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Abstract

This study aimed to evaluate the utility of the systemic immune-inflammation index (SII) in predicting the development of Cardiac Implantable Electronic Device (CIED) infections. A retrospective analysis was performed using the medical records of 2185 patients who underwent CIED (including de novo, revision, or upgrade) implantation at our institution from January 2012 to December 2019. We recorded CIED infections and risk factors according to the patient, device, and procedural characteristics during the median 28.7 months (6.2–56.8) follow-up. CIED infections were identified in 52 patients. Diabetes, chronic renal disease, SII before implantation, new cardiac resynchronization therapy (CRT) implantation, CRT-battery replacement, revision or upgrade, and the number of previous procedures were independent predictors of CIED infections. The area under the curve (AUC) of SII to predict CIED infection was .733 (95% CI: .654-.811). A raised SII may be a useful predictor of CIED infection.

Keywords

infection cardiac implantable electronic device risk factors predictors

Introduction

Cardiac Implantable Electronic Devices (CIEDs) were defined in the 1930s as aiming to monitor and treat heart contraction or rhythm disturbance by acting on electrical activity and heart muscle contraction.¹ Although CIED (pacemakers [CPM], implantable cardioverter defibrillator [ICD], and cardiac resynchronization therapies [CRT]) implantation procedures are safe and with a low risk of severe complication rates (infection, thrombosis, lead failure, and pneumothorax),² CIED infections remain a problem causing morbidity and mortality. The infection rate is reported as .5%–5% in different studies.³ In Turkey, this rate was 2.5% after 7 years of follow-up.⁴

The European and American Infective Endocarditis and Pacemaker Guidelines recommend antibiotic prophylaxis to prevent infection before implantation of CIED.^5-7 It is important to identify high-risk patients so as to minimize infection risk (e.g., by using an antibiotic envelope, a prolonged perioperative antibiotic regime, and other actions).

Recently, the systemic immune-inflammation index (SII) has been developed based on the platelet counts and neutrophil/lymphocyte ratio (NLR) to simultaneously consider inflammatory and immunothrombosis status. The SII was related to adverse outcomes in chronic heart failure and coronary artery disease.^8-10 However, the relationship between SII and the development of CIED infection in patients who underwent CIED implantation remains unclear. Thus, we evaluated the predictive value of SII for CIEDs infections.

Materials and Methods

We retrospectively examined 2185 patients admitted to our hospital for CIED implantation, replacement, upgrade, or revision between January 2012 and December 2019. The patients were divided into 2 groups: those with CIED infections (n = 52) or without CIED infection (n = 2133). Our center in Izmir, Turkey, provides tertiary referral health care. In the cardiology unit, approximately 400 CIED procedures are performed annually. These procedures include new pacemakers, new defibrillators, new CRT implantation, battery replacement for ICDs/CPMs, or revision and upgrade procedures. The patients received cefazolin or vancomycin/clindamycin in case of allergy to beta-lactam agents as prophylaxis before procedures in our hospital. The study was approved by the Local Ethics Committee of Izmir Katip Celebi University (approval number: 1040 date: 2020) and complied with the Declaration of Helsinki.

Blood samples were obtained from antecubital veins of all patients after admission, before the procedure. Age, sex, pacemaker type, laboratory parameters as hemoglobin, creatinine, estimated glomerular filtration rate, white blood cell count levels; co-morbid diseases such as chronic kidney disease and diabetes mellitus; isolated microorganism from microbiological culture, susceptibility of the isolated microorganism; the infection type as the pocket infection or endocarditis; and the result of extraction were collected. These parameters were obtained from an institutional database. In our final analysis, we only included patients with pacemaker pocket infection and infective endocarditis as device infections. Infection was confirmed via positive cultures from a pocket swab or tissue sample obtained at the time of device removal, from a lead tip, from blood, or with clinical criteria. The Duke criteria for diagnosis of endocarditis were applied to systemic infections related to implantable cardiac devices.¹¹

