Pulmonary artery diameter on chest CT predicts in-hospital mortality in patients with COVID-19 pneumonia

Pulmonary artery diameter on chest CT predicts in-hospital mortality in patients with COVID-19 pneumonia | 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 Pulmonary artery diameter on chest CT predicts in-hospital mortality in patients with COVID-19 pneumonia Nart Zafer Baytugan, Aziz İnan Çelik, Metin Çağdaş, Tahir Bezgin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1748853/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: Enlargement of pulmonary artery (PA) trunk diameter could be helpful in risk stratification by the chest CT on the admission of COVID-19 patients. Methods: The aim was to investigate the association between pulmonary artery enlargement and overall mortality in COVID-19 pneumonia. We conducted a single-center, retrospective, observational study between January 2021 and May 2021 in tertiary level hospitals in Gebze, Turkey. According to their survivor status, subjects were divided into two groups (survivors and non-survivors). Then biochemical, demographic, and clinical parameters were compared via the two groups to assess the predictive value of PA diameter on chest CT images. Results: In the enrolled 594 COVID-19 in-hospital patients (median age was 45 (34-58) years, and 263 patients (44.3%) were female), 44 patients (7.4%) died during their hospitalization. The time-dependent multivariate Cox-proportion regression model yielded main PA ≥ 29 mm on admission showed that as independent predictors of subsequent death (long rank <0.001, median survival time 28 days). Cumulative survival rates were MPAD ≥ 29 mm 45% and < 29 mm 90% respectively (p < 0.001). Conclusions: PA dilatation is strongly associated with in-hospital mortality in hospitalized patients with COVID-19 pneumonia. Thus increased PA diameter on chest CT at admission may guide rapid and early diagnosis of high-risk patients. COVID-19 Computed tomography pulmonary artery mortality pneumonia Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The coronavirus 2019 (COVID-19) disease has become a global health problem that affects millions of people quickly over the world ( 1 – 2 ). Its clinical presentation ranges from asymptomatic patients to acute respiratory distress syndrome, multiple organ dysfunction, and death. Also, it impairs the vascular endothelial structure and function ( 3 ). Severe complications more frequently occur in advanced age, smoking, and comorbidities like hypertension (HT), diabetes mellitus (DM), cardiovascular disease, cardiac arrhythmia, dementia, cancer, chronic kidney, cerebrovascular, and respiratory disease ( 4 – 6 ). Chest computed tomography (CT) may have a crucial role in diagnosing COVID-19 pneumonia ( 7 – 8 ). CT is widely used, especially in the emergency department, to assess risk and evaluate lung involvement and differential diagnosis. Pulmonary artery (PA) enlargement is a predictor of hemodynamic instability such as; right ventricular failure, pulmonary embolism, and pulmonary hypertension (PH) ( 9 – 11 ). Although PA dilatation reflects vascular injury, abnormal coagulation, hypoxia, and inflammation, the optimal cut-off value of PA diameter in COVID-19 patients is unknown. We hypothesized that the enlargement of PA could be helpful in risk stratification on the admission to hospital in the COVID-19 patient population. Therefore, we aimed to investigate the relationship between PA diameters and in-hospital mortality of COVID-19 pneumonia. Material And Methods Patients population We conducted a single-center, retrospective, observational study between January 2021 and May 2021. Five hundred ninety-four COVID-19 patients, which were diagnosed by real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test and non-cardiac gated thoracic CT scans, were enrolled in the study. Baseline demographic and laboratory findings were recorded from the hospital's electronic database system. Complete blood counts and biochemical parameters including blood glucose, creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), high sensitive CRP (hs-CRP), ferritin, fibrinogen, D- Dimer, and high sensitive cardiac troponin I (hs-cTnI) were evaluated on admission. Patients with < 18 years old, without CT imaging, pneumonia other than COVID-19 infection, non-hospitalized patients, and history of PH and thromboembolism were excluded. The study conforms to the principles in the Declaration of Helsinki and the local ethics committee's approval. CT imaging Thoracic CT imaging was performed using a 64-slice CT scanner (Aquilion 64, Toshiba Medical Systems, Japan) with 3-mm reconstructed slice thickness. All patients were examined supine, with the end of inspiration and hands raised by the side. Tube current and tube voltages were 300 mA and 120 kV, respectively, and gantry rotation time was 0.4s. All images were unenhanced and non-gated. The main PA diameter (MPAD), left PA diameter (LPAD), and right PA diameter (RPAD) were measured at the level of PA bifurcation from CT images by two cardiologists who were blinded to the study (Figure-1). Statistical analysis Data were analyzed via the SPSS 22.0 version (SPSS Inc, Chicago, Illinois). Descriptive statistics were given as mean ± standard deviation and median (25th-75th percentiles) with minimum-maximum values for continuous variables depending on their distribution. Numbers and percentages were used for categorical variables. The normal distribution of the numerical variables was analyzed by the Shapiro-Wilk, Kolmogorov-Smirnov, and Anderson-Darling tests. The Independent Samples t-test was used in comparing two independent groups where numerical variables had a normal distribution. The One-Way ANOVA test compared more than two independent groups where numerical variables had a normal distribution. For variables without normal distribution, the Kruskal Wallis test was applied. Receiver operating characteristic (ROC) curve analyses were conducted to determine and compare the optimal cut-off values of MPAD, LPAD, and RPAD that predict in-hospital mortality. The area under the ROC curve (AUC) was reported with a % 95 confidence interval (CI). Pearson Chi-Square and Fisher's Exact tests compared the differences between categorical variables. For the analyses in which parametric tests were used, the differences between the groups were evaluated with the Tukey or LSD tests when data was homogeneous based on its distribution. Multivariable cox regression analysis assessed the relationship between CT parameters (MPAD, LPAD, RPAD) and death as the outcome, summarized by hazard ratios (HR) and associated 95% confidence intervals. Survival analysis was performed by the Kaplan-Meier method, and differences in survival parameters were evaluated using the log-rank test. A p-value < 0.05 was considered statistically significant. Results A total of 594 SARS-CoV-2 patients were hospitalized and categorized according to their survivor status [survivor (n = 550) and non-survivor (n = 44)]. Baseline characteristics, clinical and laboratory parameters of the study population are demonstrated in Table-1. The median age of the overall study cohort was 45 (34–58), and 263 patients (44.3%) were female. One hundred eighty-five patients (31.1%) were smokers, 79 patients (13.3%) had DM, 133 patients (22.4%) had HT, 