{"paper_id":"1dae3ecb-34de-407f-acfc-93368c981331","body_text":"Blood Culture Negative Endocarditis; What Do Concurrent Cardiac Disorders Tell Us? | 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 Blood Culture Negative Endocarditis; What Do Concurrent Cardiac Disorders Tell Us? Mine Filiz, Hakan Erdem, Handan Ankarali, Edmond Puca, Yvon Ruch, and 58 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4177924/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 Purpose: Blood culture negative endocarditis (BCNE) presents diagnostic challenges, necessitating precise identification of high-risk patient cohorts. Hence, our objective was to pinpoint high-risk cohorts for BCNE. Methods: The study included adult patients with definite endocarditis. Data was collected via the Infectious Diseases International Research Initiative (ID-IRI). The primary study analysing one of the largest case series ever reported and was published in the European Journal of Clinical Microbiology and Infectious Diseases in 2019, was conducted across 41 centres in 13 countries. We performed a re-analysis of the database to determine the predictors of BCNE. Results: 867 cases with definite endocarditis were included in the study. Blood cultures were negative in 101 (11.65%) patients. We disclosed that as patients age, the likelihood of a negative blood culture significantly decreases (OR 0.975, 95% CI 0.963-0.987, p<0.001). Additionally, factors such as rheumatic heart disease (OR 2.036, 95% CI 0.970-4.276, p=0.049), aortic stenosis (OR 3.066, 95% CI 1.564-6.010, p=0.001), mitral regurgitation (OR 1.693, 95% CI 1.012-2.833, p=0.045), and prosthetic valves (OR 2.539, 95% CI 1.599-4.031, p<0.001) are associated with higher likelihoods of negative blood cultures. Our model can predict whether a patient falls into the culture-negative or culture-positive groups with a threshold of 0.104 (AUC±SE=0.707±0.027). The final model demonstrates a sensitivity of 70.3% and a specificity of 57.0%. Conclusion: Our study reveals a high prevalence of negative blood cultures in patients with concurrent cardiac disorders, particularly in younger cases. Therefore, caution is indicated in diagnosing and treating endocarditis in these particular patient subgroups. Infective endocarditis blood culture negative endocarditis rheumatic heart disease prosthetic valves cardiac disorders Figures Figure 1 Figure 2 INTRODUCTION Infective endocarditis (IE) poses a significant challenge to public health, claiming > 66,000 lives globally in 2019 [ 1 ]. Despite advances in diagnosis and treatment, IE morbidity and mortality rates remain high [ 2 , 3 ]. The primary aim of the clinician managing endocarditis is to decrease complications and deaths by rapid diagnosis and administering appropriate treatment. Accordingly, early targeted therapy hinges on identifying the responsible microorganism through blood culture [ 4 ]. Nonetheless, blood culture negative endocarditis (BCNE) constitutes a notable portion of all cases (2.5–31%) and presents diagnostic complexities, leading to higher long-term mortality rates compared to blood culture positive endocarditis (BCPE) [ 5 , 6 ]. Our understanding is constrained by the scarcity of randomized trials and meta-analyses on BCNE. To address this gap, we conducted an analysis to pinpoint risk groups for BCNE by contrasting the features of BCNE and BCPE utilizing the dataset from our previously published international study [ 7 ], recognized as one of the largest case series ever documented in literature. METHODS Study Design The study was conducted between January 1, 2015, and October 1, 2018, involving 41 centers spanning 13 countries and published in the European Journal of Clinical Microbiology and Infectious Diseases [ 7 ]. The primary data collection was facilitated by the Infectious Diseases International Research Initiative (ID-IRI), and we conducted a re-evaluation of the database to determine the predictors of BCNE. Participants & Definitions The study comprised patients aged 18 and above, diagnosed with definite infective endocarditis according to the modified Duke criteria and the European Society of Cardiology guideline [ 8 ]. BCPE was defined as the presence of growth in blood culture and/or tissue culture obtained from the excised valve or vegetation, while BCNE was defined as the failure to identify the pathogen. Thus, comparisons were made between these two groups. Statistical Methods Descriptive statistics were presented as frequency and percent or mean ± standard deviation (SD) or median and range. Chi-square and Fisher’s exact tests were used to compare categorical variables and Student’s t test and Mann-Whitney U test were used for comparisons of continuous variables. A logistic regression test was performed for multivariate analysis with the composite endpoint. In the univariate analysis, risk factors influencing the blood culture results at a significance level of p < 0.05 were incorporated into the multivariate binary logistic regression model. The \"Backward Variable Elimination Method\" was employed in this process, removing variables without statistical significance until only significant variables remained. A p value < 0.05 was considered significant. RESULTS A total of 867 cases with a definite diagnosis of IE were included in the study. Among these, 292 (33.7%) were female and the median age was 59.5 (16–96) years. A total of 711 cases had 2 major criteria and 136 cases had 1 major and ≥ 3 minor criteria. Microbiological data Blood cultures were negative in a total of 101 (11.65%) patients. Among the remaining 766 patients (88.4%) with identifiable microorganisms from blood cultures, the predominant pathogens were Staphylococcus aureus (n = 267, 33.6%), Streptococcus viridans (n = 149, 18.7%), enterococci (n = 128, 16.1%), and coagulase-negative staphylococci (n = 92, 11.6%). The details can be found elsewhere [ 7 ]. Univariate analyses Demographic characteristics and concurrent cardiac disorders/conditions based on blood culture results were shown in Table 1 , along with univariate comparisons. BCNE cases were younger than BCPE cases [median age 55 (18–86) versus 63 (16–96), p < 0.001]. The percentage of BCNE was found to be statistically significantly higher in the presences of rheumatic heart disease, congenital heart disease, prosthetic valves, mitral regurgitation, aortic stenosis, mitral stenosis, and was lower in the absence of a pacemaker (Table 1 ). Table 1 Demographic characteristics and coexistent cardiac disorders/conditions according to blood culture results Culture Negative Endocarditis (n = 101, 11.65%) Total (N = 867) p value Age Median (Min – Max) 55 (18–86) 59,5 (16–96) < 0.001 Gender (n,%) Female 27 (9.2%) 292 0.116 Male 74 (12.9%) 575 Ischemic Heart Disease (n,%) Absent 92 (12.2%) 756 0.213 Present 9 (8.1%) 111 Congestive Heart Failure (n,%) Absent 90 (11.7%) 766 0.800 Present 11 (10.9%) 101 Degenerative Cardiac Lesions (n,%) Absent 94 (11.7%) 802 0.818 Present 7 (10.8%) 65 Cardiomyopathy (n,%) Absent 96 (12%) 800 0.266 Present 5 (7.5%) 67 Rheumatic Heart Disease (n,%) Absent 89 (10.9%) 817 0.005 Present 12 (24%) 50 Congenital Heart Disease ( n,%) Absent 94 (11.2%) 838 0.033 Present 7 (24.1%) 29 Cardiac