Development and validation of a nomogram to predict acute aortic dissection in sudden chest pain patients

Development and validation of a nomogram to predict acute aortic dissection in sudden chest pain patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and validation of a nomogram to predict acute aortic dissection in sudden chest pain patients Shuangshuang Li, Pengcheng Du, Qingsheng Lu, Jian Dong, Jian Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7388708/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The aim of this study was to construct a model by combining routine laboratory biomarkers and clinical characteristics to distinguish acute aortic dissection (AAD) patients from other sudden chest pain patients with AMI, APE and AAA. Methods and Results: Qualified patients were randomly divided into training and validation cohorts. Independent predictive factors for differentiating AAD were filtered out via backward stepwise logistic regression. A nomogram containing the included factors was constructed. The discrimination and calibration abilities were verified via receiver operating characteristic (ROC) curves and calibration curves. The clinical use of the nomogram was evaluated via DCA. A total of 860 eligible patients were randomly allocated to the training (602) and validation (258) cohorts. The WBC count, Baso%, NLR, age, DD and alcohol status were established as independent factors for patients with AAD after multiple logistic regression analysis. A nomogram was constructed. The AUC values were 0.775 (0.733--0.817) and 0.709 (0.637--0.781) for the training and validation cohorts, respectively. The Hosmer–Lemeshow test revealed no significant difference (P>0.05), indicating that the nomogram was reliable. DCA showed favorable clinical benefit. Conclusion. This study constructed a prediction model for AAD. Validation revealed excellent discrimination and calibration, indicating that the nomograms may provide clinical reference information and increase the diagnostic efficiency of AAD. Acute aortic dissection Biomarker Diagnosis Nomogram Training cohort Validation cohort Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction AAD is a life-threatening clinical conundrum with a mortality risk of 1–2% per hour immediately without prompt diagnosis and treatment. 1 , 2 Moreover, the reported misdiagnosis rate of AAD is as high as 40%. 3 However, the diagnosis of AAD on the basis of clinical symptoms is challenging, as the disease may have varied clinical presentations and similar symptoms to several other diseases, especially acute myocardial infarction (AMI), acute pulmonary embolism (APE), and acute abdominal aortic aneurysm (AAA). Currently, imaging is recognized as the gold standard for suspicion of AAD, which is costly, time-consuming, and prone to radiation and anaphylaxis. 4 Moreover, remote and impoverished areas lack corresponding imaging equipment. Therefore, timely diagnostic strategies are needed to help physicians discriminate AAD patients and avoid excessive imaging. The contributory role of biomarkers in AAD diagnosis has been widely discussed. The application of biomarkers, such as D-dimer, calponin, and soluble ST2, represents alternative strategies for risk stratification of AAD patients. At present, only D-dimer is used in clinical practice, as recommended by the European Society of Cardiology guidelines. 5 However, single biomarkers do not perform well, as many studies have reported low sensitivity and specificity. 6 , 7 However, their clinical applicability in the diagnostic evaluation of aortic dissection remains to be determined. In this context, combining multiple different indicators may be a useful way to further improve the diagnostic accuracy of AADs compared with the use of a single indicator. Owing to the lack of simple, sensitive insights, this method can provide clinicians with an optimal diagnosis of AAD. The development of a diagnostic model that incorporates factors associated with aortic dissection in the clinic could be a powerful weapon. To accelerate the diagnosis of AAD, we measured plasma concentrations of biomarkers combined with clinical presentation and risk factors in AAD patients, healthy participants and patients with other acute chest pain diseases (e.g., AMI, APE, AAA). We established the first nomogram for the diagnostic evaluation of the multi-index linkage of aortic dissection, which can provide personalized, evidence-based, and highly accurate risk estimates. This study may provide clinicians with simple, sensitive insights into the optimal diagnosis of AAD. Biomarker-aided diagnosis of AAD in the clinic may be a powerful weapon and is incorporated into several well-validated risk prediction algorithms. Methods Study population. This study included patients with AAD, APE, AMI, and AAA in Changhai Hospital between September 1, 2016, and December 31, 2020. Data regarding patient characteristics were collected retrospectively from medical records during the patient’s hospital stay, confirmed by the study physicians, and recorded in the database. The study was approved by the Institutional Ethics Committee of Changhai Hospital. After screening, patients were randomly divided into training (70%) cohorts, which were used to develop the scoring system to discriminate AAD patients from patients with APE, AMI and AAA, and validation (30%) cohorts, which were used to validate the diagnostic performance of this scoring system. The patient recruitment process is detailed in Fig. 1 . All diagnoses were confirmed on the basis of the patient's symptoms and imaging findings. The inclusion criteria were (a) patients with AAD, APE, AMI or AAA whose onset time at the hospital was less than 14 days (in the acute stage) and (b) whose medical history and examination data were complete. Data collection Demographics, clinical syndrome data and laboratory test data, such as sex, age, pain area, cholesterol, triglyceride, HDL, LDL, BG, WBC, Mono, NEUT, Eo, Baso, Eo%, Baso%, NUET, D-dimer, PT, FIB, FDP, APTT, and TT data, were collected in a standard Excel form. Blood draws were drawn in the ward on admission but before surgery for surgical patients and during the hospital stay for healthy individuals. Statistical analysis We randomly divided all the participants into training (70%) and validation (30%) cohorts. The training cohort was used to develop a nomogram and perform internal validation, and the validation cohort was used to perform external validation. The data are presented as the means (SDs) for continuous variables. Categorical variables are expressed as percentages and were compared via the chi-square test. Univariate logistic regression analysis was used to identify predictive factors at a significant level (P < 0.1). Variables deemed significant were further screened by backward stepwise multivariate logistic analysis to determine the predictive model. The odds ratio (OR) and associated 95% confidence interval (CI) were also calculated. 8 A nomogram was constructed using independent predictive factors in the training set data. 9 To improve the visualization and clinical application value of the nomogram, if the screened laboratory test indicators were continuous variables, then we transformed them into categorical variables according to the clinical threshold. To validate the performance of the model, discrimination and calibration power were calculated via receiver operating characteristic (ROC) curves and the Hosmer–Lemeshow test. 10 The clinical use of the nomogram was evaluated via decision curve analysis (DCA). 