14 patients (2.3%) had congestive heart failure (CHF), and 66 patients (11.1%) had chronic obstructive pulmonary disease. Non-survivors were older [median age 72 (63–80) vs 44 (33–55), p < 0.001] and had a higher prevalence of HT (50% vs 21.2%, p < 0.001), CHF (18.2% vs 1.1%, p < 0.001), coronary artery disease (CAD) [13.6% vs 2.9%, p < 0.001] and chronic obstructive pulmonary disease (COPD) (34.1% vs 9.8%, p < 0.001). There was no difference between the groups in terms of DM [18.2% vs 13.6%, p = 0.397]. According to the hemodynamic parameters and laboratory assays on admission there were significant differences between two groups. Compared to survivors, non-survivors had higher fever [37.5 (38.3–36.8) vs 37.2 (36.4–38.0) C, p = 0.019], and heart rate [98 (91–106) vs 94 (89–102), p = 0.04], lower systolic blood pressure [110 ± 11 mm/Hg vs 114 ± 8 mm/Hg, p = 0.002], and lower oxygen saturation on admission [90 (83–97) vs 94 (91–97), p < 0.001]. On laboratory examination, non-survivors had higher fasting blood glucose [134 (106–235) vs 100 (87–115) mg/dL, p < 0.001], creatinine [1.2 (0.8–2.2) vs 0.8 (0.7–0.9) mg/dL, p < 0.001], AST [31.5 (23-46.5) vs 22 (17–30) U/L, p < 0.001], D-Dimer [1.2 (0.52–3.1) vs 0.37 (0.27–0.68) ng/ml], hs-CRP [93.2 (43.8–192) vs 7.4 (2-22.6) mg/L, p < 0.001], ferritin [401 (153.5–585) vs 98 (41-220.1) ng/mL, p < 0.001], white blood cell count (WBC) [11.8 ± 6.5 vs 6.6 ± 2.6 x103/ml, p < 0.001], fibrinogen [447 (389–525) vs 382(321 vs 446) mg/dl] and hs-cTnI [30 (9-132) vs 1 (0.1-3) pg/mL, p < 0.001] levels. However, hemoglobin levels [11.5 ± 2.6 vs 13.6 ± 1.6 g/dL, p < 0.001] were lower in non-survivors, and ALT levels were similar in both groups (20 (13.5–37.5) vs 22(16–36) U/L, p = 0.352). MPAD [32.1 ± 4.4 vs 25.7 ± 3.4, p < 0.001] LAPD [23.7 ± 3.7 vs 17.6 ± 2.9, p < 0.001], and RPAD [24.1 ± 4.2 vs 17.8 ± 3.2, p < 0.001] were significantly higher in non-survivor group compared to survivor group. Median length of hospitalization period was 5 ( 3 – 7 ) days and hospitalization period was longer in non-survivor group than survivor group [8 ( 4 – 12 ) days vs 5 ( 3 – 7 ) days (p 29 mm 45% and < 29 mm 90% respectively (p < 0.001) (Figure-2). Receiver operator characteristic curve of main, left and right PA diameter for predicting deaths. MPA ≥ 29 mm, with 79.55% sensitivity and 87.19% specificity. Area under the rock curve (AUC) was 0.879 (p < 0.001) (Figure-3) At cox’s regression analysis adjusted with ages, comorbidities, oxygen saturation, fewer, hs-cTnI and inflammatory parameters were predicting in-hospital mortality in patients with COVID-19 infection (Figure-4, table-2). Discussion The role of Chest CT imaging in the COVID-19 infection is apparent as to determine the prevalence and severity of the disease, early screening, and making different diagnoses. In a study by Fang et al., the sensitivity of chest CT with COVID-19 was 98% ( 12 ). The typical CT findings are the multifocal bilateral distribution of ground-glass opacities, consolidations, air bronchogram, crazy-paving pattern, pulmonary vascular enlargement, linear opacification, and airway and pleural changes COVID-19 ( 7 – 8 ). Our retrospective study showed a well-established cut-off value of MPAD ≥ 29 mm was an independent predictor of the severity of the COVID-19 infection. Enlargement of PA was an independent predictor of mortality and in-hospital duration. It was found to negatively correlate with the oxygen saturation at the time of the admission. Enlargement of PA, which can be detected by CT imaging, is a parameter that helps to predict adverse outcomes ( 13 ). Although PA enlargement is associated with poor prognosis in acute pulmonary edema, embolism, and heart failure, insufficient data on its prognostic significance and optimal cut-off PA diameter in COVID-19 infection. A normally mean PA diameter calculated in a healthy population was 26.1 ± 2.4 mm in men and 22.9 ± 1.9 mm in women ( 14 ). This value was 25.74 ± 3.48 mm in the entire study group. A study conducted by Esposito et al., which included 1461 patients, determined that an MPAD ≥ 31 mm in COVID-19 patients was an independent predictor of mortality ( 15 ). The study by Zhu et al. points to MPAD ≥ 29 mm as a significant predictor of subsequent death ( 10 ). Truong et al. demonstrated that the predictive value of MPAD is 31 mm or greater in diagnosis PH and associated with 2–3 fold increased mortality risk compared to normal ( 11 ). In parallel, we found similar findings in our study cohort with an MPAD ≥ 29 mm, and these patients have more inflammation, heart injuries, and co-morbid disease. MPAD, both as a continuous and categorical variable, predicted in-hospital mortality in various regression models adjusted with age, comorbidities, clinical status, and inflammatory parameters. Besides being a primary lung disease, COVID-19 is an infectious pathology that disrupts the endothelial system by activating numerous inflammatory and prothrombotic cascades. Erdoğan et al. have suggested that disrupts the endothelial system, increased inflammatory process, myocarditis, and active coagulopathy are associated with the severity of COVID-19 and ultimately predict adverse outcomes ( 16 ). Increased inflammatory status is accompanied by the severity of the disease and increased mortality rates ( 13 , 16 ). It may result in a decrease in lung capacity and increased PA pressure. In addition, many patients had elevated inflammatory parameters, liver enzymes, CPK, and prothrombin time ( 13 ). Furthermore, Cai et al. demonstrated the increase in liver enzymes from severe pneumonia might be related to increased pulmonary pressure ( 17 ). In our cohort, similar to these results, AST and inflammatory levels, hs-CRP, ferritin, troponin, BUN, WBC, D-Dimer, and creatinine levels were significantly associated with PA diameter. Although thrombocytopenia is a common finding in COVID-19 patients in previous studies, no correlation was found between platelet count and PA diameter in our study ( 18 – 19 ). PH's etiology is considered multifactorial; pulmonary small vessel thrombosis, vasculopathy, hypoxemia, and vasoconstriction were reported as the leading cause of PH in COVID-19 disease. PH can rapidly worsen right heart function and impair oxygenation. Thus the length of hospital stay is prolonged, and the risk of the patient's multi-organ failure, bacterial infections, sepsis, hypercoagulation, and thrombosis. We found that severe CT findings of pneumonia and relation with hypoxemia were correlated with higher MPAD. It is the most severe reason for poorer outcomes. COVID-19 has maybe affected the cardiovascular system. The underlying mechanism of cardiac damage is not clearly understood. Increased cardiac stress secondary to acute respiratory failure and progressive hypoxemia, direct myocardial infection of the virus, increased inflammatory status, or combination. Also, SARS-CoV-2 infects host cells by angiotensin-converting enzyme 2 (ACE2) receptors, leading to myocardial injury. It has been shown that cardiovascular complications and heart failure may be responsible for 40% of deaths in COVID-19 patients ( 20 ). There is a need for criteria to predict the severity and prognosis of the disease in COVID-19 patients. Thus, increased MPAD may guide rapid and early diagnosis and treatment of high-risk patients. In our