Definitions

The SII was defined as follows: SII = platelet count × neutrophil count/lymphocyte count. The mean value and corresponding 95% reference interval for the SII were 459 (189-1168) in a previous study.¹² The neutrophil-lymphocyte ratio (NLR) was defined as the ratio of the neutrophil count to the lymphocyte count. CKD (chronic kidney disease) was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m² which was calculated using the Modification of Diet in Renal Disease (MDRD) formula.¹³

Initial CIED implantation was defined as a de novo procedure. Generator replacement, lead revision, and system upgrades were considered revision procedures. These groups within the procedure type are mutually exclusive. For example, if a patient is undergoing an upgrade to CRT, then he or she is counted in the revision/upgrade group. An immunocompromised status was defined as having at least 1 of the following conditions: (1) immunosuppression (defined as viral immunosuppression, neoplastic disease, immunosuppressive drugs including steroids, chemotherapy, or congenital immunosuppression), (2) active hematologic malignancy (i.e., still requiring treatment), or (3) active neoplasm (i.e., a neoplasm that has not been resected, still requires treatment or with metastasis).

Time of Infection

Early infection

The time between implant or revision procedure and CIED infection diagnosis was 1 month or less.

Late infection

The time between implant or revision procedure and CIED infection diagnosis was 1-12 months.

Delayed Infection

When CIED infection occurred 12 months after implant or new procedure.

Statistical Analysis

Statistical tests were carried out using SPSS version 19 (SPSS Inc, Chicago, IL, USA). Continuous variables were provided as mean ± standard deviation, and categorical variables were presented as the number of patients, with the percentage of the total number. Either the Student’s t-test or the Mann–Whitney U test was used to compare values between the 2 groups, as appropriate. The Chi-squared test was used when comparing categorical variables. A multivariate Cox regression analysis was used to identify independent predictors for CIED infection. Variables with a P < .1 and known predictive values from the analysis without variables were entered into the multivariate model. The estimation of predictive values of the multivariable model and multivariable model plus SII were made by the areas under the receiver operating characteristic curve. We used the DeLong test to compare the area under the curve (AUC) with each of these parameters.¹⁴ Moreover, the increased discriminative value of the multivariable model was also estimated using net reclassification improvement (NRI) and integrated discrimination improvement (IDI).¹⁵ A 2-sided P < .05 was considered statistically significant.

Results

The mean age was 65.0 ± 13.9 years in the non-infected group and 60.0 ± 16.5 years in the infected group. Male sex dominance was observed in the infected group 41 (79%) and non-infected group 1397 (66%). Infection was more common in the younger population (60.0 ± 16.5 vs 65.0 ± 13.9 years, P = 0.010). Histories of diabetes mellitus and renal insufficiency were more common comorbidities in the CIED infection patients group (Table 1). The most frequent procedure was new cardiac pacemaker implantation in 997 patients (45.6%).

Table 1.

Baseline Characteristics of the Study Population.

Variables	No infection (n = 2133)	Infection (n = 52)	P
Age, years	65.0 ± 13.9	60.0 ± 16.5	.010
Male gender (%)	1397 (66)	41 (79)	.045
Immunocompromised, n (%)	25 (1)	1 (2)	.639
Diabetes mellitus, n (%)	667 (31)	27 (52)	.002
eGFR < 60 mL/min/1.73 m² n (%)	47 (2)	8 (15)	<.001
Type of procedure, n (%)
New pacemaker	989 (46)	8 (15)	<.001
New ICD	446 (21)	16 (31)	.085
New CRT pacemaker/ICD implantation	55 (3)	6 (12)	<.001
Pacemaker generator replacement	502 (24)	7 (14)	.090
ICD generator replacement	75 (4)	3 (6)	.421
CRT generator replacement	36 (2)	3 (6)	.028
Revision or upgrade	30 (1)	9 (17)	<.001
Number of previous procedures
1	475 (23)	7 (14)	<.001
2	167 (8)	15 (29)
>2	79 (4)	8 (15)
PADIT score	2.27 ± 1.56	4.17 ± 3.23	<.001
Mortality
At 1 year, n (%)	84 (4)	5 (10)	.041
At 2 years, n (%)	223 (11)	10 (19)	.043
At 3 years, n (%)	334 (16)	17 (33)	.001

Abbreviations: CRT, cardiac resynchronization therapy; eGFR, estimated glomerular filtration rate; ICD, implantable cardioverter defibrillator; PADIT, Prevention of Arrhythmia Device Infection Trial.

