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Infective Endocarditis – Predictors of In-Hospital Mortality, 17 Years, Single-Center Experience in Bulgaria

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28 August 2024

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Abstract
Background: Despite tremendous advances in medicine, infective endocarditis (IE) continues to be a challenge for physicians due to increasing morbidity and unchanged high mortality. Objectives: Our aim was to evaluate clinical outcomes in patients with IE and to identify predictors of in-hospital mortality. Material and methods: The study was retrospective, single-centered, and included 270 patients diagnosed with IE, for the period 2005-2021 (median age 65 (51–74), man 177 (65.6%). Results: Native IE (NVIE) was found in 180 (66.7%), prosthetic IE (PVIE) in 88 (33.6%) and cardiac device-related IE (CDRIE) in 2 (0.7%), significantly more in the non-survivors group. Healthcare associated IE (HAIE) was 72 (26.7%), the most common pathogen were Staphylococci and the proportion of Gram-negative bacteria (GNB) non-HACEK were significantly higher in non-survivor’s vs survivors (11 (15%) vs 9 (4.5%), p=0.004). Overall, early surgery was performed in 54 (20%) patients, with a significant difference between deceased/alive (3 (4.5%) vs 51 (25.1%, p=0.000). The all-cause in-hospital mortality rate was 24.8% (67). The risk factors identified that increased the risk of death were septic shock (OR – 83 1; 95% CI (17.0-405.2), p=0.000) and acute heart failure (OR –24.6; 95% CI (9.2-65.0), p=0.000). Early surgery (OR – 0.03, 95% CI (0.01-0.16), p=0.000) and low Charlson comorbidity index (OR – 0.85, 95% CI (0.74-0.98, p=0.026) in turn reduce this risk. Conclusion: We found that acute heart failure and septic shock are independent predictors of in-hospital mortality. Low Charlson comorbidity index and early surgery increased survival. Knowing the predictors of death would change the therapeutic approach to a more aggressive one and improve the near and long-term prognosis of patients with IE.
Keywords: 
Subject: Medicine and Pharmacology  -   Cardiac and Cardiovascular Systems

1. Introduction

IE was first described 350 years ago [1], but it remains a major challenge for clinicians for several reasons. IE is a changing disease, and despite the availability of modern imaging and microbiological techniques, diagnosis is often difficult and delayed. The incidence of IE increased up to 13/100 000 worldwide [2]. Improvements in medical and surgical treatment in recent decades have not changed the mortality and serious complication rates [2,3].
The mortality rate in IE is still high, at 15-30% [2,3,4] and is linked to the changing profile of the disease. Patients are older with multiple comorbidities. High Charlson Comorbidity Index (>3), diabetes, kidney failure, prosthetic valve IE (PVIE) and hemodialysis associated with bad prognosis [4,5,6,7,8]. The incidence of PVIE has raised in recent decades [2,9,10]. The new type of IE - indwelling device-related IE and IE after TAVI (Transcatheter aortic valve implantation) is increasing as a result of scientific and technological advances in the field of medicine. They are difficult to treat and have an unfavorable outcome, as they generally occur in older patients with high comorbidity and very often require surgery [11,12,13,14,15]. Healthcare-associated IE (HAIE) accounts for almost a third of all cases and has been driven by medical progress, improved treatment and increased patient survival and is associated with an adverse outcome [16,17]. There has been a change in the microbiological spectrum of IE with the predominance of staphylococci and enterococci and the associated difficulties in treatment [10]. The most important predictors of mortality are complications - acute or worsening heart failure, septic shock, acute neurological events, acute kidney failure as a result of valvular dysfunction, embolisms and/or persistent infection [18]. These conditions are and the main indications
Early risk stratification for complications and death is of great importance. The timely identification of high-risk patients would change the therapeutic approach to a more aggressive one, especially when deciding on surgical treatment. Early surgery is associated with a reduction in in-hospital mortality and an improvement in the long-term prognosis of IE patients [9,10]. Knowledge of the predictors of death and complications would be helpful in our treatment decisions.
There is a lack of information on the predictors of in-hospital mortality in Bulgaria in the last few decades. We examined predictors of in-hospital death in patients with IE over 17 years.

2. Material and Method

This study is retrospective, single-centered, including 270 patients with a diagnosis of IE, according to the modified Duke criteria , treated at the University Hospital “St. Georgi”, in the city of Plovdiv for the period January 2005–December 2021. The hospital capacity is 1500 beds, and the cardiology clinic is a reference center for the treatment of IE for a large part of southern Bulgaria. The medical records of treated patients with codes I33, I38, and I39 for the described period were used. Variables studied included demographics, risk group, presence of predisposing heart disease, comorbidities, Charlson comorbidity index (CCI) [19], entry gate, predictors for transient bacteremia, clinical, echocardiographic findings, causative organisms, complications, and clinical outcome.