11 SPSS 26.0 and Stata 15.0 were used for the statistical analyses. A two­tailed P value < 0.05 was considered statistically significant. Results Baseline Characteristics. Finally, this study included 860 eligible patients. There were 602 patients allocated to the training cohort and 258 patients allocated to the validation cohort. Among them, 518 (60.2%) were diagnosed with aortic dissection, and 658 (76.5%) of the patients were male. In addition to demographic characteristics, risk factors, symptoms, and broad hematologic indicators, including coagulation and inflammatory markers, of the training and validation cohorts are summarized in Table 1 .(at the end of the document text file.) Table 1 Patient demographic characteristics and laboratory test results Variable ALL Training cohort Validation cohort N 860 602 258 Age (≤ 55) 292(34%) 88(14.6%) 204(79.1%) Sex(Male) 658(76.5%) 468(77.7%) 190(73.6%) Aortic dissection 518(60.2%) 361(60%) 157(60.9%) Alcohol(Yes) 125(14.5%) 88(14.6%) 37(14.3%) Smoke(Yes) 265(30.8%) 185(30.7%) 80(31%) Laceration pain 182(21.2%) 132(21.9%) 50(19.3%) Chest/back pain 361(42%) 262(43.5%) 100(38.7%) Hypertension 650(75.6%) 352(58.5%) 138(53.5%) Cholesterol 4.41 ± 0.96 4.38 ± 0.91 4.47 ± 0.97 LDL 2.61 ± 0.74 2.57 ± 0.71 2.66 ± 0.74 NEUT% 70.21 ± 15.92 71.46 ± 16.3 71.6 ± 14.92 Lymp 1.37 ± 0.73 1.36 ± 0.84 1.38 ± 0.73 NLR 65.83 ± 46.29 70.42 ± 53.78 64.44 ± 40.66 PLT 194.34 ± 74.48 189.13 ± 76.30 193.79 ± 71.52 Triglyceride 1.36 ± 0.68 1.35 ± 0.68 1.4 ± 0.62 HDL 1.17 ± 0.42 1.17 ± 0.4 1.16 ± 0.36 BG 6.67 ± 2.89 6.72 ± 2.9 6.57 ± 2.16 WBC 8.88 ± 4.73 9.24 ± 4.86 9.36 ± 4.9 MONO 0.64 ± 0.64 0.66 ± 0.72 0.65 ± 0.48 NEUT 7.32 ± 13.85 7.67 ± 14.42 7.34 ± 5.69 Eo 0.12 ± 0.18 0.13 ± 0.42 0.15 ± 0.17 Baso 0.02 ± 0.03 0.02 ± 0.05 0.02 ± 0.05 Eo% 1.21 ± 1.66 2.24 ± 2.43 0.12 ± 0.19 Baso% 0.5 ± 3.35 0.51 ± 3.54 0.03 ± 0.05 DD 6.18 ± 56.64 4.55 ± 4.26 10.02 ± 91.52 PT 14.19 ± 2.55 14.49 ± 4.61 14.07 ± 1.73 FIB 4.26 ± 2.93 4.2 ± 3.03 4.2 ± 1.77 FDP 14.64 ± 20.23 16.12 ± 20.02 15.84 ± 20.82 APTT 41.13 ± 16.84 41.36 ± 16.97 41.73 ± 17.86 TT 19.14 ± 19.69 19.28 ± 18.70 20.42 ± 23.28 INR 1.10 ± 0.27 1.14 ± 0.65 1.09 ± 0.17 BNP 197.44 ± 266.40 191.54 ± 208.49 220.86 ± 335.25 Nomogram construction. Univariate analyses revealed that 15 factors were statistically significant (P < 0.1). Independent variables were screened via backward stepwise regression. The WBC count, Baso%, NLR, age, DD, and alcohol status were established as independent risk factors for patients with aortic dissection during hospitalization. The risk of aortic dissection was 0.133-fold greater in patients with abnormal WBCs (95% CI = 1.044–1.23), 0.012-fold greater in patients with abnormal NLRs (95% CI = 1.005–1.019), 0.116-fold greater in patients with abnormal DD (95% CI = 1.042–1.195), and 0.877-fold greater in drinking patients (95% CI = 1.029–3.422). The protective factors for AAD were baso% and age {OR and 95% CI: 0.378 (0.142–1.007) and 0.316 (0.186–0.537)} (Table 2 ). (at the end of the document text file.)A nomogram was constructed for estimating the risk of aortic dissection in patients with sudden chest pain. To facilitate visualization and application of the nomogram, we transformed the four continuous variables into categorical variables according to clinical thresholds. (Fig. 2 ) Table 2 Independent influencing factors for aortic dissection according to univariate and multivariate logistic analyses. Univariate analysis Multivariate analysis OR 95%CI P OR 95%CI P WBC 1.248 1.179–1.322 0.000 1.133 1.044–1.23 0.003 Baso% 0.071 0.034–0.148 0.000 0.378 0.142–1.007 0.05 EO% 0.698 0.631–0.773 0.000 0.943 0.823–1.080 0.396 NLR 1.020 1.014–1.026 0.000 1.012 1.005–1.019 0.001 NUET% 1.044 1.031–1.057 0.000 0.984 0.962–1.006 0.159 Age 0.345 0.235–0.506 0.000 0.316 0.186–0.537 0.001 DD 1.147 1.091–1.205 0.000 1.116 1.042–1.195 0.002 BG 1.260 1.136–1.398 0.000 0.013 0.925–1.110 0.775 FDP 1.021 1.009–1.034 0.001 0.995 0.975–1.014 0.592 Alcohol 2.072 1.312–3.274 0.002 1.877 1.029–3.422 0.04 Lymp 0.764 0.607–0.963 0.022 1.370 0.847–2.214 0.199 NEUT 1.044 1.003–1.088 0.037 0.994 0.973–1.017 0.620 Mono 1.508 0.990–2.296 0.056 1.162 0859-1.573 0.331 LDL 0.813 0.643–1.028 0.084 0.912 0.696–1.195 0.502 Cholesterol 0.865 0.720–1.039 0.121 NA NA NA Baso 0.010 0.000-4.485 0.139 NA NA NA FIB 1.070 0.974–1.174 0.157 NA NA NA HDL 1.326 0.825–2.132 0.244 NA NA NA Triglyceride 0.908 0.715–1.152 0.426 NA NA NA Eo% 0.838 0.539–1.303 0.432 NA NA NA PT 1.019 0.971–1.070 0.443 NA NA NA APTT 0.997 0.987–1.006 0.518 NA NA NA TT 0.997 0.989–1.006 0.551 NA NA NA INR 1.088 0.792–1.493 0.603 NA NA NA Sex 1.085 0.714–1.651 0.702 NA NA NA BNP 1.000 0.999–1.001 0.788 NA NA NA PLT 1.000 0.998–1.002 0.843 NA NA NA Smoke 0.999 0.698–1.429 0.994 NA NA NA Nomogram validation. Discrimination ability was estimated via receiver operating characteristic (ROC) analysis. The AUC values were 0.775 (0.733–0.817) and 0.709 (0.637–0.781) for the training and validation cohorts, respectively. The best cutoff for the nomogram in the training cohort was 0.437, and the sensitivity and specificity were 62% and 84%, respectively. In the validation cohort, the corresponding cutoff was 0.376, and the sensitivity and specificity were 60% and 82%, respectively. (Fig. 3 ) The Hosmer–Lemeshow test revealed no significant difference (training cohort: χ 2 = 14.91, P = 0.061; validation cohort: χ 2 = 12.43, P = 0.257), indicating that the nomogram was reliable. (Fig. 4 ) Decision curve analysis (DCA) was performed to evaluate the predictive model from the perspective of clinical consequences. When the score is within the range of 0–0.9, the use of the nomogram to predict AAD may result in a net benefit. (Fig. 5) Discussion This study established a risk prediction model for acute aortic dissection and aortic aneurysm, myocardial infarction, and pulmonary infarction. The prediction model included 51 items, mainly hematological tests. The model has good predictive ability in both the training queue (AUC: 0.775) and the external validation queue (AUC: 0.709). In addition, a nomogram was constructed on the basis of the above predictors to individually predict the incidence of AAD. At present, the diagnosis of acute aortic dissection is mainly based on clinical symptoms, which are highly suspected and are subsequently confirmed by imaging. However, the results of autopsies revealed that 40% of the patients were misdiagnosed or miss diagnosed, 3 which clearly indicates that the needs of clinical emergencies are difficult to meet. The lack of clinical experience or even accurate imaging equipment for such diseases has made the situation particularly acute in late-night emergency cases in remote areas of western China. Biomarkers have been widely studied in the cardiovascular field because they are widely available, easy to use, highly sensitive and specific. However, the ability of a single biomarker to be effectively and truly applied to the clinical diagnosis of aortic dissection is still difficult. For example, smooth muscle myosin heavy chain has high sensitivity and specificity for acute aortic dissection, but its half-life is very short, which means that patients have to visit the hospital to detect smooth muscle myosin heavy chain once they are attacked or that a short detection window may be missed. D-dimer has good sensitivity, but its specificity is only 46.6% − 68.6%. 12–14 Our former studies on biomarkers also revealed that miR-23a has good sensitivity and specificity; the diagnostic accuracy in our studies reached 92.5%, 15 but it still needs to be verified in a larger sample. The optimization of the detection of its RNA and the time-shorting during the RT‒PCR test would allow miR-23a to be widely used in clinical diagnosis; therefore, we constructed the first combined biomarker array for the diagnosis of aortic dissection. Aortic dissection is an age dependent, life-threatening cardiovascular disease, and age has been identified as a clearly associated risk factor. 