study, pulmonary disease, CAD, CHF, and HT at the time of admission adversely affected the prognosis in COVID-19 patients. On the contrary, the presence of DM did not affect the prognosis in our patient population. Limitation Of The Study Although our study emphasized the association of PA diameter with mortality, there are several limitations. We did not know about the clinical condition and PA diameters of the patients before the COVID-19. There was also no follow-up data. Dynamic measurement of PA trunk diameter will reveal more information. Furthermore, our cohort included only hospitalized patients because these results cannot be generalized to all COVID-19 patients. The frequency of pulmonary embolism that could lead to PA enlargement was unknown. And lack of data on electrocardiography and echocardiography imaging. Conclusions Chest CT imaging in the diagnosis of COVID-19 is the obvious, simple, and great value of early screening. Rapid diagnosis of high-risk COVID-19 patients is critical, significantly dissolving the emergency department's patient density. Enlargement of PA on chest CT may indicate hemodynamic instability and worse outcomes. It should be considered that these patients may be at high risk and should be evaluated carefully. Declarations Declaration to interest: none Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Pradhan D, Biswasroy P, Kumar Naik P et al (2020 Jul) A Review of Current Interventions for COVID-19 Prevention. Arch Med Res 51(5):363–374 Sharma A, Tiwari S, Deb MK et al (2020 Aug) Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2): a global pandemic and treatment strategies. Int J Antimicrob Agents 56(2):106054 Libby P, Lüscher T COVID-19 is, in the end, an endothelial disease. Eur Heart J. 2020 Sep 1;41(32):3038-44 Wu Z, McGoogan JM (2020) Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA 323:1239–1242 Izcovich A, Ragusa MA, Tortosa et al Prognostic factors for severity and mortality in patients infected with COVID-19: A systematic review.PLoS One. 2020 Nov17;15(11). Zhou F, Yu T, Du R et al (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395:1054–1062 Ufuk F, Savas R Chest CT features of the novel coronavirus disease (COVID-19). Turk J Med Sci.2020. Ding X, Xu J, Zhou J et al (2020) Chest CT findings of COVID-19 pneumonia by duration of symptoms.Eur J Radiol:109009 Chien CH, Shih FC, Chen CY et al Unenhanced multidetector computed tomography findings in acute central pulmonary embolism. BMC Med Imaging. 2019 Aug 14;19(1):65 Zhu QQ, Gong T, Huang G et al (2021 Jun) Pulmonary artery trunk enlargement on admission as a predictor of mortality in in-hospital patients with COVID-19. Jpn J Radiol 39(6):589–597 Truong QA, Bhatia SH, Szymonifka J et al (2018) A Four-Tier Severity Classification System of Pulmonary Artery Metrics on Computed Tomography for the Diagnosis and Prognosis of Pulmonary Hypertension. J Cardiovasc Comput Tomogr 12(1):60–66 Fang Y, Zhang H, Xie J et al Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR.Radiology296:115–17 Yildiz M, Yadigar S, Yildiz B et al (2021 Feb) Evaluation of the relationship between COVID-19 pneumonia severity and pulmonary artery diameter measurement. Herz 46(1):56–62 Nevsky G, Jacobs JE, Lim RP et al (2011 Apr) Sex-specific normalized reference values of heart and great vessel dimensions in cardiac CT angiography. AJR Am J Roentgenol 196(4):788–794 Esposito A, Palmisano A, Toselli M et al (2021 Jun) Chest CT-derived pulmonary artery enlargement at the admission predicts overall survival in COVID-19 patients: insight from 1461 consecutive patients in Italy. Eur Radiol 31(6):4031–4041 Erdoğan M, Öztürk S, Erdöl MA et al (2021) Aug Prognostic utility of pulmonary artery and ascending aorta diameters derived from computed tomography in COVID-19 patients.Echocardiography. 6;10.1111. Cai Q, Huang D, Yu H et al (2020 Sep) COVID-19:Abnormal liver function test. J Hepatol 73(3):566–574 Lippi G, Plebani M, Henry BM (2020 Jul) Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: A meta-analysis. Clin Chim Acta 506:145–148 Terpos E, Ntanasis-Stathopoulos I, Elalamy I et al (2020 Jul) Hematological findings and complications of COVID-19. Am J Hematol 95(7):834–847 Ruan Q, Yang K, Wang W, Jiang L et al (2020) Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive CareMed46:846–848 Tables Table 1. The baseline clinical and laboratory characteristics of the patients according to the survival status Survivors Non-survivors Total p value Age 45±15 71±13 47±17 <0.001 Gender (Female), n (%) Diabetes mellitus, n (%) 71 (13.6) 8 (18.2) 79 (13.9) 0.397 Hypertension, n (%) 111 (21.2) 22 (50) 133 (23.5) <0.001 Congestive heart failure, n (%) 6 (1.1) 8 (18.2) 14 (2.5) <0.001 Coronary artery disease, n (%) 15 (2.9) 6 (13.6) 21 (3.7) <0.001 Chronic obstructive pulmonary disease, n (%) 51 (9.8) 15 (34.1) 66 (11.6) <0.001 Smoking, n (%) 171 (32.7) 14 (31.8) 185 (32.6) 0.905 Oxygen saturation, % 94±3 90±7 93±4 <0.001 Fever, o C 37.2±0.8 37.5±0.8 37.2±0.8 0.019 Heart rate, bpm 96 98 96 0.053 Systolic blood pressure, mmHg 114±8 110±11 115±9 0.002 Main pulmonary artery diameter, mm 25.7±3.4 32.1±4.4 26.2±3.9 <0.001 Right pulmonary artery diameter, mm 17.8±3.2 24.1±4.2 18.3±3.7 <0.001 Left pulmonary artery diameter, mm 17.6±2.9 23.7±3.8 18.1±3.4 <0.001 Glucose, mg/dL 100±89 134±106 101±90 <0.001 Creatinine, mg/dL 0.8±0.7 1.2±0.8 0.8±0.7 <0.001 BUN, mg/dL 12±10 33±19 13±10 <0.001 AST, U/L 23±17 31.5±23 24±18 <0.001 ALT, U/L 22±16 20±13.5 22±16 0.352 hs-cTnI, ng/mL 0.001±0.003 0.03±0.13 0.013±0.042 <0.001 Ferritin, ng/mL 100.1±41.6 401±153.5 109±43.5 <0.001 CRP, mg/L 7.2±1.7 93.2±43.8 7.8±1.9 <0.001 White blood cell count, x10 3 /ml 6.7±2.6 11.8±6.5 7.1±3.4 <0.001 Hemoglobin, g/dL 13.6±1.6 11.5±2.6 13.4±1.8 <0.001 Thrombocyte, x10 3 /ml 233±81 245±129 234±85 0.344 Abbreviations: BUN: Blood urea nitrogen, AST: Aspartate transaminase, ALT: Alanine aminotransferase, hs-cTnI: High-sensitivity cardiac troponin I, CRP: C-reactive protein. Table 2. At cox’s regression analysis adjusted with ages, comorbidities, oxygen saturation, fewer, hs-cTnI and inflammatory parameters were predicting in-hospital mortality Variable HR [95% CI] p value MPA 1.252 [1.180-1.327] <0.001 MPA+age 1.168[1.085-1.258] <0.001 MPA+age+HT+CAD 1.158[1.072-1.250] <0.001 MPA+age+HT+CHF 1.156[1.074-1.244] <0.001 MPA+age+HT+COPD 1.168[1.081-1.262] <0.001 MPA+HT+saturation+fever 1.244[1.161-1.332] <0.001 MPA+glucose+creatinin+CRP 1.217[1.134-1.306] <0.001 MPA+hs-cTnI+fibrinogen+D-dimer 1.305[1.033-1.650] 0.026 Abbreviations: MPA: Main pulmonary artery, HT: hypertension, CAD: coronary artery disease, CHF: chronic heart failure, COPD: chronic obstructive pulmonary disease Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-1748853","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":113209950,"identity":"197db2e9-7f67-4c28-8972-5060fb3ba3b4","order_by":0,"name":"Nart Zafer Baytugan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYPACOQYG9h4wi4ePCOWMDQwMxkC1ZxgYDgApNuK1SOSAtTAQ1GLOfvb5wx81BnLmM98efPwxx06GjYH54aMbeLRY9qQbNkgcMzCWuZ2XbHBwWzLQYWzGxjl4tBgcSGNsMGD7kzhDOsdM4uA2ZqAWHjZpvFrOP2NsSPhnUD9D8gxISz0RWm4AbTnYZpAgIcED0nKYGC3PGGc29hkYzuDJMTY4u+04DxszIb+cT2P4+OObgbwE+xnDB5Xbqu352ZsfPsanBQtgJk35KBgFo2AUjAIsAAC2KERJl55/6QAAAABJRU5ErkJggg==","orcid":"","institution":"Gebze Fatih State