The median follow-up time was 28.7 months (6.2-56.8). Among the 2185 patients, CIED infection was present in 52 (2.4%) patients. Of the 52 CIED infection patients, 13 (25%) had an early infection (<1 month after the procedure), 18 had (35%) late infection (1-12 months after the device-related procedure), and 21 had (40%) delayed infection (>12 months after the procedure).

The CIED infection rate was lower with initial pacemaker implantation vs repeat procedures (15% vs 46%, P < .001). New CRT pacemaker (CRT-P) or defibrillator (CRT-D) implantation and CRT generator replacement, revision, or upgrade were more common in CIED infection patients compared with patients without (each P < .05, Table 1). The number of previous procedures was higher in the infection group than in the non-infected group.

Cardiac implantable electronic device infection patients had higher levels of white blood cell count and lower estimated glomerular filtration rate value compared with those without CIED infection (Table 2) (8.8 ± 2.4 vs 7.8 ± 1.5 × 10³/μL, P < .001; 85.9 ± 28.5 vs 92.7 ± 21.10 mL/min per 1.73 m² P = 0.023, respectively). Also, the SII values were higher in patients with CIED infection than in those without (11.7 [6.3-19.9] vs 6.1 [4.0-9.1], P < 0.001). In multivariate analysis, age, male gender, diabetes mellitus, SII, eGFR, new ICD implantation, new CRT-P or CRT-D implantation, CRT-battery replacement, revision, or upgrade, and a number of previous procedures were independent predictors of CIED infection (Table 3). Also, platelet count (Odds ratio [OR]: 1.007, 95% confidence interval [CI]: 1.004-1.011, P < .001), and NLR (OR: 1.178, 95% CI: 1.084-1.280, P < 0.001) were found to be independent predictors of CIED infection in multivariate analysis.

Table 2.

Laboratory Findings of the Patients.

Variable	No infection (n = 2133)	Infection (n = 52)	P
WBC count (×10³/μL)	7.8 ± 1.5	8.8 ± 2.4	<.001
Hemoglobin (g/dL)	12.9 ± 1.9	12.8 ± 1.6	.882
eGFR (mL/min/1.73 m²)	92.7 ± 21.0	85.9 ± 28.5	.023
Platelet count (×10³/μL)	244 ± 77	295 ± 99	<.001
Neutrophil count (×10³/μL)	4.6 ± 1.3	6.0 ± 2.2	<.001
Lymphocyte count (×10³/μL)	1.8 ± 0.6	1.6 ± 07	.007
NLR	2.5 (1.9-3.7)	4.3 (2.6-6.1)	<.001
SII	605.9 (403.3-906)	1166.2 (631.6-1993.7)	<.001

Abbreviations: WBC, white blood cell; eGFR, estimated glomerular filtration rate; NLR, neutrophil-to-lymphocyte ratio; SII, systemic immune-inflammation index.

Table 3.

Univariate and Multivariate Analysis for Prediction of CIED Infections.

