Infective Endocarditis

This survey was a nation-wide population-based register study of patients with IE. Our cohort consisted of patients who have been hospitalized and treated for IE during 1997 to 2007 in Sweden. The Swedish Hospital Discharge Register collects individual data from all hospitals and more than 99% of the discharges in somatic care are covered by the register [9] (link). In this study exposure was defined as a diagnosis of infective endocarditis (IE), and outcome was death or non-death by the end of the follow-up period. Exposure data (IE diagnosis) was collected from the nationwide inpatient register and outcome was then cross-referenced from the Swedish population register. Cases of IE were identified using ICD-10 codes: I33, I38, and I39. In order to refine the resulting mortality rates the cohort has been divided in several categories: native-valve IE, prosthetic-valve IE, intravenous drug users (IVDUs); as well as two age-groups; <65 years and ≥65 years. It can readily be assumed that different patient categories have different mortality risks, and therefore we wanted to isolate the rates specific to a particular patient profile. In addition to exposure (IE diagnosis) and outcome data, we also collected information on whether valve surgery was performed, or not performed, at any time point during follow-up up after initial hospital admission. The surgical operations of interest were identified using ICD-10 codes: FG, FJ, FK, and FM.
For the patients in our cohort we have established the date, but not specific cause, of death (if it occurred before the end of our follow-up period). Therefore it was the all-cause attributable IE mortality rates which were determined. Crude rates were determined by counting the number of deaths in our patient cohort within a specific time period after IE was diagnosed. The crude mortality rates represented the absolute mortality risk in the cohort. Another way to illustrate the absolute mortality risk associated with IE is to look at the Kaplan-Meier survival estimator, where the crude survival rate is 1- crude mortality rate.
In order to explore possible increases in long-term relative mortality risks, the crude mortality rates were then directly standardized using age- and sex- stratified mortality rates from the general population of Sweden as the reference population. This data was available from the Statistics Sweden. The standardized mortality ratio (SMR), was then the ratio between the observed number of deaths and the expected number of deaths, where the expected number of deaths was obtained by multiplying the person-years in the cohort with the age- and sex specific mortality rates in the Swedish general population. Ninety-five percent confidence intervals for the SMR were calculated assuming that the observed number of deaths was Poisson distributed. Comparisons of mortality between early surgery and medical therapy were done within each patient category (native and prosthetic valve IE) using age- and sex- stratified Mantel-Haenszel estimates of the odds ratio. The time trend for the annual incidence and mortality rate of IE was explored in a linear regression model using a quasipoisson distribution and t-test for significance.

T2DM status represents the treatment indicator introduced in the theoretical part of this study. As can be seen in Table 1, significant differences are found between people with or without T2DM for age and for almost every clinical variable analyzed. Therefore, it was deemed necessary to use a statistical method, such as PSM (PSM), to control the confounding effect of baseline characteristics. The main aim of the propensity score method is to obtain an unbiased estimate of treatment effect adjusted for the impact of given confounding factors in non-randomized and observational studies [36 (link)]. The PSM method allows selecting participants with and without T2DM with the same, or nearly the same propensity score obtained with logistic regression, so that we can match the structure of the confounding factors for both group of patients [36 (link), 37 (link)]. By using multivariable logistic regression, we could estimate a propensity score for each person with and without T2DM in our investigation [36 (link)]. The variables included in the model were year of admission, sex, age, all comorbidities analyzed (Additional file 1: Table S1), previous aortic valve disease and prosthetic valve carriers.Table 1

Incidence, clinical characteristics and in-hospital outcomes of patients hospitalized with infective endocarditis in Spain from 2001 to 2015 according to T2DM status

