Lipid-to-neutrophil ratios in predicting in-hospital outcomes in pulmonary thromboembolism

Lipid-to-neutrophil ratios in predicting in-hospital outcomes in pulmonary thromboembolism | 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 Short Report Lipid-to-neutrophil ratios in predicting in-hospital outcomes in pulmonary thromboembolism Neda Roshanravan, Sina Hamzehzadeh, Samad Ghaffari, Sami Rassouli, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3946464/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Acute pulmonary thromboembolism (PTE) is one of the leading causes of death and severe disability. Considering the availability and accessibility of complete blood count (CBC) and lipid profiles, our study was conducted to assess the predictive value of lipid-to-neutrophil count ratios for the short-term survival of PTE patients. Results The population of this analytical cross-sectional study consists of 547 PTE patients of which 41 patients (7.5%) died during hospitalization. There was a significant difference between death and survived groups regarding cholesterol (146.00[60.50] vs. 165.50[59.75]; p-value < 0.01), LDL (80.00[48.00] vs. 102.00[52.00]; p-value < 0.01), HDL (31.00[19.00] vs. 35.00[14.00]; p-value = 0.04). Cholesterol/neutrophil*1000 with a cut-off value of 22.014 (sensitivity: 56.7%; specificity: 61.3%), LDL/neutrophil*1000 with a cut-off value of 10.909 (sensitivity: 69.3%; specificity: 51.9%) and HDL/neutrophile *1000 with a cut-off value of 4.150 (sensitivity: 61.9%; specificity: 58.1%) can predict short-term survival in patients with acute PTE. Based on our findings, patients with higher cholesterol/neutrophil, LDL/neutrophil, and HDL/neutrophil ratios have a better in-hospital prognosis and measurement of lipid-to-neutrophil ratio in the first 24 hours of hospitalization may be a valuable marker for determining the early prognosis of PTE. pulmonary thromboembolism prognosis mortality neutrophil to high-density lipoprotein Figures Figure 1 Introduction Pulmonary thromboembolism (PTE) is the third most common cause of cardiovascular mortality worldwide after stroke and myocardial infarction (MI) [ 1 ]. The annual incidence of PTE is approximately 300,000 to 600,000 cases in the United States (US) and Europe [ 2 ] and it is increasing over the past 20 years [ 3 ]. PTE imposes a significant economic burden on the healthcare system [ 4 ]. Risk stratification plays an important role in patients with acute PTE. Thrombolytic therapy or surgical embolectomy should be considered in patients with high-risk PTE [ 5 ]. There are many clinical risk scores for evaluating PTE prognosis. The most common one is the Pulmonary Embolism Severity Index (PESI) [ 6 ]; beyond that, the availability of some blood parameters can also help us determine the PTE prognosis [ 7 , 8 ]. Neutrophils are one of the major components of leukocytes in the peripheral blood and play a significant role in thrombosis. It can be determined in a cheap and easily available way. Recent studies showed an increase in the blood level of neutrophils in patients with PTE [ 9 , 10 ]. Also, cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglyceride (TG) are found to be associated with the incidence and prognosis of venous thromboembolism (VTE) and PTE [ 11 – 13 ]. Best of our knowledge, there is no study on the association between lipid-to-neutrophil ratios and the mortality of acute PTE. With these considerations, our study was conducted to assess the predictive value of lipid-to-neutrophil ratios for in-hospital mortality (IHM) in patients with acute PTE. Methods This study is an analytical cross-sectional study that was conducted between April 2019 and September 2022 at Shahid Madani Medical and Training Heart Hospital, affiliated with Tabriz University of Medical Sciences (TUOMS). The study process was reviewed and approved by the ethics committee of TUOMS, according to the declaration of Helsinki. The data for this research was retrieved from the Tabriz PTE registry. Informed consent was obtained from all subjects and/or their legal guardian(s) who have participated in our study. Inclusion criteria were hospitalization with confirmed acute PTE diagnosis based on computed tomography (CT) pulmonary angiography by two expert radiologists, age ≥ 18, and availability of complete blood count data (CBC with differential measures of the number of each type of these white blood cells). The exclusion criteria were chronic PTE. IHM was defined as any mortality during hospitalization due to PTE. Therefore, patients with mortality due to other causes were excluded from our sample. Information regarding the demographic characteristics, past medical history, and vital signs (were taken in the emergency unit), laboratory variables based on first fasting results after administration, and outcomes of hospitalization, were collected and demanded ratios were calculated by dividing the lipid levels by the neutrophil counts. Statistics The twenty-sixth version of SPSS Statistics was utilized for statistical analysis. The normality of the destitution of the numeric variables was assessed using the Kolmogorov–Smirnov test. Categorical variables are presented in number and percent. The Chi-square test was utilized for comparing these variables. Numerical parameters are reported in mean ± standard deviation (SD) or median and interquartile range (IQR), based on normality. The comparison between these variables was conducted using the independent sample t-test or Mann-Whitney U test. In addition, multivariate logistic regression analysis was conducted. Finally, the receiver operating characteristics (ROC) curve was used to find the best cut-off for IHM prediction. The cut-off values were calculated using the Youden index [ 14 ], which calculates by this formula: $$sensitivity \left(\%\right)+specificty \left(\%\right)-100=Youden index$$ The cut-offs with the highest Youden index were reported as the optimal cut-offs. In all applied statistics, 95% confidence intervals and a 0.05 level of significance for p-value were observed. Results 547 patients, including 253 males (46.2%) and 294 females (53.8%) participated in this study. In Table 1 demographic characteristics and laboratory findings of patients who died due to PTE are