Inflammatory biomarkers in infective endocarditis: machine learning to predict mortality

The ratio of blood urea nitrogen to high-density lipoprotein cholesterol: a novel risk-stratifying tool for infective endocarditis

BACKGROUND

Increased blood urea nitrogen (BUN) and decreased high-density lipoprotein cholesterol (HDL) are common in infectious diseases. However, the prognostic significance of the BUN-to-HDL ratio (BHR) in patients with infective endocarditis (IE) is yet unknown.

METHODS

In all, 1441 patients with confirmed IE were included and divided into four groups according to the level of BUN and HDL: BUN > 5.2mmol/L and HDL < 0.7mmol/L (n = 296), BUN > 5.2mmol/L and HDL ≥ 0.7mmol/L (n = 364), BUN ≤ 5.2mmol/L and HDL < 0.7mmol/L (n = 327), and BUN ≤ 5.2mmol/L and HDL ≥ 0.7mmol/L (n = 454). BHR was calculated as BUN/HDL. Multivariable analyses were conducted to determine the association of BHR with adverse events.

RESULTS

The in-hospital mortality was 6.4%. Patients with BUN > 5.2mmol/L and HDL < 0.7mmol/L had a significantly higher risk of in-hospital death than those with BUN ≤ 5.2mmol/L and HDL ≥ 0.7mmol/L (adjusted odds ratio [aOR] = 4.65, 95% confidence interval [CI]: 1.91-11.35, p = 0.001). The decision curve analysis indicated that the combination of BUN and HDL had higher net benefit than either alone. BHR presented a higher predictive value than BUN (AUC: 0.744 vs 0.693, p = 0.01) or HDL (AUC: 0.744 vs 0.648, p < 0.001) for in-hospital mortality, and the optimal cut-off value was 7.4 (sensitivity, 79.3%; specificity, 60.3%). Furthermore, the cumulative 6-month mortality risk was significantly higher in patients with BHR > 7.4 than those with BHR ≤ 7.4 (log-rank = 93.4, p < 0.001). BHR > 7.4 was an independent risk factor for 6-month mortality in IE (adjusted hazard ratio [aHR] = 3.77, 95%CI: 2.32-6.11, p < 0.001).

CONCLUSIONS

BHR offers a high predictive value for short-term mortality in IE, positioning it as a potential stratification tool for critical care triage.

Change of Heart: Can Artificial Intelligence Transform Infective Endocarditis Management?

Artificial intelligence (AI) has emerged as a promising adjunct in the diagnosis and management of infective endocarditis (IE), a disease characterized by diagnostic complexity and significant morbidity. Machine learning (ML) models such as SABIER and SYSUPMIE have demonstrated strong predictive accuracy for early IE diagnosis, embolic risk stratification, and postoperative mortality, surpassing traditional clinical scoring systems. In imaging, AI-enhanced echocardiography and advanced modalities like FDG-PET/CT offer improved sensitivity, specificity, and reduced inter-observer variability, potentially transforming clinical decision making. Additionally, AI-powered microbiological techniques, including MALDI-TOF mass spectrometry combined with ML and neural network-based metagenomic classifiers, show promise in rapidly identifying pathogens and predicting antimicrobial resistance. Despite encouraging early results, widespread adoption faces barriers, including data limitations, interpretability issues, ethical concerns, and the need for robust validation. Future directions include leveraging generative AI as clinical consultative tools, provided their capabilities and limitations are carefully managed. Ultimately, collaborative efforts addressing these challenges could transform IE care, enhancing diagnostic accuracy, clinical outcomes, and patient safety.