16 Several large, long-term studies of the characteristics of aortic dissection in Eastern and Western countries have also revealed that the population is generally older, although the age of onset is lower in East China than in West China. 17 – 19 Increasing evidence indicates that the involvement of inflammatory mechanisms in the process of arterial wall remodeling plays a key role in the development and progression of aortic dissection. 20 – 22 In addition, changes in systemic inflammatory biomarkers are associated with the acute phase response to aortic dissection. 22 In our study, we found significant differences in several important inflammatory cells. Nonspecific inflammatory markers such as white blood cells have been proposed as diagnostic biomarkers for AAD in previous studies. 23 , 24 Guo Z et al. and Zhou J. et al. reported that the WBC count is associated with preoperative hypoxemia in patients with aortic dissection. 25 , 26 In addition, a growing number of studies suggest that it is an independent risk factor associated with the prognosis of type A AAD. 27 , The NLR 28 , another marker that is significantly associated with systemic inflammation, has good value in the prognostic assessment of AAD but also shows good advantages in diagnosis. 29 , 30 Zhou et al. included 606 AD patients, 202 aortic aneurysm patients and 140 healthy people as controls. A receiver operating characteristic (ROC) curve was generated to evaluate the diagnostic value of the NLR and FIB in TBAD patients and showed good specificity. 31 Zhang Hx et al. compared AD with other acute chest pain diseases and reported that the NLR had good predictive and clinical effects on AD. 32 Guo-Ping Shi et al. reported that eosinophils protect mice from angiotensin-II perfusion-induced abdominal aortic aneurysm and that increased blood EOS counts in AAA patients serve as independent risk factors for human AAA. 33 However, the relationship between eosinophils and aortic dissection needs further study in the future. DD is a fibrin degradation product present in the circulation following fibrinolysis of the thrombus. Weber et al. were the first to prospectively assess DD levels in patients with AAD relative to other patients with acute cardiogenic chest pain and reported that DD levels are of good diagnostic value. 34 However, owing to its good negative likelihood ratio, an increasing number of studies have shown that negative results may be helpful for excluding acute aortic dissection in low-risk patients. 13 , 35 , 36 The European Society of Cardiology guidelines, updated in 2014, introduced the use of D-dimer as a secondary filter in patients with low aortic dissection detection risk scores (ADD-RSs) (0–1). 37 The relationship between alcohol and aortic dissection deserves scientific attention. Excessive alcohol consumption may be the most common cause of arterial hypertension, 38 – 40 and severe and poorly controlled arterial hypertension induced by alcohol may lead to AD genesis. 41 – 43 Excessive alcohol consumption may also lead to AD by inducing holiday heart syndrome (most commonly atrial fibrillation) with typically elevated heart rates. 44 , 45 Therefore, studies on the etiological link between alcohol and AD are of clinical value. Different biomarkers may have different sensitivities to different subtypes of AAD, and our study incorporates well-performing variables into the model, which also provides insights into patient status and inflammation. One advantage of our nomogram is that it is built from readily available blood indicators and clinical information and is well validated in both training sets and external validation sets. Thus, it can be used in resource-limited settings, where clinicians may still have all the data they need to use effectively. In addition, decision curve analysis revealed that the nomogram has good net benefit. Therefore, in our study, a diagnostic model of acute aortic dissection with a grade pattern was built via consultation and physical examination to obtain symptoms and signs quickly in combination with routine blood indicators that can be quickly obtained. It would be helpful to identify a patient with chest pain as acute aortic dissection or others beside it to obtain more accurate imaging examinations, thus improving the efficiency of diagnosis. Deficiency . The main limitation of our research is that it was a single-center retrospective study. The sample size was one of the limitations, even though a certain number of patients and control model samples were reviewed by us, and cooperation with other centers aimed to further verify the diagnostic model of acute aortic dissection. Some favorable indicators reported in previous studies were abandoned to make the indicators in this diagnostic model easy to obtain because the purpose of our study was simple, rapid and accurate. Abbreviations AAD Acute aortic dissection; AMI Acute myocardial infarction; APE Acute pulmonary embolism; AAA abdominal aortic aneurysm; BP Blood pressure; WBC White blood cell count; DD D-dimer; HDL High-density lipoprotein; LDL Low-density lipoprotein; NEUT Neutrophil count; NEUT% Neutrophil ratio; PLT Platelet; BG Blood glucose; MONO Monocyte count; EO Eosinophil count; Eo% Eosinophil ratio; Baso Basophil count; Baso% Basophil ratio; APTT Activation of partial thromboplastin time; PT Prothrombin time; FIB Fibrinogen; FDP Fibrinogen degradation product; TT Thrombin time; INR International normalized ratio; NLR Neutrophil lymphocyte ratio; Lymp Lymphocyte count; CRP C-reactive protein; BNP Brain natriuretic peptide; DCA Decision curve analysis ; AUC Area under curve ; ROC Receiver operating characteristic. Declarations Author Contributions SS L, PC D and J D conceived and wrote the main manuscript; SS L and PC D carried out the data collection and statistical analysis; SS L, PC D and QS Lprepared the figures and tables; J Z and D J designed the study and revised the manuscript. All the authors read and approved the final manuscript. Acknowledgments The authors thank the staff of the medical records management unit and colleagues who helped review the records. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. Funding. This work was supported by the National Natural Science Foundation of China [82170500]; the Special application and plan for basic medical research [2021JCSQ01]; and the Research project of the Shanghai Municipal Health Commission [20224Y0351]. 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Liu CL, Liu X, Zhang Y, Liu J, Yang C, Luo S, Liu T, Wang Y, Lindholt JS, Diederichsen A, Rasmussen LM, Dahl M, Sukhova GK, Lu G, Upchurch GR, Libby P, Guo J, Zhang J, Shi GP. Eosinophils Protect Mice From Angiotensin-II Perfusion-Induced Abdominal Aortic Aneurysm. Circ Res. 2021; 128(2):188–202. Weber T, Högler S, Auer J, Berent R, Lassnig E, Kvas E, Eber B. D-dimer in acute aortic dissection. Chest. 2003;123(5):1375–1378. Asha S E, Miers J W. A Systematic Review and Meta-analysis of D-dimer as a Rule-out Test for Suspected Acute Aortic Dissection. Ann Emerg Med. 2015;66(4): 368–378. Sodeck G, Domanovits H, Schillinger M, Ehrlich MP, Endler G, Herkner H, Laggner A. D-dimer in ruling out acute aortic dissection: a systematic review and prospective cohort study. Eur Heart J. 2007;28(24):3067–3075. Suzuki T, Eagle KA. Biomarker-Assisted Diagnosis of Acute Aortic Dissection. Circulation. 2018;137(3):270–272. Tomson J, Lip GY. Alcohol and hypertension: an old relationship revisited. Alcohol & Alcoholism. 2006;41(1):3–4. Stewart SH, Oroszi G, Randall PK, Anton RF. COMT genotype influences the effect of alcohol on blood pressure: results from the COMBINE study. Am J Hypertens. 2009; 22(1):87–91. Klatsky A L, Gunderson E. Alcohol and hypertension: a review. Journal of the American Society of Hypertension. 