compared to patients who survived it. There was a significant difference between death and survived groups regarding blood urea nitrogen (BUN) (p-value < 0.01), creatinine (p-value < 0.01), red cell distribution width (RDW) (p-value < 0.01), lymphocyte (p-value < 0.01), platelet (PLT) (p-value = 0.04), systolic blood pressure (SBP) (p-value < 0.01), cholesterol (p-value < 0.01), LDL (p-value < 0.01), HDL (p-value = 0.04), and HTN (p-value < 0.01). In Table 2 results of logistic regression analysis for factors associated with PTE IHM are shown. This analysis showed that none of these factors independently play a role in PTE IHM. Table 1 the comparison of characteristics of the patients. The numeric data are presented in median [IQR] or mean ± SD, based on the normality of distributions and nominal data are presented in number (percentage). CTNI: Cardiac Troponin I; BUN: Blood urea nitrogen; Hb: Hemoglobin; HCT: Hematocrit; MCV: Mean corpuscular volume; RDW: Red cell distribution width; WBC: White blood cells; PLT: Platelets; MPV: Mean platelet volume; SBP: Systolic blood pressure; HR: Heart rate; RR: Respiratory rate; BT: Body temperature; TG: Triglycerides; LDL: Low-density lipoprotein; HDL: High-density lipoprotein; HTN: Hypertension; DM: Diabetes mellitus. Characteristics Death (n = 41) Alive (n = 506) p-value Age 77.00 [20.00] 71.00 [28.00] 0.07 Sex (male: female) 20 (48.8%): 21 (51.2%) 233 (46.0%): 273 (54.0%) 0.52 peak CTNI 0.10 [0.17] 0.10 [0.10] 0.16 D-dimer 3.45 [505.93] 2.30 [11.63] 0.97 BUN 27.00 [32.50] 20.00 [13.00] < 0.01* Creatinine 1.25 [0.70] 1.10 [0.40] < 0.01* Blood sugar 116.00 [58.00] 116.00 [58.00] 0.56 Hb 12.97 ± 3.08 13.23 ± 2.26 0.40 HCT 40.40 [12.60] 40.63 [8.40] 0.84 MCV 88.00 [7.70] 86.00 [8.10] 0.08 RDW 50.05 [10.2] 46.90 [6.70] < 0.01* WBC*1000 11.00 [7.30] 9.90 [4.90] 0.32 Neutrophil*1000 7.80 [6.79] 6.95 [4.33] 0.50 Lymphocyte*1000 1.35 [1.06] 1.70 [1.27] < 0.01* PLT*1000 175.00 [149.00] 197.00 [97.25] 0.04* MPV 10.00 [1.90] 9.80 [1.30] 0.16 SBP 110.00 [30.00] 120.00 [30.00] < 0.01* HR 98.00 [26.50] 99.00 [30.00] 0.99 RR 20.00 [6.00] 20.00 [6.00] 0.44 BT 37.00 [0.30] 37.00 [0.30] 0.71 TG 113.00 [72.50] 117.00 [71.00] 0.46 Cholesterol 146.00 [60.50] 165.50 [59.75] < 0.01* LDL 80.00 [48.00] 102.00 [52.00] < 0.01* HDL 31.00 [19.00] 35.00 [14.00] 0.04* HTN 25 (61.0%) 201 (39.7%) < 0.01* DM 12 (29.3%) 90 (17.8%) 0.06 Smoking 8 (19.5%) 65 (12.8%) 0.22 Ratios Cholesterol/Neutrophile*1000 17.75 [15.24] 23.51 [17.45] < 0.01* LDL/Neutrophile*1000 9.47 [9.74] 14.60 [13.42] < 0.01* HDL/Neutrophile*1000 3.37 [3.36] 4.97 [3.81] < 0.01* Table 2 factors associated with in-hospital mortality in multivariant logistic regression analysis. Characteristics B (95%CI) p-value SBP 1.011 (0.994–1.029) 0.207 HTN 2.093 (0.865–5.067) 0.101 DM 1.080 (0.402–2.901) 0.879 TG 1.000 (0.995–1.004) 0.973 Cholesterol 1.010 (0.972–1.049) 0.624 LDL 1.012 (0.964–1.062) 0.641 HDL 0.962 (0.889–1.041) 0.340 Hb 0.951 (0.791–1.142) 0.589 RDW 0.999 (0.979–1.020) 0.937 Neutrophil*1000 1.000 (1.000–1.000) 0.715 Lymphocyte*1000 1.000 (1.000–1.001) 0.102 PLT*1000 1.000 (1.000–1.000) 0.399 peak CTNI 1.181 (0.634–2.199) 0.601 BUN 0.979 (0.952–1.007) 0.143 Creatinine 0.866 (0.579–1.294) 0.482 Cholesterol/Neutrophile 1.013 (0.766–1.339) 0.930 LDL/Neutrophile 0.902 (0.639–1.274) 0.559 HDL/Neutrophile 1.382 (0.808–2.362) 0.238 CTNI: Cardiac Troponin I; Hb: Hemoglobin; RDW: Red cell distribution width; SBP: Systolic blood pressure; HR: Heart rate; RR: Respiratory rate; BT: Body temperature; TG: Triglycerides; LDL: Low-density lipoprotein; HDL: High-density lipoprotein; HTN: Hypertension; DM: Diabetes mellitus; PLT: Platelets; BUN: Blood urea nitrogen. Based on our findings, cholesterol/neutrophil*1000 with a cut-off value of 22.014 can predict short-term survival in PTE patients with 56.7% sensitivity and 61.3% specificity (AUC: 0.71 [95%CI: 0.62–0.80], p-value < 0.01). Also, LDL/neutrophil*1000 with a cut-off value of 10.909 (sensitivity: 69.3%; specificity: 51.9%; AUC: 0.72 [95%CI: 0.63–0.81], p-value < 0.01) and HDL/neutrophile*1000 with a cut-off value of 4.150 (sensitivity: 61.9%; specificity: 58.1%; AUC: 0.59 [95%CI: 0.52–0.67], p-value < 0.01) can predict short-term survival in patients with acute PTE, too. Between the mentioned parameters, LDL/neutrophil, cholesterol/neutrophil, and HDL/neutrophil was found to be the best prognostic factor for the short-term survival of patients with PTE. ROC curves for each of the discussed ratios are presented in Fig. 1 . Discussion This study was designed to assess the predictive value of lipid-to-neutrophile ratios for the short-term survival of patients with PTE. Based on our findings, cholesterol/neutrophil, LDL/neutrophil, and HDL/neutrophil ratios were found to be appropriate predictive factors in PTE patients. PTE has an incidence rate of approximately 60–70 per 100,000, among the general population and if untreated, its mortality can be as high as 30% [ 15 – 17 ]. Because most PTE patients ultimately die within the first hours of presentation, early diagnosis and having an insight into its possible prognosis are of paramount importance [ 18 ]. Few scores are used to determine the prognosis of PTE patients like PESI and simplified PESI score, Geneva score, and 2014 European Society of Cardiology (ESC) mode, however recent studies indicate that the Geneva risk score and 2014 ESC model are not reliable to identify the high-risk PTE patients. Moreover, although the PESI score can be reliable for identifying the low risk of early mortality in PTE patients, clinicians still question its ability to identify the high risk of early mortality in them [ 19 – 21 ]. Recent studies have indicated several laboratory parameters including brain natriuretic peptide (BNP), N-terminal-proB-type Natriuretic Peptide (NT-proBNP), interleukin (IL)-6, IL-8, heart-type fatty acid binding protein (H-FABP), troponin and myoglobin as a possible prognostic factor for PTE patients [ 16 , 17 ], however, accessibility, availability, and cost-effectiveness limit their use in the clinical practice. In this condition, the widely available and accessible parameters such as CBC. diff findings and lipid profile are suggested as an appropriate predictive factor for mortality in PTE patients. Studies