2.1. Definition and Classification of IE

The diagnosis was defined as definite IE or possible IE according to the modified Duke criteria [20]. Surgical treatment of IE was defined as early when the surgery was performed during antibiotic treatment. Valvular involvement of IE is determined based on findings from echocardiography, other imaging studies, cardiac surgery, or in some cases by clinical presentation. Episodes of IE were categorized by mode of acquisition as: community-acquired IE (CAIE), healthcare-associated IE (HAIE), and intravenous-druguse-associated IE (IDUIE). These categories are mutually exclusive. IE was defined as HAIE according to the following criteria: (1) occurrence of IE > 48 h after hospital admission or within 6 months after hospital discharge for ≥ 2 days; (2) IE developed within 6 months after a significant invasive procedure performed during hospitalization or in an outpatient setting; (3) extensive outpatient healthcare contacts, defined as receiving wound care or intravenous treatment within 1 month before the onset of IE; or (4) stay in a clinic-home to receive similar care [12,13,14,15]. IE occurring on a prosthetic valve within 12 months of surgery is defined as prosthetic valve early endocarditis (PVIE) and is classified as HAIE. Patients with a recent (within 1 month) or longer history of intravenous drug use were classified as IDUIE. Patients with no medical history and no history of injecting drug use were classified as CAIE. IE following dental treatment is considered to be CAIE if there is no other healthcare contact. The presence of septic emboli and an extracardiac focus of infection was defined as a focus of infection detected by imaging or based on typical clinical presentation. Complications were diagnosed according to the established diagnostic criteria and recommendations.

2.2. Statistical Methods

Quantitative data are presented as arithmetic mean ± standard deviation (mean ± SD) or median and interquartile range (25–75 percentiles) according to the type of distribution of the variables (Kolmogorov–Smirnov test). Categorical variables were summarized using absolute (n) and relative (%) magnitudes. Mann–Whitney test for independent samples was used to compare quantitative variables between two groups. Z-test was used to compare the relative shares of categorical variables between the studied groups. Logistic regression was performed to determine the simultaneous influence of significant independent variables identified in the univariate analysis to predict belonging to one of two mutually exclusive categories (alive/dead) of the dependent variable output. To determine whether there were differences in the distribution of survival between patients who underwent early surgery and those who did not, a log rank test was performed. A p-value < 0.05 (two-tailed test) was considered statistically significant for all tests. A statistical analysis was performed using SPSS, version 26.0 (IBM Corp., Binghamton, NY, USA).

