Abstract
Background
Blood culture-negative endocarditis (BCNE) is a diagnostic challenge, therefore our objective was to pinpoint high-risk cohorts for BCNE.
Methods
The study included adult patients with definite endocarditis. Data were collected via the Infectious Diseases International Research Initiative (ID-IRI). The study analysing one of the largest case series ever reported was conducted across 41 centers in 13 countries. We analysed the database to determine the predictors of BCNE using univariate and logistic regression analyses.
Results
Blood cultures were negative in 101 (11.65 %) of 867 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
Caution should be exercised when diagnosing endocarditis in patients with concurrent cardiac disorders, particularly in younger cases.
Keywords: Infective endocarditis, Blood culture negative endocarditis, Rheumatic heart disease, Prosthetic valves, Cardiac disorders
Highlights
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Endocarditis is a severe disease and early diagnosis is crucial to better prognosis.
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Diagnosis becomes difficult when BCNE emerges from uncommon or fastidious bacteria.
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BCNE requires precise identification of high-risk patient groups.
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The prevalence of BCNE is high in patients with concurrent cardiac disorders.
1. 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, recognized as one of the largest case series ever documented in literature [7].
2. Methods
2.1. Study design
The study was conducted between January 1, 2015, and October 1, 2018, involving 41 centers spanning 13 countries [7]. The primary data collection was facilitated by the Infectious Diseases International Research Initiative (ID-IRI). The data input was made through a web-based questionnaire. The questionnaire included demographic, clinical, laboratory, microbiologic, special tests, echocardiographic findings and outcome. We conducted a re-analysis of this database to determine the predictors of BCNE.
2.2. Case definition
The study comprised patients aged 18 and above, diagnosed with definite infective endocarditis according to the modified Duke criteria and the 2015 European Society of Cardiology (ESC) guideline [8]. The updated 2023 ESC guideline was very similar to the previous version and did not alter the diagnostic criteria for definitive endocarditis [9]. 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 absence of growth in any of the blood cultures and/or tissue culture obtained from the excised valve or vegetation. Thus, comparisons were made between these two groups.
2.3. Statistical methods
The collected data were analysed with the software SPSS version 23 and the statistical package Stata/MP version 14.1 was used for the logistic regression analysis and the nomogram tables. 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.
3. 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 %) [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 | ||
|
Absent | 62 (9.8 %) | 635 | 0.004 |
| Present | 39 (16.8 %) | 232 | ||
|
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 | ||
|
|
75 (10.6 %) | 708 | 0.041 |
|
26 (16.4 %) | 159 | ||
|
|
88 (11.3 %) | 782 | 0.270 |
|
13 (15.3 %) | 85 | ||
|
|
87 (10.8 %) | 805 | 0.005 |
|
14 (22.6 %) | 62 | ||
|
|
93 (11.5 %) | 812 | 0.489 |
|
8 (14.5 %) | 55 | ||
|
|
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 %.
Fig. 1.
Receiver operating characteristics (ROC) curve based on the probabilities of patients belonging to culture-negative or culture-positive groups. The final model cut-off value: 0.104, sensitivity 70.3 % and specificity 57.0 %.
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.
Figure-2.
A nomogram of the final predictive model. The score is calculated separately for each line, based on the presence or absence of cardiac disorders and the patient's age. The ‘Prob’ value at the bottom of the figure corresponds to the calculated total score, and it indicates the probability that a patient's blood culture result will be negative. The higher the ‘Prob’ value, the greater the likelihood of a negative blood culture.
4. Discussion
Identifying IE is easier in individuals with ongoing bacteremia [8,10]. 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 [11,12]. 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 [13,14]. However, detailed subgroup analysis of this data were lacking. Another paper analysing IE shows that BCNE is more common in the southern countries of this region, where rheumatic heart disease is prevalent, and points to the heterogeneity of the epidemiology [15]. 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 [6,16,17]. 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 [[18], [19], [20], [21]]. However, the data on this subject are limited and lack 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 [22]. 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 %) [23]. Similar findings were observed in other studies analysing BCNE characteristics, indicating that the most commonly noted cardiac predisposing factor was prosthetic valves [16,17]. 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 analysed 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 [24]. 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. Moreover, the use of antibacterial medications at the time of taking blood cultures may have contributed to the negative culture results. This is because people with pre-existing cardiac problems might have been more likely to receive early antibiotic treatment, which could potentially confound the findings. In addition, updated studies with more recent data are required to reflect the current clinical and microbiological landscape. It is hypothesized that our model can be further validated by conducting new studies. While we agree that external validation is essential for a comprehensive evaluation of the model's performance, we believe that the high sensitivity of our model is a valuable asset in identifying patients with blood culture-negative endocarditis, which can be challenging to diagnose as one of the strengths of this study.