	T2DM	2001–2003	2004–2006	2007–2009	2010–2012	2013–2015	Total	Trend
Number of endocarditis	Yes	345	486	603	962	1040	3436	< 0.001
	No	2019	2128	2304	3165	3574	13,190	< 0.001
Incidence per 100,000 per year*	Yes	6.0	7.8	8.9	12.8	13.1	10.0	< 0.001
	No	3.9	3.8	3.8	5.1	5.5	4.5	< 0.001
Age, mean (SD)*	Yes	68.7 (9.8)	69.2 (9.9)	70.4 (9.9)	71.6 (10.2)	71.8 (10.3)	70.8 (10.2)	< 0.001
	No	66.2 (12.1)	66.0 (12.3)	67.4 (12.5)	69.0 (12.2)	69.4 (12.4)	67.9 (12.4)	< 0.001
Female sex, n (%)	Yes	130 (37.7)	193 (39.7)	233 (38.6)	348 (36.2)	336 (32.3)	1240 (36.1)	0.023
	No	667 (33.0)	704 (33.1)	786 (34.1)	1037 (32.8)	1131 (31.7)	4325 (32.8)	0.393
Charlson Comorbidity Index, mean (SD)*	Yes	1.0 (0.8)	1.0 (0.8)	1.0 (0.8)	1.1 (0.8)	1.1 (0.8)	1.1 (0.8)	< 0.001
	No	0.8 (0.8)	0.8 (0.8)	0.9 (0.8)	1.0 (0.8)	1.0 (0.8)	0.9 (0.8)	< 0.001
Prosthetic valve carriers, n (%)	Yes	27 (7.8)	41 (8.4)	57 (9.5)	106 (11.0)	102 (9.8)	333 (9.7)	0.375
	No	170 (8.4)	191 (9.0)	205 (8.9)	294 (9.3)	360 (10.1)	1220 (9.3)	0.280
Previous mitral valve disease, n (%)*	Yes	99 (28.7)	122 (25.1)	156 (25.9)	271 (28.2)	264 (25.4)	912 (26.5)	0.478
	No	540 (26.8)	616 (29.0)	681 (29.6)	1034 (32.7)	1100 (30.8)	3971 (30.1)	< 0.001
Previous aortic valve disease, n (%)*	Yes	75 (21.7)	114 (23.5)	149 (24.7)	251 (26.1)	280 (26.9)	869 (25.3)	0.280
	No	511 (25.3)	602 (28.3)	673 (29.2)	972 (30.7)	1122 (31.4)	3880 (29.4)	< 0.001
Readmissions, n (%)*	Yes	58 (16.8)	91 (18.7)	116 (19.2)	199 (20.7)	223 (21.4)	687 (20.0)	0.338
	No	300 (14.9)	314 (14.8)	354 (15.4)	526 (16.6)	597 (16.7)	2091 (15.9)	0.128
Length of hospital stay, mean (SD)	Yes	28.24 (20.9)	28.9 (21.4)	28.48 (20.4)	27.69 (21.0)	24.76 (18.9)	27.17 (20.4)	< 0.001
	No	28.33 (22.6)	27.87 (21.8)	28.25 (22.4)	27.18 (22.4)	26.66 (21.4)	27.51 (22.1)	0.018
In-hospital mortality, n (%)	Yes	87 (25.2)	101 (20.8)	135 (22.4)	209 (21.7)	182 (17.5)	714 (20.8)	0.015
	No	431 (21.4)	397 (18.7)	439 (19.1)	638 (20.2)	657 (18.4)	2562 (19.4)	0.053
Costs, mean (SD)*	Yes	9137.6 (5486)	12,380.7 (8116.2)	14,928.4 (8646.3)	13,507.4 (11,465.3)	13,744.5 (10,822.8)	13,230.4 (10,000.7)	< 0.001
	No	10,171.7 (7450.2)	12,746.6 (8463.6)	16,366.4 (11,276.8)	16,588.5 (17,974.3)	14,759.3 (12,184.5)	14,452.0 (12,845.6)	< 0.001

T2DM type 2 diabetes mellitus, SD standard deviation

*Denotes a p value < 0.05 for the difference when comparing total values between patients with and without T2DM (namely, the results in the “TOTAL” column). Trends for incidence were assessed using multivariable Poisson regression models adjusted by age and sex

For PSM we used the PSMATCH2 Stata module. The Matching method chosen was one-to-one within caliper of width equal to 0.2 of the standard deviation of the logit of the propensity score [38 , 39 (link)]. Using this method, we could match all case of T2DM with a non-diabetic patient. To assess the balance of the samples after PSM we estimated the absolute standardized difference before and after matching. As can be seen in Additional file 3: Table S2 none of the absolute standardized differences after PSM were above 10%, that represents meaningful imbalance.

Exclusion criteria commonly used in the past ordinary clinical trials are as follows [15 (link),16 (link),17 (link)]:

Age < 18 years, >80 years.

Coexisting comorbidities or medical conditions that could make the evaluation of infective endocarditis difficult such as severe liver dysfunction, severe renal dysfunction, or HIV/AIDS. (Severe liver dysfunction was defined as serum total bilirubin, or aspartate aminotransferase/alanine aminotransferase > the upper limit of the normal reference range ×3. Severe renal dysfunction was defined as Creatinine clearance < 30 mL/min.) Unassessable malignancies were defined as any terminal stage malignancy) or any with metastatic lesions. Unassessable diabetes mellitus was defined as serum-hemoglobin A1c (NGSP) ≧ 7.0%.