Variables	Univariate			Multivariate
Variables	OR	95% CI	P	OR	95% CI	P
Age	.97	.95-.99	.010	0.94	0.92-0.97	<.001
Male gender	1.96	1.03-3.84	.049	2.45	1.22-4.91	.012
DM	2.35	1.35-4.09	.02	2.26	1.19-4.29	.013
Number of previous procedures ≥ 2	2.45	1.86-3.27	<.001	3.10	2.00-4.80	<.001
New pacemaker implantation	.21	.09-.44	<.001
New ICD implantation	1.68	.92-3.05	.089	2.94	1.37-6.30	.006
New CRT-pacemaker/CRT-ICD implantation	4.92	2.02-12.02	<.001	2.26	1.27-4.01	.005
Pacemaker generator replacement	.50	.22-1.12	.096
CRT generator replacement	3.56	1.62-11.97	.040	1.68	1.01-2.78	.046
Revision/upgrade	2.45	1.87-3.20	<.001	1.70	1.30-2.68	.013
eGFR	.98	.97-.99	<.001	.96	.94-.98	<.001
WBC count^a	1.35	1.15-1.59	<.001
NLR^a	1.16	1.08-1.23	<.001
Platelet count^a	1.1	1.00-1.01	<.001
SII	1.00	1.00-1.01	<.001	1.00	1.00-1.01	<.001

^aThese parameters were not included in multivariate analysis in order to prevent multicollinearity.

Abbreviations: OR, odds ratio; CI, confidence interval; CIED, Cardiac Implantable Electronic Device; DM, diabetes mellitus; CRT, cardiac resynchronization therapy; ICD, implantable cardioverter defibrillator; eGFR, estimated glomerular filtration rate; WBC, White blood cell; SII, Systemic immune-inflammation index; NLR, neutrophil-to-lymphocyte Ratio.

The AUC of SII in the prediction of CIED infection was .733 (95% CI: .654-.811, P < .001, Figure 1). We found that SII had higher accuracy in predicting CIED infection compared with platelet count alone (SII vs platelet: AUC: .733 vs .646, z = 2.421, P = .0155, Figure 1). Although SII and NLR had similar accuracy for predicting CIED infection (SII vs NLR: AUC: .733 vs .715, z = .981, P = 0.326), SII was related to a significant NRI of 51.4% (z = 3.663, P = .0002) and an IDI of .014 (P < .05) compared with NLR alone (Figure 1).

Figure 1.

Receiver operating characteristic (ROC) curves of platelet count, neutrophil-to-lymphocyte Ratio (NLR), and the systemic immune-inflammation index (SII) in predicting Cardiac Implantable Electronic Device (CIED) infection.

The adding of SII to a multivariable model including age, male gender, diabetes mellitus, eGFR, new ICD implantation, new CRT-P or CRT-D implantation, CRT-battery replacement, revision, or upgrade, and number of previous procedures provided a better predictive value than multivariable model alone (AUCs: .824 vs .749, z = 3.762, P < .001; NRI: 80.2%, P < .001; IDI: .085, P < .05, Figure 2). The predictive values of combination of SII with Prevention of Arrhythmia Device Infection Trial (PADIT) score¹⁶ are provided in Figure 3 (PADIT plus SII vs PADIT; AUCs: .806 vs .726, z = 2.008, P = .044; IDI: .054, P < .05; NRI: 55.4%, z = 3.951, P < .001).

Figure 2.

Receiver operating characteristic (ROC) curves of the multivariable model and multivariable model plus systemic immune-inflammation index (SII) in predicting Cardiac Implantable Electronic Device (CIED) infection.

Figure 3.

Receiver operating characteristic (ROC) curves of the Prevention of Arrhythmia Device Infection Trial (PADIT) score and PADIT score plus systemic immune-inflammation index (SII) in predicting Cardiac Implantable Electronic Device (CIED) infection.

Bacteremia was confirmed in 27 (51.9%) of the 52 infected patients. The microbiological blood culture results are summarized in Table 4. The most encountered microorganisms were methicillin susceptible Staphylococcus aureus (MSSA), methicillin resistant coagulase negative Staphylococcus (MRKNS), and methicillin resistant S aureus (MRSA).

Table 4.

Microorganisms Isolated From Blood Culture.