3. Results

Of all 270 patients, 205 (75.9%) had definite IE, with 133 (65%) of them having two major criteria and 72 (35%) having one major and three minor criteria. There were 65 (24.1%) diagnosed with possible IE, 62 (95%) of them with one major and one minor criterion and three with three minor criteria. The all-cause mortality rate was 24.8% (67). The patients’ baseline characteristics are shown in Table 1. The median age was 65 (51–74), and the non-survivors were significantly older than survivors (67 (53–75) vs. 62 (44–73), p = 0.003). In the entire sample, man were 177 (65.6%), without distinction between survivors and non-survivors. The most patients presented with native valve IE 180 (66.7%) and 88 (33.6%) with prosthetic valve IE. We found CDRIE only in the non-survivors’ group (2 (3%), p=0.013). No significant difference was identified between deceased and alive patients in terms of risk group, predisposing cardiac conditions, port of entry and type of IE according to mode of acquisition.
Patients were presented with a wide range of comorbidity, the most common being arterial hypertension 171 (63.3%), chronic heart failure 124 (45.9%), previous cardiac surgery 95 (35.2%), chronic kidney failure 70 (25.9%), coronary artery disease 64 (23.7%), diabetes 51 (18.9%), atrial fibrillation 49 (18.1%) and more. We found a significant difference with more cases in the deceased vs alive for CCI (4 (3-6) vs 3 (1-5), p=0.000), chronic kidney disease (26 (38.8%) vs 44 (24.7%), p=0.006), atrial fibrillation (18 (26.9%) vs 31 (15.3%), (p=0.033) and previous stroke (15 (22.4%) vs 25 (12.3%), p=0.044). The most common clinical symptoms were fever 263 (97.4%), anemia 248 (92.5%) and heart murmur 178 (66.2%). Cases with splenomegaly 49 (8.1%) and skin changes 14 (5.5%) have decreased dramatically. There were significantly fewer febrile cases in the deceased group (63 (94%) vs 200 (98.5%), p=0.045). In terms of complications, we found that there were significantly more cases of acute heart failure (57 (85.1%), vs 71 (35%), p=0.000), septic shock (20 (29.9%) vs 3 (1.5%), p=0.000) and worsening kidney function (36 (53.7%) vs 75 (36.9%), p=0.015) in those who died compared with those who survived. Early surgery was performed overall in 54 (20%), but less frequency in non-survivors than in survivors (3 (4.5%) vs 51 (25.1%, p=0.000) (Table 2).
The echocardiographic results are shown in Supplementary File S1. Transesophageal echocardiography (TOE) was performed more often in survivors than in deceased (86 (42.4%) vs 11(16.4%), p=0.000). We found that AV-TV involvement was significantly more common in the deceased group (3 (4.5%) vs 1 (0.5%, p=0.019) and ejection fraction was significantly lower in the same group (55 (51-66) vs 62 (55-68), p=0.001).
Hemoculture results were negative in 111 (41.1%) and the most common pathogens were Staphylococci in 89 (33%). Enterococci were more prevalent (25 (9.3%)) than streptococci (21 (7.8%)). The GNB non-HACEK were significantly more frequent in the deceased group (10 (14.9%) vs 9 (4.5%), p=0.004), especially for Escherichia coli ( 5 (7.4%) vs 4 (2.0 %), p=0.030) and Serratia marcescens (3 (4.5%) vs 1 (0.5%) p=0.019) (Supplementary File S2).
The predictors of in-hospital mortality from univariate analysis are summarized in Table 3.
Logistic regression was performed to determine the simultaneous influence of significant independent variables identified in the univariate analysis to predict belonging to one of two mutually exclusive categories (alive/dead) of the dependent variable output. The logistic regression model was statistically significant, χ2(4)=138.07, p=0.000 and explained 59.4% (Nagelkerke R2) of the variation in outcome and correctly classified 86.3% of cases. The presence of septic shock increased the odds of death by a factor of 83.1 and the complication of acute heart failure (AHF) by a factor of 24.64. Early surgery and low CCI reduce this risk (Table 4).
A log-rank survival analysis was performed to determine whether there was a difference in the survival distribution between patients indicated for early surgery who underwent surgery and those who did not. The result of the test was statistically significant, i.e. the survival distribution between the two groups was different – ​​χ2(1)=25.20, p=0.000 (Figure 1). The Median time to event (death), with the estimated time to death being 6 days after hospitalization for the group of patients indicated for early surgery that was not performed. In addition, the 75% percentile of the data showed survival 3 days from hospitalization.