Implants (Subtotal) (n,%) Absent 59 (10.1%) 584 0.041 Present 42 (14.8%) 283 1. Prosthetic valves (n,%) Absent 62 (9.8%) 635 0.004 Present 39 (16.8%) 232 2. Pacemaker use (n,%) Absent 97 (12.4%) 785 0.045 Present 4 (4.9%) 82 Valvular Problems (Subtotal) (n,%) Absent 61 (10.3%) 592 0.070 Present 40 (14.5%) 275 1. Mitral regurgitation (n,%) Absent 75 (10.6%) 708 0.041 Present 26 (16.4%) 159 2. Aortic regurgitation (n,%) Absent 88 (11.3%) 782 0.270 Present 13 (15.3%) 85 3. Aortic stenosis (n,%) Absent 87 (10.8%) 805 0.005 Present 14 (22.6%) 62 4. Tricuspid regurgitation (n,%) Absent 93 (11.5%) 812 0.489 Present 8 (14.5%) 55 5. Mitral stenosis (n,%) Absent 92 (11.1%) 829 0.018 Present 9 (23.7%) 38 Antibiotic use prior to blood culture sampling (n = 377) (n, %) Yes 53 (36.1%) 147 < 0.001 No 31 (13.5%) 230 Final model The outcomes of our ultimate model indicate that the likelihood of a negative blood culture decreases significantly by 0.975 times (OR 0.975, 95% CI 0.963–0.987, p < 0.001) with each year increase in age. Furthermore, it was noted to increase by 2.036 times (OR 2.036, 95% CI 0.970–4.276, p = 0.049) in patients with rheumatic heart disease, 3.066 times (OR 3.066, 95% CI 1.564–6.010, p = 0.001) in those with aortic stenosis, 1.693 times (OR 1.693, 95% CI 1.012–2.833, p = 0.045) in individuals with mitral regurgitation, and 2.539 times (OR 2.539, 95% CI 1.599–4.031, p < 0.001) in patients with prosthetic valves (Table 2 ). Table 2 Final model results B S.E. OR 95% C.I.for OR Lower Upper p Age -0.025 0.006 0.975 0.963 0.987 < 0.001 Rheumatic Heart Disease (Present versus Absent) 0.711 0.378 2.036 0.970 4.276 0.049 Aortic Stenosis (Present versus Absent) 1.120 0.343 3.066 1.564 6.010 0.001 Mitral Regurgitation (Present versus Absent) 0.527 0.263 1.693 1.012 2.833 0.045 Prosthetic Valves (Present versus Absent) 0.932 0.236 2.539 1.599 4.031 < 0.001 Constant -1.157 0.374 0.314 0.002 B: Regression coefficient, S.E.: Standard error, C.I.: Confidence interval, OR: Odds ratio The power of the model The predictions of our model indicate the probabilities of patients belonging to culture-negative or culture-positive groups. When the cut-off value for these probabilities was calculated using the ROC analysis, the cut-off value corresponding to the best diagnostic success was found to be 0.104. The area under curve (AUC ± SE) was calculated to be 0.707 ± 0.027 (Fig. 1 ). The classification of patients based on the cut-off value determined according to the model is shown in Table 3 (p < 0.001). With a cut-off value of 0.104, the sensitivity of the final model was 70.3%, the specificity 57.0%, the positive predictive value 93.6% and the negative predictive value 17.8%. Table 3 The classification of patients based on the cut-off value determined according to the nomogram Blood Culture Negative Endocarditis Blood Culture Positive Endocarditis Total (N, %) n % within Model predictions % within Blood Culture n % within Model predictions % within Blood Culture Model Predictions Negative 71 17.8% (NPV) 70.3% (Sensitivity) 329 --- 43.0% (FN) 400 Positive 30 --- 29.7% (FP) 437 93.6% (PPV) 57.0% (Specificity) 467 Total (N, %) 101 766 867 NPV: Negative predictive value, PPV: Positive predictive value, FP: False positive, FN: False negative, --- : The % in these cells were not written because they do not have any meaning. The nomogram, which facilitates the interpretation of the final model, is shown in Fig. 2 . The value \"Prob\" at the bottom of the figure corresponds to the probability of a patient's blood culture result being negative. The higher this value is, the greater the probability of a negative blood culture. DISCUSSION Identifying IE is easier in individuals with ongoing bacteremia [ 8 , 9 ]. Yet, when BCNE emerges from uncommon or fastidious bacteria, or less virulent bacteria from normal flora, diagnosis becomes challenging. Due to their lower aggressiveness, detecting these bacteria through blood cultures is less likely [ 10 ]. In promptly formulating diagnostic approaches for BCNE cases, it is crucial to pinpoint specific patient cohorts warranting suspicion. Hence, in this study we demonstrated a significant reduction in the likelihood of a negative blood culture as age advances, while it rises in patients with underlying cardiac tissue damage like rheumatic heart disease or valvular problems such as aortic stenosis, mitral regurgitation, and implanted prosthetic valves. Research conducted in developing nations identified rheumatic heart disease as the primary risk factor for IE, with nearly half of the patients experiencing BCNE [ 11 , 12 ]. However, detailed subgroup analysis of this data was lacking. Our study revealed a twofold increase in BCNE incidence among individuals with rheumatic heart disease. On the other hand, a younger demographic was more prevalent in BCNE cases compared to BCPE patients in our study, aligning with the existing literature [ 6 , 13 , 14 ]. This suggests that rheumatic heart disease may lead to the early onset of IE, particularly in BCNE format. There are BCNE case reports in the literature related to patients with damaged heart tissues [ 15 – 18 ]. However, the data on this subject is limited and lacks of a holistic perspective. In a review discussing BCNE risk factors, beyond well-known factors such as exposure to fastidious bacteria and pre-blood culture antibiotic usage, individuals with underlying valvular heart disease, right-sided endocarditis, the presence of intra-cardiac or vascular devices, and contact with foreign bodies in the bloodstream have also been recognized as risk factors [ 19 ]. Accordingly, in a 23-years descriptive analysis conducted at a tertiary center in Switzerland, the most common cardiac predisposing factor for BCNE was the implanted prosthetic valves (42%) [ 20 ]. Similar findings were observed in other studies analyzing BCNE characteristics, indicating that the most commonly noted cardiac predisposing factor was prosthetic valves [ 13 , 14 ]. Similarly, our study indicated a 2.5-fold increase in the probability of negative blood cultures when prosthetic valves were present. The European Endocarditis (EURO-ENDO) international study represented one of the most extensive case series concerning BCNE (16.8%), involving the evaluation of 3113 patients. Unlike our research, definite and possible cases of IE were collectively analyzed in that study. Throughout the follow-up period, heart failure resulting from valve dysfunction was more commonly observed in BCNE patients compared to BCPE patients [ 6 ]. In addition, in a Spanish cohort study where 1001 patients with a definitive IE were evaluated, aortic problems in terms of valvular dysfunction were more commonly detected in BCNE patients (8.3%) despite having fewer other comorbidities [ 21 ]. Thus, our study revealed a rise in the occurrence of BCNE in patients with heart valve dysfunction like aortic stenosis and mitral regurgitation, suggesting valvular damage as a potential surrogate marker for BCNE. The strength of our study is that it included definite endocarditis cases and is one of the largest case series ever reported in the literature. Nevertheless, its primary limitation stems from its retrospective design. IE is