have reported ratios like monocytes to HDL ratio or neutrophil to lymphocyte ratio as probable prognostic factors for PTE [ 22 , 23 ]. This study founds cholesterol/neutrophil and LDL/neutrophil, and HDL/neutrophil ratios appropriate predictive factors for IHM in PTE patients. Recently, a new concept called “lipid paradox” has been introduced which means that a lower rate of lipid parameters like serum total cholesterol, LDL, and TG have a significant relationship with a higher rate of IHM in cardiovascular diseases like acute coronary syndrome and myocardial infarction [ 24 ]. From the pathophysiological point of view, the basis of the thrombotic process is inflammation leading to oxidative changes that can decrease cholesterol synthesis. Also, acute-phase reactants can increase cholesterol uptake by hepatocytes [ 25 , 26 ]. In addition, recent studies on mice have shown that HDL and Chol have an important role in lung normal function and have a vital role in the regulation of pulmonary inflammatory response after tissue injury [ 27 , 28 ]. On the other hand, HDL can protect endothelial cells against inflammation and oxidative stress by preventing monocyte flow to the arterial wall, which reduces the expression of CD11b on monocyte and endothelial molecules and prevents the adhesion of monocytes to the endothelial wall [ 29 – 31 ]. Finally, TGs are known as important energy sources for peripheral organs. An increase in acute phase reactions increases the function of lipoprotein lipase that breaks down circulating TGs and results in lower TG levels [ 32 ]. In a study by Karatas et al. serum total cholesterol, LDL, HDL, and TG levels were significantly lower in deceased patients when compared to the surviving PTE patients [ 12 ]. In another study by Avci et al., serum levels of HDL were also significantly lower in deceased PTE patients [ 22 ]. In this study also the Serum total cholesterol, LDL, HDL, and TG levels were significantly lower in PTE patients who died during their hospitalization. On the other hand, studies indicate that leukocyte count could be related to fibrinogen, factor VII, and factor VIII levels and can cause local thrombogenic activity [ 33 , 34 ]. Moreover, stimulated neutrophils may be responsible for vascular injury due to increased cytokines secretion [ 35 ], which can be a result of severe hypoxia caused by pulmonary artery obstruction and an increase in neuro-hormone and adrenergic system activity. This reaction may aggravate thrombosis and the severity of the disease in patients [ 36 ]. In a study by Kayrak et al. WBC, neutrophil, and lymphocyte counts were significantly higher in deceased PTE patients in comparison to survivors [ 37 ]. Another study by ÇAVUŞ et al. also indicates the same result [ 23 ]. In this study lymphocyte count was significantly higher in the mortality group however WBC and neutrophil count didn't have a significant difference between the death and alive groups. Recent investigations suggested the neutrophil to HDL ratio as a prognostic factor for the severity of coronary arteries stenosis [ 38 ], clinical outcomes of patients with MI [ 39 ], and all-cause and cardiovascular mortality in the general population [ 40 ]. As one of the first tries, we investigated the relationship between lipids to neutrophil ratios and IHM of PTE patients in a great cohort and we found cholesterol/neutrophil, HDL/neutrophil and LDL/neutrophil ratios good predictors of short-term survival in PTE patients. Some limitations may affect our findings. One of them is the retrospective design of the study and the second one is the lack of long-term follow-up in the study. Also, its recommended to compare the suggested ratios in this study with other risk scores such as PESI in future studies. In addition, there is a need for future prospective multicenter studies to provide a higher level of evidence in this regard. Conclusion PTE patients with cholesterol/neutrophil*1000 under 22.014, LDL/neutrophil*1000 under 10.909 and HDL/neutrophile*1000 under 4.150 have a higher rate of IHM, which suggests these ratios are a good prognostic factor for predicting short-term mortality in PTE patients. Measurement of lipid to neutrophil in the first 24 hours of hospitalization may be a valuable marker for determining the early prognosis of PTE. Declarations Acknowledgments The research protocol was approved and supported by the Student Research Committee, Tabriz University of Medical Sciences (grant number: 70814). Ethical approval The study process was reviewed and approved by the ethics committee of Tabriz University of Medical Sciences, according to the declaration of Helsinki (ethics code: IR.TBZMED.REC.1401.1011). Consent to participate Informed consent was obtained from all subjects and/or their legal guardian(s) who have participated in our study. Consent for publication Not applicable. Availability of data and materials The dataset analyzed during the current study is available from the corresponding author on a reasonable request. Competing interest None. Funding This work was supported by Deputy for Research of Tabriz University of Medical Sciences. Author Contribution Conceptualization; N.R., S.G., E.J., E.B.; Methodology: N.R., S.G., A.N., E.J., E.B.; Validation: N.R., S.G., E.J.; Formal analysis: A.N., E.B.; Investigation: N.R., S.G., E.J., E.B.; Resources: N.R., S.G., E.J., T.Y.; Writing - Original Draft: S.H., S.R., A.N., E.B.; Writing - Review & Editing: N.R., S.G., T.Y., E.J.; Visualization: S.R., A.N., E.B.; Supervision: N.R., S.G., T.Y., E.J.; Project administration: S.H., E.B., A.N., E.J.; Funding acquisition: E.B., A.N., E.J. All authors reviewed the manuscript. References Essien EO, Rali P, Mathai SC. Pulmonary Embolism. Med Clin North Am. 2019;103(3):549-64. Cohen AT, Agnelli G, Anderson FA, Arcelus JI, Bergqvist D, Brecht JG, et al. 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Am J Cardiol. 2012;109(1):128-34. Kayrak M, Erdoğan HI, Solak Y, Akilli H, Gül EE, Yildirim O, et al. Prognostic value of neutrophil to lymphocyte ratio in patients with acute pulmonary embolism: a restrospective study. Heart Lung Circ. 2014;23(1):56-62. Kou T, Luo H, Yin L. Relationship between neutrophils to HDL-C ratio and severity of coronary stenosis. BMC Cardiovasc Disord. 