4. Discussion

The overall in-hospital mortality rate in our cohort was 24.4%. These data are consistent with those reported in the literature of 15-30% [2,3,21,22]. The data from the univariate analysis, which are associated with increased mortality (Table 3) can be summarized as follows:
1. Patient characteristics - age, high CCI >3, chronic kidney diseases, previous stroke, atrial fibrillation.
2. Complications occurred – acute heart failure, acute kidney failure, septic shock.
3. Echocardiographic data - low EF (%), severe tricuspid regurgitation, bivalve IE - aortic and tricuspid valve involvement. 4. Microbiological characteristics - GNB non-HASEK - Escherichia coli and Serratia marcescens. 5. Failure to perform early surgery when indicated.
Regarding the microbiological causative agent, gram-negative bacteria (GNB non HASEK) were significantly more frequently represented in the deceased group especially Escherichia coli and Serratia marcescens. Marco Falcone and author also found Escherichia coli as the most common cause of GNB-IE [23]. GNB non HASEK-IE is a rare infection, associated with high in-hospital mortalityi and characterized by the involvement of elderly patients with high comorbidity, nosocomial acquisition, and unfavorable outcome [24,25].
Acute heart failure and septic shock were strong predictors for in-hospital morality, while performed early surgery and low CCI were protective ones.
Acute heart failure is the most common complications of IE and the main indication for early surgery [9,10]. The frequency of heart failure in IE is reported in a wide range between 19-75% [26,27]. This complication is most commonly the result of leaflet perforation or rupture, mitral chordal rupture, valve dehiscence in PVIE and less frequently intracardiac fistula, valve obstruction, myocardial infarction due to embolization [10]. The severity of the manifestation depends on previous cardiac function and coexisting comorbidities. Heart failure in IE is a well-documented independent predictor of in-hospital and 1-year mortality. Surgery remains the only effective therapy linked to improved survival [26,27,28].
Septic shock is a life-threatening complication of IE and occurs in about 5 to 12% of patients [2,3,29,30,31,32,33]. It is well known independent predictor for in-hospital mortality. Risk factors for septic shock include S. aureus and gram-negative bacteria, nosocomial acquisition, acute renal failure, diabetes mellitus, central nervous system embolism [29]. Almost 2/3 of patients with septic shock die. In the context of persistent infection, septic shock is a main indication for early surgery. There is sufficient evidence that early surgical intervention reduces in-hospital and 1-year mortality in these patients [30].
Comorbidities are an important part of the IE patient profile and a predictor of disease outcome. We used the Charlson Comorbidity Index (CCI) [19], the most widely validated measure of the prognostic impact of multiple chronic diseases. Our data are similar to those of other studies for the CCI as an independent prognostic indicator in IE [34,35,36].
The independent predictors of death were found in our study are consistent with the literature. The most frequently reported predictors of death are heart failure, age >70 years [2,3,32,37]; Septic shock – [31,38,39], high CCI [2,40]. In other studies PVIE, Staphylococcus aureus IE, cerebrovascular complicatio ns, paravalvular abscess were reported as independent predictors of in-hospital death [3,21,32,37,41].
Early surgery is a protective indicator and failure to perform early surgery when indicated is a strong predictor of in-hospital death [2]. In our study, early surgery was performed in 20%, which is quite less compared to other countries 45-62% [2,3,21,41]. Data from the literature show that almost half of patients with IE are indicated for early surgery, but more than half of them do not receive it. The reasons are of a different nature. In EURO-ENDO, these are most often: high perioperative risk – 58.2%; death before surgery – 22.5%; patient refusal – 18.8%; neurological complications – 11.2%; lack of cardiac surgery in the medical facility – 6.2%; others – 20.7% [2]. Our data show the benefit of early surgery and the improvement in survival in IE. We found early surgery to be an independent predictor that reduced in-hospital mortality.