a severe disease, and early diagnosis and treatment are crucial to improve the prognosis. However, our study illustrates that negative blood cultures are prevalent among patients with concurrent cardiac comorbidities, particularly within the non-elderly cohort, in cases with endocarditis. Therefore, caution is advised in the diagnosis and evidence-based treatment of IE in this subgroup of patients. Declarations Compliance with Ethical Standards Funding: We did not receive any kind of funding. Conflict of Interest: None to declare. Ethical approval: Yes, it is obtained from Fatih Sultan Mehmet Training and Research Hospital’s Review Board. Informed consent: Not applicable. The study has a retrospective design. References Momtazmanesh S, Moghaddam SS, Rad EM, Azadnajafabad S, Ebrahimi N, Mohammadi E et al (2022) Global, regional, and national burden and quality of care index of endocarditis: the global burden of disease study 1990–2019. Eur J Prev Cardiol 29(8). https://doi.org/10.1093/eurjpc/zwab211 Habib G, Erba PA, Iung B, Donal E, Cosyns B, Laroche C et al (2019) Clinical presentation, aetiology and outcome of infective endocarditis. Results of the ESC-EORP EURO-ENDO (European infective endocarditis) registry: A prospective cohort study. 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Eur Heart J 43(29). https://doi.org/10.1093/eurheartj/ehac307 Erdem H, Puca E, Ruch Y, Santos L, Ghanem-Zoubi N, Argemi X et al (2019) Portraying infective endocarditis: results of multinational ID-IRI study. Eur J Clin Microbiol Infect Dis 38(9). https://doi.org/10.1007/s10096-019-03607-x Habib G, Lancellotti P, Antunes MJ, Bongiorni MG, Casalta JP, Zotti F, Del et al (2015) 2015 ESC guidelines for the management of infective endocarditis: The task force for the management of infective endocarditis of the European society of cardiology (ESC): Endorsed by: European association for cardio-thoracic surgery (EACTS), the European association of nuclear medicine (EANM). Eur Heart J 36(44). https://doi.org/10.1093/eurheartj/ehv319 van der Vaart TW, Stuifzand M, Boekholdt SM, Cramer MJ, Bonten MJM, Prins JM et al (2022) The prevalence of persistent bacteraemia in patients with a non-staphylococcal infective endocarditis, a retrospective cohort study. 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Pathogens 10(5). https://doi.org/10.3390/pathogens10050551 Lamas CC, Eykyn SJ (2003) Blood culture negative endocarditis: Analysis of 63 cases presenting over 25 years. Heart 89(3). https://doi.org/10.1136/heart.89.3.258 Lwin MT, Tsoi V, Yam T, Carroll A, Salmon T, Harden S et al (2021) Blood culture negative infective endocarditis in adult congenital heart disease patients with prosthetic grafts: A case series. Eur Heart J Case Rep 5(3). https://doi.org/10.1093/ehjcr/ytab106 Rocha C, Clérigo V, Fernandes L, Rodrigues A, Sargento D, Silva G (2014) Infective Endocarditis with Negative Cultures: A Defiant Diagnosis. Case Rep Clin Med 03(08). https://doi.org/10.4236/crcm.2014.38101 Hansen AW, Jalli S, Day M, Schulz T, Henery D (2023) Culture Negative Endocarditis Presenting as Transient Complete Heart Block in an Adult with Congenital Heart Disease. J Am Coll Cardiol 81(8). https://doi.org/10.1016/S0735-1097(23)04221-3 Dash SKN, Sugunakar C, Kar MK (2012) Culture negative infective endocarditis associated with osler’s nodes. J Clin Diagn Res 6(2). https://doi.org/10.7860/JCDR/2012/.1894 Katsouli A, Massad MG (2013) Current issues in the diagnosis and management of blood culture-negative infective and non-infective endocarditis. Ann Thorac Surg 95(4). https://doi.org/10.1016/j.athoracsur.2012.10.044 Dähler R, Brugger SD, Frank M, Greutmann M, Sromicki J, Marques-Maggi E et al (2022) A retrospective analysis of blood culture-negative endocarditis at a tertiary care centre in Switzerland. Swiss Med Wkly 152(4950). https://doi.org/10.57187/smw.2022.40016 Suardi LR, de Alarcón A, García MV, Ciezar AP, Hidalgo Tenorio C, Martinez-Marcos FJ et al (2021) Blood culture-negative infective endocarditis: a worse outcome? Results from a large multicentre retrospective Spanish cohort study. Infect Dis 53(10). https://doi.org/10.1080/23744235.2021.1925342 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-4177924\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":284882003,\"identity\":\"3c01790e-7c6c-48ba-93df-ce2c2e2cc3f5\",\"order_by\":0,\"name\":\"Mine 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18:38:33\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4177924/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4177924/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":53878195,\"identity\":\"827df658-34e3-4898-a60b-fca42c7e6990\",\"added_by\":\"auto\",\"created_at\":\"2024-04-01 16:59:16\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":13652,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eReceiver Operating Characteristics (ROC) curve based on final model predictions\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4177924/v1/2807dd91a8fd661f76c4b28b.png\"},{\"id\":53878196,\"identity\":\"c96834b3-ca80-431e-92a2-7cd4f3231d8d\",\"added_by\":\"auto\",\"created_at\":\"2024-04-01 16:59:17\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":29247,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eNomogram of the final model\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4177924/v1/ae41f13a75f6c3dffb2fb384.png\"},{\"id\":53924585,\"identity\":\"d92e8d5d-88bd-4a6e-ba41-775d19902d9d\",\"added_by\":\"auto\",\"created_at\":\"2024-04-02 09:35:33\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":778219,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4177924/v1/95f1fca9-63ff-45a1-8af6-87f314cd1932.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Blood Culture Negative Endocarditis; What Do Concurrent Cardiac Disorders Tell Us?\",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eInfective endocarditis (IE) poses a significant challenge to public health, claiming\\u0026thinsp;\\u0026gt;\\u0026thinsp;66,000 lives globally in 2019 [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Despite advances in diagnosis and treatment, IE morbidity and mortality rates remain high [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. The primary aim of the clinician managing endocarditis is to decrease complications and deaths by rapid diagnosis and administering appropriate treatment. Accordingly, early targeted therapy hinges on identifying the responsible microorganism through blood culture [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Nonetheless, blood culture negative endocarditis (BCNE) constitutes a notable portion of all cases (2.5\\u0026ndash;31%) and presents diagnostic complexities, leading to higher long-term mortality rates compared to blood culture positive endocarditis (BCPE) [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eOur understanding is constrained by the scarcity of randomized trials and meta-analyses on BCNE. To address this gap, we conducted an analysis to pinpoint risk groups for BCNE by contrasting the features of BCNE and BCPE utilizing the dataset from our previously published international study [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e], recognized as one of the largest case series ever documented in literature.