Hospital","correspondingAuthor":true,"prefix":"","firstName":"Nart","middleName":"Zafer","lastName":"Baytugan","suffix":""},{"id":113209951,"identity":"ee9d6d26-fd45-4561-8303-33948ae55951","order_by":1,"name":"Aziz İnan Çelik","email":"","orcid":"","institution":"Gebze Fatih State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Aziz","middleName":"İnan","lastName":"Çelik","suffix":""},{"id":113209952,"identity":"2961fbe7-95b8-41f0-93f7-12db89e758c1","order_by":2,"name":"Metin Çağdaş","email":"","orcid":"","institution":"Gebze Fatih State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Metin","middleName":"","lastName":"Çağdaş","suffix":""},{"id":113209953,"identity":"c4ebf666-1378-4940-8450-8711f3d45a77","order_by":3,"name":"Tahir Bezgin","email":"","orcid":"","institution":"Gebze Fatih State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tahir","middleName":"","lastName":"Bezgin","suffix":""},{"id":113209954,"identity":"04a0d734-f3c9-406e-aced-f612e2d2e3d2","order_by":4,"name":"Hasan Çağlayan Kandemir","email":"","orcid":"","institution":"Kocaeli State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hasan","middleName":"Çağlayan","lastName":"Kandemir","suffix":""}],"badges":[],"createdAt":"2022-06-11 16:44:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1748853/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1748853/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22692033,"identity":"87ff5546-9ccd-4cc5-bc00-14f7986d22d7","added_by":"auto","created_at":"2022-06-15 17:04:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":925120,"visible":true,"origin":"","legend":"\u003cp\u003eFrom chest CT the diameter of the main, left and right PA was measured at the level of bifurcation on the mediastinal window\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1748853/v1/95d9a1a28663585d2322edbe.png"},{"id":22692030,"identity":"b7f832a5-100f-4bd7-ae15-b58e6beb068b","added_by":"auto","created_at":"2022-06-15 17:04:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":244110,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for PA trunk diameter showed that PA diameter \u0026gt;29 mm was significant predictor of mortality. ( long-rank p\u0026lt;0.001, median survival time was 28 days)\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1748853/v1/18426edb1b2f3fd05d363f6a.png"},{"id":22692031,"identity":"fd6914e5-5ddc-4803-a432-91d93a379047","added_by":"auto","created_at":"2022-06-15 17:04:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":333436,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operator characteristic curve of main, left and right PA diameter for predicting deaths. MPA \u0026gt;29 mm, with 79.55% sensitivity and 87.19% specificity. Area under the rock curve (AUC) was 0.879. (p\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1748853/v1/a2bd8495cb9d5b614e12cab2.png"},{"id":22692032,"identity":"8d74a1e1-8ea0-43ec-8e84-1889017ab32f","added_by":"auto","created_at":"2022-06-15 17:04:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":105545,"visible":true,"origin":"","legend":"\u003cp\u003eAt cox’s regression analysis adjusted with ages, comorbidities, oxygen saturation, fewer, hs-cTnI and inflammatory parameters were predicting in-hospital mortality.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1748853/v1/d2a2e7efc57aab45571f5ebf.png"},{"id":23698946,"identity":"aa08d23a-91bc-451d-b585-605722493e4b","added_by":"auto","created_at":"2022-07-11 09:44:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1977915,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1748853/v1/8125fd32-dfbe-4e47-a04e-d642f4792c06.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pulmonary artery diameter on chest CT predicts in-hospital mortality in patients with COVID-19 pneumonia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe coronavirus 2019 (COVID-19) disease has become a global health problem that affects millions of people quickly over the world (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Its clinical presentation ranges from asymptomatic patients to acute respiratory distress syndrome, multiple organ dysfunction, and death. Also, it impairs the vascular endothelial structure and function (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Severe complications more frequently occur in advanced age, smoking, and comorbidities like hypertension (HT), diabetes mellitus (DM), cardiovascular disease, cardiac arrhythmia, dementia, cancer, chronic kidney, cerebrovascular, and respiratory disease (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Chest computed tomography (CT) may have a crucial role in diagnosing COVID-19 pneumonia (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). CT is widely used, especially in the emergency department, to assess risk and evaluate lung involvement and differential diagnosis. Pulmonary artery (PA) enlargement is a predictor of hemodynamic instability such as; right ventricular failure, pulmonary embolism, and pulmonary hypertension (PH) (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Although PA dilatation reflects vascular injury, abnormal coagulation, hypoxia, and inflammation, the optimal cut-off value of PA diameter in COVID-19 patients is unknown. We hypothesized that the enlargement of PA could be helpful in risk stratification on the admission to hospital in the COVID-19 patient population. Therefore, we aimed to investigate the relationship between PA diameters and in-hospital mortality of COVID-19 pneumonia.\u003c/p\u003e"},{"header":"Material And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients population\u003c/h2\u003e \u003cp\u003eWe conducted a single-center, retrospective, observational study between January 2021 and May 2021. Five hundred ninety-four COVID-19 patients, which were diagnosed by real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test and non-cardiac gated thoracic CT scans, were enrolled in the study. Baseline demographic and laboratory findings were recorded from the hospital's electronic database system. Complete blood counts and biochemical parameters including blood glucose, creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), high sensitive CRP (hs-CRP), ferritin, fibrinogen, D- Dimer, and high sensitive cardiac troponin I (hs-cTnI) were evaluated on admission. Patients with \u0026lt;\u0026thinsp;18 years old, without CT imaging, pneumonia other than COVID-19 infection, non-hospitalized patients, and history of PH and thromboembolism were excluded. The study conforms to the principles in the Declaration of Helsinki and the local ethics committee's approval.