5. Limitations

This study is retrospective, and the data were based on the clinical database of a single center. Another limitation is the long study period and the changing recommendations and evidence, especially regarding early surgery, the use of new imaging techniques 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for diagnosis and new antibiotic molecules. Technological advances in echocardiography also have implications for echocardiographic data. Despite these limitations, our study is the only such study performed in Bulgaria in the last several decades, and it included a large number of patients over a long period of time (17 years).

6. Conclusions

Acute heart failure and septic shock are independent predictors of in-hospital mortality. Low Charlson comorbidity index and early surgery increased survival. Knowing the predictors of death would change the therapeutic approach to a more aggressive one and improve the near and long-term prognosis of patients with IE.

Institutional Review Board Statement

This study was conducted with the consent of the Local Ethics Committee (decision #2/09.03.2023) and in accordance with the principles of the Declaration of Helsinki.

References

  1. Geller SA. Infective endoarditis: a history of the development of its undestanding. Autops Case Rep. 2013;3(4):5–12. [CrossRef]
  2. Habib G, Erba PA, 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 Oct 14;40(39):3222–32.
  3. Murdoch DR. Clinical Presentation, Etiology, and Outcome of Infective Endocarditis in the 21st Century. Arch Intern Med. 2009 Mar 9;169(5):463. [CrossRef]
  4. Armiñanzas C, Fariñas-Alvarez C, Zarauza J, Muñoz P, González Ramallo V, Martínez Sellés M, et al. Role of age and comorbidities in mortality of patients with infective endocarditis. Eur J Intern Med. 2019 Jun;64:63–71. [CrossRef]
  5. Pericàs JM, Llopis J, Jiménez-Exposito MJ, Kourany WM, Almirante B, Carosi G, et al. Infective Endocarditis in Patients on Chronic Hemodialysis. J Am Coll Cardiol. 2021 Apr;77(13):1629–40. [CrossRef]
  6. Duval X, Alla F, Doco-Lecompte T, Le Moing V, Delahaye F, Mainardi JL, et al. Diabetes mellitus and infective endocarditis: the insulin factor in patient morbidity and mortality. Eur Heart J. 2006 Jun 13;28(1):59–64. [CrossRef]
  7. Chu VH, Cabell CH, Benjamin DK, Kuniholm EF, Fowler VG, Engemann J, et al. Early Predictors of In-Hospital Death in Infective Endocarditis. Circulation. 2004 Apr 13;109(14):1745–9. [CrossRef]
  8. Menchi-Elanzi M, Ramos-Rincón JM, Merino-Lucas E, Reus-Bañuls S, Torrús-Tendero D, Clíment-Paya V, et al. Infective endocarditis in elderly and very elderly patients. Aging Clin Exp Res. 2020 Jul 19;32(7):1383–8. [CrossRef]
  9. Habib G, Lancellotti P, Antunes MJ, Bongiorni MG, Casalta JP, Del Zotti F, 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 Nov 21;36(44):3075–128.
  10. 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. [CrossRef]
  11. Infective endocarditis after TAVI: a meta-analysis and systematic review of epidemiology, risk factors and clinical consequences. Rev Cardiovasc Med. 2020;21(2):263.
  12. Panagides V, Cuervo G, Llopis J, Abdel-Wahab M, Mangner N, Habib G, et al. Infective Endocarditis After Transcatheter Versus Surgical Aortic Valve Replacement. Clinical Infectious Diseases. 2024 Jan 25;78(1):179–87. [CrossRef]
  13. del Val D, Panagides V, Mestres CA, Miró JM, Rodés-Cabau J. Infective Endocarditis After Transcatheter Aortic Valve Replacement. J Am Coll Cardiol. 2023 Jan;81(4):394–412. [CrossRef]
  14. Sanghavi R, Ravikumar N, Sarodaya V, Haq M, Sherif M, Harky A. Outcomes in Cardiac Implantable Electronic device-related Infective Endocarditis: A Systematic Review of Current Literature. Future Cardiol. 2022 Nov 8;18(11):891–9. [CrossRef]
  15. Jędrzejczyk-Patej E, Mazurek M, Kowalski O, Sokal A, Kozieł M, Adamczyk K, et al. Device-related infective endocarditis in cardiac resynchronization therapy recipients — Single center registry with over 2500 person-years follow up. Int J Cardiol. 2017 Jan;227:18–24.
  16. Fernández-Hidalgo N, Almirante B, Tornos P, Pigrau C, Sambola A, Igual A, et al. Contemporary Epidemiology and Prognosis of Health Care–Associated Infective Endocarditis. Clinical Infectious Diseases. 2008 Nov 15;47(10):1287–97. [CrossRef]