2021;21(1):127. Huang J-B, Chen Y-S, Ji H-Y, Xie W-M, Jiang J, Ran L-S, et al. Neutrophil to high-density lipoprotein ratio has a superior prognostic value in elderly patients with acute myocardial infarction: a comparison study. Lipids in Health and Disease. 2020;19(1):59. Jiang M, Sun J, Zou H, Li M, Su Z, Sun W, et al. Prognostic Role of Neutrophil to High-Density Lipoprotein Cholesterol Ratio for All-Cause and Cardiovascular Mortality in the General Population. Front Cardiovasc Med. 2022;9:807339. Additional Declarations No competing interests reported. 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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-3946464","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":273122283,"identity":"2451ba50-44c5-47a4-b9bf-c35ea795edd3","order_by":0,"name":"Neda Roshanravan","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Neda","middleName":"","lastName":"Roshanravan","suffix":""},{"id":273122284,"identity":"18a210cb-c648-4520-a3be-5fd68edf197c","order_by":1,"name":"Sina Hamzehzadeh","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sina","middleName":"","lastName":"Hamzehzadeh","suffix":""},{"id":273122285,"identity":"af898ceb-95f5-4f6a-82e8-97cee8741161","order_by":2,"name":"Samad Ghaffari","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Samad","middleName":"","lastName":"Ghaffari","suffix":""},{"id":273122286,"identity":"a304f4c8-36d6-4d18-9a37-e1f57dab3b93","order_by":3,"name":"Sami Rassouli","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sami","middleName":"","lastName":"Rassouli","suffix":""},{"id":273122287,"identity":"db16c3ca-b62c-4a24-9046-3c548e3bb1c5","order_by":4,"name":"Amirreza Naseri","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Amirreza","middleName":"","lastName":"Naseri","suffix":""},{"id":273122288,"identity":"6606c7d0-fc5a-4a5b-9800-7dd0ea1bfcb1","order_by":5,"name":"Tohid Yahyapoor","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tohid","middleName":"","lastName":"Yahyapoor","suffix":""},{"id":273122289,"identity":"66eb8690-9a28-4c6d-9d9c-9d38799e9523","order_by":6,"name":"Elnaz Javanshir","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Elnaz","middleName":"","lastName":"Javanshir","suffix":""},{"id":273122290,"identity":"71f3ecea-0d69-457b-9153-cc7385827aa0","order_by":7,"name":"Erfan Banisefid","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBAC+wbGBwyMDQwMbOxAgsHAgrAWAwZmA4gWngMgrgQJWhgkEkB8YrRIH2b8XLjDLo9P8vnVDT8KJBj427sT8Gqx50tmlp55JrmYTTqn7GYP0GESZ85uwG8LD/8Bad425sQ26Zy0GzxALQYSuYS0MDP/5m2rT2yTPJN28w+RWtiAthxObJNgP3abWFvYrHnPHE9s48lhuy1jIMFD0C/2PczMt3l3VCfObz/+7OabPzZy/O29+LUgAR4DMEmschBgf0CK6lEwCkbBKBhBAABuFj+2sQ5u+gAAAABJRU5ErkJggg==","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Erfan","middleName":"","lastName":"Banisefid","suffix":""}],"badges":[],"createdAt":"2024-02-10 18:02:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3946464/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3946464/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51331408,"identity":"be607ab1-b816-47ff-99fe-3ccf8278a044","added_by":"auto","created_at":"2024-02-19 17:53:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43641,"visible":true,"origin":"","legend":"\u003cp\u003ethe ROC curves for predicting short-term survival for the variables investigated in this study.\u003c/p\u003e\n\u003cp\u003eLDL: Low-density lipoprotein; HDL: High-density lipoprotein; Chol: Total cholesterol.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3946464/v1/1a6589b5ecc354f526dc9b41.png"},{"id":52198189,"identity":"41484419-dd08-4631-9e84-a6c03a2adba4","added_by":"auto","created_at":"2024-03-07 20:29:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":405347,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3946464/v1/3cd20060-62f4-4344-82ce-b7b531c4273e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lipid-to-neutrophil ratios in predicting in-hospital outcomes in pulmonary thromboembolism","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePulmonary thromboembolism (PTE) is the third most common cause of cardiovascular mortality worldwide after stroke and myocardial infarction (MI) [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The annual incidence of PTE is approximately 300,000 to 600,000 cases in the United States (US) and Europe [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e] and it is increasing over the past 20 years [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. PTE imposes a significant economic burden on the healthcare system [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eRisk stratification plays an important role in patients with acute PTE. Thrombolytic therapy or surgical embolectomy should be considered in patients with high-risk PTE [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. There are many clinical risk scores for evaluating PTE prognosis. The most common one is the Pulmonary Embolism Severity Index (PESI) [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]; beyond that, the availability of some blood parameters can also help us determine the PTE prognosis [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eNeutrophils are one of the major components of leukocytes in the peripheral blood and play a significant role in thrombosis. It can be determined in a cheap and easily available way. Recent studies showed an increase in the blood level of neutrophils in patients with PTE [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Also, cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglyceride (TG) are found to be associated with the incidence and prognosis of venous thromboembolism (VTE) and PTE [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eBest of our knowledge, there is no study on the association between lipid-to-neutrophil ratios and the mortality of acute PTE. With these considerations, our study was conducted to assess the predictive value of lipid-to-neutrophil ratios for in-hospital mortality (IHM) in patients with acute PTE.