Predicting the risk of postoperative death risk in Acute Type A Aortic Dissection: development and evaluation of a new predictive nomogram

Predicting the risk of postoperative death risk in Acute Type A Aortic Dissection: development and evaluation of a new predictive nomogram | 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 Predicting the risk of postoperative death risk in Acute Type A Aortic Dissection: development and evaluation of a new predictive nomogram Chenxi Ying, Zhedong Wan, Ruoshi Chen, Weidong Li, Xin Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4509101/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Acute type A aortic dissection (ATAAD) is an emergency condition characterized by severe chest pain and back pain, with rapid disease progression and a very high mortality rate. The current methods for predicting postoperative mortality rate in acute type A aortic dissection are inadequate, necessitating the urgent need for new prediction methods. Methods This study is a retrospective analysis of 309 patients with ATAAD in The First Affiliated Hospital Zhejiang University of Medicine. By utilizing the LASSO and logistic regression analysis, we have developed a novel predictive model for postoperative mortality rate. The model incorporates factors such as platelet count (PLT), lactic acid (LA), hydroxybutyrate dehydrogenase (HBDH) , activated partial thromboplastin time (APTT) , deep hypothermic circulatory arrest (DHCA) time to predict the risk of mortality in patients. Results The predictive nomogram included predictors such as PLT, LA, HBDH, APTT, and DHCA time. With a C-index of 0.9787, the model demonstrated good discrimination power, calibration, and ROC curve. It was able to maintain a high C-index value of 0.984 even during interval verification. Conclusions We have developed and validated a novel predictive model for assessing postoperative mortality risk in Chinese ATAAD patients. This predictive tool demonstrates good discriminatory ability and calibration, which can assist clinicians in making more accurate risk assessments and devising personalized treatment plans. ATAAD postoperative hospital death risk prediction nomogram prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Acute type A aortic dissection (ATAAD) is a fatal cardiovascular disease, with a urgent onset, rapid progression, and high mortality. The main symptom is severe thoracic and back pain [ 1 ] . When the extracellular matrix undergoes inflammation and degradation, it often results in damage to the integrity of the aorta, causing blood to enter the aortic media and form new blood spaces [ 2 ] . With the dissection progresses, the aortic wall becomes more susceptible to rupture, resulting in severe bleeding. This can rapidly progress to hemorrhagic shock and multiple organ failure. Without treatment, individuals suffering from ATAAD face mortality rates of 33%, 50%, and 75% after 1, 2, and 7 days, respectively [ 3 ] . However, even after undergoing emergency surgical repair treatment, certain patients with advanced age, a history of hypertension, previous interventions, chronic renal insufficiency, and other underlying diseases may still lead to severe postoperative arrhythmia, atelectasis, renal failure and a high risk of mortality. Therefore, it is essential to promptly identify and intervene for patients with ATAAD in order to enhance the survival rate and prognosis of individuals [ 4 ] , and identifying patients with a higher risk index and providing enhanced post-operative care in the ICU is necessary for improving outcomes. The current methods for predicting postoperative mortality in patients with acute type A aortic dissection rely on scoring systems such as the American Association of Surgeons (ASA) grading system and the Cardiovascular Risk assessment (ACC/AHA) guidelines [ 5 ] . These methods assess the patient's overall health status and other disease risk factors to estimate surgical risk. However, they may lack individualized features and have limited data sources, which can limit their accuracy. While these traditional methods can provide some risk assessment, they heavily rely on the patient's clinical characteristics and medical history, introducing subjectivity and uncertainty to the prediction results. Therefore, there is an urgent need to develop a new prediction method that addresses these limitations. By analyzing and modeling clinical data from patients undergoing surgery for ATAAD at our hospital, we have developed a new predictive nomogram that effectively predicts the risk of mortality after surgery. This nomogram takes into account factors such as age, gender, previous illnesses, myocardial enzymes, blood routine, and other indicators, as well as the potential for postoperative complications. Through a systematic analysis and assessment of these factors, we can make more precise predictions regarding the mortality risk for patients and establish suitable preventive and treatment measures. This study aims to offer a practical method for predicting the risk of postoperative mortality in Chinese patients with ATAAD, with the ultimate aim of providing clinicians with more accurate prediction results to evaluate postoperative risk and devise personalized treatment plans. Additionally, this nomogram will serve as a valuable tool for cardiac surgeons to enhance the clinical management and outcomes of patients. Patients and Methods Patients A retrospective analysis was conducted on 309 patients undergoing surgical treatment for ATAAD at The First Affiliated Hospital of Zhejiang University of Medicine, from January 2021 to January 2024. Inclusion criteria were as follows: (1) patients with symptoms and confirmed ATAAD based on symptoms and comprehensive computed tomography angiography (CTA); (2) patients with symptom onset within 14 days; (3) patients aged 18 years and above; (4) patients who underwent open chest surgery under cardiopulmonary bypass support. Exclusion criteria were as follows: (1) patients with missing important data; (2) patients who declined surgery or did not undergo surgical treatment for ruptured dissection preoperatively; (3) patients with rheumatic immune diseases, hematological disorders, malignancies, or severe trauma that could affect laboratory test results; (4) patients in a pregnant state; (5) patients undergoing repeat cardiac surgery; (6) patients on long-term use of glucocorticoids, immunosuppressants, or medications that could affect laboratory test results. All included patients underwent comprehensive imaging and laboratory examinations prior to surgery for a definite diagnosis. Based on the postoperative outcomes within 30 days, patients were categorized into the death group (n = 44) and the survival group (n = 265). The death group comprised 36 males and 8 females, with a mean age of (56.34 ± 11.53) years, while the survival group comprised 201 males and 64 females, with a mean age of (52.39 ± 12.25) years. This study has been approved by the Ethics Committee of the First Affiliated Hospital, Zhejiang University School of Medicine. General information The clinical data of patients who underwent surgery at the Department of Cardiovascular Surgery, the First Affiliated Hospital of Zhejiang University School of Medicine from January 2021 to January 2024 was collected. All patients were diagnosed with ATAAD using preoperative CTA and color Doppler echocardiography, which had a sensitivity of 100% and specificity of 99%. Upon admission, blood samples were taken from all patients via the cubital vein for initial testing, to assess biochemical markers such as Brain natriuretic peptide, Troponin I, lactate dehydrogenase, hydroxybutyrate dehydrogenase, creatine kinase, creatine kinase isoenzymes, D-Dimer, creatinine, and glomerular filtration rate. The collection of data included clinical symptoms, past medical history, laboratory test results, CTA imaging findings, cardiac color Doppler ultrasound data, and perioperative information in Table 1 . Statistical analysis Demographic, disease, and treatment characteristics data were expressed as counts (%), and continuous variables such as blood biochemical indicators and intraoperative cardiopulmonary bypass time were reported as mean ± standard deviation (SD). We added a collection of preset prediction factors for perioperative variables, consisting of clinical characteristics and data(Table 1 ). Statistical analysis was performed using the R software 4.3.2. The least absolute shrinkage and selection operator (LASSO) method, which is appropriate for the reducing high dimensional data [ 6 , 7 ] , was used to select the optimal predictive features in death risk factors from the patients with ATAAD. The LASSO regression model was used to select features with nonzero coefficients, which were then incorporated into a multivariable logistic regression analysis to build a predictive model [ 8 ] . The selected features were considered as odds ratio (OR) with a 95% confidence interval (CI) and a P-value. All statistical significance levels were two-sided. Sociodemographic variables with a P-value of 0.05 were included in the model, while variables associated