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.
Funding
We did not receive any kind of funding.
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.
CRediT authorship contribution statement
Mine Filiz: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Hakan Erdem: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation. Handan Ankarali: Writing – review & editing, Visualization, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation. Edmond Puca: Writing – review & editing, Validation, Supervision, Investigation, Data curation. Yvon Ruch: Writing – review & editing, Validation, Supervision, Investigation, Data curation. Lurdes Santos: Writing – review & editing, Validation, Supervision, Investigation, Data curation. Teresa Fasciana: Writing – review & editing, Validation, Supervision, Investigation, Funding acquisition, Data curation. Anna M. Giammanco: Writing – review & editing, Validation, Supervision, Investigation, Funding acquisition, Data curation. Nesrin Ghanem-Zoubi: Writing – review & editing, Validation, Investigation, Data curation. Xavier Argemi: Writing – review & editing, Validation, Investigation, Data curation. Yves Hansmann: Writing – review & editing, Validation, Investigation, Data curation. Rahmet Guner: Writing – review & editing, Validation, Investigation, Data curation. Gilda Tonziello: Writing – review & editing, Validation, Investigation, Data curation. Jean-Philippe Mazzucotelli: Writing – review & editing, Validation, Investigation, Data curation. Najada Como: Writing – review & editing, Validation, Investigation, Data curation. Sukran Kose: Validation, Investigation, Data curation, Writing – review & editing. Ayse Batirel: Writing – review & editing, Validation, Investigation, Data curation. Asuman Inan: Writing – review & editing, Validation, Investigation, Data curation. Necla Tulek: Writing – review & editing, Validation, Investigation, Data curation. Abdullah Umut Pekok: Writing – review & editing, Validation, Investigation, Data curation. Ejaz Ahmed Khan: Writing – review & editing, Validation, Investigation, Data curation. Atilla Iyisoy: Writing – review & editing, Validation, Investigation, Data curation. Meliha Meric-Koc: Writing – review & editing, Validation, Investigation, Data curation. Ayse Kaya-Kalem: Writing – review & editing, Validation, Investigation, Data curation. Pedro Palma Martins: Writing – review & editing, Validation, Investigation, Data curation. Imran Hasanoglu: Writing – review & editing, Validation, Investigation, Data curation. André Silva-Pinto: Writing – review & editing, Validation, Investigation, Data curation. Nefise Oztoprak: Writing – review & editing, Validation, Investigation, Data curation. Raquel Duro: Writing – review & editing, Validation, Investigation, Data curation. Fahad Almajid: Writing – review & editing, Validation, Investigation, Data curation. Mustafa Dogan: Writing – review & editing, Validation, Investigation, Data curation. Nicolas Dauby: Writing – review & editing, Validation, Investigation, Data curation. Jesper Damsgaard Gunst: Writing – review & editing, Validation, Investigation, Data curation. Recep Tekin: Writing – review & editing, Validation, Investigation, Data curation. Deborah Konopnicki: Writing – review & editing, Validation, Investigation, Data curation. Nicola Petrosillo: Writing – review & editing, Validation, Investigation, Data curation. Ilkay Bozkurt: Writing – review & editing, Validation, Investigation, Data curation. Jamal Wadi Al Ramahi: Writing – review & editing, Validation, Investigation, Data curation. Corneliu Popescu: Writing – review & editing, Validation, Investigation, Data curation. Ilker Inanc Balkan: Writing – review & editing, Validation, Investigation, Data curation. Safak Ozer-Balin: Writing – review & editing, Validation, Investigation, Data curation. Tatjana Lejko Zupanc: Writing – review & editing, Validation, Investigation, Data curation. Antonio Cascio: Writing – review & editing, Validation, Investigation, Data curation. Irina Magdalena Dumitru: Writing – review & editing, Validation, Investigation, Data