\u003c/p\u003e"},{"header":"Methods ","content":"\u003cp\u003eThis study is an analytical cross-sectional study that was conducted between April 2019 and September 2022 at Shahid Madani Medical and Training Heart Hospital, affiliated with Tabriz University of Medical Sciences (TUOMS). The study process was reviewed and approved by the ethics committee of TUOMS, according to the declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eThe data for this research was retrieved from the Tabriz PTE registry. Informed consent was obtained from all subjects and/or their legal guardian(s) who have participated in our study. Inclusion criteria were hospitalization with confirmed acute PTE diagnosis based on computed tomography (CT) pulmonary angiography by two expert radiologists, age\u0026thinsp;\u0026ge;\u0026thinsp;18, and availability of complete blood count data (CBC with differential measures of the number of each type of these white blood cells). The exclusion criteria were chronic PTE. IHM was defined as any mortality during hospitalization due to PTE. Therefore, patients with mortality due to other causes were excluded from our sample. Information regarding the demographic characteristics, past medical history, and vital signs (were taken in the emergency unit), laboratory variables based on first fasting results after administration, and outcomes of hospitalization, were collected and demanded ratios were calculated by dividing the lipid levels by the neutrophil counts.\u003c/p\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eThe twenty-sixth version of SPSS Statistics was utilized for statistical analysis. The normality of the destitution of the numeric variables was assessed using the Kolmogorov\u0026ndash;Smirnov test. Categorical variables are presented in number and percent. The Chi-square test was utilized for comparing these variables. Numerical parameters are reported in mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median and interquartile range (IQR), based on normality. The comparison between these variables was conducted using the independent sample t-test or Mann-Whitney U test. In addition, multivariate logistic regression analysis was conducted. Finally, the receiver operating characteristics (ROC) curve was used to find the best cut-off for IHM prediction. The cut-off values were calculated using the Youden index [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], which calculates by this formula:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$sensitivity \\left(\\%\\right)+specificty \\left(\\%\\right)-100=Youden index$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe cut-offs with the highest Youden index were reported as the optimal cut-offs. In all applied statistics, 95% confidence intervals and a 0.05 level of significance for p-value were observed.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e547 patients, including 253 males (46.2%) and 294 females (53.8%) participated in this study. In Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demographic characteristics and laboratory findings of patients who died due to PTE are compared to patients who survived it. There was a significant difference between death and survived groups regarding blood urea nitrogen (BUN) (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), creatinine (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), red cell distribution width (RDW) (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), lymphocyte (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), platelet (PLT) (p-value\u0026thinsp;=\u0026thinsp;0.04), systolic blood pressure (SBP) (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), cholesterol (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), LDL (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), HDL (p-value\u0026thinsp;=\u0026thinsp;0.04), and HTN (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e results of logistic regression analysis for factors associated with PTE IHM are shown. This analysis showed that none of these factors independently play a role in PTE IHM.\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\u003ethe comparison of characteristics of the patients. The numeric data are presented in median [IQR] or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, based on the normality of distributions and nominal data are presented in number (percentage). CTNI: Cardiac Troponin I; BUN: Blood urea nitrogen; Hb: Hemoglobin; HCT: Hematocrit; MCV: Mean corpuscular volume; RDW: Red cell distribution width; WBC: White blood cells; PLT: Platelets; MPV: Mean platelet volume; SBP: Systolic blood pressure; HR: Heart rate; RR: Respiratory rate; BT: Body temperature; TG: Triglycerides; LDL: Low-density lipoprotein; HDL: High-density lipoprotein; HTN: Hypertension; DM: Diabetes mellitus.\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\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeath (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive (n\u0026thinsp;=\u0026thinsp;506)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.00 [20.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.00 [28.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex (male: female)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (48.8%): 21 (51.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233 (46.0%): 273 (54.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epeak CTNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10 [0.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10 [0.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD-dimer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.45 [505.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.30 [11.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBUN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.00 [32.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.00 [13.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25 [0.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10 [0.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood sugar\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.00 [58.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116.00 [58.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.40 [12.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.63 [8.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMCV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.00 [7.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.00 [8.