with disease and treatment characteristics were all included [ 9 ] . Calibration and ROC curves were used to assess the calibration of the nomogram. A significant test statistic indicated that the model did not calibrate perfectly [ 10 ] . To measure the discrimination performance of the nomogram, Harrell’s C-index was calculated. The nomogram underwent bootstrapping validation (1,000 bootstrap resamples) to calculate a relatively corrected C-index [ 11 ] . Results Patients’ characteristics This study included a total of 309 patients who underwent surgery for ATAAD. After surgery, the patients were divided into the survival group and the death group (251 males, 58 females; mean age 52.95±12.21 years [range 25-82 years]). All data, including demographic, disease, postoperative biochemical indicators, and treatment features of the two groups of patients（Table 1）. Table 1 Differences between demographic and clinical characteristics of survival and death groups Demographic characteristics Survival(n=265) Death(n=44) Age(years) 52.39±12.25 56.34±11.53 CM(n%) axillary arterial 201(75.85) 36(81.82) axillary＆femoral arterial 64(24.15) 8(18.18) Gender(n%) Male 214(80.75) 37(84.09) Female 51(19.25) 7(15.91) Debakey type(n%) I 178(67.17) 28(63.64) II 87(32.83) 16(36.36) IVH(n%) Yes 18(6.79) 8(18.18) No 247(93.21) 36(81.82) AR(n%) no 30(11.32) 1(2.27) mild 114(43.02) 18(40.91) moderate 75(28.30) 20(45.45) severe 46(17.36) 5(11.36) PLT(10E9/L) 107.39±46.48 48.11±32.89 LA(mmol/L) 2.96±1.99 15.16±7.62 FIB(g/L) 2.88±1.09 1.61±0.93 D-2(ug/L FEU) 7291.40±8655.24 29584.75±25401.25 APTT(s) 35.51±26.33 58.98±34.32 TT(s) 27.01±29.71 59.11±51.66 CRP(mg/L) 77.63±128.31 50.05±51.06 GFR(mL/min) 63.79±29.13 43.76±25.56 CR(umol/L) 142.19±118.23 203.80±172.01 ALT(U/L) 61.94±162.79 533.45±1022.39 AST(U/L) 94.89±171.51 1098.80±2040.14 TnI(ng/mL) 4.80±9.59 59.62±115.88 BNP(pg/mL) 443.32±742.60 1003.08±1570.33 LDH(U/L) 474.26±354.78 2599.39±2683.39 HBDH(U/L) 380.37±237.21 1655.95±1804.60 CK(U/L) 1774.49±12182.872 8768.82±16636.63 CKMB(U/L) 45.70±97.36 272.16±456.96 DHCA(min) 25.43±13.48 32.77±12.36 CPB 3:23:15.44±0:55:22.98 4:13:57.75±1:54:13.72 ACCT 2:25:13.24±0:46:53.65 3:02:51.71±1:39:06.49 Feature selection Out of the 26 demographic, disease, and treatment characteristics, only 9 potential predictors with nonzero coefficients in the LASSO regression model were identified based on the cohort of 309 patients（~3:1 ratio ；Figure 1A and B). These features included patients' preoperative intervention history, intraoperative cardiopulmonary bypass cannulation mode, deep hypothermic circulatory arrest time, postoperative platelet count, lactate level, D2 dimer, fibrinogen, activated partial thromboplastin time, and hydroxybutyrate dehydrogenase (Table 2). Table 2 Predictors of death risk in patients undergoing aortic dissection surgery Factors Prediction model β Odds ratio (95% CI) P-value Intercept -17.036 ＜0.001 ＜0.001 CM(double) -1.313 0.0459 - 1.3071 0.118 IVH(Yes ） 1.604 0.4231 - 89.6515 0.245 PLT -1.506 0.0871 - 0.4779 ＜0.001 LA 2.964 4.9172 - 133.9899 ＜0.001 D-2 0.374 0.5908 - 3.9575 0.434 FIB -0.226 0.3609 - 1.7364 0.565 APTT 1.0162 1.3171 - 6.7580 0.013 HBDH 0.773 1.2350 - 4.0904 0.010 DHCA 0.720 1.1347 - 4.0740 0.025 Note: β is the regression coefficient. Development of death prediction model Table 2 displays the 9 potential factor features that were identified through LASSO regression analysis. Significant factors, such as deep hypothermic circulatory arrest time, platelet count, lactate level, activated partial thromboplastin time, and hydroxybutyrate dehydrogenase value on the first postoperative day, were selected based on logistics regression analysis results. These factors were categorized into quartiles, and a predictive model was developed using these independent factors. The results were presented in a nomogram graph (Figure 2). Apparent performance of the death risk nomogram in the cohort The calibration curve of the death risk nomogram for ATAAD patients undergoing surgery indicated strong agreement in the cohort (Figure 3,4). With a C-index of 0.9787 in the cohort, further validation through bootstrapping confirmed a value of 0.984, underscoring the model's excellent discriminatory power. The death risk nomogram exhibited robust predictive performance. The above validation findings indicate that the model has good fitting, high discriminability, and calibration. In clinical practice, it is essential to comprehensively consider patient data and clinical images, as these data and images contribute to improving disease analysis, diagnosis, prognosis prediction, and particularly assessing potential mortality after treatment. Discussion Due to the rapid onset, aggressive progression, high severity, and complex clinical manifestations of ATAAD, it is a severe and life-threatening cardiovascular disease [ 12 ] . Even with timely surgical intervention, patients with ATAAD still face a high risk of mortality during the perioperative period and prognosis, attributed to a range of complications and other factors [ 13 ] . According to data from the International Registry of Acute Aortic Dissection (IRAD), the in-hospital mortality rate during the perioperative period for patients with Stanford type A aortic dissection is 29.5% [ 14 ] . Therefore, analyzing the perioperative survival rate of patients with ATAAD and exploring the major risk factors influencing short-term and long-term prognosis have significant clinical implications for developing targeted treatment plans and interventions, reducing patient mortality, and improving prognosis. Currently, nomograms are widely used as prognostic tools in oncology and medicine. Nomograms rely on user-friendly digital interfaces to enhance accuracy and provide a more easily understandable prognosis, aiding in better clinical decision-making [ 15 ] . Our study is the first to apply the nomogram to predict postoperative mortality in patients with ATAAD. We developed and validated a novel predictive tool that uses only five easily accessible factors to estimate the risk of postoperative mortality in patients with ATAAD. By incorporating intraoperative deep hypothermic circulatory arrest time, postoperative day 1 platelet count, lactate level, activated partial thromboplastin time, and hydroxybutyrate dehydrogenase value into the nomogram, it facilitates individualized prediction of postoperative mortality in aortic dissection patients . This study provides a relatively accurate predictive tool for patients with ATAAD. Internal validation of the cohort demonstrated good discriminative ability and calibration. In particular, the high c-index in the interval validation suggests that the nomogram can be widely and accurately used due to its large sample size. Based on our developed predictive nomogram, for patients with higher scores after evaluation, it is recommended that postoperative respiratory therapists promptly assess the patient's breathing pattern, evaluate lung function, and work collaboratively with SICU doctors to adjust ventilator parameters and determine the timing of extubation. Radiologists should perform bedside chest X-rays and ultrasound examinations on postoperative patients daily to provide guidance for treatment and recovery. Additionally, SICU nurses should intensify their monitoring and provide targeted nursing care based on the established nursing goals. In AD, the rupture of the intimal layer of the aortic wall exposes the endothelial tissue, triggering a clotting reaction and stimulating exogenous coagulation function, leading to the consumption of a large amount of coagulation substances. Platelets play a crucial role in the clotting reaction, and a significant number of platelets are consumed during thrombus formation [ 16 – 18 ] . As a result, a lower postoperative platelet count indicates more severe inflammation and greater damage to the aortic wall, which in turn leads to a higher risk of postoperative bleeding and a poorer prognosis for patients. This statement is from a prospective study that included 183 patients [ 19 ] . The results revealed a significant negative correlation between postoperative platelet levels and in-hospital mortality in ATAAD patients. Patients with lower platelet levels exhibited a significantly higher mortality rate compared to those with higher levels, which is consistent with our research findings. APTT is primarily used to assess the intrinsic coagulation pathway within the clotting system. Previous studies have indicated significant fluctuations in levels of clotting-related substances such as thrombin, fibrinogen, and plasminogen activator in patients undergoing hypothermic circulatory arrest surgery [ 17 , 20 ] . In contrast to the extrinsic coagulation pathway's clotting factor FVII, FXII is more susceptible to inhibition from hypothermic circulatory arrest and exhibits a slower recovery rate after surgery. FXII is mainly responsible for the stability of blood clots [ 21 ] . Due to the extensive consumption of FVII during the preoperative thrombosis process in the false lumen, there is a shortage of its substrate. Therefore, postoperatively, FXII is activated and consumed in large