Yasin J, Hanifa H, Sabet CJ, Dinh NH, Harky A, Jain J, Jain H. Presurgery and postsurgery: advancements in artificial intelligence and machine learning models for enhancing patient management in infective endocarditis

Infective endocarditis (IE) is a severe infection of the inner lining of the heart, known as the endocardium. It is characterized by a range of symptoms and has a complicated pattern of occurrence, leading to a significant number of deaths. IE poses significant diagnostic and treatment difficulties. This evaluation examines the utilization of artificial intelligence (AI) and machine learning (ML) models in addressing IE management. It focuses on the most recent advancements and possible applications. Through this paper, the authors observe that AI/ML can significantly enhance and outperform traditional diagnostic methods leading to more accurate risk stratification, personalized therapies, as well and real-time monitoring facilities. For example, early postsurgical mortality prediction models like SYSUPMIE achieved 'very good' area under the curve (AUROC) values exceeding 0.81. Additionally, AI/ML has improved diagnostic accuracy for prosthetic valve endocarditis, with PET-ML models increasing sensitivity from 59 to 72% when integrated into ESC criteria and reaching a high specificity of 83%. Furthermore, inflammatory biomarkers such as IL-15 and CCL4 have been identified as predictive markers, showing 91% accuracy in forecasting mortality, and identifying high-risk patients with specific CRP, IL-15, and CCL4 levels. Even simpler ML models, like Naïve Bayes, demonstrated an excellent accuracy of 92.30% in death rate prediction following valvular surgery for IE patients. Furthermore, this review provides a vital assessment of the advantages and disadvantages of such AI/ML models, such as better-quality decision support approaches like adaptive response systems on one hand, and data privacy threats or ethical concerns on the other hand. In conclusion, Al and ML must continue, through multicentric and validated research, to advance cardiovascular medicine, and overcome implementation challenges to boost patient outcomes and healthcare delivery.

Diagnostic work-up and current management strategies for infective endocarditis in the pediatric population

Infective endocarditis (IE) remains a serious disease that is associated with significant morbidity and mortality, and despite the significant advances that have been made in understanding the disease process in past decades, its incidence appears to be on the rise recently. Endocarditis in children is no longer a rare occurrence. This appeared to be related to a combination of the improved survival of children with congenital heart diseases (CHDs), increase use of intracardiac protheses, and catheter-related interventions. The American Heart Association (AHA) 2007 guidelines reduced the recommendations for use of prophylactic antibiotics in those with CHDs which occurred despite the noticeable increase in endocarditis incidence around that time. In general, the recommendations for managing children with IE are derived from the adults' guidelines, and the evidence-base is lacking in many clinical scenarios. Understanding the epidemiology, clinical presentations, microbiology, and outcomes of different management strategies for endocarditis is needed to have a clear and optimal plan for these children. In the current narrative review, we discuss IE in the pediatric population in terms of etiology, predisposing factors, and different treatment strategies for this unique population.

A decision support system to recommend appropriate therapy protocol for AML patients

Introduction

Acute Myeloid Leukemia (AML) is one of the most aggressive hematological neoplasms, emphasizing the critical need for early detection and strategic treatment planning. The association between prompt intervention and enhanced patient survival rates underscores the pivotal role of therapy decisions. To determine the treatment protocol, specialists heavily rely on prognostic predictions that consider the response to treatment and clinical outcomes. The existing risk classification system categorizes patients into favorable, intermediate, and adverse groups, forming the basis for personalized therapeutic choices. However, accurately assessing the intermediate-risk group poses significant challenges, potentially resulting in treatment delays and deterioration of patient conditions.

Methods

This study introduces a decision support system leveraging cutting-edge machine learning techniques to address these issues. The system automatically recommends tailored oncology therapy protocols based on outcome predictions.

Results

The proposed approach achieved a high performance close to 0.9 in F1-Score and AUC. The model generated with gene expression data exhibited superior performance.

Discussion

Our system can effectively support specialists in making well-informed decisions regarding the most suitable and safe therapy for individual patients. The proposed decision support system has the potential to not only streamline treatment initiation but also contribute to prolonged survival and improved quality of life for individuals diagnosed with AML. This marks a significant stride toward optimizing therapeutic interventions and patient outcomes.