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRDW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.05 [10.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.90 [6.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC*1000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.00 [7.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.90 [4.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutrophil*1000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.80 [6.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.95 [4.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphocyte*1000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35 [1.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70 [1.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLT*1000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175.00 [149.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197.00 [97.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMPV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.00 [1.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.80 [1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110.00 [30.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.00 [30.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.00 [26.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.00 [30.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.00 [6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.00 [6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.00 [0.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.00 [0.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113.00 [72.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117.00 [71.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCholesterol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146.00 [60.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165.50 [59.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.00 [48.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.00 [52.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.00 [19.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.00 [14.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHTN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (61.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201 (39.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRatios\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCholesterol/Neutrophile*1000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.75 [15.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.51 [17.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL/Neutrophile*1000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.47 [9.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.60 [13.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL/Neutrophile*1000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.37 [3.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.97 [3.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01*\u003c/b\u003e\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 \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\u003e factors associated with in-hospital mortality in multivariant logistic regression analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.011 (0.994\u0026ndash;1.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHTN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.093 (0.865\u0026ndash;5.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.080 (0.402\u0026ndash;2.901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000 (0.995\u0026ndash;1.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCholesterol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.010 (0.972\u0026ndash;1.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.012 (0.964\u0026ndash;1.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.962 (0.889\u0026ndash;1.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.951 (0.791\u0026ndash;1.142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRDW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.999 (0.979\u0026ndash;1.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutrophil*1000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000 (1.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphocyte*1000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000 (1.000\u0026ndash;1.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLT*1000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000 (1.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epeak CTNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.181 (0.634\u0026ndash;2.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBUN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.979 (0.952\u0026ndash;1.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.866 (0.579\u0026ndash;1.294)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCholesterol/Neutrophile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.013 (0.766\u0026ndash;1.339)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL/Neutrophile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.902 (0.639\u0026ndash;1.274)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL/Neutrophile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.382 (0.808\u0026ndash;2.362)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCTNI: Cardiac Troponin I; Hb: Hemoglobin; RDW: Red cell distribution width; SBP: Systolic blood pressure; HR: Heart rate; RR: Respiratory rate; BT: Body temperature; TG: Triglycerides; LDL: Low-density lipoprotein; HDL: High-density lipoprotein; HTN: Hypertension; DM: Diabetes mellitus; PLT: Platelets; BUN: Blood urea nitrogen.