  17. Lomas JM, Martínez-Marcos FJ, Plata A, Ivanova R, Gálvez J, Ruiz J, et al. HEALTH CARE - ASSOCIATED INFECTIVE ENDOCARDITIS. AN UNDESIRABLE EFFECT OF HEALTH CARE UNIVERSALIZATION. Clinical Microbiology and Infection. 2009 Sep. [CrossRef]
  18. Park LP, Chu VH, Peterson G, Skoutelis A, Lejko-Zupa T, Bouza E, et al. Validated Risk Score for Predicting 6-Month Mortality in Infective Endocarditis. J Am Heart Assoc. 2016 Apr 3;5(4). [CrossRef]
  19. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis. 1987 Jan;40(5):373–83. [CrossRef]
  20. Durack DT, Lukes AS, Bright DK. New criteria for diagnosis of infective endocarditis: utilization of specific echocardiographic findings. Duke Endocarditis Service. Am J Med. 1994 Mar;96(3):200–9. [CrossRef]
  21. Sunder S, Grammatico-Guillon L, Lemaignen A, Lacasse M, Gaborit C, Boutoille D, et al. Incidence, characteristics, and mortality of infective endocarditis in France in 2011. PLoS One. 2019 Oct 25;14(10):e0223857. [CrossRef]
  22. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019. J Am Coll Cardiol. 2020 Dec;76(25):2982–3021. [CrossRef]
  23. Falcone M, Tiseo G, Durante-Mangoni E, Ravasio V, Barbaro F, Ursi MP, et al. Risk Factors and Outcomes of Endocarditis Due to Non-HACEK Gram-Negative Bacilli: Data from the Prospective Multicenter Italian Endocarditis Study Cohort. Antimicrob Agents Chemother. 2018 Apr;62(4). [CrossRef]
  24. Calderón Parra J, De Castro-Campos D, Muñoz García P, Olmedo Samperio M, Marín Arriaza M, De Alarcón A, et al. Non-HACEK gram negative bacilli endocarditis: Analysis of a national prospective cohort. Eur J Intern Med. 2021 Oct;92:71–8.
  25. Sebillotte M, Boutoille D, Declerck C, Talarmin JP, Lemaignen A, Piau C, et al. Non-HACEK gram-negative bacilli endocarditis: a multicentre retrospective case-control study. Infect Dis. 2023 Sep 2;55(9):599–606. [CrossRef]
  26. Pericàs JM, Hernández-Meneses M, Muñoz P, Martínez-Sellés M, Álvarez-Uria A, de Alarcón A, et al. Characteristics and Outcome of Acute Heart Failure in Infective Endocarditis: Focus on Cardiogenic Shock. Clinical Infectious Diseases. 2021 Sep 7;73(5):765–74. [CrossRef]
  27. Mir T, Uddin M, Qureshi WT, Regmi N, Tleyjeh IM, Saydain G. Predictors of Complications Secondary to Infective Endocarditis and Their Associated Outcomes: A Large Cohort Study from the National Emergency Database (2016–2018). Infect Dis Ther. 2022 Feb 24;11(1):305–21. [CrossRef]
  28. Bohbot Y, Habib G, Laroche C, Stöhr E, Chirouze C, Hernandez-Meneses M, et al. Characteristics, management, and outcomes of patients with left-sided infective endocarditis complicated by heart failure: a substudy of the ESC-EORP EURO-ENDO (European infective endocarditis) registry. Eur J Heart Fail. 2022 Jul 16;24(7):1253–65.
  29. Pericàs JM, Hernández-Meneses M, Muñoz P, Álvarez-Uría A, Pinilla-Llorente B, de Alarcón A, et al. Outcomes and Risk Factors of Septic Shock in Patients With Infective Endocarditis: A Prospective Cohort Study. Open Forum Infect Dis. 2021 Jun 1;8(6). [CrossRef]
  30. Pericàs JM, Hernández-Meneses M, Muñoz P, Álvarez-Uría A, Pinilla-Llorente B, de Alarcón A, et al. Outcomes and Risk Factors of Septic Shock in Patients With Infective Endocarditis: A Prospective Cohort Study. Open Forum Infect Dis. 2021 Jun 1;8(6). [CrossRef]
  31. Sousa C, Nogueira P, Pinto FJ. Insight into the epidemiology of infective endocarditis in Portugal: a contemporary nationwide study from 2010 to 2018. BMC Cardiovasc Disord. 2021 Dec 16;21(1):138. [CrossRef]
  32. Urina-Jassir M, Jaimes-Reyes MA, Martinez-Vernaza S, Quiroga-Vergara C, Urina-Triana M. Clinical, Microbiological, and Imaging Characteristics of Infective Endocarditis in Latin America: A Systematic Review. International Journal of Infectious Diseases. 2022 Apr;117:312–21. [CrossRef]
  33. Vahabi A, Gül F, Garakhanova S, Sipahi H, Sipahi OR. Pooled analysis of 1270 infective endocarditis cases in Turkey. The Journal of Infection in Developing Countries. 2019 Feb 28;13(02):93–100. [CrossRef]