\\u003c/p\\u003e\"},{\"header\":\"METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy Design\\u003c/h2\\u003e \\u003cp\\u003eThe study was conducted between January 1, 2015, and October 1, 2018, involving 41 centers spanning 13 countries and published in the European Journal of Clinical Microbiology and Infectious Diseases [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. The primary data collection was facilitated by the Infectious Diseases International Research Initiative (ID-IRI), and we conducted a re-evaluation of the database to determine the predictors of BCNE.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eParticipants \\u0026amp; Definitions\\u003c/h2\\u003e \\u003cp\\u003eThe study comprised patients aged 18 and above, diagnosed with definite infective endocarditis according to the modified Duke criteria and the European Society of Cardiology guideline [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. BCPE was defined as the presence of growth in blood culture and/or tissue culture obtained from the excised valve or vegetation, while BCNE was defined as the failure to identify the pathogen. Thus, comparisons were made between these two groups.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical Methods\\u003c/h2\\u003e \\u003cp\\u003eDescriptive statistics were presented as frequency and percent or mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation (SD) or median and range. Chi-square and Fisher\\u0026rsquo;s exact tests were used to compare categorical variables and Student\\u0026rsquo;s t test and Mann-Whitney U test were used for comparisons of continuous variables. A logistic regression test was performed for multivariate analysis with the composite endpoint. In the univariate analysis, risk factors influencing the blood culture results at a significance level of p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 were incorporated into the multivariate binary logistic regression model. The \\\"Backward Variable Elimination Method\\\" was employed in this process, removing variables without statistical significance until only significant variables remained. A p value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered significant.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cp\\u003eA total of 867 cases with a definite diagnosis of IE were included in the study. Among these, 292 (33.7%) were female and the median age was 59.5 (16\\u0026ndash;96) years. A total of 711 cases had 2 major criteria and 136 cases had 1 major and \\u0026ge;\\u0026thinsp;3 minor criteria.\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eMicrobiological data\\u003c/strong\\u003e \\u003cp\\u003eBlood cultures were negative in a total of 101 (11.65%) patients. Among the remaining 766 patients (88.4%) with identifiable microorganisms from blood cultures, the predominant pathogens were \\u003cem\\u003eStaphylococcus aureus\\u003c/em\\u003e (n\\u0026thinsp;=\\u0026thinsp;267, 33.6%), \\u003cem\\u003eStreptococcus viridans\\u003c/em\\u003e (n\\u0026thinsp;=\\u0026thinsp;149, 18.7%), enterococci (n\\u0026thinsp;=\\u0026thinsp;128, 16.1%), and coagulase-negative staphylococci (n\\u0026thinsp;=\\u0026thinsp;92, 11.6%). The details can be found elsewhere [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eUnivariate analyses\\u003c/strong\\u003e \\u003cp\\u003eDemographic characteristics and concurrent cardiac disorders/conditions based on blood culture results were shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, along with univariate comparisons. BCNE cases were younger than BCPE cases [median age 55 (18\\u0026ndash;86) versus 63 (16\\u0026ndash;96), p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001]. The percentage of BCNE was found to be statistically significantly higher in the presences of rheumatic heart disease, congenital heart disease, prosthetic valves, mitral regurgitation, aortic stenosis, mitral stenosis, and was lower in the absence of a pacemaker (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\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\\u003eDemographic characteristics and coexistent cardiac disorders/conditions according to blood culture results\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCulture Negative Endocarditis\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;101, 11.65%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTotal (N\\u0026thinsp;=\\u0026thinsp;867)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003e value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u003c/b\\u003e Median (Min \\u0026ndash; Max)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e55 (18\\u0026ndash;86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e59,5 (16\\u0026ndash;96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGender\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27 (9.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e292\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.116\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e74 (12.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e575\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eIschemic Heart Disease\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e92 (12.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e756\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.213\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 (8.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e111\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCongestive Heart Failure\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e90 (11.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e766\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.800\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11 (10.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e101\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDegenerative Cardiac Lesions\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e94 (11.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e802\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.818\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7 (10.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e65\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCardiomyopathy\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e96 (12%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e800\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.266\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (7.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e67\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRheumatic Heart Disease\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e89 (10.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e817\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.005\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12 (24%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCongenital Heart Disease (\\u003c/b\\u003en,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e94 (11.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e838\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.033\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7 (24.