2008;2(5):307–317. Mancia G, De Backer G, Dominiczak A, Cifkova R, Fagard R, Germano G, Grassi G, Heagerty AM, Kjeldsen SE, Laurent S, Narkiewicz K, Ruilope L, Rynkiewicz A, Schmieder RE, Struijker Boudier HA, Zanchetti A, Vahanian A, Camm J, De Caterina R, Dean V, Dickstein K, Filippatos G, Funck-Brentano C, Hellemans I, Kristensen SD, McGregor K, Sechtem U, Silber S, Tendera M, Widimsky P, Zamorano JL, Kjeldsen SE, Erdine S, Narkiewicz K, Kiowski W, Agabiti-Rosei E, Ambrosioni E, Cifkova R, Dominiczak A, Fagard R, Heagerty AM, Laurent S, Lindholm LH, Mancia G, Manolis A, Nilsson PM, Redon J, Schmieder RE, Struijker 2007 Guidelines for the management of arterial hypertension: The Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Eur Heart J . 2007;28(12):1462–1536. Schirpenbach C, Reincke M. Epidemiology and etiology of therapy-resistant hypertension. Der Internist. 2009;50(1):7–16.. Goran KP. Pheochromocytomas in aortic dissection patients: have they been missing or missed? Am J Emerg Med. 2008;26(5):626–627.. Kodama K, Nishigami K, Sakamoto T, Sawamura T, Hirayama T, Misumi H, Nakao K. Tight heart rate control reduces secondary adverse events in patients with type B acute aortic dissection. Circulation. 2008;118(14_suppl_1): S167-S170. Koracevic G, Djordjevic D, Glasnovic J. Is significance of atrial fibrillation in acute aortic dissection underestimated? J Emerg Med. 2009;37(2):168–170. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7388708","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507302370,"identity":"81e1b64a-653d-4738-bd93-dd2e8ee4175c","order_by":0,"name":"Shuangshuang Li","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Shuangshuang","middleName":"","lastName":"Li","suffix":""},{"id":507302371,"identity":"5b2df597-5f75-4f5e-9926-255d18007c25","order_by":1,"name":"Pengcheng Du","email":"","orcid":"","institution":"the First Affiliated Hospital of Naval Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Pengcheng","middleName":"","lastName":"Du","suffix":""},{"id":507302372,"identity":"6bb1f3a7-968c-4128-bb53-b6b22ec8f04c","order_by":2,"name":"Qingsheng Lu","email":"","orcid":"","institution":"the First Affiliated Hospital of Naval Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qingsheng","middleName":"","lastName":"Lu","suffix":""},{"id":507302373,"identity":"6df34c8b-cdb5-4b03-81b3-33b8ac7bc6e0","order_by":3,"name":"Jian Dong","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Dong","suffix":""},{"id":507302374,"identity":"3a8cfd2c-18d2-4924-9919-b3db74363dae","order_by":4,"name":"Jian Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3PMQuCQBTA8ScHN0muLwj9CjdJU5/FQ3CqEFqEhIRCh2gPCvoKja0SXIvtQot9g9waGlJcmk7bgu4Pb3jwfsMDUKl+MayHAVAgaeEE4RekB9RlRSY6kjoTdLt/X5F2Ye2WAof+aEoB7IBHFIxk7UiJthceInNnFfFyfhoAZtejlBAc2xUhPLYKkfOMAsOJnNCGLHgMWuzzmLQTvSHnihAKnQii5w6RXSpCCTqZ0Ft/sbZuesPXnB8ioyyfQWgayUZO6gh+LHrreZ326HSmUqlUf9sbdfk9t/EO+AAAAAAASUVORK5CYII=","orcid":"","institution":"the Third Affiliated Hospital of the Naval Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-08-16 16:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7388708/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7388708/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90813542,"identity":"2d49d669-6261-4733-8318-7c8d8cb8fd19","added_by":"auto","created_at":"2025-09-08 12:34:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23125,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of screening study cohort\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7388708/v1/c8fa763eca4632090800fba7.png"},{"id":90812297,"identity":"b91db667-f07b-4562-9bb2-0b3e2b8bd639","added_by":"auto","created_at":"2025-09-08 12:18:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8296,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram predicts the probability of aortic dissection in patients with acute chest pain. When using the nomogram, drawing a vertical line from each variable upward to the terms and then recording the corresponding points. The point of each variable was then summed up to obtain a total score that corresponds to a predicted probability of AAD at the bottom of the nomogram. Codes annotation: for alcohol, 0 means that the patient does not drink alcohol, 1 means that the patient has. for age，1means less than 55 years old, 2 means more than 55 years old；for D-dimer，0 means normal, 1means abnormal (＞0.5mg/L); NLR 0≤40，1：40-80，2≥80; for Basophil ratio, 0 means normal, 1means abnormal (＞1mg/L), WBC 0：≤4，1: 4-10，2：＞10.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7388708/v1/9d51dc2f9b17a5a7dbaac862.png"},{"id":90812291,"identity":"f3aba86a-fb04-4ff6-8653-39427925291e","added_by":"auto","created_at":"2025-09-08 12:18:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":18837,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of the predictive model in the training and validation cohort. Area under the ROC curve (blue line) shows the predictive ability of the models.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7388708/v1/c8340fe159b5e98be482d434.png"},{"id":90812292,"identity":"8d57846f-c724-4d04-9e43-089c469b09c5","added_by":"auto","created_at":"2025-09-08 12:18:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18233,"visible":true,"origin":"","legend":"\u003cp\u003eThe calibration curve (training set: A; validation set: B) for the training and validation cohort.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7388708/v1/4496f00cf83e9d67a80bf77f.png"},{"id":93754214,"identity":"51fd12c0-31ad-45da-8f7d-2a549bd1a2bc","added_by":"auto","created_at":"2025-10-17 08:24:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":897314,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7388708/v1/c2d379c7-54c6-4409-8815-44ab71a7b676.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDevelopment and validation of a nomogram to predict acute aortic dissection in sudden chest pain patients\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAAD is a life-threatening clinical conundrum with a mortality risk of 1\u0026ndash;2% per hour immediately without prompt diagnosis and treatment.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Moreover, the reported misdiagnosis rate of AAD is as high as 40%.\u003csup\u003e3\u003c/sup\u003e However, the diagnosis of AAD on the basis of clinical symptoms is challenging, as the disease may have varied clinical presentations and similar symptoms to several other diseases, especially acute myocardial infarction (AMI), acute pulmonary embolism (APE), and acute abdominal aortic aneurysm (AAA). Currently, imaging is recognized as the gold standard for suspicion of AAD, which is costly, time-consuming, and prone to radiation and anaphylaxis.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Moreover, remote and impoverished areas lack corresponding imaging equipment. Therefore, timely diagnostic strategies are needed to help physicians discriminate AAD patients and avoid excessive imaging.\u003c/p\u003e\u003cp\u003eThe contributory role of biomarkers in AAD diagnosis has been widely discussed. The application of biomarkers, such as D-dimer, calponin, and soluble ST2, represents alternative strategies for risk stratification of AAD patients. At present, only D-dimer is used in clinical practice, as recommended by the European Society of Cardiology guidelines.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e However, single biomarkers do not perform well, as many studies have reported low sensitivity and specificity.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e However, their clinical applicability in the diagnostic evaluation of aortic dissection remains to be determined. In this context, combining multiple different indicators may be a useful way to further improve the diagnostic accuracy of AADs compared with the use of a single indicator.