Number of Positive blood Culture	Microorganism
8	MSSA
6	MRKNS
4	MRSA
1	Staphylococcus lagdunensis
1	MSSE
1	Enterococcus faecalis
1	Enterobacter aerogenes
1	Escherichia coli
1	Enterobacter cloacae
1	Stenotrophomonas maltophila
1	Non albicans candida
1	Candida parapsilosis

Abbrevations: MSSA, Methicillin susceptible Staphylococcus aureus; MRKNS, Methicillin resistant Coagulase Negative Staphylococcus; MRSA, Methicillin resistant S aureus; MSSE, Methicillin susceptible Staphylococcus epidermidis.

Of the bacteremic patients, 4 (14.8) had lead or valvular endocarditis and 23 (85%) had a pocket infection. In the lead-infected group, no mortality was recorded, whereas in the pocket-infected group 1 death due to heart failure occurred. Also, 1 death was recorded due to pneumonia, 2 years after pocket infection.

Discussion

The present study found that SII was independently associated with the development of CIED infection. Also, SII improved risk stratification, and enabled a more accurate prediction of CIED infection compared with the PADIT score alone.

The most important factors in preventing CIEDs are sterilization of the procedure area and obeying the rules of antisepsis. The infection rate is predicted to be low with formally educated and experienced clinicians since the duration of the process is expected to be shorter. Antibiotic prophylaxis is recommended by American and European Guidelines^17,18 but there are fluctuating data and there is no decision about the duration of antibiotic therapy after the implantation process. Dwivedi et al¹⁹ compared 2 groups that had antibiotic therapy for 48 h and 7 days/procedure; they found no significant difference in infection rate between the 2 groups. The authors usually use 1 dose antibiotic therapy before implantation and one dose afterward. In different centers, the study groups did not obtain any significant difference between prolonged antibiotic use for 1 day or 2 days after the procedure. According to these data, there is still a debate on a prophylactic antibiotic dose, duration, and type to prevent infection.

White blood cell and its subgroups are traditional inflammatory markers and have been widely used in clinical practice. Lymphocytes are the smallest cells among WBC. They mainly include T lymphocytes and B lymphocytes, which participate in cellular immunity and humoral immunity, respectively. Lymphocytes may secrete specific antibodies or produce cytotoxicity to those participating in immune inflammatory reactions.²⁰ It has been shown that a decreased lymphocyte count is associated with poor outcomes in cardiovascular disease.²¹ Neutrophils play an important role in the early stage of vegetation formation.²² Neutrophils also have an essential role in the development of immunothrombosis.²³ As platelets release numerous inflammatory mediators, they play a pivotal role in inflammation.²⁴ A previous study has shown that pathogens can promote vegetation formation by inducing intravascular neutrophil extracellular traps through activated platelets.²⁵ Bacterial adhesion to the damaged or inflamed structure such as valve and device may be promoted by platelets.²⁵ Therefore this may lead to the infection of sterile platelet-fibrin clots and the expanded vegetation. In a synergetic manner, the NLR can reflect more about disease severity than either of its constituent leukocyte counts.²⁶ Previous studies have demonstrated the prognostic value of immune biomarkers such as NLR in many diseases including coronary artery disease, and pneumonia.^27-29 NLR has been shown to have predictive value for infective endocarditis.^11,30 NLR was an independent predictor of adverse outcomes including in-hospital mortality or an intra-cerebral event (cerebrovascular event, meningitis, embolism, or brain hemorrhage) in patients with infective endocarditis.²⁶ In the present study, both platelet count and NLR were independent predictors of the development of CIED infection in patients who underwent CIED implantation. SII as a novel marker consists of the neutrophil count, lymphocyte count, and platelet count, and indicates the inflammatory and immune response simultaneously in patients. Furthermore, increased SII may represent the impaired balance of inflammatory and immunologic status. SII was found to be useful in predicting in-hospital mortality in patients with infective endocarditis.²⁴ Having higher SII was associated with embolic events in these patients.³¹ In the present study, patients with CIED infection had higher SII values before the procedure compared with those without CIED infection. Higher SII before the procedure may be a sign of infection or inflammation, especially in cases having early infection risk.