Hematologic Parameters as Predictors of Long-Term Mortality in Infective Endocarditis Patients

BACKGROUND Infective endocarditis (IE) is an endothelial infection that is associated with high mortality and morbidity rates. Early and accurate risk prediction is important in patients with IE. Neutrophil-to-lymphocyte ratio (NLR), which is one of the hematological parameters that can be performed anywhere and is easily accessible, is a predictor of poor prognosis in many infectious and cardiovascular diseases. This study aimed to evaluate the association between laboratory parameters and 3-year mortality in 155 patients with infective endocarditis at a single center in Turkey. MATERIAL AND METHODS We retrospectively analyzed the clinical and echocardiographic data of 155 adult patients with definite IE according to the modified Duke Criteria, and we analyzed all laboratory results, such as hemoglobin, white blood cell, neutrophil, lymphocyte, platelet, platelet distribution width, NLR, urea, creatinine, albumin, procalcitonin, and blood culture results. RESULTS The median follow-up time was 341 days (range, 2-4003 days). The out-of-hospital mortality rate was 31.6%. Among the discharged patients (n=106), there were 46 non-survivors, with an out-of- hospital mortality rate of 43.4%. The overall mortality rate was 61.3%. During the follow-up, the 1-year mortality rate was 47.1% and the 3-year mortality rate was 54.8%. We detected significant differences in the admission values of NLR between the patients with and without 3-year mortality (P<0.001). CONCLUSIONS The NLR on admission to a tertiary center was independently associated with 3-year mortality in IE patients (P<0.001). NLR is a parameter that can be obtained from a simple, widely available and inexpensive hemagroma as a useful marker in predicting long-term mortality in IE.

Prognostic Value of Blood Urea Nitrogen for Short-Term Mortality in Patients with Infective Endocarditis

INTRODUCTION

Blood urea nitrogen (BUN) is a metabolic product validated to be an independent risk factor in the prognosis of several diseases. However, the prognostic value of BUN in patients with infective endocarditis (IE) remains unevaluated.

METHODS

A total of 1371 patients with a diagnosis of IE were included and divided into four groups according to BUN (mmol/L) at admission: < 3.5 (n = 343), 3.5-4.8 (n = 343), 4.8-6.8 (n = 341), and ≥ 6.8 (n = 344). Restricted cubic spline was used to assess the association of BUN with in-hospital mortality. Multivariate analysis was performed to identify the independent risk factors for adverse outcomes.

RESULTS

The in-hospital mortality reached 7.4%, while the 6-month mortality was 9.8%. The restricted cubic spline plot exhibited an approximately linear relationship between BUN and in-hospital mortality. Receiver operating characteristics curve analysis showed that the optimal cut-off of BUN for predicting in-hospital death was 6.8 mmol/L. Kaplan-Meier analysis showed that patients with BUN > 6.8 mmol/L had a higher 6-month mortality than other groups (log rank = 97.9, P < 0.001). Multivariate analysis indicated that BUN > 6.8 mmol/L was an independent predictor indicator for both in-hospital [adjusted odds ratio (aOR) = 2.365, 95% confidence interval (CI) 1.292-4.328, P = 0.005] and 6-month mortality [adjusted hazard ratio (aHR) = 2.171, 95% CI 1.355-3.479, P = 0.001].

CONCLUSIONS

BUN is suitable for independently predicting short-term mortality in patients with IE.

C-Reactive Protein: Pathophysiology, Diagnosis, False Test Results and a Novel Diagnostic Algorithm for Clinicians

The current literature provides a body of evidence on C-Reactive Protein (CRP) and its potential role in inflammation. However, most pieces of evidence are sparse and controversial. This critical state-of-the-art monography provides all the crucial data on the potential biochemical properties of the protein, along with further evidence on its potential pathobiology, both for its pentameric and monomeric forms, including information for its ligands as well as the possible function of autoantibodies against the protein. Furthermore, the current evidence on its potential utility as a biomarker of various diseases is presented, of all cardiovascular, respiratory, hepatobiliary, gastrointestinal, pancreatic, renal, gynecological, andrological, dental, oral, otorhinolaryngological, ophthalmological, dermatological, musculoskeletal, neurological, mental, splenic, thyroid conditions, as well as infections, autoimmune-supposed conditions and neoplasms, including other possible factors that have been linked with elevated concentrations of that protein. Moreover, data on molecular diagnostics on CRP are discussed, and possible etiologies of false test results are highlighted. Additionally, this review evaluates all current pieces of evidence on CRP and systemic inflammation, and highlights future goals. Finally, a novel diagnostic algorithm to carefully assess the CRP level for a precise diagnosis of a medical condition is illustrated.