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e29\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCardiac Implants (Subtotal)\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e59 (10.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e584\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.041\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e42 (14.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e283\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1. \\u003cb\\u003eProsthetic valves\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e62 (9.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e635\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.004\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e39 (16.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e232\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e2. \\u003cb\\u003ePacemaker use\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e97 (12.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e785\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.045\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4 (4.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e82\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eValvular Problems (Subtotal)\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e61 (10.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e592\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.070\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e40 (14.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e275\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1. \\u003cb\\u003eMitral regurgitation\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75 (10.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e708\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.041\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26 (16.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e159\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e2. \\u003cb\\u003eAortic regurgitation\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e88 (11.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e782\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.270\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13 (15.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e85\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e3. \\u003cb\\u003eAortic stenosis\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e87 (10.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e805\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.005\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14 (22.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e62\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e4. \\u003cb\\u003eTricuspid regurgitation\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e93 (11.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e812\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.489\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8 (14.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e55\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e5. \\u003cb\\u003eMitral stenosis\\u003c/b\\u003e (n,%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbsent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e92 (11.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e829\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.018\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 (23.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e38\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAntibiotic use prior to blood culture sampling\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;377) (n, %)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e53 (36.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e147\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e31 (13.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e230\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eFinal model\\u003c/strong\\u003e \\u003cp\\u003eThe outcomes of our ultimate model indicate that the likelihood of a negative blood culture decreases significantly by 0.975 times (OR 0.975, 95% CI 0.963\\u0026ndash;0.987, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) with each year increase in age. Furthermore, it was noted to increase by 2.036 times (OR 2.036, 95% CI 0.970\\u0026ndash;4.276, p\\u0026thinsp;=\\u0026thinsp;0.049) in patients with rheumatic heart disease, 3.066 times (OR 3.066, 95% CI 1.564\\u0026ndash;6.010, p\\u0026thinsp;=\\u0026thinsp;0.001) in those with aortic stenosis, 1.693 times (OR 1.693, 95% CI 1.012\\u0026ndash;2.833, p\\u0026thinsp;=\\u0026thinsp;0.045) in individuals with mitral regurgitation, and 2.539 times (OR 2.539, 95% CI 1.599\\u0026ndash;4.031, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) in patients with prosthetic valves (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eFinal model results\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"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 \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eB\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eS.E.\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eOR\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e95% C.I.for OR\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c7\\\" namest=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLower\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eUpper\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c7\\\" namest=\\\"c7\\\"\\u003e\\u0026nbsp;\\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=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.025\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.975\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.963\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.987\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRheumatic Heart Disease (Present\\u003c/b\\u003e \\u003cb\\u003eversus\\u003c/b\\u003e \\u003cb\\u003eAbsent)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.711\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.378\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.036\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.970\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.276\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.049\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAortic Stenosis (Present\\u003c/b\\u003e \\u003cb\\u003eversus\\u003c/b\\u003e \\u003cb\\u003eAbsent)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.120\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.343\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.066\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.564\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6.010\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMitral Regurgitation (Present\\u003c/b\\u003e \\u003cb\\u003eversus\\u003c/b\\u003e \\u003cb\\u003eAbsent)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.527\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.263\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.693\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.012\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.833\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.045\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eProsthetic