  34. Alves SG, Pivatto F, Filippini FB, Dannenhauer GP, Seroiska G, Bischoff HM, et al. Desempenho do Escore SHARPEN e do Índice de Comorbidade de Charlson para Predição de Mortalidade durante a Internação Hospitalar e após a Alta na Endocardite Infecciosa. Arq Bras Cardiol. 2023 Dec 31;120(12).
  35. Lu KJ, Kearney LG, Ord M, Jones E, Burrell LM, Srivastava PM. Age adjusted Charlson Co-morbidity Index is an independent predictor of mortality over long-term follow-up in infective endocarditis. Int J Cardiol. 2013 Oct;168(6):5243–8. [CrossRef]
  36. Wei D, Sun Y, Chen R, Meng Y, Wu W. Age-adjusted Charlson comorbidity index and in-hospital mortality in critically ill patients with cardiogenic shock: A retrospective cohort study. Exp Ther Med. 2023 May 5;25(6):299. [CrossRef]
  37. Noubiap JJ, Nkeck JR, Kwondom BS, Nyaga UF. Epidemiology of infective endocarditis in Africa: a systematic review and meta-analysis. Lancet Glob Health. 2022 Jan;10(1):e77–86.
  38. Hase R, Otsuka Y, Yoshida K, Hosokawa N. Profile of infective endocarditis at a tertiary-care hospital in Japan over a 14-year period: characteristics, outcome and predictors for in-hospital mortality. International Journal of Infectious Diseases. 2015 Apr;33:62–6. [CrossRef]
  39. Álvarez-Zaballos S, González-Ramallo V, Quintana E, Muñoz P, de la Villa-Martínez S, Fariñas MC, et al. Multivalvular Endocarditis: A Rare Condition with Poor Prognosis. J Clin Med. 2022 Aug 13;11(16):4736. [CrossRef]
  40. Kim JH, Lee HJ, Ku NS, Lee SH, Lee S, Choi JY, et al. Infective endocarditis at a tertiary care hospital in South Korea. Heart. 2021 Jan;107(2):135–41. [CrossRef]
  41. Maguire DJ, Arora RC, Hiebert BM, Dufault B, Thorleifson MD. The Epidemiology of Endocarditis in Manitoba: A Retrospective Study. CJC Open. 2021 Dec;3(12):1471–81. [CrossRef]
Preprints 116541 g001
Figure 1. Survival function showing the difference in the distribution of survival between patients indicated for early surgery that underwent early surgery and those that didn't.
Figure 1. Survival function showing the difference in the distribution of survival between patients indicated for early surgery that underwent early surgery and those that didn't.
Preprints 116541 g001
Table 1. Baseline characteristics.
Table 1. Baseline characteristics.
Variables Total IE cases
2005 – 2021
n=270		 IE Non-Survivors
n=67		 IE Survivors
n=203		 p=value
Age in yrs., Χ ± SD
median (IQR) 		60.86±16.83
65 (51-74)		 65.99±15.69
69 (59-78)		 59.17±16.89
63 (46-73)		 0.003Ϯ
Gender – male, n (%) 177 (65.6) 41 (61,2) 136 (67) 0.386*
Тime symptoms-hospitalization,
median (IQR) 		30 (20-60) 30 (14-60) 30 (20-60) 0.188Ϯ
Risk groups, n (%)
  Low 136 (50.4) 36 (53.7) 100 (49.3) 0.532*
  Moderate 44 (16.3) 10 (14.9) 34 (16.7) 0.729*
  High 90 (33.3) 21 (31.3) 69 (34.0) 0.684*
Type of valves, n (%)
Native IE 180 (66.7) 45 (67.2) 135 (66.5) 0.916
Prosthetic IE 88 (33.6) 20 (29.9) 68 (33.5) 0.586
  Early prosthetic 9 (3.3) 1 (1.5) 8 (3.9) 0.341
 Late prosthetic 79 (29.3) 19 (28.4) 60 (29.6) 0.852
CDRIE 2 (0.7) 2 (3.0) 0 (0.0) 0.013*
 Entry door, n (%)
  Unknown 125 (46.3) 35 (52.2) 86 (42.4) 0.162
  Non-dental manipulation/procedures 44 (16.3) 9 (13.4) 35 (17.2) 0.465
  Dental 30 (11.1) 5 (7.5) 25 (12.3) 0.278
   I.v. drug users 24 (8.9) 7 (10.4) 17 (8.4) 0.618
   Hemodialysis 13 (4.8) 5 (7.5) 8 (3.9) 0.232
   Skin 10 (3.7) 3 (4.50) 7 (3.4) 0.678
   Urogenital 9 (3.3) 1 (1.5) 8 (3.9) 0.341
  Gastrointestinal 5 (1.9) 1 (1.5) 8 (3.9) 0.341
   Respirators 5 (1.9) 0 (0.0) 5 (2.5) 0.191
   Ear Nose Throat 4 (1.5) 1 (1.5) 3 (1.5) 1.000
  Others 1 (0.4) 0 (0.0) 1 (0.5) 0.562
Predisposing heart conditions, n (%)
  Prosthetic valve 76 (28.2) 19 (28.3) 57 (28.0) 0.949
  Past IE 20 (7.4) 6 (9.0) 14 (6.9) 0.569
      Past IE prosthetic 14 (5.2) 3 (4.5) 11(5.4) 0.773
      Past IE native valves 6 (2.2) 3 (4.5) 3 (1.5) 0.151
   Rheumatic heart disease 11 (4.0) 1 (1.5) 10 (4.9) 0.221
   Congenital heart disease 21 (7.8) 6 (9.0) 15 (7.5) 0.693
      Bicuspid Ao valve 11 (4.1) 4 (6.0) 7 (3.5) 0.372
      Mitral valve prolapse 8 (3.0) 1 (1.5) 7 (3.5) 0.406
      Others 2 (0.7) 1 (1.5) 1 (0.5) 0.410
   Degenerative valve 19 (7.0) 7 (10.5) 12 (5.9) 0.202
   Intact valves 123 (45.6) 28 (41.7) 95 (46.8) 0.467
Type of acquisition