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCT imaging\u003c/h2\u003e \u003cp\u003eThoracic CT imaging was performed using a 64-slice CT scanner (Aquilion 64, Toshiba Medical Systems, Japan) with 3-mm reconstructed slice thickness. All patients were examined supine, with the end of inspiration and hands raised by the side. Tube current and tube voltages were 300 mA and 120 kV, respectively, and gantry rotation time was 0.4s. All images were unenhanced and non-gated. The main PA diameter (MPAD), left PA diameter (LPAD), and right PA diameter (RPAD) were measured at the level of PA bifurcation from CT images by two cardiologists who were blinded to the study (Figure-1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were analyzed via the SPSS 22.0 version (SPSS Inc, Chicago, Illinois). Descriptive statistics were given as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and median (25th-75th percentiles) with minimum-maximum values for continuous variables depending on their distribution. Numbers and percentages were used for categorical variables. The normal distribution of the numerical variables was analyzed by the Shapiro-Wilk, Kolmogorov-Smirnov, and Anderson-Darling tests. The Independent Samples t-test was used in comparing two independent groups where numerical variables had a normal distribution. The One-Way ANOVA test compared more than two independent groups where numerical variables had a normal distribution. For variables without normal distribution, the Kruskal Wallis test was applied. Receiver operating characteristic (ROC) curve analyses were conducted to determine and compare the optimal cut-off values of MPAD, LPAD, and RPAD that predict in-hospital mortality. The area under the ROC curve (AUC) was reported with a % 95 confidence interval (CI). Pearson Chi-Square and Fisher's Exact tests compared the differences between categorical variables. For the analyses in which parametric tests were used, the differences between the groups were evaluated with the Tukey or LSD tests when data was homogeneous based on its distribution. Multivariable cox regression analysis assessed the relationship between CT parameters (MPAD, LPAD, RPAD) and death as the outcome, summarized by hazard ratios (HR) and associated 95% confidence intervals. Survival analysis was performed by the Kaplan-Meier method, and differences in survival parameters were evaluated using the log-rank test. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 594 SARS-CoV-2 patients were hospitalized and categorized according to their survivor status [survivor (n\u0026thinsp;=\u0026thinsp;550) and non-survivor (n\u0026thinsp;=\u0026thinsp;44)]. Baseline characteristics, clinical and laboratory parameters of the study population are demonstrated in Table-1. The median age of the overall study cohort was 45 (34\u0026ndash;58), and 263 patients (44.3%) were female. One hundred eighty-five patients (31.1%) were smokers, 79 patients (13.3%) had DM, 133 patients (22.4%) had HT, 14 patients (2.3%) had congestive heart failure (CHF), and 66 patients (11.1%) had chronic obstructive pulmonary disease.\u003c/p\u003e \u003cp\u003eNon-survivors were older [median age 72 (63\u0026ndash;80) vs 44 (33\u0026ndash;55), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and had a higher prevalence of HT (50% vs 21.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CHF (18.2% vs 1.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), coronary artery disease (CAD) [13.6% vs 2.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and chronic obstructive pulmonary disease (COPD) (34.1% vs 9.8%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was no difference between the groups in terms of DM [18.2% vs 13.6%, p\u0026thinsp;=\u0026thinsp;0.397]. According to the hemodynamic parameters and laboratory assays on admission there were significant differences between two groups. Compared to survivors, non-survivors had higher fever [37.5 (38.3\u0026ndash;36.8) vs 37.2 (36.4\u0026ndash;38.0) C, p\u0026thinsp;=\u0026thinsp;0.019], and heart rate [98 (91\u0026ndash;106) vs 94 (89\u0026ndash;102), p\u0026thinsp;=\u0026thinsp;0.04], lower systolic blood pressure [110\u0026thinsp;\u0026plusmn;\u0026thinsp;11 mm/Hg vs 114\u0026thinsp;\u0026plusmn;\u0026thinsp;8 mm/Hg, p\u0026thinsp;=\u0026thinsp;0.002], and lower oxygen saturation on admission [90 (83\u0026ndash;97) vs 94 (91\u0026ndash;97), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. On laboratory examination, non-survivors had higher fasting blood glucose [134 (106\u0026ndash;235) vs 100 (87\u0026ndash;115) mg/dL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], creatinine [1.2 (0.8\u0026ndash;2.2) vs 0.8 (0.7\u0026ndash;0.9) mg/dL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], AST [31.5 (23-46.5) vs 22 (17\u0026ndash;30) U/L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], D-Dimer [1.2 (0.52\u0026ndash;3.1) vs 0.37 (0.27\u0026ndash;0.68) ng/ml], hs-CRP [93.2 (43.8\u0026ndash;192) vs 7.4 (2-22.6) mg/L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], ferritin [401 (153.5\u0026ndash;585) vs 98 (41-220.1) ng/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], white blood cell count (WBC) [11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5 vs 6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 x103/ml, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], fibrinogen [447 (389\u0026ndash;525) vs 382(321 vs 446) mg/dl] and hs-cTnI [30 (9-132) vs 1 (0.1-3) pg/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] levels. However, hemoglobin levels [11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 vs 13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 g/dL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] were lower in non-survivors, and ALT levels were similar in both groups (20 (13.5\u0026ndash;37.5) vs 22(16\u0026ndash;36) U/L, p\u0026thinsp;=\u0026thinsp;0.352). MPAD [32.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 vs 25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] LAPD [23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7 vs 17.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], and RPAD [24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 vs 17.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] were significantly higher in non-survivor group compared to survivor group. Median length of hospitalization period was 5 (\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) days and hospitalization period was longer in non-survivor group than survivor group [8 (\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9 CR10 CR11\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) days vs 5 (\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) days (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)]. Cumulative survival rates were MPAD\u0026thinsp;\u0026gt;\u0026thinsp;29 mm 45% and \u0026lt;\u0026thinsp;29 mm 90% respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Figure-2). Receiver operator characteristic curve of main, left and right PA diameter for predicting deaths. MPA\u0026thinsp;\u0026ge;\u0026thinsp;29 mm, with 79.55% sensitivity and 87.19% specificity. Area under the rock curve (AUC) was 0.879 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Figure-3) At cox\u0026rsquo;s regression analysis adjusted with ages, comorbidities, oxygen saturation, fewer, hs-cTnI and inflammatory parameters were predicting in-hospital mortality in patients with COVID-19 infection (Figure-4, table-2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe role of Chest CT imaging in the COVID-19 infection is apparent as to determine the prevalence and severity of the disease, early screening, and making different diagnoses. In a study by Fang et al., the sensitivity of chest CT with COVID-19 was 98% (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The typical CT findings are the multifocal bilateral distribution of ground-glass opacities, consolidations, air bronchogram, crazy-paving pattern, pulmonary vascular enlargement, linear opacification, and airway and pleural changes COVID-19 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur retrospective study showed a well-established cut-off value of MPAD\u0026thinsp;\u0026ge;\u0026thinsp;29 mm was an independent predictor of the severity of the COVID-19 infection. Enlargement of PA was an independent predictor of mortality and in-hospital duration. It was found to negatively correlate with the oxygen saturation at the time of the admission. Enlargement of PA, which can be detected by CT imaging, is a parameter that helps to predict adverse outcomes (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Although PA enlargement is associated with poor prognosis in acute pulmonary edema, embolism, and heart failure, insufficient data on its prognostic significance and optimal cut-off PA diameter in COVID-19 infection. A normally mean PA diameter calculated in a healthy population was 26.