Several risk scores have been developed for the pre-operative assessment of CIED infection risk, combining device-related and patient factors.^16,32 PADIT study has an important place in this area.¹⁶ Five independent predictors of CIED infection for PADIT are prior procedures [P], age [A], depressed renal function [D], immunocompromisation [I], and procedure type [T]. This risk score system did not include markers of inflammation such as WBC, NLR, and SII. In our study group, being young aged males, the primary application of CIED, procedures containing multi-lead like the implementation of CRT were found independently statistically significant for infection. In addition to having comorbidity as DM, CKD also increased SII before the implantation procedure was obtained as independent risk factors for post-procedure infections. Our study showed that SII can be used in addition to the PADIT score in predicting CIED infection in patients who underwent CIED implantation. The present study has some limitations. This is a retrospective, single-center study, including selection bias. As some patients were transferred from other hospitals to our hospital, there were no data regarding the presence of postoperative pocket hematoma, procedure time, anticoagulant use, or operator experience which are associated with the development of CIED infection. The lack of inflammatory markers, such as C-reactive protein, was another limitation. In the present study, SII was applied to single-center data. Thus, its performance should be externally validated in future studies.

Conclusion

We found that platelet count, NLR, and SII were predictive markers for CIEDs infection in patients who underwent CIED implantation. Early detection of SII may assist in early diagnosis, risk stratification, and management of these patients.

Footnotes

Author Contribution

All authors contributed to: (1) substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, and, (3) final approval of the version to be published.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Banu Karaca

Tuncay Kırış

References

Nelson

. A brief history of cardiac pacing. Tex Heart Inst J. 1993;20:12-8.

Marsch

Mashaqi

Burgwitz

, et al. Prevention of pacemaker infections with perioperative antimicrobial treatment: An in vitro study. Europace. 2013;16:604-11.

Kirkfeldt

Johansen

Nielsen

. Management of cardiac electronic device infections: Challenges and outcomes. Arrhythmia Electrophysiol Rev. 2016;5:183-7.

Cengiz

Okutucu

Ascioglu

, et al. Permanent pacemaker and implantable cardioverter defibrillator infection: Seven years of diagnostic and therapeutic experience of a single center. Clin Cardiol. 2010;33:406-11.

Barbar

Patel

Thomas

Cheung

. Strategies to prevent cardiac implantable electronic device infection. J Innov Card Rhythm Manag. 2020;11:3949-56.

Leung

Danik

. Diagnosis, and treatment of cardiac implantable electronic device infections. Curr Cardiol Rep. 2016;18:58.

Sohail

Uslan

Khan

, et al. Management and outcome of permanent pacemaker and implantable cardioverter-defibrillator infections. J Am Coll Cardiol. 2007;49:1851-9.

Yang

Hsu

, et al. Systemic immune inflammation index (SII) predicted clinical outcome in patients with coronary artery disease. Eur J Clin Invest. 2020;50:e13230.

Seo

Yamada

Morita

, et al. P589 Prognostic value of the systemic immune-inflammation index in patients with chronic heart failure. Eur Heart J. 2018;39:589.

10.

Vatan

Çakmak

Ağaç

Eynel

Erkan

. The systemic immune-inflammation index predicts impaired myocardial perfusion and short-term mortality in st-segment elevation myocardial infarction patients. Angiology. 2022;7:33197221106886.

11.

Sexton

Mick

, et al. Proposed modifications to the Duke criteria for the diagnosis of infective endocarditis. Clin Infect Dis. 2000;30:633-8.

12.

Fest

Ruiter

Ikram

Voortman

van Eijck

CHJ

Stricker

. Reference values for white blood-cell-based inflammatory markers in the Rotterdam study: A population-based prospective cohort study. Sci Rep. 2018;8:10566.

13.

Coresh

Auguste

. Reliability of GFR formulas based on serum creatinine, with special reference to the MDRD Study equation. Scand J Clin Lab Invest Suppl 2008;241:30-8.