quantities, playing a role in the formation of false lumen thrombus. High APTT levels in patients suggest inadequate compensation of the intrinsic coagulation pathway, leading to postoperative bleeding or thrombus formation. In addition, increased blood loss and transfusion of blood products increase the risk of hypoxemia and renal dysfunction, thereby affecting the prognosis. Therefore, platelet and APTT levels are independent factors for postoperative mortality in patients. In the surgical treatment of ATAAD, the technique of deep hypothermic circulatory arrest (DHCA) is employed. The basis of DHCA is that low temperature can reduce the release of neurotoxic substances and inhibit oxidative stress and calcium influx, thereby exerting a protective effect on the brain [ 22 , 23 ] . However, prolonged DHCA time may result in ischemia-reperfusion injury to the heart and brain, increasing the risk of postoperative cardiac and neurological complications, and consequently affecting survival rates [ 22 ] . According to our research, the DHCA time is positively correlated with postoperative mortality in patients. This may be due to the longer DHCA duration leading to more severe inflammatory reactions and organ damage, increasing the risk of postoperative multiorgan failure and complications, thereby increasing the risk of death. Additionally, the prolonged DHCA time may be associated with longer surgical duration, which can increase the risk of surgical trauma, bleeding, and surgery-related complications. In order to shorten the DHCA time, the double arterial cannulation(DAC) is commonly used in clinical practice. The DAC effectively reduces the DHCA time, thereby reducing secondary injuries to major organs including the brain [ 24 , 25 ] . Thus, even though the DAC can effectively reduce DHCA time and decrease the risk of postoperative mortality, the specific strategies for cannulation must be based on the individual patient's condition and the surgeon's proficiency level [ 26 ] . Lactate is a sensitive indicator representing microcirculation and early organ dysfunction [ 27 , 28 ] . High lactate levels are typically a result of ischemic and hypoxic conditions and are often associated with multiorgan dysfunction. In patients with ATAAD, the complex interplay from the underlying disease, surgery, and cardiopulmonary bypass can lead to poor early postoperative organ perfusion. Clinical symptoms may not be prominent, and objective indicators such as imaging studies may not provide the optimal assessment. In such cases, lactate serves as a relatively convenient and rapid surrogate marker for ischemia [ 29 ] . Our study revealed that lactate was the most reliable indicator of postoperative mortality. As a result, prompt identification of elevated lactate levels is imperative. For patients with elevated lactate levels, doctors can develop and adjust treatment plans specifically to reduce their risk of death. For example, increased monitoring, timely correction of fluid balance and renal function, improvement of blood perfusion and oxygenation, and prevention of the development of complications such as multiple organ dysfunction syndromes. HBDH, an enzyme found in myocardial cells, plays a crucial role in energy metabolism and is a sensitive indicator of myocardial injury. High levels of HBDH levels indicate the extent of myocardial cell damage potentially due to decreased cardiac pumping function. According to Lee et al [ 30 ] , increased HBDH levels are associated with higher in-hospital mortality in patients with non-ischemic dilated cardiomyopathy(NIDCM) and a greater risk of atherosclerotic events in those undergoing lower limb arterial interventions. Another research focusing on patients with NIDCM indicated that heightened alpha-HBDH levels demonstrated a high level of sensitivity and specificity in forecasting mortality, with an AUC of 0.810, suggesting a reliable predictive value for disease risk and prognosis [ 31 ] . These findings underscore the value of HBDH as a marker of myocardial injury severity and risk of mortality in cardiovascular disease, including ATAAD. Therefore, monitoring changes in HBDH levels can provide a more precise assessment of the degree of myocardial injury and multiorgan involvement in ATAAD, serving as a critical factor for predicting postoperative mortality. This understanding can inform future clinical strategies for managing patients with ATAAD. This study has some limitations that should be noted. Firstly, it is a retrospective study, which may introduce selection bias. Secondly, the results are based on a small number of patients, which may limit the generalizability of the findings to the broader population. Therefore, larger, prospective studies with more diverse patient populations are needed to provide more robust evidence in this area. Additionally, the study only looked at in-hospital mortality and did not include post-discharge mortality or long-term follow-up results. Future research should include long-term outcomes, adverse events, and reoperations to provide a more comprehensive understanding of the effectiveness of the results. A prospective, multicenter, large-scale study with long-term follow-up is necessary to evaluate the findings before they can be implemented in clinical practice. Conclusion This study established a new nomogram with relatively good accuracy for predicting in-hospital mortality in Chinese patients with ATAAD based on perioperative indicators. It aims to help clinicians identify ATAAD patients who may be at higher risk of in-hospital mortality through personalized risk assessment. The classification system developed in this study will assist clinicians in selecting personalized treatment strategies for ATAAD patients. To further validate these findings, future research will involve expanding the sample size and conducting a prospective cohort study. Declarations Ethics approval and consent to participate We solemnly make a statement that written informed consent were obtained from all subjects, and all participants were aware of the study purpose, risks and benefits. All participants provided written consent before participating in the study. The study was approved by the Ethics Committee of The First Affiliated Hospital of Zhejiang University of Medicine, with the ethical Approval Number: IIT20210395A Consent for publication Not applicable. Competing interests The authors declare no competing interests. Availability of data and materials The datasets used and/or analysed during the current study are available from the coresponding author on reasonable request. Funding No funding Author information Authors and Affiliations Department of Cardiovascular Surgery, The First Affiliated Hospital Zhejiang University of Medicine, Hangzhou, Zhejiang, China Chenxi Ying, Weidong Li, Ruoshi Chen, Zhedong Wan Contributions CX and YCX contributed to the concept and design. YCX and WZD contributed to the acquisition, analysis, and interpretation of data. YCX and CRS contributed to the drafting of the manuscript. CX and LWD had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analyses. All authors contributed to the critical revision of the manuscript for important intellectual content. All authors contributed to the article and approved the submitted version. Corresponding authors Correspondence to Xin Chen. References Morello, F., Santoro, M., Fargion, A. T., Grifoni, S. & Nazerian, P. Diagnosis and management of acute aortic syndromes in the emergency department. 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Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 10 Dec, 2024 Reviews received at journal 05 Dec, 2024 Reviews received at journal 27 Nov, 2024 Reviewers agreed at journal 26 Nov, 2024 Reviewers agreed at journal 26 Nov, 2024 Reviews received at journal 26 Nov, 2024 Reviewers agreed at journal 24 Nov, 2024 Reviewers agreed at journal 23 Nov, 2024 Editor invited by journal 13 Nov, 2024 Reviewers invited by journal 01 Jul, 2024 Editor assigned by journal 19 Jun, 2024 Submission checks completed at journal 19 Jun, 2024 First submitted to journal 31 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4509101","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322187635,"identity":"7cd9f6d9-3616-4fa9-96d8-5f5476ba5f01","order_by":0,"name":"Chenxi Ying","email":"","orcid":"","institution":"The First Affiliated Hospital Zhejiang University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chenxi","middleName":"","lastName":"Ying","suffix":""},{"id":322187636,"identity":"f71f68a5-011d-46ab-ab9c-a7f073751e84","order_by":1,"name":"Zhedong Wan","email":"","orcid":"","institution":"The First Affiliated Hospital Zhejiang University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhedong","middleName":"","lastName":"Wan","suffix":""},{"id":322187637,"identity":"3fd45834-b66b-418e-9fda-991bed0bbbd8","order_by":2,"name":"Ruoshi Chen","email":"","orcid":"","institution":"The First Affiliated Hospital Zhejiang University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ruoshi","middleName":"","lastName":"Chen","suffix":""},{"id":322187638,"identity":"f0e79ef6-62de-4894-8785-a6dcd00f14b5","order_by":3,"name":"Weidong