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 mm in men and 22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 mm in women (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This value was 25.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48 mm in the entire study group. A study conducted by Esposito et al., which included 1461 patients, determined that an MPAD\u0026thinsp;\u0026ge;\u0026thinsp;31 mm in COVID-19 patients was an independent predictor of mortality (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The study by Zhu et al. points to MPAD\u0026thinsp;\u0026ge;\u0026thinsp;29 mm as a significant predictor of subsequent death (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Truong et al. demonstrated that the predictive value of MPAD is 31 mm or greater in diagnosis PH and associated with 2\u0026ndash;3 fold increased mortality risk compared to normal (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In parallel, we found similar findings in our study cohort with an MPAD\u0026thinsp;\u0026ge;\u0026thinsp;29 mm, and these patients have more inflammation, heart injuries, and co-morbid disease. MPAD, both as a continuous and categorical variable, predicted in-hospital mortality in various regression models adjusted with age, comorbidities, clinical status, and inflammatory parameters.\u003c/p\u003e \u003cp\u003eBesides being a primary lung disease, COVID-19 is an infectious pathology that disrupts the endothelial system by activating numerous inflammatory and prothrombotic cascades. Erdoğan et al. have suggested that disrupts the endothelial system, increased inflammatory process, myocarditis, and active coagulopathy are associated with the severity of COVID-19 and ultimately predict adverse outcomes (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Increased inflammatory status is accompanied by the severity of the disease and increased mortality rates (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). It may result in a decrease in lung capacity and increased PA pressure. In addition, many patients had elevated inflammatory parameters, liver enzymes, CPK, and prothrombin time (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Furthermore, Cai et al. demonstrated the increase in liver enzymes from severe pneumonia might be related to increased pulmonary pressure (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In our cohort, similar to these results, AST and inflammatory levels, hs-CRP, ferritin, troponin, BUN, WBC, D-Dimer, and creatinine levels were significantly associated with PA diameter. Although thrombocytopenia is a common finding in COVID-19 patients in previous studies, no correlation was found between platelet count and PA diameter in our study (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePH's etiology is considered multifactorial; pulmonary small vessel thrombosis, vasculopathy, hypoxemia, and vasoconstriction were reported as the leading cause of PH in COVID-19 disease. PH can rapidly worsen right heart function and impair oxygenation. Thus the length of hospital stay is prolonged, and the risk of the patient's multi-organ failure, bacterial infections, sepsis, hypercoagulation, and thrombosis. We found that severe CT findings of pneumonia and relation with hypoxemia were correlated with higher MPAD. It is the most severe reason for poorer outcomes.\u003c/p\u003e \u003cp\u003eCOVID-19 has maybe affected the cardiovascular system. The underlying mechanism of cardiac damage is not clearly understood. Increased cardiac stress secondary to acute respiratory failure and progressive hypoxemia, direct myocardial infection of the virus, increased inflammatory status, or combination. Also, SARS-CoV-2 infects host cells by angiotensin-converting enzyme 2 (ACE2) receptors, leading to myocardial injury. It has been shown that cardiovascular complications and heart failure may be responsible for 40% of deaths in COVID-19 patients (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is a need for criteria to predict the severity and prognosis of the disease in COVID-19 patients. Thus, increased MPAD may guide rapid and early diagnosis and treatment of high-risk patients.\u003c/p\u003e \u003cp\u003eIn our study, pulmonary disease, CAD, CHF, and HT at the time of admission adversely affected the prognosis in COVID-19 patients. On the contrary, the presence of DM did not affect the prognosis in our patient population.\u003c/p\u003e\n\u003ch2\u003eLimitation Of The Study\u003c/h2\u003e\n\u003cp\u003eAlthough our study emphasized the association of PA diameter with mortality, there are several limitations. We did not know about the clinical condition and PA diameters of the patients before the COVID-19. There was also no follow-up data. Dynamic measurement of PA trunk diameter will reveal more information. Furthermore, our cohort included only hospitalized patients because these results cannot be generalized to all COVID-19 patients. The frequency of pulmonary embolism that could lead to PA enlargement was unknown. And lack of data on electrocardiography and echocardiography imaging.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eChest CT imaging in the diagnosis of COVID-19 is the obvious, simple, and great value of early screening. Rapid diagnosis of high-risk COVID-19 patients is critical, significantly dissolving the emergency department\u0026apos;s patient density. Enlargement of PA on chest CT may indicate hemodynamic instability and worse outcomes. It should be considered that these patients may be at high risk and should be evaluated carefully.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration \u0026nbsp;to interest:\u003c/strong\u003e none\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003ePradhan D, Biswasroy P, Kumar Naik P et al (2020 Jul) A Review of Current Interventions for COVID-19 Prevention. Arch Med Res 51(5):363\u0026ndash;374\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSharma A, Tiwari S, Deb MK et al (2020 Aug) Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2): a global pandemic and treatment strategies. Int J Antimicrob Agents 56(2):106054\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLibby P, L\u0026uuml;scher T COVID-19 is, in the end, an endothelial disease. Eur Heart J. 2020 Sep 1;41(32):3038-44\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWu Z, McGoogan JM (2020) Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA 323:1239\u0026ndash;1242\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eIzcovich A, Ragusa MA, Tortosa et al Prognostic factors for severity and mortality in patients infected with COVID-19: A systematic review.PLoS One. 2020 Nov17;15(11).