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on our findings, cholesterol/neutrophil*1000 with a cut-off value of 22.014 can predict short-term survival in PTE patients with 56.7% sensitivity and 61.3% specificity (AUC: 0.71 [95%CI: 0.62\u0026ndash;0.80], p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Also, LDL/neutrophil*1000 with a cut-off value of 10.909 (sensitivity: 69.3%; specificity: 51.9%; AUC: 0.72 [95%CI: 0.63\u0026ndash;0.81], p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and HDL/neutrophile*1000 with a cut-off value of 4.150 (sensitivity: 61.9%; specificity: 58.1%; AUC: 0.59 [95%CI: 0.52\u0026ndash;0.67], p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) can predict short-term survival in patients with acute PTE, too. Between the mentioned parameters, LDL/neutrophil, cholesterol/neutrophil, and HDL/neutrophil was found to be the best prognostic factor for the short-term survival of patients with PTE. ROC curves for each of the discussed ratios are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study was designed to assess the predictive value of lipid-to-neutrophile ratios for the short-term survival of patients with PTE. Based on our findings, cholesterol/neutrophil, LDL/neutrophil, and HDL/neutrophil ratios were found to be appropriate predictive factors in PTE patients.\u003c/p\u003e \u003cp\u003ePTE has an incidence rate of approximately 60\u0026ndash;70 per 100,000, among the general population and if untreated, its mortality can be as high as 30% [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Because most PTE patients ultimately die within the first hours of presentation, early diagnosis and having an insight into its possible prognosis are of paramount importance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Few scores are used to determine the prognosis of PTE patients like PESI and simplified PESI score, Geneva score, and 2014 European Society of Cardiology (ESC) mode, however recent studies indicate that the Geneva risk score and 2014 ESC model are not reliable to identify the high-risk PTE patients. Moreover, although the PESI score can be reliable for identifying the low risk of early mortality in PTE patients, clinicians still question its ability to identify the high risk of early mortality in them [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Recent studies have indicated several laboratory parameters including brain natriuretic peptide (BNP), N-terminal-proB-type Natriuretic Peptide (NT-proBNP), interleukin (IL)-6, IL-8, heart-type fatty acid binding protein (H-FABP), troponin and myoglobin as a possible prognostic factor for PTE patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], however, accessibility, availability, and cost-effectiveness limit their use in the clinical practice. In this condition, the widely available and accessible parameters such as CBC. diff findings and lipid profile are suggested as an appropriate predictive factor for mortality in PTE patients. Studies have reported ratios like monocytes to HDL ratio or neutrophil to lymphocyte ratio as probable prognostic factors for PTE [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This study founds cholesterol/neutrophil and LDL/neutrophil, and HDL/neutrophil ratios appropriate predictive factors for IHM in PTE patients.\u003c/p\u003e \u003cp\u003eRecently, a new concept called \u0026ldquo;lipid paradox\u0026rdquo; has been introduced which means that a lower rate of lipid parameters like serum total cholesterol, LDL, and TG have a significant relationship with a higher rate of IHM in cardiovascular diseases like acute coronary syndrome and myocardial infarction [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. From the pathophysiological point of view, the basis of the thrombotic process is inflammation leading to oxidative changes that can decrease cholesterol synthesis. Also, acute-phase reactants can increase cholesterol uptake by hepatocytes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In addition, recent studies on mice have shown that HDL and Chol have an important role in lung normal function and have a vital role in the regulation of pulmonary inflammatory response after tissue injury [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. On the other hand, HDL can protect endothelial cells against inflammation and oxidative stress by preventing monocyte flow to the arterial wall, which reduces the expression of CD11b on monocyte and endothelial molecules and prevents the adhesion of monocytes to the endothelial wall [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Finally, TGs are known as important energy sources for peripheral organs. An increase in acute phase reactions increases the function of lipoprotein lipase that breaks down circulating TGs and results in lower TG levels [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In a study by Karatas et al. serum total cholesterol, LDL, HDL, and TG levels were significantly lower in deceased patients when compared to the surviving PTE patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In another study by Avci et al., serum levels of HDL were also significantly lower in deceased PTE patients [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this study also the Serum total cholesterol, LDL, HDL, and TG levels were significantly lower in PTE patients who died during their hospitalization.\u003c/p\u003e \u003cp\u003eOn the other hand, studies indicate that leukocyte count could be related to fibrinogen, factor VII, and factor VIII levels and can cause local thrombogenic activity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, stimulated neutrophils may be responsible for vascular injury due to increased cytokines secretion [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], which can be a result of severe hypoxia caused by pulmonary artery obstruction and an increase in neuro-hormone and adrenergic system activity. This reaction may aggravate thrombosis and the severity of the disease in patients [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In a study by Kayrak et al. WBC, neutrophil, and lymphocyte counts were significantly higher in deceased PTE patients in comparison to survivors [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Another study by \u0026Ccedil;AVUŞ et al. also indicates the same result [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study lymphocyte count was significantly higher in the mortality group however WBC and neutrophil count didn't have a significant difference between the death and alive groups.