Li","email":"","orcid":"","institution":"The First Affiliated Hospital Zhejiang University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Weidong","middleName":"","lastName":"Li","suffix":""},{"id":322187639,"identity":"cf961eab-3b6f-49de-b9e3-a13b16f3bb3a","order_by":4,"name":"Xin Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDACCRBhwMDDxt/Y+ICBB8RLIE6LHJ/E4WYDErQwMBjLMaS3QdkEtPDPbj72mKfgTmIbw8G2yh8yhxn42XMMGH7uwGPJnWPphjMMniW2MTe23ZDgOcwg2fPGgLH3DG4tBhI5ZhIfDA6DbblhANRicCPHgJmxDZ+W/G8SCWAtiW0FCUAt9oS15LCBbDFmA2phOACyRYKAFokbaWaSMwwOy7FJHGyWbOBJ55E486zgYC8eLfwzkp9J8/w5zCPf3/7w488eazn+9uSND37i0YIKGHsgkXmAWA1A8IMEtaNgFIyCUTBiAAAN0U5xQ3LTOAAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital Zhejiang University of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-05-31 12:54:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4509101/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4509101/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60530817,"identity":"5094c48d-e4c7-4d0e-9d60-1d84e5127a84","added_by":"auto","created_at":"2024-07-17 20:09:34","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":521822,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic and clinical feature selection using the LASSO binary logistic regression model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) The LASSO model's optimal parameter (lambda) was chosen through fivefold cross-validation using the minimum criteria\u003csup\u003e[8]\u003c/sup\u003e. A plot of the partial likelihood deviance (binomial deviance) versus log(lambda) was created, with dotted vertical lines indicating the optimal values based on the minimum criteria and the 1 SE of the minimum criteria (the 1-SE criteria).\u003c/p\u003e\n\u003cp\u003e(B) The LASSO was used to plot the coefficient profiles of the 26 features against a sequence of log(lambda) values. A vertical line was then drawn at the value determined through fivefold cross-validation, resulting in an optimal lambda and nine features with nonzero coefficients.\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4509101/v1/8cbe6efa33466e02ada340e4.jpeg"},{"id":60530056,"identity":"d7c6d057-2357-4333-a18c-17c796a5d6cc","added_by":"auto","created_at":"2024-07-17 20:01:34","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55000,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDeveloped ATAAD patients ‘death risk after surgery nomogram.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The ATAAD patients ‘death risk after surgery nomogram was developed in the cohort, with PLT, LA, APTT, HBDH, and DHCA incorporated.\u003c/p\u003e\n\u003cp\u003ePLT, platelet counts (1: 10.0~68.0; 2: 68.0~90.0; 3: 90~124.5; 4: 124.5~314.0 10E9/L);\u003c/p\u003e\n\u003cp\u003eLA, lactic acid (1: 0.4~1.8; 2: 1.8~2.8; 3: 2.8~4.4; 4: 4.4~29 mmol/L)\u003c/p\u003e\n\u003cp\u003eAPTT, activated partial thromboplastin time (1: 12.9~27.50; 2: 27.50~31.40; 3: 31.40~38.75; 4: 37.75~401.00 s)\u003c/p\u003e\n\u003cp\u003eHBDH, hydroxybutyrate dehydrogenase (1: 136.00~291.50; 2: 291.50~364.00; 3: 364.00~458.50; 4: 458.50~9445.00 U/L)\u003c/p\u003e\n\u003cp\u003eDHCA, deep hypothermic circulatory arrest time (1: 0~21; 2: 21~26; 3: 26~32; 4: 32~101 min)\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4509101/v1/f966d7155b2411263fea864d.jpeg"},{"id":60530055,"identity":"2fb12605-7502-4648-836c-fc88431346c0","added_by":"auto","created_at":"2024-07-17 20:01:34","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curves of the death nomogram prediction in the cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes: \u003c/strong\u003eThe x-axis depicts the predicted death risk, the y-axis shows the actual diagnosed death. The diagonal dotted line signifies a perfect prediction by an ideal model, whereas the solid line represents the performance of the nomogram. A closer fit to the diagonal dotted line indicates a better prediction.\u003c/p\u003e","description":"","filename":"Figure3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4509101/v1/8d3fc7cec0f9818927459a88.jpeg"},{"id":60530816,"identity":"faca8d06-f10c-4540-b4c3-654e1c31a525","added_by":"auto","created_at":"2024-07-17 20:09:34","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53787,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve, derived from the multivariate regression analysis of the test data, shows an AUC of 0.9836. This indicates that the prediction model demonstrates a high level of differentiation. The regression equation is logit= −17.036 + -1.056 × PLT + 2.964 × LA + 1.0162 × APTT + 0.773 × HBDH + 0.720 × DHCA.\u003c/p\u003e","description":"","filename":"Figure4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4509101/v1/f67979271b437b421e14de36.jpeg"},{"id":60530818,"identity":"14781fe0-fbb5-417f-8d24-490fb03c0afd","added_by":"auto","created_at":"2024-07-17 20:09:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1359688,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4509101/v1/1bd1bf33-4691-4e91-a95c-8965cb6a0704.pdf"},{"id":60530052,"identity":"67e5a65a-0266-4b28-ac8d-0cc2db03137d","added_by":"auto","created_at":"2024-07-17 20:01:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41104,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4509101/v1/02989634c0ff1e3added2053.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting the risk of postoperative death risk in Acute Type A Aortic Dissection: development and evaluation of a new predictive nomogram","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute type A aortic dissection (ATAAD) is a fatal cardiovascular disease, with a urgent onset, rapid progression, and high mortality. The main symptom is severe thoracic and back pain\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. When the extracellular matrix undergoes inflammation and degradation, it often results in damage to the integrity of the aorta, causing blood to enter the aortic media and form new blood spaces\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. With the dissection progresses, the aortic wall becomes more susceptible to rupture, resulting in severe bleeding. This can rapidly progress to hemorrhagic shock and multiple organ failure. Without treatment, individuals suffering from ATAAD face mortality rates of 33%, 50%, and 75% after 1, 2, and 7 days, respectively\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. However, even after undergoing emergency surgical repair treatment, certain patients with advanced age, a history of hypertension, previous interventions, chronic renal insufficiency, and other underlying diseases may still lead to severe postoperative arrhythmia, atelectasis, renal failure and a high risk of mortality. Therefore, it is essential to promptly identify and intervene for patients with ATAAD in order to enhance the survival rate and prognosis of individuals\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, and identifying patients with a higher risk index and providing enhanced post-operative care in the ICU is necessary for improving outcomes.\u003c/p\u003e \u003cp\u003eThe current methods for predicting postoperative mortality in patients with acute type A aortic dissection rely on scoring systems such as the American Association of Surgeons (ASA) grading system and the Cardiovascular Risk assessment (ACC/AHA) guidelines\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. These methods assess the patient's overall health status and other disease risk factors to estimate surgical risk. However, they may lack individualized features and have limited data sources, which can limit their accuracy. While these traditional methods can provide some risk assessment, they heavily rely on the patient's clinical characteristics and medical history, introducing subjectivity and uncertainty to the prediction results. Therefore, there is an urgent need to develop a new prediction method that addresses these limitations.\u003c/p\u003e \u003cp\u003eBy analyzing and modeling clinical data from patients undergoing surgery for ATAAD at our hospital, we have developed a new predictive nomogram that effectively predicts the risk of mortality after surgery. This nomogram takes into account factors such as age, gender, previous illnesses, myocardial enzymes, blood routine, and other indicators, as well as the potential for postoperative complications. Through a systematic analysis and assessment of these factors, we can make more precise predictions regarding the mortality risk for patients and establish suitable preventive and treatment measures. This study aims to offer a practical method for predicting the risk of postoperative mortality in Chinese patients with ATAAD, with the ultimate aim of providing clinicians with more accurate prediction results to evaluate postoperative risk and devise personalized treatment plans. Additionally, this nomogram will serve as a valuable tool for cardiac surgeons to enhance the clinical management and outcomes of patients.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eA retrospective analysis was conducted on 309 patients undergoing surgical treatment for ATAAD at The First Affiliated Hospital of Zhejiang University of Medicine, from January 2021 to January 2024.