Valves (Present\\u003c/b\\u003e \\u003cb\\u003eversus\\u003c/b\\u003e \\u003cb\\u003eAbsent)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.932\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.236\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.539\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.599\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.031\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eConstant\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-1.157\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.374\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.314\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"7\\\"\\u003eB: Regression coefficient, S.E.: Standard error, C.I.: Confidence interval, OR: Odds ratio\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eThe power of the model\\u003c/strong\\u003e \\u003cp\\u003eThe predictions of our model indicate the probabilities of patients belonging to culture-negative or culture-positive groups. When the cut-off value for these probabilities was calculated using the ROC analysis, the cut-off value corresponding to the best diagnostic success was found to be 0.104. The area under curve (AUC\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SE) was calculated to be 0.707\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.027 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The classification of patients based on the cut-off value determined according to the model is shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). With a cut-off value of 0.104, the sensitivity of the final model was 70.3%, the specificity 57.0%, the positive predictive value 93.6% and the negative predictive value 17.8%.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThe classification of patients based on the cut-off value determined according to the nomogram\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"9\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" morerows=\\\"1\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eBlood Culture Negative Endocarditis\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eBlood Culture Positive Endocarditis\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003cp\\u003e(N, %)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003en\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e% within Model predictions\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e% within Blood Culture\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003en\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e% within Model predictions\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e% within Blood Culture\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eModel Predictions\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNegative\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17.8%\\u003c/p\\u003e \\u003cp\\u003e(NPV)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e70.3% (Sensitivity)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e329\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e---\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e43.0% (FN)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e400\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePositive\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e---\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e29.7%\\u003c/p\\u003e \\u003cp\\u003e(FP)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e437\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e93.6%\\u003c/p\\u003e \\u003cp\\u003e(PPV)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e57.0% (Specificity)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e467\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTotal (N, %)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e101\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e766\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e867\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"9\\\"\\u003eNPV: Negative predictive value, PPV: Positive predictive value, FP: False positive, FN: False negative, \\u003cb\\u003e---\\u003c/b\\u003e: The % in these cells were not written because they do not have any meaning.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe nomogram, which facilitates the interpretation of the final model, is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. The value \\\"Prob\\\" at the bottom of the figure corresponds to the probability of a patient's blood culture result being negative. The higher this value is, the greater the probability of a negative blood culture.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eIdentifying IE is easier in individuals with ongoing bacteremia [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Yet, when BCNE emerges from uncommon or fastidious bacteria, or less virulent bacteria from normal flora, diagnosis becomes challenging. Due to their lower aggressiveness, detecting these bacteria through blood cultures is less likely [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. In promptly formulating diagnostic approaches for BCNE cases, it is crucial to pinpoint specific patient cohorts warranting suspicion. Hence, in this study we demonstrated a significant reduction in the likelihood of a negative blood culture as age advances, while it rises in patients with underlying cardiac tissue damage like rheumatic heart disease or valvular problems such as aortic stenosis, mitral regurgitation, and implanted prosthetic valves.\\u003c/p\\u003e \\u003cp\\u003eResearch conducted in developing nations identified rheumatic heart disease as the primary risk factor for IE, with nearly half of the patients experiencing BCNE [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. However, detailed subgroup analysis of this data was lacking. Our study revealed a twofold increase in BCNE incidence among individuals with rheumatic heart disease. On the other hand, a younger demographic was more prevalent in BCNE cases compared to BCPE patients in our study, aligning with the existing literature [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. This suggests that rheumatic heart disease may lead to the early onset of IE, particularly in BCNE format.\\u003c/p\\u003e \\u003cp\\u003eThere are BCNE case reports in the literature related to patients with damaged heart tissues [\\u003cspan additionalcitationids=\\\"CR16 CR17\\\" citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. However, the data on this subject is limited and lacks of a holistic perspective. In a review discussing BCNE risk factors, beyond well-known factors such as exposure to fastidious bacteria and pre-blood culture antibiotic usage, individuals with underlying valvular heart disease, right-sided endocarditis, the presence of intra-cardiac or vascular devices, and contact with foreign bodies in the bloodstream have also been recognized as risk factors [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Accordingly, in a 23-years descriptive analysis conducted at a tertiary center in Switzerland, the most common cardiac predisposing factor for BCNE was the implanted prosthetic valves (42%) [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Similar findings were observed in other studies analyzing BCNE characteristics, indicating that the most commonly noted cardiac predisposing factor was prosthetic valves [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Similarly, our study indicated a 2.5-fold increase in the probability of negative blood cultures when prosthetic valves were present.