14.

DeLong

Clarke-Pearson

. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics. 1988;44:837-45.

15.

Pencina

D’Agostino RB

Steyerberg

. Extensions of net reclassification improvement calculations to measure the usefulness of new biomarkers. Stat Med. 2011;30:11-21.

16.

Birnie

Wang

Alings

, et al. Risk factors for infections involving cardiac implanted electronic devices. J Am Coll Cardiol. 2019;74:2845-54.

17.

Habib

Lancellotti

Antunes

, 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;36:3075-128.

18.

Baddour

Wilson

Bayer

, et al. Infective endocarditis in adults: Diagnosis, antimicrobial therapy, and management of complications: A scientific statement for healthcare professionals from the American heart association. Circulation. 2015;132:1435-86.

19.

Dwivedi

Saran

Khera

, et al. Short-term (48 hours) versus long-term (7 days) antibiotic prophylaxis for permanent pacemaker implantation. Indian Heart J. 2001;53:740-2.

20.

Luo

Xie

Zhang

, et al. Combination of lymphocyte number and function in evaluating host immunity. Aging. 2019;11:12685-707.

21.

Kim

Eliot

Koestler

Kelsey

. Association of neutrophil-to-lymphocyte ratio with mortality and cardiovascular disease in the Jackson heart study and modification by the duffy antigen variant. JAMA Cardiol. 2018;3:455-62.

22.

Liu

Gao

, et al. Prognostic value of systemic immune inflammation index in acute/subacute patients with cerebral venous sinus thrombosis. Stroke Vasc Neurol. 2020;5:368-73.

23.

Wartha

Beiter

Normark

Henriques-Normark

. Neutrophil extracellular traps: casting the NET over pathogenesis. Curr Opin Microbiol. 2007;10:52-6.

24.

Agus

Kahraman

Arslan

, et al. Systemic immune inflammation index predicts mortality in infective endocarditis. J Saudi Heart Assoc. 2020;32:58-64.

25.

Jung

Yeh

Hsu

Lee

Shun

Chia

. Endocarditis pathogen promotes vegetation formation by inducing intravascular neutrophil extracellular traps through activated platelets. Circulation. 2015;131:571-81.

26.

Turak

Özcan

İşleyen

, et al. Usefulness of neutrophil to lymphocyte ratio to predict in-hospital outcomes in infective endocarditis. Can J Cardiol. 2013;29:1672-8.

27.

Cataudella

Giraffa

Di Marca

, et al. Neutrophil-to-lymphocyte ratio: An emerging marker predicting prognosis in elderly adults with community-acquired pneumonia. J Am Geriatr Soc. 2017;65:1796-801.

28.

Corriere

Di Marca

Cataudella

, et al. Neutrophil-to-lymphocyte ratio is a strong predictor of atherosclerotic carotid plaques in older adults. Nutr Metabol Cardiovasc Dis. 2018;28:23-7.

29.

Regolo

Vaccaro

Sorce

, et al. Neutrophil-to-lymphocyte ratio (NLR) Is a promising predictor of mortality and admission to intensive care unit of COVID-19 patients. J Clin Med. 2022;16:2235.

30.

Chen

L-J

, et al. Neutrophil-lymphocyte ratio in predicting infective endocarditis: A case-control retrospective study. Mediators Inflamm. 2020;2020:8586418. doi:10.1155/2020/8586418.

31.

Zhu

Zhou

Shuai

. Systematic immune-inflammation index predicts embolic events in infective endocarditis. Int Heart J. 2022;63:510-6.

32.

Shariff

Eby

Adelstein

, et al. Health and economic outcomes associated with the use of an antimicrobial envelope as a standard of care for cardiac implantable electronic device implantation. J Cardiovasc Electrophysiol. 2015;26:783-9.

The Systemic Immune-Inflammation Index and Predicting Cardiac Implantable Electronic Device Infections