\u003c/p\u003e \u003cp\u003eInclusion criteria were as follows: (1) patients with symptoms and confirmed ATAAD based on symptoms and comprehensive computed tomography angiography (CTA); (2) patients with symptom onset within 14 days; (3) patients aged 18 years and above; (4) patients who underwent open chest surgery under cardiopulmonary bypass support.\u003c/p\u003e \u003cp\u003eExclusion criteria were as follows: (1) patients with missing important data; (2) patients who declined surgery or did not undergo surgical treatment for ruptured dissection preoperatively; (3) patients with rheumatic immune diseases, hematological disorders, malignancies, or severe trauma that could affect laboratory test results; (4) patients in a pregnant state; (5) patients undergoing repeat cardiac surgery; (6) patients on long-term use of glucocorticoids, immunosuppressants, or medications that could affect laboratory test results.\u003c/p\u003e \u003cp\u003eAll included patients underwent comprehensive imaging and laboratory examinations prior to surgery for a definite diagnosis. Based on the postoperative outcomes within 30 days, patients were categorized into the death group (n\u0026thinsp;=\u0026thinsp;44) and the survival group (n\u0026thinsp;=\u0026thinsp;265). The death group comprised 36 males and 8 females, with a mean age of (56.34\u0026thinsp;\u0026plusmn;\u0026thinsp;11.53) years, while the survival group comprised 201 males and 64 females, with a mean age of (52.39\u0026thinsp;\u0026plusmn;\u0026thinsp;12.25) years. This study has been approved by the Ethics Committee of the First Affiliated Hospital, Zhejiang University School of Medicine.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGeneral information\u003c/h2\u003e \u003cp\u003eThe clinical data of patients who underwent surgery at the Department of Cardiovascular Surgery, the First Affiliated Hospital of Zhejiang University School of Medicine from January 2021 to January 2024 was collected. All patients were diagnosed with ATAAD using preoperative CTA and color Doppler echocardiography, which had a sensitivity of 100% and specificity of 99%. Upon admission, blood samples were taken from all patients via the cubital vein for initial testing, to assess biochemical markers such as Brain natriuretic peptide, Troponin I, lactate dehydrogenase, hydroxybutyrate dehydrogenase, creatine kinase, creatine kinase isoenzymes, D-Dimer, creatinine, and glomerular filtration rate. The collection of data included clinical symptoms, past medical history, laboratory test results, CTA imaging findings, cardiac color Doppler ultrasound data, and perioperative information in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDemographic, disease, and treatment characteristics data were expressed as counts (%), and continuous variables such as blood biochemical indicators and intraoperative cardiopulmonary bypass time were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). We added a collection of preset prediction factors for perioperative variables, consisting of clinical characteristics and data(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Statistical analysis was performed using the R software 4.3.2. The least absolute shrinkage and selection operator (LASSO) method, which is appropriate for the reducing high dimensional data\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, was used to select the optimal predictive features in death risk factors from the patients with ATAAD. The LASSO regression model was used to select features with nonzero coefficients, which were then incorporated into a multivariable logistic regression analysis to build a predictive model\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The selected features were considered as odds ratio (OR) with a 95% confidence interval (CI) and a P-value. All statistical significance levels were two-sided. Sociodemographic variables with a P-value of 0.05 were included in the model, while variables associated with disease and treatment characteristics were all included\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Calibration and ROC curves were used to assess the calibration of the nomogram. A significant test statistic indicated that the model did not calibrate perfectly\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. To measure the discrimination performance of the nomogram, Harrell\u0026rsquo;s C-index was calculated. The nomogram underwent bootstrapping validation (1,000 bootstrap resamples) to calculate a relatively corrected C-index\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch3\u003ePatients\u0026rsquo; characteristics\u003c/h3\u003e\n\u003cp\u003eThis study included a total of 309 patients who underwent surgery for ATAAD. After surgery, the patients were divided into the survival group and the death group (251 males, 58 females; mean age 52.95\u0026plusmn;12.21 years [range 25-82 years]). All data, including demographic, disease, postoperative biochemical indicators, and treatment features of the two groups of patients（Table 1）.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDifferences between demographic and clinical characteristics of survival and death groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"607\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.07236842105263%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvival(n=265) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Death(n=44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e52.39\u0026plusmn;12.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e56.34\u0026plusmn;11.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCM(n%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003eaxillary\u0026nbsp;arterial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e201(75.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e36(81.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003eaxillary＆femoral arterial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e64(24.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e8(18.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender(n%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e214(80.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e37(84.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e51(19.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e7(15.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDebakey type(n%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n 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\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e18(6.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e8(18.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e247(93.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e36(81.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAR(n%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n 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width=\"24.835526315789473%\"\u003e\n \u003cp\u003e1098.80\u0026plusmn;2040.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTnI(ng/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e4.80\u0026plusmn;9.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e59.62\u0026plusmn;115.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBNP(pg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e443.32\u0026plusmn;742.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e1003.08\u0026plusmn;1570.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDH(U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e474.26\u0026plusmn;354.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e2599.39\u0026plusmn;2683.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBDH(U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e380.37\u0026plusmn;237.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e1655.95\u0026plusmn;1804.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCK(U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e1774.49\u0026plusmn;12182.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e8768.82\u0026plusmn;16636.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKMB(U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e45.70\u0026plusmn;97.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e272.16\u0026plusmn;456.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDHCA(min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e25.43\u0026plusmn;13.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e32.77\u0026plusmn;12.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCPB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e3:23:15.44\u0026plusmn;0:55:22.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e4:13:57.75\u0026plusmn;1:54:13.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.92763157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eACCT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.23684210526316%\"\u003e\n \u003cp\u003e2:25:13.24\u0026plusmn;0:46:53.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.835526315789473%\"\u003e\n \u003cp\u003e3:02:51.71\u0026plusmn;1:39:06.