\u003c/p\u003e\u003cp\u003eOwing to the lack of simple, sensitive insights, this method can provide clinicians with an optimal diagnosis of AAD. The development of a diagnostic model that incorporates factors associated with aortic dissection in the clinic could be a powerful weapon.\u003c/p\u003e\u003cp\u003eTo accelerate the diagnosis of AAD, we measured plasma concentrations of biomarkers combined with clinical presentation and risk factors in AAD patients, healthy participants and patients with other acute chest pain diseases (e.g., AMI, APE, AAA). We established the first nomogram for the diagnostic evaluation of the multi-index linkage of aortic dissection, which can provide personalized, evidence-based, and highly accurate risk estimates. This study may provide clinicians with simple, sensitive insights into the optimal diagnosis of AAD. Biomarker-aided diagnosis of AAD in the clinic may be a powerful weapon and is incorporated into several well-validated risk prediction algorithms.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy population.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study included patients with AAD, APE, AMI, and AAA in Changhai Hospital between September 1, 2016, and December 31, 2020. Data regarding patient characteristics were collected retrospectively from medical records during the patient\u0026rsquo;s hospital stay, confirmed by the study physicians, and recorded in the database. The study was approved by the Institutional Ethics Committee of Changhai Hospital. After screening, patients were randomly divided into training (70%) cohorts, which were used to develop the scoring system to discriminate AAD patients from patients with APE, AMI and AAA, and validation (30%) cohorts, which were used to validate the diagnostic performance of this scoring system. The patient recruitment process is detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll diagnoses were confirmed on the basis of the patient's symptoms and imaging findings. The inclusion criteria were (a) patients with AAD, APE, AMI or AAA whose onset time at the hospital was less than 14 days (in the acute stage) and (b) whose medical history and examination data were complete.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData collection\u003c/h2\u003e\u003cp\u003eDemographics, clinical syndrome data and laboratory test data, such as sex, age, pain area, cholesterol, triglyceride, HDL, LDL, BG, WBC, Mono, NEUT, Eo, Baso, Eo%, Baso%, NUET, D-dimer, PT, FIB, FDP, APTT, and TT data, were collected in a standard Excel form. Blood draws were drawn in the ward on admission but before surgery for surgical patients and during the hospital stay for healthy individuals.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eWe randomly divided all the participants into training (70%) and validation (30%) cohorts. The training cohort was used to develop a nomogram and perform internal validation, and the validation cohort was used to perform external validation. The data are presented as the means (SDs) for continuous variables. Categorical variables are expressed as percentages and were compared via the chi-square test.\u003c/p\u003e\u003cp\u003eUnivariate logistic regression analysis was used to identify predictive factors at a significant level (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Variables deemed significant were further screened by backward stepwise multivariate logistic analysis to determine the predictive model. The odds ratio (OR) and associated 95% confidence interval (CI) were also calculated.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eA nomogram was constructed using independent predictive factors in the training set data.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e To improve the visualization and clinical application value of the nomogram, if the screened laboratory test indicators were continuous variables, then we transformed them into categorical variables according to the clinical threshold.\u003c/p\u003e\u003cp\u003eTo validate the performance of the model, discrimination and calibration power were calculated via receiver operating characteristic (ROC) curves and the Hosmer\u0026ndash;Lemeshow test.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e The clinical use of the nomogram was evaluated via decision curve analysis (DCA).\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e SPSS 26.0 and Stata 15.0 were used for the statistical analyses. A two\u0026shy;tailed \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline Characteristics.\u003c/b\u003e Finally, this study included 860 eligible patients. There were 602 patients allocated to the training cohort and 258 patients allocated to the validation cohort. Among them, 518 (60.2%) were diagnosed with aortic dissection, and 658 (76.5%) of the patients were male. In addition to demographic characteristics, risk factors, symptoms, and broad hematologic indicators, including coagulation and inflammatory markers, of the training and validation cohorts are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.(at the end of the document text file.)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient demographic characteristics and laboratory test results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTraining cohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eValidation cohort\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e258\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (\u0026le;\u0026thinsp;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e292(34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88(14.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e204(79.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex(Male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e658(76.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e468(77.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e190(73.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAortic dissection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e518(60.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e361(60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e157(60.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol(Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125(14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88(14.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37(14.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke(Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e265(30.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e185(30.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80(31%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaceration pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e182(21.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132(21.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50(19.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChest/back pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e361(42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e262(43.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100(38.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e650(75.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e352(58.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138(53.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEUT%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.21\u0026thinsp;\u0026plusmn;\u0026thinsp;15.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.46\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.83\u0026thinsp;\u0026plusmn;\u0026thinsp;46.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.42\u0026thinsp;\u0026plusmn;\u0026thinsp;53.