curation. Aysegul Erdem: Writing – review & editing, Validation, Investigation, Data curation. Gulden Ersoz: Writing – review & editing, Validation, Investigation, Data curation. Meltem Tasbakan: Writing – review & editing, Validation, Investigation, Data curation. Oday Abu Ajamieh: Writing – review & editing, Validation, Investigation, Data curation. Fatma Sirmatel: Writing – review & editing, Validation, Investigation, Data curation. Simin Florescu: Writing – review & editing, Validation, Investigation, Data curation. Serda Gulsun: Writing – review & editing, Validation, Investigation, Data curation. Hacer Deniz Ozkaya: Writing – review & editing, Validation, Investigation, Data curation. Sema Sari: Writing – review & editing, Validation, Investigation, Data curation. Selma Tosun: Writing – review & editing, Validation, Investigation, Data curation. Meltem Avci: Writing – review & editing, Validation, Investigation, Data curation. Yasemin Cag: Writing – review & editing, Validation, Investigation, Data curation. Guven Celebi: Writing – review & editing, Validation, Investigation, Data curation. Ayse Sagmak-Tartar: Writing – review & editing, Validation, Investigation, Data curation. Sumeyra Karakus: Writing – review & editing, Validation, Investigation, Data curation. Alper Sener: Writing – review & editing, Validation, Investigation, Data curation. Arjeta Dedej: Writing – review & editing, Validation, Investigation, Data curation. Serkan Oncu: Writing – review & editing, Validation, Investigation, Data curation. Rosa Fontana Del Vecchio: Writing – review & editing, Validation, Investigation, Data curation. Derya Ozturk-Engin: Writing – review & editing, Validation, Investigation, Data curation. Canan Agalar: Writing – review & editing, Validation, Investigation, Data curation.
Declaration of competing interest
None to declare.
Handling Editor: Patricia Schlagenhauf
References
- 1.Momtazmanesh S., Moghaddam S.S., Rad E.M., Azadnajafabad S., Ebrahimi N., Mohammadi E., et al. Global, regional, and national burden and quality of care index of endocarditis: the global burden of disease study 1990–2019. Eur J Prev Cardiol. 2022;29(8) doi: 10.1093/eurjpc/zwab211. [DOI] [PubMed] [Google Scholar]
- 2.Habib G., Erba P.A., Iung B., Donal E., Cosyns B., Laroche C., et al. 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. 2019;40(39) doi: 10.1093/eurheartj/ehz620. [DOI] [PubMed] [Google Scholar]
- 3.Thuny F., Avierinos J.F., Tribouilloy C., Giorgi R., Casalta J.P., Milandre L., et al. Impact of cerebrovascular complications on mortality and neurologic outcome during infective endocarditis: a prospective multicentre study. Eur Heart J. 2007;28(9) doi: 10.1093/eurheartj/ehm005. [DOI] [PubMed] [Google Scholar]
- 4.Rajani R., Klein J.L. Infective endocarditis: a contemporary update. Clin Med J Royal College Physic London. 2020;20(1) doi: 10.7861/clinmed.cme.20.1.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Houpikian P., Raoult D. Blood culture-negative endocarditis in a reference center: etiologic diagnosis of 348 cases. Medicine. 2005;84(3) doi: 10.1097/01.md.0000165658.82869.17. [DOI] [PubMed] [Google Scholar]
- 6.Kong W.K.F., Salsano A., Giacobbe D.R., Popescu B.A., Laroche C., Duval X., et al. Outcomes of culture-negative vs. culture-positive infective endocarditis: the ESC-EORP EURO-ENDO registry. Eur Heart J. 2022;43(29) doi: 10.1093/eurheartj/ehac307. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Erdem H., Puca E., Ruch Y., Santos L., Ghanem-Zoubi N., Argemi X., et al. Portraying infective endocarditis: results of multinational ID-IRI study. Eur J Clin Microbiol Infect Dis. 2019;38(9) doi: 10.1007/s10096-019-03607-x. [DOI] [PubMed] [Google Scholar]
- 8.Habib G., Lancellotti P., Antunes M.J., Bongiorni M.G., Casalta J.P., Zotti F Del, et al. 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. 2015;36(44) doi: 10.1093/eurheartj/ehv319. [DOI] [PubMed] [Google Scholar]
- 9.Delgado V., Ajmone Marsan N., de Waha S., Bonaros N., Brida M., Burri H., et al. 2023 ESC Guidelines for the management of endocarditis. Eur Heart J. 2023 Oct 14;44(39):3948–4042. doi: 10.1093/eurheartj/ehad193. [DOI] [PubMed] [Google Scholar]