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eFeature selection\u003c/h3\u003e\n\u003cp\u003eOut of the 26 demographic, disease, and treatment characteristics, only 9 potential predictors with nonzero coefficients in the LASSO regression model were identified based on the cohort of 309 patients（~3:1 ratio ；Figure 1A and B). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese features included patients\u0026apos; preoperative intervention history, intraoperative cardiopulmonary bypass cannulation mode, deep hypothermic circulatory arrest time, postoperative platelet count, lactate level, D2 dimer, fibrinogen, activated partial thromboplastin time, and hydroxybutyrate dehydrogenase (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e \u003cstrong\u003ePredictors of death risk in patients undergoing aortic dissection surgery\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"594\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.20875420875421%\" colspan=\"4\"\u003e\n \u003cp\u003ePrediction model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.61616161616162%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.696969696969695%\"\u003e\n \u003cp\u003eOdds ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.68686868686869%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.410774410774412%\" colspan=\"3\"\u003e\n \u003cp\u003e-17.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.7979797979798%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCM(double)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.410774410774412%\" colspan=\"3\"\u003e\n \u003cp\u003e-1.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.7979797979798%\"\u003e\n \u003cp\u003e0.0459 - 1.3071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eIVH(Yes\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.410774410774412%\" colspan=\"3\"\u003e\n \u003cp\u003e1.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.7979797979798%\"\u003e\n \u003cp\u003e0.4231 - 89.6515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.410774410774412%\" colspan=\"3\"\u003e\n \u003cp\u003e-1.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.7979797979798%\"\u003e\n \u003cp\u003e0.0871 - 0.4779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eLA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.410774410774412%\" colspan=\"3\"\u003e\n \u003cp\u003e2.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.7979797979798%\"\u003e\n \u003cp\u003e4.9172 - 133.9899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e\u003cstrong\u003eD-2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.263069139966273%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.05564924114671%\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.69814502529511%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.69814502529511%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.84822934232715%\"\u003e\n \u003cp\u003e0.5908 - 3.9575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFIB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.263069139966273%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.05564924114671%\"\u003e\n \u003cp\u003e-0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.69814502529511%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.69814502529511%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.84822934232715%\"\u003e\n \u003cp\u003e0.3609 - 1.7364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPTT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.263069139966273%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.05564924114671%\"\u003e\n \u003cp\u003e1.0162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.69814502529511%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.69814502529511%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.84822934232715%\"\u003e\n \u003cp\u003e1.3171 - 6.7580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBDH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.263069139966273%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.05564924114671%\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.69814502529511%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.69814502529511%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.84822934232715%\"\u003e\n \u003cp\u003e1.2350 - 4.0904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDHCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.263069139966273%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.05564924114671%\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.69814502529511%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.69814502529511%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.84822934232715%\"\u003e\n \u003cp\u003e1.1347 - 4.0740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.478920741989882%\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;is the regression coefficient.\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003eDevelopment of death prediction model\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eTable 2 displays the 9 potential factor features that were identified through LASSO regression analysis. Significant factors, such as deep hypothermic circulatory arrest time, platelet count, lactate level, activated partial thromboplastin time, and hydroxybutyrate dehydrogenase value on the first postoperative day, were selected based on logistics regression analysis results. These factors were categorized into quartiles, and a predictive model was developed using these independent factors. The results were presented in a nomogram graph (Figure 2).\u003c/p\u003e\n\u003ch3\u003eApparent performance of the death risk nomogram in the cohort\u003c/h3\u003e\n\u003cp\u003eThe calibration curve of the death risk nomogram for ATAAD patients undergoing surgery indicated strong agreement in the cohort (Figure 3,4). With a C-index of 0.9787 in the cohort, further validation through bootstrapping confirmed a value of 0.984, underscoring the model\u0026apos;s excellent discriminatory power. The death risk nomogram exhibited robust predictive performance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe above validation findings indicate that the model has good fitting, high discriminability, and calibration. In clinical practice, it is essential to comprehensively consider patient data and clinical images, as these data and images contribute to improving disease analysis, diagnosis, prognosis prediction, and particularly assessing potential mortality after treatment.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDue to the rapid onset, aggressive progression, high severity, and complex clinical manifestations of ATAAD, it is a severe and life-threatening cardiovascular disease\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Even with timely surgical intervention, patients with ATAAD still face a high risk of mortality during the perioperative period and prognosis, attributed to a range of complications and other factors \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. According to data from the International Registry of Acute Aortic Dissection (IRAD), the in-hospital mortality rate during the perioperative period for patients with Stanford type A aortic dissection is 29.5%\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Therefore, analyzing the perioperative survival rate of patients with ATAAD and exploring the major risk factors influencing short-term and long-term prognosis have significant clinical implications for developing targeted treatment plans and interventions, reducing patient mortality, and improving prognosis.\u003c/p\u003e \u003cp\u003eCurrently, nomograms are widely used as prognostic tools in oncology and medicine. Nomograms rely on user-friendly digital interfaces to enhance accuracy and provide a more easily understandable prognosis, aiding in better clinical decision-making\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Our study is the first to apply the nomogram to predict postoperative mortality in patients with ATAAD. We developed and validated a novel predictive tool that uses only five easily accessible factors to estimate the risk of postoperative mortality in patients with ATAAD. By incorporating intraoperative deep hypothermic circulatory arrest time, postoperative day 1 platelet count, lactate level, activated partial thromboplastin time, and hydroxybutyrate dehydrogenase value into the nomogram, it facilitates individualized prediction of postoperative mortality in aortic dissection \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003epatients\u003c/span\u003e. This study provides a relatively accurate predictive tool for patients with ATAAD. Internal validation of the cohort demonstrated good discriminative ability and calibration. In particular, the high c-index in the interval validation suggests that the nomogram can be widely and accurately used due to its large sample size.