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.44\u0026thinsp;\u0026plusmn;\u0026thinsp;40.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194.34\u0026thinsp;\u0026plusmn;\u0026thinsp;74.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e189.13\u0026thinsp;\u0026plusmn;\u0026thinsp;76.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e193.79\u0026thinsp;\u0026plusmn;\u0026thinsp;71.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglyceride\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.72\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.24\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.36\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMONO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEUT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.32\u0026thinsp;\u0026plusmn;\u0026thinsp;13.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.67\u0026thinsp;\u0026plusmn;\u0026thinsp;14.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.34\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaso\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEo%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaso%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.18\u0026thinsp;\u0026plusmn;\u0026thinsp;56.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.02\u0026thinsp;\u0026plusmn;\u0026thinsp;91.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.49\u0026thinsp;\u0026plusmn;\u0026thinsp;4.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.64\u0026thinsp;\u0026plusmn;\u0026thinsp;20.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.12\u0026thinsp;\u0026plusmn;\u0026thinsp;20.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.84\u0026thinsp;\u0026plusmn;\u0026thinsp;20.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.13\u0026thinsp;\u0026plusmn;\u0026thinsp;16.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.36\u0026thinsp;\u0026plusmn;\u0026thinsp;16.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.73\u0026thinsp;\u0026plusmn;\u0026thinsp;17.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.14\u0026thinsp;\u0026plusmn;\u0026thinsp;19.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.28\u0026thinsp;\u0026plusmn;\u0026thinsp;18.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.42\u0026thinsp;\u0026plusmn;\u0026thinsp;23.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBNP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e197.44\u0026thinsp;\u0026plusmn;\u0026thinsp;266.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e191.54\u0026thinsp;\u0026plusmn;\u0026thinsp;208.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e220.86\u0026thinsp;\u0026plusmn;\u0026thinsp;335.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eNomogram construction.\u003c/b\u003e Univariate analyses revealed that 15 factors were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Independent variables were screened via backward stepwise regression. The WBC count, Baso%, NLR, age, DD, and alcohol status were established as independent risk factors for patients with aortic dissection during hospitalization. The risk of aortic dissection was 0.133-fold greater in patients with abnormal WBCs (95% CI\u0026thinsp;=\u0026thinsp;1.044\u0026ndash;1.23), 0.012-fold greater in patients with abnormal NLRs (95% CI\u0026thinsp;=\u0026thinsp;1.005\u0026ndash;1.019), 0.116-fold greater in patients with abnormal DD (95% CI\u0026thinsp;=\u0026thinsp;1.042\u0026ndash;1.195), and 0.877-fold greater in drinking patients (95% CI\u0026thinsp;=\u0026thinsp;1.029\u0026ndash;3.422). The protective factors for AAD were baso% and age {OR and 95% CI: 0.378 (0.142\u0026ndash;1.007) and 0.316 (0.186\u0026ndash;0.537)} (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). (at the end of the document text file.)A nomogram was constructed for estimating the risk of aortic dissection in patients with sudden chest pain. To facilitate visualization and application of the nomogram, we transformed the four continuous variables into categorical variables according to clinical thresholds. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndependent influencing factors for aortic dissection according to univariate and multivariate logistic analyses.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.179\u0026ndash;1.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.044\u0026ndash;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaso%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.034\u0026ndash;0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.142\u0026ndash;1.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEO%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.631\u0026ndash;0.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.823\u0026ndash;1.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.014\u0026ndash;1.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.005\u0026ndash;1.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNUET%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.031\u0026ndash;1.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.962\u0026ndash;1.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.235\u0026ndash;0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.186\u0026ndash;0.537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.091\u0026ndash;1.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.042\u0026ndash;1.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.136\u0026ndash;1.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.925\u0026ndash;1.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.009\u0026ndash;1.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.975\u0026ndash;1.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.312\u0026ndash;3.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.029\u0026ndash;3.422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.607\u0026ndash;0.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.847\u0026ndash;2.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEUT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.003\u0026ndash;1.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.973\u0026ndash;1.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMono\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.990\u0026ndash;2.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0859-1.573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.643\u0026ndash;1.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.696\u0026ndash;1.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.720\u0026ndash;1.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaso\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000-4.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.974\u0026ndash;1.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.825\u0026ndash;2.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglyceride\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.715\u0026ndash;1.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEo%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.539\u0026ndash;1.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.971\u0026ndash;1.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.987\u0026ndash;1.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.989\u0026ndash;1.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.792\u0026ndash;1.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.714\u0026ndash;1.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBNP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.999\u0026ndash;1.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.998\u0026ndash;1.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.698\u0026ndash;1.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eNomogram validation.