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZhou F, Yu T, Du R et al (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395:1054\u0026ndash;1062\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eUfuk F, Savas R Chest CT features of the novel coronavirus disease (COVID-19). Turk J Med Sci.2020.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDing X, Xu J, Zhou J et al (2020) Chest CT findings of COVID-19 pneumonia by duration of symptoms.Eur J Radiol:109009\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChien CH, Shih FC, Chen CY et al Unenhanced multidetector computed tomography findings in acute central pulmonary embolism. BMC Med Imaging. 2019 Aug 14;19(1):65\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZhu QQ, Gong T, Huang G et al (2021 Jun) Pulmonary artery trunk enlargement on admission as a predictor of mortality in in-hospital patients with COVID-19. Jpn J Radiol 39(6):589\u0026ndash;597\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTruong QA, Bhatia SH, Szymonifka J et al (2018) A Four-Tier Severity Classification System of Pulmonary Artery Metrics on Computed Tomography for the Diagnosis and Prognosis of Pulmonary Hypertension. J Cardiovasc Comput Tomogr 12(1):60\u0026ndash;66\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFang Y, Zhang H, Xie J et al Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR.Radiology296:115\u0026ndash;17\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYildiz M, Yadigar S, Yildiz B et al (2021 Feb) Evaluation of the relationship between COVID-19 pneumonia severity and pulmonary artery diameter measurement. Herz 46(1):56\u0026ndash;62\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eNevsky G, Jacobs JE, Lim RP et al (2011 Apr) Sex-specific normalized reference values of heart and great vessel dimensions in cardiac CT angiography. AJR Am J Roentgenol 196(4):788\u0026ndash;794\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eEsposito A, Palmisano A, Toselli M et al (2021 Jun) Chest CT-derived pulmonary artery enlargement at the admission predicts overall survival in COVID-19 patients: insight from 1461 consecutive patients in Italy. Eur Radiol 31(6):4031\u0026ndash;4041\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eErdoğan M, \u0026Ouml;zt\u0026uuml;rk S, Erd\u0026ouml;l MA et al (2021) Aug Prognostic utility of pulmonary artery and ascending aorta diameters derived from computed tomography in COVID-19 patients.Echocardiography. 6;10.1111.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eCai Q, Huang D, Yu H et al (2020 Sep) COVID-19:Abnormal liver function test. J Hepatol 73(3):566\u0026ndash;574\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLippi G, Plebani M, Henry BM (2020 Jul) Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: A meta-analysis. Clin Chim Acta 506:145\u0026ndash;148\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTerpos E, Ntanasis-Stathopoulos I, Elalamy I et al (2020 Jul) Hematological findings and complications of COVID-19. Am J Hematol 95(7):834\u0026ndash;847\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRuan Q, Yang K, Wang W, Jiang L et al (2020) Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive CareMed46:846\u0026ndash;848\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e The baseline clinical and laboratory characteristics of the patients according to the survival status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003eSurvivors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003eNon-survivors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e45\u0026plusmn;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e71\u0026plusmn;13 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e47\u0026plusmn;17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eGender (Female), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e71 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e8 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e79 (13.9) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e111 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e22 (50) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e133 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eCongestive heart failure, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e6 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e8 (18.2) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e14 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eCoronary artery disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e15 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e6 (13.6) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e21 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eChronic obstructive pulmonary disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e51 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e15 (34.1) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e66 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e171 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e14 (31.8) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e185 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eOxygen saturation, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e94\u0026plusmn;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e90\u0026plusmn;7 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;93\u0026plusmn;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eFever, \u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e37.2\u0026plusmn;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e37.5\u0026plusmn;0.8 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e37.2\u0026plusmn;0.8 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eHeart rate, bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eSystolic blood pressure, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e114\u0026plusmn;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e110\u0026plusmn;11 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e115\u0026plusmn;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eMain pulmonary artery diameter, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e25.7\u0026plusmn;3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e32.1\u0026plusmn;4.4 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e26.2\u0026plusmn;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eRight pulmonary artery diameter, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e17.8\u0026plusmn;3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e24.1\u0026plusmn;4.2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e18.3\u0026plusmn;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eLeft pulmonary artery diameter, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e17.6\u0026plusmn;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e23.7\u0026plusmn;3.8 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e18.1\u0026plusmn;3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eGlucose, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e100\u0026plusmn;89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e134\u0026plusmn;106 