\u003c/p\u003e \u003cp\u003eRecent investigations suggested the neutrophil to HDL ratio as a prognostic factor for the severity of coronary arteries stenosis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], clinical outcomes of patients with MI [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and all-cause and cardiovascular mortality in the general population [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. As one of the first tries, we investigated the relationship between lipids to neutrophil ratios and IHM of PTE patients in a great cohort and we found cholesterol/neutrophil, HDL/neutrophil and LDL/neutrophil ratios good predictors of short-term survival in PTE patients. Some limitations may affect our findings. One of them is the retrospective design of the study and the second one is the lack of long-term follow-up in the study. Also, its recommended to compare the suggested ratios in this study with other risk scores such as PESI in future studies. In addition, there is a need for future prospective multicenter studies to provide a higher level of evidence in this regard.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePTE patients with cholesterol/neutrophil*1000 under 22.014, LDL/neutrophil*1000 under 10.909 and HDL/neutrophile*1000 under 4.150 have a higher rate of IHM, which suggests these ratios are a good prognostic factor for predicting short-term mortality in PTE patients. Measurement of lipid to neutrophil in the first 24 hours of hospitalization may be a valuable marker for determining the early prognosis of PTE.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe research protocol was approved and supported by the Student Research Committee, Tabriz University of Medical Sciences (grant number: 70814).\u003c/p\u003e\n\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eThe study process was reviewed and approved by the ethics committee of Tabriz University of Medical Sciences, according to the declaration of Helsinki (ethics code: IR.TBZMED.REC.1401.1011).\u003c/p\u003e\n\u003ch2\u003eConsent to participate\u003c/h2\u003e\n\u003cp\u003eInformed consent was obtained from all subjects and/or their legal guardian(s) who have participated in our study.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe dataset analyzed during the current study is available from the corresponding author on a reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interest\u003c/h2\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by Deputy for Research of Tabriz University of Medical Sciences.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization; N.R., S.G., E.J., E.B.; Methodology: N.R., S.G., A.N., E.J., E.B.; Validation: N.R., S.G., E.J.; Formal analysis: A.N., E.B.; Investigation: N.R., S.G., E.J., E.B.; Resources: N.R., S.G., E.J., T.Y.; Writing - Original Draft: S.H., S.R., A.N., E.B.; Writing - Review \u0026amp; Editing: N.R., S.G., T.Y., E.J.; Visualization: S.R., A.N., E.B.; Supervision: N.R., S.G., T.Y., E.J.; Project administration: S.H., E.B., A.N., E.J.; Funding acquisition: E.B., A.N., E.J. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEssien EO, Rali P, Mathai SC. Pulmonary Embolism. Med Clin North Am. 2019;103(3):549-64.\u003c/li\u003e\n\u003cli\u003eCohen AT, Agnelli G, Anderson FA, Arcelus JI, Bergqvist D, Brecht JG, et al. Venous thromboembolism (VTE) in Europe. The number of VTE events and associated morbidity and mortality. Thromb Haemost. 2007;98(4):756-64.\u003c/li\u003e\n\u003cli\u003eBarco S, Valerio L, Gallo A, Turatti G, Mahmoudpour SH, Ageno W, et al. Global reporting of pulmonary embolism-related deaths in the World Health Organization mortality database: Vital registration data from 123 countries. Res Pract Thromb Haemost. 2021;5(5):e12520.\u003c/li\u003e\n\u003cli\u003eGrosse SD, Nelson RE, Nyarko KA, Richardson LC, Raskob GE. The economic burden of incident venous thromboembolism in the United States: A review of estimated attributable healthcare costs. 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Front Cardiovasc Med. 2022;9:807339.\u003c/li\u003e\n\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":"pulmonary thromboembolism, prognosis, mortality, neutrophil to high-density lipoprotein","lastPublishedDoi":"10.21203/rs.3.rs-3946464/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3946464/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eAcute pulmonary thromboembolism (PTE) is one of the leading causes of death and severe disability. Considering the availability and accessibility of complete blood count (CBC) and lipid profiles, our study was conducted to assess the predictive value of lipid-to-neutrophil count ratios for the short-term survival of PTE patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe population of this analytical cross-sectional study consists of 547 PTE patients of which 41 patients (7.5%) died during hospitalization. There was a significant difference between death and survived groups regarding cholesterol (146.00[60.50] vs. 165.50[59.75]; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), LDL (80.00[48.00] vs. 102.00[52.00]; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), HDL (31.00[19.00] vs. 35.00[14.00]; p-value\u0026thinsp;=\u0026thinsp;0.04). Cholesterol/neutrophil*1000 with a cut-off value of 22.014 (sensitivity: 56.7%; specificity: 61.3%), LDL/neutrophil*1000 with a cut-off value of 10.909 (sensitivity: 69.3%; specificity: 51.9%) and HDL/neutrophile *1000 with a cut-off value of 4.150 (sensitivity: 61.9%; specificity: 58.1%) can predict short-term survival in patients with acute PTE. Based on our findings, patients with higher cholesterol/neutrophil, LDL/neutrophil, and HDL/neutrophil ratios have a better in-hospital prognosis and measurement of lipid-to-neutrophil ratio in the first 24 hours of hospitalization may be a valuable marker for determining the early prognosis of PTE.\u003c/p\u003e","manuscriptTitle":"Lipid-to-neutrophil ratios in predicting in-hospital outcomes in pulmonary thromboembolism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-19 17:53:12","doi":"10.21203/rs.3.rs-3946464/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":"c8a3dddf-ce8f-409f-8b10-d244fb1c2b7a","owner":[],"postedDate":"February 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-19T21:29:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-19 17:53:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3946464","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3946464","identity":"rs-3946464","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}