\u003c/p\u003e \u003cp\u003eBased on our developed predictive nomogram, for patients with higher scores after evaluation, it is recommended that postoperative respiratory therapists promptly assess the patient's breathing pattern, evaluate lung function, and work collaboratively with SICU doctors to adjust ventilator parameters and determine the timing of extubation. Radiologists should perform bedside chest X-rays and ultrasound examinations on postoperative patients daily to provide guidance for treatment and recovery. Additionally, SICU nurses should intensify their monitoring and provide targeted nursing care based on the established nursing goals.\u003c/p\u003e \u003cp\u003eIn AD, the rupture of the intimal layer of the aortic wall exposes the endothelial tissue, triggering a clotting reaction and stimulating exogenous coagulation function, leading to the consumption of a large amount of coagulation substances. Platelets play a crucial role in the clotting reaction, and a significant number of platelets are consumed during thrombus formation\u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. As a result, a lower postoperative platelet count indicates more severe inflammation and greater damage to the aortic wall, which in turn leads to a higher risk of postoperative bleeding and a poorer prognosis for patients. This statement is from a prospective study that included 183 patients\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The results revealed a significant negative correlation between postoperative platelet levels and in-hospital mortality in ATAAD patients. Patients with lower platelet levels exhibited a significantly higher mortality rate compared to those with higher levels, which is consistent with our research findings.\u003c/p\u003e \u003cp\u003eAPTT is primarily used to assess the intrinsic coagulation pathway within the clotting system. Previous studies have indicated significant fluctuations in levels of clotting-related substances such as thrombin, fibrinogen, and plasminogen activator in patients undergoing hypothermic circulatory arrest surgery\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. In contrast to the extrinsic coagulation pathway's clotting factor FVII, FXII is more susceptible to inhibition from hypothermic circulatory arrest and exhibits a slower recovery rate after surgery. FXII is mainly responsible for the stability of blood clots\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Due to the extensive consumption of FVII during the preoperative thrombosis process in the false lumen, there is a shortage of its substrate. Therefore, postoperatively, FXII is activated and consumed in large quantities, playing a role in the formation of false lumen thrombus. High APTT levels in patients suggest inadequate compensation of the intrinsic coagulation pathway, leading to postoperative bleeding or thrombus formation. In addition, increased blood loss and transfusion of blood products increase the risk of hypoxemia and renal dysfunction, thereby affecting the prognosis. Therefore, platelet and APTT levels are independent factors for postoperative mortality in patients.\u003c/p\u003e \u003cp\u003eIn the surgical treatment of ATAAD, the technique of deep hypothermic circulatory arrest (DHCA) is employed. The basis of DHCA is that low temperature can reduce the release of neurotoxic substances and inhibit oxidative stress and calcium influx, thereby exerting a protective effect on the brain\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. However, prolonged DHCA time may result in ischemia-reperfusion injury to the heart and brain, increasing the risk of postoperative cardiac and neurological complications, and consequently affecting survival rates\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. According to our research, the DHCA time is positively correlated with postoperative mortality in patients. This may be due to the longer DHCA duration leading to more severe inflammatory reactions and organ damage, increasing the risk of postoperative multiorgan failure and complications, thereby increasing the risk of death. Additionally, the prolonged DHCA time may be associated with longer surgical duration, which can increase the risk of surgical trauma, bleeding, and surgery-related complications. In order to shorten the DHCA time, the double arterial cannulation(DAC) is commonly used in clinical practice. The DAC effectively reduces the DHCA time, thereby reducing secondary injuries to major organs including the brain\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Thus, even though the DAC can effectively reduce DHCA time and decrease the risk of postoperative mortality, the specific strategies for cannulation must be based on the individual patient's condition and the surgeon's proficiency level\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLactate is a sensitive indicator representing microcirculation and early organ dysfunction\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. High lactate levels are typically a result of ischemic and hypoxic conditions and are often associated with multiorgan dysfunction. In patients with ATAAD, the complex interplay from the underlying disease, surgery, and cardiopulmonary bypass can lead to poor early postoperative organ perfusion. Clinical symptoms may not be prominent, and objective indicators such as imaging studies may not provide the optimal assessment. In such cases, lactate serves as a relatively convenient and rapid surrogate marker for ischemia\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Our study revealed that lactate was the most reliable indicator of postoperative mortality. As a result, prompt identification of elevated lactate levels is imperative. For patients with elevated lactate levels, doctors can develop and adjust treatment plans specifically to reduce their risk of death. For example, increased monitoring, timely correction of fluid balance and renal function, improvement of blood perfusion and oxygenation, and prevention of the development of complications such as multiple organ dysfunction syndromes.\u003c/p\u003e \u003cp\u003eHBDH, an enzyme found in myocardial cells, plays a crucial role in energy metabolism and is a sensitive indicator of myocardial injury. High levels of HBDH levels indicate the extent of myocardial cell damage potentially due to decreased cardiac pumping function. According to Lee et al\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, increased HBDH levels are associated with higher in-hospital mortality in patients with non-ischemic dilated cardiomyopathy(NIDCM) and a greater risk of atherosclerotic events in those undergoing lower limb arterial interventions. Another research focusing on patients with NIDCM indicated that heightened alpha-HBDH levels demonstrated a high level of sensitivity and specificity in forecasting mortality, with an AUC of 0.810, suggesting a reliable predictive value for disease risk and prognosis\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. These findings underscore the value of HBDH as a marker of myocardial injury severity and risk of mortality in cardiovascular disease, including ATAAD. Therefore, monitoring changes in HBDH levels can provide a more precise assessment of the degree of myocardial injury and multiorgan involvement in ATAAD, serving as a critical factor for predicting postoperative mortality. This understanding can inform future clinical strategies for managing patients with ATAAD.\u003c/p\u003e \u003cp\u003eThis study has some limitations that should be noted. Firstly, it is a retrospective study, which may introduce selection bias. Secondly, the results are based on a small number of patients, which may limit the generalizability of the findings to the broader population. Therefore, larger, prospective studies with more diverse patient populations are needed to provide more robust evidence in this area. Additionally, the study only looked at in-hospital mortality and did not include post-discharge mortality or long-term follow-up results. Future research should include long-term outcomes, adverse events, and reoperations to provide a more comprehensive understanding of the effectiveness of the results. A prospective, multicenter, large-scale study with long-term follow-up is necessary to evaluate the findings before they can be implemented in clinical practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study established a new nomogram with relatively good accuracy for predicting in-hospital mortality in Chinese patients with ATAAD based on perioperative indicators. It aims to help clinicians identify ATAAD patients who may be at higher risk of in-hospital mortality through personalized risk assessment. The classification system developed in this study will assist clinicians in selecting personalized treatment strategies for ATAAD patients. To further validate these findings, future research will involve expanding the sample size and conducting a prospective cohort study.