Having prosthetic valve involvement.

Receiving immunosuppressive therapy due to any cause.

Receiving chemotherapy for malignancy.

Receiving palliative therapy for malignancy.

Receiving hemodialysis due to any cause.

Having other complicated infection such as mycobacterial tuberculosis.

Inability to give full informed consent such as cognitive impairment due to any cause.

Causative pathogens are fungi.

Multiple pathogens identified.

Need of prolonged antibiotic therapy due to spondylodiscitis or other septic complication.

Patients who need urgent cardiac surgery but are considered inoperable due to high surgical risks.

Poor prognosis (anticipated life expectancy <90 days or patients who are not expected to survive until the end of the trial).

Pregnant or breastfeeding women.

Our institute, which is a 900-bed tertiary care center, is located in the countryside at Aichi Prefecture in the central area of Japan. For the purpose of estimating how many infective endocarditis (IE) patients in our institute could participate in any randomized clinical trials for the treatment of IE, we reviewed all IE patients who were admitted in our hospital between 2007 and 2019. Patients diagnosed as definite IE according to the modified Duke criteria or by surgical procedure were included in this study. Patients diagnosed as having possible IE according to the modified Duke criteria [8 (link)] were excluded in this study.
The severity of IE was determined by a systemic inflammatory response syndrome (SIRS) score [9 (link)], quick SOFA (qSOFA), and SOFA scores [10 (link)]. The Charlson comorbidity index (CCI) evaluated patents’ comorbidity [11 (link)]. We previously reported that SOFA score was a good predictive marker among IE patients for in-hospital death [7 (link)]. Thus, we evaluated severity of IE by SOFA score.
The patients were divided into two groups: patients who were eligible for clinical trials (RCT appropriate group), and those who were not (RCT inappropriate group). Then, patients’ characteristics (age and sex); pathogens isolated; clinical outcomes such as the treatments and/or results; 30-day or in-hospital mortality; and the reasons of exclusion from the clinical trial were evaluated.

A multidisciplinary team, composed of one intensive care physician (B.V.), one infectious disease consultant (P.V.), one clinical microbiologist (G.M.R.), and one MD clinical pharmacologist (F.P.), had several virtual meetings for developing algorithms for targeted antimicrobial therapy of severe MSSA and MRSA infections. Algorithms were organized for different sites of infection (namely infective endocarditis [IE], primary bloodstream infections [BSIs], infections associated with intracardiac/intravascular devices, community-acquired pneumonia [CAP], infection-related ventilator-associated complications (IVACs), central nervous system [CNS] infections, and necrotizing skin and soft tissue infections), and therapeutic strategies were based also on PK/PD features [10 (link),14 (link)]. A researcher (M.G.) retrieved the scientific evidence for supporting the specific proposals by means of a PubMed-MEDLINE literature search (until August 2022). Key terms concerning selected antibiotics, genotype of resistance and/or antibiotic susceptibility pattern of S. aureus, and site of infections were searched in combination. The searched terms were oxacillin, cefazolin, daptomycin, fosfomycin, ceftobiprole, ceftaroline, vancomycin, teicoplanin, linezolid, methicillin-susceptible Staphylococcus aureus, methicillin-resistant Staphylococcus aureus, MRSA, MSSA, bloodstream infections, bacteremia, infective endocarditis, cardiac implantable electronic device infections, community-acquired pneumonia, hospital-acquired pneumonia, nosocomial pneumonia, pneumonia, ventilator-associated pneumonia, infection-related ventilator-associated complications, central nervous system infections, ventriculitis, meningitis, complicated skin and soft tissue infections, necrotizing soft tissue infections, necrotizing fasciitis, intensive care unit, and critically ill patients. Quality of evidence was established according to a hierarchical scale of the study design, as reported in the evidence pyramid [15 (link)]: randomized controlled trials (RCTs), prospective observational studies, retrospective observational studies, case series, case reports, and in vivo/in vitro preclinical studies. International guidelines/guidance documents issued by the Infectious Disease Society of America and/or by the European Society of Clinical Microbiology and Infectious Diseases, systematic reviews, and meta-analyses were also consulted. Consistence between retrieved studies was also considered by assessing the concordance in clinical outcome of the included studies at each level of the evidence pyramid. Only articles published in the English language were considered, with a main focus on those published in the last ten years.
Agreement between all of the four team members was reached on each of the options provided in the therapeutic algorithms after thoroughly discussion based on specific long-standing experience and expertise of each single member.