647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.5963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eEntry of the tear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eAortic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.07%\u0026nbsp;（\u0026plusmn;2.12）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e19.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.6426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.5297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.7359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.6328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eAscending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e17.97%（\u0026plusmn;1.97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e20.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.2793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.4804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.6692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.5748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eDescending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e18.36%（\u0026plusmn;2.01）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e19.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.8911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.2493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.8707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.34908700322234%\" valign=\"top\"\u003e\n \u003cp\u003eRoot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.267454350161117%\"\u003e\n \u003cp\u003e17.88%（\u0026plusmn;1.62）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.252416756176155%\"\u003e\n \u003cp\u003e7.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.270676691729323%\"\u003e\n \u003cp\u003e0.01095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.7433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.848549946294307%\" valign=\"top\"\u003e\n \u003cp\u003e0.9832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"top\"\u003e\n \u003cp\u003e0.8632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"type A aortic dissection, surgery, risk factors, prediction score, mortality","lastPublishedDoi":"10.21203/rs.3.rs-3933237/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3933237/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study seeks to assess the predictive precision of the GERAADA score for 30-day mortality in patients with Type A aortic dissection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A retrospective study analyzed data from 382 survivors and 90 non-survivors, examining demographic, clinical, and surgical variables. GERAADA scores were calculated by a blinded cardiac surgeon using a web-based application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The overall mortality is 19.06% and 18.18% for GERAADA prediction.\u003c/p\u003e\n\u003cp\u003eThe presence of malperfusion in more than two organs emerged as a significant risk factor for hospital mortality p=0.028. Longer surgery times were significantly associated with elevated mortality p=0.002. Moreover, postoperative ECMO, CPR, and IABP were significantly linked to increased mortality rates. Additionally, ICU stay duration, lung infection, MODS, and respiratory failure p\u0026lt;0.05 independently posed as risk factors for hospital mortality.\u003c/p\u003e\n\u003cp\u003ePatients with Hemiparesis and peripheral malperfusion experienced no deaths, as predicted by GERAADA score. Additionally, patients with a dissection tear located at the root of the aorta exhibited a lower mortality rate of 7.14%, contrasting the higher 19.87% GERAADA prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: GERAADA predictions were mostly accurate, but exceptions occurred with inotrope use, hemiparesis, peripheral malperfusion, and aortic dissection at the root. We suggest enhancing the GERAADA score by incorporating intraoperative and postoperative factors.\u003c/p\u003e","manuscriptTitle":"Assessing GERAADA Score Mortality Predictions in Type A Aortic Dissection Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-19 18:03:13","doi":"10.21203/rs.3.rs-3933237/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1221a684-b980-42ba-8a4f-0196d486162a","owner":[],"postedDate":"February 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-17T14:53:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-19 18:03:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3933237","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3933237","identity":"rs-3933237","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}