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eWe solemnly make a statement that written informed consent were obtained from all subjects, and all participants were aware of the study purpose, risks and benefits. All participants provided written consent before participating in the study. The study was approved by the Ethics Committee of The First Affiliated Hospital of Zhejiang University of Medicine, with the ethical Approval Number: IIT20210395A\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the coresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo funding\u003c/p\u003e\n\u003cp\u003eAuthor information\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eDepartment of Cardiovascular Surgery, The First Affiliated Hospital Zhejiang University of Medicine, Hangzhou, Zhejiang, China\u003c/p\u003e\n\u003cp\u003eChenxi Ying,\u0026nbsp;Weidong Li, Ruoshi Chen, Zhedong Wan\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003eCX and YCX contributed to the concept and design. YCX and WZD contributed to the acquisition, analysis, and interpretation of data. YCX and CRS contributed to the drafting of the manuscript. CX and LWD had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analyses. All authors contributed to the critical revision of the manuscript for important intellectual content. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003eCorresponding authors\u003c/p\u003e\n\u003cp\u003eCorrespondence to Xin Chen.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMorello, F., Santoro, M., Fargion, A. T., Grifoni, S. \u0026amp; Nazerian, P. Diagnosis and management of acute aortic syndromes in the emergency department. Intern Emerg Med 16, 171-181, doi:10.1007/s11739-020-02354-8 (2021).\u003c/li\u003e\n\u003cli\u003eNakashima, Y. \u0026amp; Sueishi, K. 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Insights From the International Registry of Acute Aortic Dissection: A 20-Year Experience of Collaborative Clinical Research. Circulation 137, 1846-1860, doi:10.1161/CIRCULATIONAHA.117.031264 (2018).\u003c/li\u003e\n\u003cli\u003eWei, L. et al. Beliefs about medicines and non-adherence in patients with stroke, diabetes mellitus and rheumatoid arthritis: a cross-sectional study in China. BMJ Open 7, e017293, doi:10.1136/bmjopen-2017-017293 (2017).\u003c/li\u003e\n\u003cli\u003eBalduini, C. L. et al. Activation of the hemostatic process in patients with unruptured aortic aneurysm before and in the first week after surgical repair. Haematologica 82, 581-583 (1997).\u003c/li\u003e\n\u003cli\u003ePaparella, D. et al. Hemostasis alterations in patients with acute aortic dissection. Ann Thorac Surg 91, 1364-1369, doi:10.1016/j.athoracsur.2011.01.058 (2011).\u003c/li\u003e\n\u003cli\u003eten Cate, J. W., Timmers, H. \u0026amp; Becker, A. E. Coagulopathy in Ruptured or Dissecting Aortic Aneurysms. Am J Med 59, 171-176, doi:10.1016/0002-9343(75)90351-4 (1975).\u003c/li\u003e\n\u003cli\u003eBaigent, C. et al. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet 366, 1267-1278, doi:10.1016/S0140-6736(05)67394-1 (2005).\u003c/li\u003e\n\u003cli\u003eGuan, X., Li, J., Gong, M., Lan, F. \u0026amp; Zhang, H. The hemostatic disturbance in patients with acute aortic dissection: A prospective observational study. Medicine (Baltimore) 95, e4710, doi:10.1097/MD.0000000000004710 (2016).\u003c/li\u003e\n\u003cli\u003eKuijpers, M. J. et al. Factor XII regulates the pathological process of thrombus formation on ruptured plaques. Arterioscler Thromb Vasc Biol 34, 1674-1680, doi:10.1161/ATVBAHA.114.303315 (2014).\u003c/li\u003e\n\u003cli\u003ePatel, H. J. et al. Open arch reconstruction in the endovascular era: analysis of 721 patients over 17 years. J Thorac Cardiovasc Surg 141, 1417-1423, doi:10.1016/j.jtcvs.2011.02.020 (2011).\u003c/li\u003e\n\u003cli\u003eMontisci, A. et al. Cerebral Perfusion and Neuromonitoring during Complex Aortic Arch Surgery: A Narrative Review. J Clin Med 12, doi:10.3390/jcm12103470 (2023).\u003c/li\u003e\n\u003cli\u003eZhang, H. et al. Double arterial cannulation versus right axillary artery cannulation for acute type A aortic dissection: a retrospective study. J Cardiothorac Surg 16, 326, doi:10.1186/s13019-021-01714-5 (2021).\u003c/li\u003e\n\u003cli\u003eGhoreishi, M. et al. Factors associated with acute stroke after type A aortic dissection repair: An analysis of the Society of Thoracic Surgeons National Adult Cardiac Surgery Database. J Thorac Cardiovasc Surg 159, 2143-2154 e2143, doi:10.1016/j.jtcvs.2019.06.016 (2020).\u003c/li\u003e\n\u003cli\u003eGokalp, O. et al. Comparison of Femoral and Axillary Artery Cannulation in Acute Type A Aortic Dissection Surgery. Braz J Cardiovasc Surg 35, 28-33, doi:10.21470/1678-9741-2018-0354 (2020).\u003c/li\u003e\n\u003cli\u003eGrimm, J. C. et al. Differential outcomes of type A dissection with malperfusion according to affected organ system. Ann Cardiothorac Surg 5, 202-208, doi:10.21037/acs.2016.03.11 (2016).\u003c/li\u003e\n\u003cli\u003eYang, Y., Xue, J., Li, H., Tong, J. \u0026amp; Jin, M. Perioperative risk factors predict one-year mortality in patients with acute type-A aortic dissection. J Cardiothorac Surg 15, 249, doi:10.1186/s13019-020-01296-8 (2020).\u003c/li\u003e\n\u003cli\u003eLi, H. W. et al. Reconsidering the Impact of Pre-Operative Malperfusion on Acute Type A Dissection: The Modified Penn Classification. J Am Coll Cardiol 67, 121-122, doi:10.1016/j.jacc.2015.09.102 (2016).\u003c/li\u003e\n\u003cli\u003eLee, S., Koppensteiner, R., Kopp, C. W. \u0026amp; Gremmel, T. alpha-Hydroxybutyrate dehydrogenase is associated with atherothrombotic events following infrainguinal angioplasty and stenting. Sci Rep 9, 18200, doi:10.1038/s41598-019-54899-0 (2019).\u003c/li\u003e\n\u003cli\u003eLiu, Z. et al. Elevated alpha-hydroxybutyrate dehydrogenase as an independent prognostic factor for mortality in hospitalized patients with COVID-19. ESC Heart Fail 8, 644-651, doi:10.1002/ehf2.13151 (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ATAAD, postoperative hospital death, risk prediction, nomogram, prediction model","lastPublishedDoi":"10.21203/rs.3.rs-4509101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4509101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcute type A aortic dissection (ATAAD) is an emergency condition characterized by severe chest pain and back pain, with rapid disease progression and a very high mortality rate. The current methods for predicting postoperative mortality rate in acute type A aortic dissection are inadequate, necessitating the urgent need for new prediction methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a retrospective analysis of 309 patients with ATAAD in The First Affiliated Hospital Zhejiang University of Medicine. By utilizing the LASSO and logistic regression analysis, we have developed a novel predictive model for postoperative mortality rate. The model incorporates factors such as platelet count (PLT), lactic acid (LA), hydroxybutyrate dehydrogenase (HBDH) , activated partial thromboplastin time (APTT) , deep hypothermic circulatory arrest (DHCA) time to predict the risk of mortality in patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predictive nomogram included predictors such as PLT, LA, HBDH, APTT, and DHCA time. With a C-index of 0.9787, the model demonstrated good discrimination power, calibration, and ROC curve. It was able to maintain a high C-index value of 0.984 even during interval verification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have developed and validated a novel predictive model for assessing postoperative mortality risk in Chinese ATAAD patients. This predictive tool demonstrates good discriminatory ability and calibration, which can assist clinicians in making more accurate risk assessments and devising personalized treatment plans.\u003c/p\u003e","manuscriptTitle":"Predicting the risk of postoperative death risk in Acute Type A Aortic Dissection: development and evaluation of a new predictive nomogram","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-17 20:01:29","doi":"10.21203/rs.3.rs-4509101/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-12-11T00:53:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-05T22:18:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-27T07:31:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81556350127911505630370249659582852857","date":"2024-11-27T01:08:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273691965692148483705281697314659668156","date":"2024-11-27T01:06:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-26T19:14:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83910007650622573210281745315074670002","date":"2024-11-24T17:20:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241457306878659155329906651718743376343","date":"2024-11-24T03:38:07+00:00","index":"hide","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-13T14:21:25+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-01T11:08:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-19T17:37:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-19T11:34:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2024-05-31T12:52:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1462e9a8-6332-433c-a6ce-3b027b48d9b8","owner":[],"postedDate":"July 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-17T20:01:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-17 20:01:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4509101","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4509101","identity":"rs-4509101","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}