- 10.van der Vaart T.W., Stuifzand M., Boekholdt S.M., Cramer M.J., Bonten M.J.M., Prins J.M., et al. The prevalence of persistent bacteraemia in patients with a non-staphylococcal infective endocarditis, a retrospective cohort study. Int J Cardiol. 2022;367(49) doi: 10.1016/j.ijcard.2022.08.038. [DOI] [PubMed] [Google Scholar]
- 11.Boyle K.K., Wood S., Tarity T.D. Low-virulence organisms and periprosthetic joint infection—biofilm considerations of these organisms. Curr Rev Musculoskelet Med. 2018;11(3) doi: 10.1007/s12178-018-9503-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Moradi F., Rajaee Behbahani M., Gorginpour J., Dezhkam A., Hadi N. Ralstoniapickettii bloodstream infection in the patient with Guillain-Barre syndrome under plasmapheresis. New Microbes New Infect. 2024 Jan 12;57 doi: 10.1016/j.nmni.2024.101218. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Noubiap J.J., Nkeck J.R., Kwondom B.S., Nyaga U.F. Epidemiology of infective endocarditis in Africa: a systematic review and meta-analysis. Lancet Global Health. 2022;10(1) doi: 10.1016/S2214-109X(21)00400-9. [DOI] [PubMed] [Google Scholar]
- 14.Letaief A., Boughzala E., Kaabia N., Ernez S., Abid F., Chaabane T Ben, et al. Epidemiology of infective endocarditis in Tunisia: a 10-year multicenter retrospective study. Int J Infect Dis. 2007;11(5) doi: 10.1016/j.ijid.2006.10.006. [DOI] [PubMed] [Google Scholar]
- 15.Gouriet F., Chaudet H., Gautret P., Pellegrin L., de Santi V.P., Savini H., et al. Endocarditis in the mediterranean basin. New Microbes New Infect. 2018 May 30;26:S43–S51. doi: 10.1016/j.nmni.2018.05.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Buburuz A.M., Petris A., Costache I.I., Jelihovschi I., Arsenescu-Georgescu C., Iancu L.S. Evaluation of laboratory predictors for in-hospital mortality in infective endocarditis and negative blood culture pattern characteristics. Pathogens. 2021;10(5) doi: 10.3390/pathogens10050551. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Lamas C.C., Eykyn S.J. Blood culture negative endocarditis: analysis of 63 cases presenting over 25 years. Heart. 2003;89(3) doi: 10.1136/heart.89.3.258. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Lwin M.T., Tsoi V., Yam T., Carroll A., Salmon T., Harden S., et al. Blood culture negative infective endocarditis in adult congenital heart disease patients with prosthetic grafts: a case series. Eur Heart J Case Rep. 2021;5(3) doi: 10.1093/ehjcr/ytab106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Rocha C., Clérigo V., Fernandes L., Rodrigues A., Sargento D., Silva G. Infective endocarditis with negative cultures: a defiant diagnosis. Case Rep Clin Med. 2014;3(8) doi: 10.4236/crcm.2014.38101. [DOI] [Google Scholar]
- 20.Hansen A.W., Jalli S., Day M., Schulz T., Henery D. Culture negative endocarditis presenting as transient complete heart block in an adult with congenital heart disease. J Am Coll Cardiol. 2023;81(8) doi: 10.1016/S0735-1097(23)04221-3. [DOI] [Google Scholar]
- 21.Dash S.K.N., Sugunakar C., Kar M.K. Culture negative infective endocarditis associated with osler's nodes. J Clin Diagn Res. 2012;6(2) doi: 10.7860/JCDR/2012/.1894. [DOI] [Google Scholar]
- 22.Katsouli A., Massad M.G. Current issues in the diagnosis and management of blood culture-negative infective and non-infective endocarditis. Ann Thorac Surg. 2013;95(4) doi: 10.1016/j.athoracsur.2012.10.044. 95. [DOI] [PubMed] [Google Scholar]
- 23.Dähler R., Brugger S.D., Frank M., Greutmann M., Sromicki J., Marques-Maggi E., et al. A retrospective analysis of blood culture-negative endocarditis at a tertiary care centre in Switzerland. Swiss Med Wkly. 2022;152(4950) doi: 10.57187/smw.2022.40016. [DOI] [PubMed] [Google Scholar]
- 24.Suardi L.R., de Alarcón A., García M.V., Ciezar A.P., Hidalgo Tenorio C., Martinez-Marcos F.J., et al. Blood culture-negative infective endocarditis: a worse outcome? Results from a large multicentre retrospective Spanish cohort study. Inf Disp. 2021;53(10) doi: 10.1080/23744235.2021.1925342. [DOI] [PubMed] [Google Scholar]