\\u003c/p\\u003e \\u003cp\\u003eThe European Endocarditis (EURO-ENDO) international study represented one of the most extensive case series concerning BCNE (16.8%), involving the evaluation of 3113 patients. Unlike our research, definite and possible cases of IE were collectively analyzed in that study. Throughout the follow-up period, heart failure resulting from valve dysfunction was more commonly observed in BCNE patients compared to BCPE patients [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. In addition, in a Spanish cohort study where 1001 patients with a definitive IE were evaluated, aortic problems in terms of valvular dysfunction were more commonly detected in BCNE patients (8.3%) despite having fewer other comorbidities [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Thus, our study revealed a rise in the occurrence of BCNE in patients with heart valve dysfunction like aortic stenosis and mitral regurgitation, suggesting valvular damage as a potential surrogate marker for BCNE.\\u003c/p\\u003e \\u003cp\\u003eThe strength of our study is that it included definite endocarditis cases and is one of the largest case series ever reported in the literature. Nevertheless, its primary limitation stems from its retrospective design. IE is a severe disease, and early diagnosis and treatment are crucial to improve the prognosis. However, our study illustrates that negative blood cultures are prevalent among patients with concurrent cardiac comorbidities, particularly within the non-elderly cohort, in cases with endocarditis. Therefore, caution is advised in the diagnosis and evidence-based treatment of IE in this subgroup of patients.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eCompliance with Ethical Standards\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u003c/strong\\u003e We did not receive any kind of funding.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of Interest:\\u003c/strong\\u003e\\u0026nbsp; None to declare.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval:\\u0026nbsp;\\u003c/strong\\u003eYes, it is obtained from Fatih Sultan Mehmet Training and Research Hospital\\u0026rsquo;s Review Board.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInformed consent:\\u0026nbsp;\\u003c/strong\\u003eNot applicable. The study has a retrospective design.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eMomtazmanesh S, Moghaddam SS, Rad EM, Azadnajafabad S, Ebrahimi N, Mohammadi E et al (2022) Global, regional, and national burden and quality of care index of endocarditis: the global burden of disease study 1990\\u0026ndash;2019. Eur J Prev Cardiol 29(8). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/eurjpc/zwab211\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/eurjpc/zwab211\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHabib G, Erba PA, Iung B, Donal E, Cosyns B, Laroche C et al (2019) Clinical presentation, aetiology and outcome of infective endocarditis. Results of the ESC-EORP EURO-ENDO (European infective endocarditis) registry: A prospective cohort study. Eur Heart J 40(39). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/eurheartj/ehz620\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/eurheartj/ehz620\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eThuny F, Avierinos JF, Tribouilloy C, Giorgi R, Casalta JP, Milandre L et al (2007) Impact of cerebrovascular complications on mortality and neurologic outcome during infective endocarditis: A prospective multicentre study. Eur Heart J 28(9). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/eurheartj/ehm005\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/eurheartj/ehm005\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRajani R, Klein JL (2020) Infective endocarditis: A contemporary update. Clinical Medicine. J R Coll Physicians Lond 20(1). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.7861/clinmed.cme.20.1.1\\u003c/span\\u003e\\u003cspan address=\\\"10.7861/clinmed.cme.20.1.1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHoupikian P, Raoult D (2005) Blood culture-negative endocarditis in a reference center: Etiologic diagnosis of 348 cases. Medicine 84(3). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1097/01.md.0000165658.82869.17\\u003c/span\\u003e\\u003cspan address=\\\"10.1097/01.md.0000165658.82869.17\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKong WKF, Salsano A, Giacobbe DR, Popescu BA, Laroche C, Duval X et al (2022) Outcomes of culture-negative vs. culture-positive infective endocarditis: the ESC-EORP EURO-ENDO registry. Eur Heart J 43(29). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/eurheartj/ehac307\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/eurheartj/ehac307\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eErdem H, Puca E, Ruch Y, Santos L, Ghanem-Zoubi N, Argemi X et al (2019) Portraying infective endocarditis: results of multinational ID-IRI study. Eur J Clin Microbiol Infect Dis 38(9). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s10096-019-03607-x\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s10096-019-03607-x\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHabib G, Lancellotti P, Antunes MJ, Bongiorni MG, Casalta JP, Zotti F, Del et al (2015) 2015 ESC guidelines for the management of infective endocarditis: The task force for the management of infective endocarditis of the European society of cardiology (ESC): Endorsed by: European association for cardio-thoracic surgery (EACTS), the European association of nuclear medicine (EANM). Eur Heart J 36(44). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/eurheartj/ehv319\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/eurheartj/ehv319\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003evan der Vaart TW, Stuifzand M, Boekholdt SM, Cramer MJ, Bonten MJM, Prins JM et al (2022) The prevalence of persistent bacteraemia in patients with a non-staphylococcal infective endocarditis, a retrospective cohort study. Int J Cardiol 367(49). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.ijcard.2022.08.038\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ijcard.2022.08.038\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBoyle KK, Wood S, Tarity TD (2018) Low-Virulence Organisms and Periprosthetic Joint Infection\\u0026mdash;Biofilm Considerations of These Organisms. Curr Rev Musculoskelet Med 11(3). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s12178-018-9503-2\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s12178-018-9503-2\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNoubiap JJ, Nkeck JR, Kwondom BS, Nyaga UF (2022) Epidemiology of infective endocarditis in Africa: a systematic review and meta-analysis. Lancet Glob Health 10(1). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S2214-109X(21)00400-9\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S2214-109X(21)00400-9\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLetaief A, Boughzala E, Kaabia N, Ernez S, Abid F, Chaabane T, Ben et al (2007) Epidemiology of infective endocarditis in Tunisia: a 10-year multicenter retrospective study. Int J Infect Dis 11(5). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.ijid.2006.10.006\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ijid.2006.10.006\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBuburuz AM, Petris A, Costache II, Jelihovschi I, Arsenescu-Georgescu C, Iancu LS (2021) Evaluation of laboratory predictors for in-hospital mortality in infective endocarditis and negative blood culture pattern characteristics. Pathogens 10(5). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/pathogens10050551\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/pathogens10050551\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLamas CC, Eykyn SJ (2003) Blood culture negative endocarditis: Analysis of 63 cases presenting over 25 years. Heart 89(3). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1136/heart.89.3.258\\u003c/span\\u003e\\u003cspan address=\\\"10.1136/heart.89.3.258\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLwin MT, Tsoi V, Yam T, Carroll A, Salmon T, Harden S et al (2021) Blood culture negative infective endocarditis in adult congenital heart disease patients with prosthetic grafts: A case series. Eur Heart J Case Rep 5(3). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/ehjcr/ytab106\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/ehjcr/ytab106\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRocha C, Cl\\u0026eacute;rigo V, Fernandes L, Rodrigues A, Sargento D, Silva G (2014) Infective Endocarditis with Negative Cultures: A Defiant Diagnosis. Case Rep Clin Med 03(08). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.4236/crcm.2014.38101\\u003c/span\\u003e\\u003cspan address=\\\"10.4236/crcm.2014.38101\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHansen AW, Jalli S, Day M, Schulz T, Henery D (2023) Culture Negative Endocarditis Presenting as Transient Complete Heart Block in an Adult with Congenital Heart Disease. J Am Coll Cardiol 81(8). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S0735-1097(23)04221-3\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0735-1097(23)04221-3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDash SKN, Sugunakar C, Kar MK (2012) Culture negative infective endocarditis associated with osler\\u0026rsquo;s nodes. J Clin Diagn Res 6(2). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.7860/JCDR/2012/.1894\\u003c/span\\u003e\\u003cspan address=\\\"10.7860/JCDR/2012/.1894\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKatsouli A, Massad MG (2013) Current issues in the diagnosis and management of blood culture-negative infective and non-infective endocarditis. Ann Thorac Surg 95(4). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.athoracsur.2012.10.044\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.athoracsur.2012.10.044\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eD\\u0026auml;hler R, Brugger SD, Frank M, Greutmann M, Sromicki J, Marques-Maggi E et al (2022) A retrospective analysis of blood culture-negative endocarditis at a tertiary care centre in Switzerland. Swiss Med Wkly 152(4950). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.57187/smw.2022.40016\\u003c/span\\u003e\\u003cspan address=\\\"10.57187/smw.2022.40016\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSuardi LR, de Alarc\\u0026oacute;n A, Garc\\u0026iacute;a MV, Ciezar AP, Hidalgo Tenorio C, Martinez-Marcos FJ et al (2021) Blood culture-negative infective endocarditis: a worse outcome? Results from a large multicentre retrospective Spanish cohort study. Infect Dis 53(10). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/23744235.2021.1925342\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/23744235.2021.1925342\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Infective endocarditis, blood culture negative endocarditis, rheumatic heart disease, prosthetic valves, cardiac disorders\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4177924/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4177924/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003ePurpose:\\u003c/strong\\u003e Blood culture negative endocarditis (BCNE) presents diagnostic challenges, necessitating precise identification of high-risk patient cohorts. Hence, our objective was to pinpoint high-risk cohorts for BCNE.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods:\\u003c/strong\\u003e The study included adult patients with definite endocarditis. Data was collected via the Infectious Diseases International Research Initiative (ID-IRI). The primary study analysing one of the largest case series ever reported and was published in the European Journal of Clinical Microbiology and Infectious Diseases in 2019, was conducted across 41 centres in 13 countries. We performed a re-analysis of the database to determine the predictors of BCNE.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003e 867 cases with definite endocarditis were included in the study. Blood cultures were negative in 101 (11.65%) patients. We disclosed that as patients age, the likelihood of a negative blood culture significantly decreases (OR 0.975, 95% CI 0.963-0.987, p\\u0026lt;0.001). Additionally, factors such as rheumatic heart disease (OR 2.036, 95% CI 0.970-4.276, p=0.049), aortic stenosis (OR 3.066, 95% CI 1.564-6.010, p=0.001), mitral regurgitation (OR 1.693, 95% CI 1.012-2.833, p=0.045), and prosthetic valves (OR 2.539, 95% CI 1.599-4.031, p\\u0026lt;0.001) are associated with higher likelihoods of negative blood cultures. Our model can predict whether a patient falls into the culture-negative or culture-positive groups with a threshold of 0.104 (AUC±SE=0.707±0.027). The final model demonstrates a sensitivity of 70.3% and a specificity of 57.0%.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion:\\u003c/strong\\u003e Our study reveals a high prevalence of negative blood cultures in patients with concurrent cardiac disorders, particularly in younger cases. Therefore, caution is indicated in diagnosing and treating endocarditis in these particular patient subgroups.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Blood Culture Negative Endocarditis; What Do Concurrent Cardiac Disorders Tell Us?\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-04-01 16:59:08\",\"doi\":\"10.21203/rs.3.rs-4177924/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"a2656af6-1228-4887-a5bc-e59e2e042d97\",\"owner\":[],\"postedDate\":\"April 1st, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-04-02T09:35:25+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-04-01 16:59:08\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4177924\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4177924\",\"identity\":\"rs-4177924\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}