\u003c/b\u003e Discrimination ability was estimated via receiver operating characteristic (ROC) analysis. The AUC values were 0.775 (0.733\u0026ndash;0.817) and 0.709 (0.637\u0026ndash;0.781) for the training and validation cohorts, respectively. The best cutoff for the nomogram in the training cohort was 0.437, and the sensitivity and specificity were 62% and 84%, respectively. In the validation cohort, the corresponding cutoff was 0.376, and the sensitivity and specificity were 60% and 82%, respectively. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Hosmer\u0026ndash;Lemeshow test revealed no significant difference (training cohort: χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;14.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.061; validation cohort: χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;12.43, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.257), indicating that the nomogram was reliable. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDecision curve analysis (DCA) was performed to evaluate the predictive model from the perspective of clinical consequences. When the score is within the range of 0\u0026ndash;0.9, the use of the nomogram to predict AAD may result in a net benefit. (Fig.\u0026nbsp;5)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study established a risk prediction model for acute aortic dissection and aortic aneurysm, myocardial infarction, and pulmonary infarction. The prediction model included 51 items, mainly hematological tests. The model has good predictive ability in both the training queue (AUC: 0.775) and the external validation queue (AUC: 0.709). In addition, a nomogram was constructed on the basis of the above predictors to individually predict the incidence of AAD.\u003c/p\u003e\u003cp\u003eAt present, the diagnosis of acute aortic dissection is mainly based on clinical symptoms, which are highly suspected and are subsequently confirmed by imaging. However, the results of autopsies revealed that 40% of the patients were misdiagnosed or miss diagnosed,\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e which clearly indicates that the needs of clinical emergencies are difficult to meet. The lack of clinical experience or even accurate imaging equipment for such diseases has made the situation particularly acute in late-night emergency cases in remote areas of western China.\u003c/p\u003e\u003cp\u003eBiomarkers have been widely studied in the cardiovascular field because they are widely available, easy to use, highly sensitive and specific. However, the ability of a single biomarker to be effectively and truly applied to the clinical diagnosis of aortic dissection is still difficult. For example, smooth muscle myosin heavy chain has high sensitivity and specificity for acute aortic dissection, but its half-life is very short, which means that patients have to visit the hospital to detect smooth muscle myosin heavy chain once they are attacked or that a short detection window may be missed. D-dimer has good sensitivity, but its specificity is only 46.6% \u0026minus;\u0026thinsp;68.6%.\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e Our former studies on biomarkers also revealed that miR-23a has good sensitivity and specificity; the diagnostic accuracy in our studies reached 92.5%,\u003csup\u003e15\u003c/sup\u003e but it still needs to be verified in a larger sample. The optimization of the detection of its RNA and the time-shorting during the RT‒PCR test would allow miR-23a to be widely used in clinical diagnosis; therefore, we constructed the first combined biomarker array for the diagnosis of aortic dissection.\u003c/p\u003e\u003cp\u003eAortic dissection is an age dependent, life-threatening cardiovascular disease, and age has been identified as a clearly associated risk factor.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Several large, long-term studies of the characteristics of aortic dissection in Eastern and Western countries have also revealed that the population is generally older, although the age of onset is lower in East China than in West China.\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIncreasing evidence indicates that the involvement of inflammatory mechanisms in the process of arterial wall remodeling plays a key role in the development and progression of aortic dissection.\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e In addition, changes in systemic inflammatory biomarkers are associated with the acute phase response to aortic dissection.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e In our study, we found significant differences in several important inflammatory cells. Nonspecific inflammatory markers such as white blood cells have been proposed as diagnostic biomarkers for AAD in previous studies.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Guo Z et al. and Zhou J. et al. reported that the WBC count is associated with preoperative hypoxemia in patients with aortic dissection.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e In addition, a growing number of studies suggest that it is an independent risk factor associated with the prognosis of type A AAD. \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, The NLR \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, another marker that is significantly associated with systemic inflammation, has good value in the prognostic assessment of AAD but also shows good advantages in diagnosis.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Zhou et al. included 606 AD patients, 202 aortic aneurysm patients and 140 healthy people as controls. A receiver operating characteristic (ROC) curve was generated to evaluate the diagnostic value of the NLR and FIB in TBAD patients and showed good specificity.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Zhang Hx et al. compared AD with other acute chest pain diseases and reported that the NLR had good predictive and clinical effects on AD.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Guo-Ping Shi et al. reported that eosinophils protect mice from angiotensin-II perfusion-induced abdominal aortic aneurysm and that increased blood EOS counts in AAA patients serve as independent risk factors for human AAA.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e However, the relationship between eosinophils and aortic dissection needs further study in the future.\u003c/p\u003e\u003cp\u003eDD is a fibrin degradation product present in the circulation following fibrinolysis of the thrombus. Weber et al. were the first to prospectively assess DD levels in patients with AAD relative to other patients with acute cardiogenic chest pain and reported that DD levels are of good diagnostic value.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e However, owing to its good negative likelihood ratio, an increasing number of studies have shown that negative results may be helpful for excluding acute aortic dissection in low-risk patients.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e The European Society of Cardiology guidelines, updated in 2014, introduced the use of D-dimer as a secondary filter in patients with low aortic dissection detection risk scores (ADD-RSs) (0\u0026ndash;1).\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe relationship between alcohol and aortic dissection deserves scientific attention. Excessive alcohol consumption may be the most common cause of arterial hypertension,\u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and severe and poorly controlled arterial hypertension induced by alcohol may lead to AD genesis.\u003csup\u003e\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e Excessive alcohol consumption may also lead to AD by inducing holiday heart syndrome (most commonly atrial fibrillation) with typically elevated heart rates.