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e101\u0026plusmn;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eCreatinine, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e0.8\u0026plusmn;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e1.2\u0026plusmn;0.8 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e0.8\u0026plusmn;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eBUN, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e12\u0026plusmn;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e33\u0026plusmn;19 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e13\u0026plusmn;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eAST, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e23\u0026plusmn;17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e31.5\u0026plusmn;23 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e24\u0026plusmn;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eALT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e22\u0026plusmn;16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e20\u0026plusmn;13.5 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e22\u0026plusmn;16 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003ehs-cTnI, ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e0.001\u0026plusmn;0.003 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e0.03\u0026plusmn;0.13 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e0.013\u0026plusmn;0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eFerritin, ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e100.1\u0026plusmn;41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e401\u0026plusmn;153.5 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e109\u0026plusmn;43.5 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eCRP, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e7.2\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e93.2\u0026plusmn;43.8 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e7.8\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eWhite blood cell count, x10\u003csup\u003e3\u003c/sup\u003e/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e6.7\u0026plusmn;2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e11.8\u0026plusmn;6.5 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e7.1\u0026plusmn;3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e13.6\u0026plusmn;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e11.5\u0026plusmn;2.6 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e13.4\u0026plusmn;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"39.0728476821192%\"\u003e\n \u003cp\u003eThrombocyte, x10\u003csup\u003e3\u003c/sup\u003e/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e233\u0026plusmn;81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.218543046357617%\"\u003e\n \u003cp\u003e245\u0026plusmn;129 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.562913907284768%\"\u003e\n \u003cp\u003e234\u0026plusmn;85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.927152317880795%\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e BUN: Blood urea nitrogen, AST: Aspartate transaminase, ALT: Alanine aminotransferase, hs-cTnI: High-sensitivity cardiac troponin I, CRP: C-reactive protein.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\n\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e At cox\u0026rsquo;s regression analysis adjusted with ages, comorbidities, oxygen saturation, fewer, hs-cTnI and inflammatory parameters were predicting in-hospital mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eMPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.252 [1.180-1.327]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eMPA+age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.168[1.085-1.258]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eMPA+age+HT+CAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.158[1.072-1.250]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eMPA+age+HT+CHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.156[1.074-1.244]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eMPA+age+HT+COPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.168[1.081-1.262]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eMPA+HT+saturation+fever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.244[1.161-1.332]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eMPA+glucose+creatinin+CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.217[1.134-1.306]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eMPA+hs-cTnI+fibrinogen+D-dimer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.305[1.033-1.650]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e MPA: Main pulmonary artery, HT: hypertension, CAD: coronary artery disease, CHF: chronic heart failure, COPD: chronic obstructive pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"}],"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":"COVID-19, Computed tomography, pulmonary artery, mortality, pneumonia","lastPublishedDoi":"10.21203/rs.3.rs-1748853/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1748853/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eEnlargement of pulmonary artery (PA) trunk diameter could be helpful in risk stratification by the chest CT on the admission of COVID-19 patients.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe aim was to investigate the association between pulmonary artery enlargement and overall mortality in COVID-19 pneumonia. We conducted a single-center, retrospective, observational study between January 2021 and May 2021 in tertiary level hospitals in Gebze, Turkey. According to their survivor status, subjects were divided into two groups (survivors and non-survivors). Then biochemical, demographic, and clinical parameters were compared via the two groups to assess the predictive value of PA diameter on chest CT images.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn the enrolled 594 COVID-19 in-hospital patients (median age was 45 (34-58) years, and 263 patients (44.3%) were female), 44 patients (7.4%) died during their hospitalization. The time-dependent multivariate Cox-proportion regression model yielded main PA ≥ 29 mm on admission showed that as independent predictors of subsequent death (long rank \u0026lt;0.001, median survival time 28 days). Cumulative survival rates were MPAD ≥ 29 mm 45% and \u0026lt; 29 mm 90% respectively (p \u0026lt; 0.001).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003ePA dilatation is strongly associated with in-hospital mortality in hospitalized patients with COVID-19 pneumonia. Thus increased PA diameter on chest CT at admission may guide rapid and early diagnosis of high-risk patients.\u003c/p\u003e","manuscriptTitle":"Pulmonary artery diameter on chest CT predicts in-hospital mortality in patients with COVID-19 pneumonia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-15 17:04:06","doi":"10.21203/rs.3.rs-1748853/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":"9d254452-c1cb-482d-9a69-fccc2da44bec","owner":[],"postedDate":"June 15th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-07-11T09:44:24+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-15 17:04:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1748853","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1748853","identity":"rs-1748853","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}