Community acquired IE 173 (64.1) 43 (64.2) 130 (64.0) 0.976
Health care–associated IE 72 (26.7) 17 (25.4) 55 (27.1) 0.785
Intravenous drug use–related IE 25 (9.3) 7 (10.4) 18 (8.9) 0.714
* z-test; † Mann–Whitney U Test; CDRIE—cardiac device related IE.
Table 2. Comorbidity, clinical symptoms and complications.
Table 2. Comorbidity, clinical symptoms and complications.
Variables Total IE cases
2005 – 2021
n=270		 IE Non-Survivors
n=67		 IE Survivors
n=203		 p=value
Comorbidity
   CCI, median (IQR) 3 (2-5) 4 (3-6) 3 (1-5) 0.000†
   AH 171 (63,3) 43 (64.2) 128 (63.1) 0.860*
   CHF 124 (45.9) 32 (47.8) 92 (45.3) 0.728*
   Heart surgery 95 (35,2) 20 (29.9) 75 (36.9) 0.292*
   CKD 70 (25.9) 26 (38.8) 44 (21.7) 0.006*
   CAD 64 (23.7) 19 (28.4) 45 (22.2) 0.302*
   Diabetes 51 (18.9) 11 (16.4) 40 (19.7) 0.551*
   Atrial fibrillation 49 (18.1) 18 (26.9) 31 (15.3) 0.033*
   Past stroke 40 (14.8) 15 (22.4) 25 (12.3) 0.044*
   Gastrointestinal 32 (11.1) 6 (9.0) 26 (12.8) 0.580*
   Malignancy 30 (11.1) 6 (9.0) 24 (11.8) 0.517*
   COPD 21 (7.8) 5 (7.6) 16 (7.9) 0.936*
   Hemodialysis 14 (5.2) 5 (7.5) 9 (4.4) 0.332*
   Chronic liver disease 13 (4.8) 4 (6.0) 9 (4.4) 0.611*
   Systemic disease 4 (1.5) 0 (2.0) 4 (2.0) 0.247*
Clinical symptoms
   Fever 263 (97.4) 63 (94) 200 (98.5) 0.045*
   Anemia 248 (92.5) 61 (91) 107 (93) 0.590*
   Cardiac murmur 178 (66.2) 46 (68.7) 132 (65.3) 0.620*
   Splenomegaly 49 (8.1) 13 (19.4) 36 (17.7) 0.759*
   Skin disorders 14 (5.5) 4 (6.0) 10 (4.9) 0.738*
Complications
   AHF 128 (47.5) 57 (85.1) 71 (35) 0.000*
  Worsening
  kidney function		 111 (41.1) 36 (53.7) 75 (36.9) 0.015*
  Embolism 56 (20.7) 16 (23.9) 40 (19.7) 0.370*
    Brain 29 (51.7) 8 (50.0) 21 (52.5) 0.866*
    Lung 5 (8.9) 0 (0.0) 5 (12.5) -
    Spleen 10 (17.9) 5 (31.25) 5 (12.5) 0.098*
    Skin 7 (12.5) 1 (6.25) 6 (15) 0.371*
 Musculoskeletal 2 (3.6) 1 (6.25) 1 (2.5) 0.495
   Combine 3 (5.4) 1 (6.25) 2 (5) 0.851*
  Strock 30 (11.1) 9 (13.4) 21 (10.3) 0.486*
  Septic shock 23 (8.5) 20 (29.9) 3 (1.5) 0.000*
Early surgery, n (%) 54 (20.0) 3 (4.5) 51 (25.1) 0.000*
*z-test; †Mann-Whitney U Test; CCI-Charlson comorbidity index; AH-arterial hypertension; CHF-chronic heart failure; CKD-chronic kidney diseases; CAD-coronary arterial diseases; COPD-chronic obstructive pulmonary diseases; AHF-acute heart failure.
Table 3. Predictors of in-hospital mortality - p value from univariate analysis.
Table 3. Predictors of in-hospital mortality - p value from univariate analysis.
Variables Total IE cases
2005 – 2021
n=270		 IE Non-
Survivors
n=67
IE Survivors
n=203		 p=value
Age in yrs., Χ ± SD,
median (IQR) 		60.86±16.83
65 (51-74)		 65.99±15.69
69 (59-78)		 59.17±16.89
63 (46-73)		 0.003Ϯ
Carlson Comorbidity Index,
median (IQR) 		3 (2-5) 4 (3-6) 3 (1-5) 0.000†
AHF, n (%) 128 (47.5) 57 (85.1) 71 (35) 0.000*
AKF, n (%) 111 (41.1) 36 (53.7) 75 (36.9) 0.015*
Septic shock, n (%) 23 (8.5) 20 (29.9) 3 (1.5) 0.000*
Atrial fibrillation, n (%) 49 (18.1) 18 (26.9) 31 (15.3) 0.033*
CKD, n (%) 70 (25.9) 26 (38.8) 44 (21.7) 0.006*
Past stroke, n (%) 40 (14.8) 15 (22.4) 25 (12.3) 0.044*
AV-TV involvement, n (%) 4 (1.5) 3 (4.5) 1 (0.5) 0.019*
EF %. медиана (IQR) 60 (54-68) 55 (51-66) 62 (55-68) 0.001Ϯ
ТR III ст, n (%) 12 (4.4) 6 (9.0) 6 (3.0) 0.040*
GNB non-HACEK, n (%), 19 (7.1) 11 (15) 9 (4.5) 0.004*
Escherichia coli, n (%) 9 (3.4) 5 (7.5) 4 (2.0) 0.030*
Serratia marcescens, n (%) 4 (1.5) 3 (4.5) 1 (0.5) 0.019*
*z-test; †Mann-Whitney U Test; AHF-Acute heart failure; AKF- Acute kidney failure; CKD-Chronic kidney diseases; AV-TV – Aortic valve-Tricuspid valve; EF-Ejection fraction; TR-Tricuspid regurgitation; GNB non-HACEK-Gram negative bacteria non-HACEK- (Hemophilus species, Actinobacillus, Cardiobacterium, Eikenella, or Kingella).
Table 4. Logistic regression for periods.
Table 4. Logistic regression for periods.
Variable B- coefficient OR 95% CI p Nagelkerke R2
2005-2021 59.4%
Early surgery -3.52 0.03 0.01-0.16 0.000
Charlson comorbidity index -0.16 0.85 0.74-0.98 0.026
Septic shock 4.42 83.06 17.03-405.18 0.000
Acute heart failure 3.20 24.64 9.20-65.98 0.000
OR-odds ratio; CI- confidence interval;.
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