\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e Therefore, studies on the etiological link between alcohol and AD are of clinical value.\u003c/p\u003e\u003cp\u003eDifferent biomarkers may have different sensitivities to different subtypes of AAD, and our study incorporates well-performing variables into the model, which also provides insights into patient status and inflammation. One advantage of our nomogram is that it is built from readily available blood indicators and clinical information and is well validated in both training sets and external validation sets. Thus, it can be used in resource-limited settings, where clinicians may still have all the data they need to use effectively. In addition, decision curve analysis revealed that the nomogram has good net benefit.\u003c/p\u003e\u003cp\u003eTherefore, in our study, a diagnostic model of acute aortic dissection with a grade pattern was built via consultation and physical examination to obtain symptoms and signs quickly in combination with routine blood indicators that can be quickly obtained. It would be helpful to identify a patient with chest pain as acute aortic dissection or others beside it to obtain more accurate imaging examinations, thus improving the efficiency of diagnosis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDeficiency\u003c/b\u003e. The main limitation of our research is that it was a single-center retrospective study. The sample size was one of the limitations, even though a certain number of patients and control model samples were reviewed by us, and cooperation with other centers aimed to further verify the diagnostic model of acute aortic dissection. Some favorable indicators reported in previous studies were abandoned to make the indicators in this diagnostic model easy to obtain because the purpose of our study was simple, rapid and accurate.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAAD\u0026nbsp;\u003c/strong\u003eAcute aortic dissection; \u003cstrong\u003eAMI\u003c/strong\u003e Acute myocardial infarction; \u003cstrong\u003eAPE\u0026nbsp;\u003c/strong\u003eAcute pulmonary embolism; \u003cstrong\u003eAAA\u003c/strong\u003e abdominal aortic aneurysm; \u003cstrong\u003eBP\u003c/strong\u003e Blood pressure; \u003cstrong\u003eWBC\u003c/strong\u003e White blood cell count; \u003cstrong\u003eDD\u003c/strong\u003e D-dimer; \u003cstrong\u003eHDL\u003c/strong\u003e High-density lipoprotein; \u003cstrong\u003eLDL\u003c/strong\u003e Low-density lipoprotein; \u003cstrong\u003eNEUT\u003c/strong\u003e Neutrophil count; \u003cstrong\u003eNEUT%\u003c/strong\u003e Neutrophil ratio; \u003cstrong\u003ePLT\u003c/strong\u003e Platelet; \u003cstrong\u003eBG\u003c/strong\u003e Blood glucose; \u003cstrong\u003eMONO\u003c/strong\u003e Monocyte count; \u003cstrong\u003eEO\u003c/strong\u003e Eosinophil count; \u003cstrong\u003eEo%\u003c/strong\u003e Eosinophil ratio; \u003cstrong\u003eBaso\u003c/strong\u003e Basophil count;\u003cstrong\u003e\u0026nbsp;Baso%\u0026nbsp;\u003c/strong\u003eBasophil ratio;\u003cstrong\u003e\u0026nbsp;APTT\u003c/strong\u003e Activation of partial thromboplastin time; \u003cstrong\u003ePT\u003c/strong\u003e Prothrombin time;\u003cstrong\u003e\u0026nbsp;FIB\u003c/strong\u003e Fibrinogen;\u003cstrong\u003e\u0026nbsp;FDP\u003c/strong\u003e Fibrinogen degradation product; \u003cstrong\u003eTT\u003c/strong\u003e Thrombin time; \u003cstrong\u003eINR\u003c/strong\u003e International normalized ratio; \u003cstrong\u003eNLR\u0026nbsp;\u003c/strong\u003eNeutrophil lymphocyte ratio; \u003cstrong\u003eLymp\u003c/strong\u003e Lymphocyte count; \u003cstrong\u003eCRP\u003c/strong\u003e C-reactive protein; \u003cstrong\u003eBNP\u0026nbsp;\u003c/strong\u003eBrain natriuretic peptide; \u003cstrong\u003eDCA\u003c/strong\u003e Decision curve analysis\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAUC\u0026nbsp;\u003c/strong\u003eArea under curve\u003cstrong\u003e; ROC\u0026nbsp;\u003c/strong\u003eReceiver operating characteristic.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSS L, PC D and J D conceived and wrote the main manuscript; SS L and PC D carried out the data collection and statistical analysis; SS L, PC D and QS Lprepared the figures and tables; J Z and D J designed the study and revised the manuscript. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the staff of the medical records management unit and colleagues who helped review the records.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [82170500]; the Special application and plan for basic medical research [2021JCSQ01]; and the Research project of the Shanghai Municipal Health Commission [20224Y0351].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHagan PG, Nienaber CA, Isselbacher EM, Bruckman D, Karavite DJ, Russman PL, Evangelista A, Fattori R, Suzuki T, Oh JK, Moore AG, Malouf JF, Pape LA, Gaca C, Sechtem U, Lenferink S, Deutsch HJ, Diedrichs H, Marcos y Robles J, Llovet A, Gilon D, Das SK, Armstrong WF, Deeb GM, Eagle KA. 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Is significance of atrial fibrillation in acute aortic dissection underestimated? \u003cem\u003eJ Emerg Med.\u003c/em\u003e 2009;37(2):168\u0026ndash;170.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute aortic dissection, Biomarker, Diagnosis, Nomogram, Training cohort, Validation cohort","lastPublishedDoi":"10.21203/rs.3.rs-7388708/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7388708/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe aim of this study was to construct a model by combining routine laboratory biomarkers and clinical characteristics to distinguish acute aortic dissection (AAD) patients from other sudden chest pain patients with AMI, APE and AAA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and Results: \u003c/strong\u003eQualified patients were randomly divided into training and validation cohorts. Independent predictive factors for differentiating AAD were filtered out via backward stepwise logistic regression. A nomogram containing the included factors was constructed. The discrimination and calibration abilities were verified via receiver operating characteristic (ROC) curves and calibration curves. The clinical use of the nomogram was evaluated via DCA. A total of 860 eligible patients were randomly allocated to the training (602) and validation (258) cohorts. The WBC count, Baso%, NLR, age, DD and alcohol status were established as independent factors for patients with AAD after multiple logistic regression analysis. A nomogram was constructed. The AUC values were 0.775 (0.733--0.817) and 0.709 (0.637--0.781) for the training and validation cohorts, respectively. The Hosmer–Lemeshow test revealed no significant difference (P\u0026gt;0.05), indicating that the nomogram was reliable. DCA showed favorable clinical benefit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion. \u003c/strong\u003eThis study constructed a prediction model for AAD. Validation revealed excellent discrimination and calibration, indicating that the nomograms may provide clinical reference information and increase the diagnostic efficiency of AAD.\u003c/p\u003e","manuscriptTitle":"Development and validation of a nomogram to predict acute aortic dissection in sudden chest pain patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 12:18:18","doi":"10.21203/rs.3.rs-7388708/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6e58d1dc-de46-4333-8d71-2662427bc3dd","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T08:24:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-08 12:18:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7388708","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7388708","identity":"rs-7388708","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}