Prognostic implications of ultra-short heart rate variability indices in hospitalized patients with infective endocarditis

BACKGROUND

Infective endocarditis (IE) is a disease that poses a serious health risk. It is important to identify high-risk patients early in the course of their treatment. In the current study, we evaluated the prognostic value of ultra-short heart-rate variability (HRV), an index of vagal nerve activity, in IE.

METHODS

Retrospective analysis was performed on adult patients admitted to a tertiary hospital due to IE. A logistic regression (LR) was used to determine whether clinical, laboratory, and HRV parameters were predictive of specific clinical features (valve type, staphylococcal infection) or severe short-term complications (cardiac, metastatic infection, and death). The accuracy of the model was evaluated through the measurement of the area under the curve (AUC) of the receiver operating characteristic curve (ROC). An analysis of survival was conducted using Cox regression. A number of HRV indices were calculated, including the standard deviation of normal heart-beat intervals (SDNN) and the root mean square of successive differences (RMSSD).

RESULTS

75 patients, aged 60.3(±18.6) years old, were examined. When compared with published age- and gender-adjusted HRV norms, SDNN and RMSSD were found to be relatively low in our cohort (75%-76% lower than the median; 33%-41% lower than the 2nd percentile). 26(34.6%) patients developed a metastatic infection, with RMSSD<7.03ms (adjusted odds ratio (aOR) 9.340, p = 0.002), incorporated in a multivariate LR model (AUC 0.833). Furthermore, 27(36.0%) patients were diagnosed with Staphylococcus IE, with SDNN<4.92ms (aOR 5.235, p = 0.004), a major component of the multivariate LR model (AUC 0.741). Multivariate Cox regression survival model, included RMSSD (HR 1.008, p = 0.012).

CONCLUSION

SDNN, and particularly RMSSD, derived from ultra-short ECG recordings, may provide prognostic information about patients presenting with IE.

Predicting Stroke and Mortality in Mitral Regurgitation: A Machine Learning Approach

We hypothesized that an interpretable gradient boosting machine (GBM) model considering comorbidities, P-wave and echocardiographic measurements, can better predict mortality and cerebrovascular events in mitral regurgitation (MR). Patients from a tertiary center were analyzed. The GBM model was used as an interpretable statistical approach to identify the leading indicators of high-risk patients with either outcome of CVAs and all-cause mortality. A total of 706 patients were included. GBM analysis showed that age, systolic blood pressure, diastolic blood pressure, plasma albumin levels, mean P-wave duration (PWD), MR regurgitant volume, left ventricular ejection fraction (LVEF), left atrial dimension at end-systole (LADs), velocity-time integral (VTI) and effective regurgitant orifice were significant predictors of TIA/stroke. Age, sodium, urea and albumin levels, platelet count, mean PWD, LVEF, LADs, left ventricular dimension at end systole (LVDs) and VTI were significant predictors of all-cause mortality. The GBM demonstrates the best predictive performance in terms of precision, sensitivity c-statistic and F1-score compared to logistic regression, decision tree, random forest, support vector machine, and artificial neural networks. Gradient boosting model incorporating clinical data from different investigative modalities significantly improves risk prediction performance and identify key indicators for outcome prediction in MR.

Infective endocarditis: Do we have an effective risk score model? A systematic review

Background

Infective endocarditis (IE) is a rare, highly morbid condition with 17% in-hospital mortality. A total of 25-30% require surgery and there is ongoing debate with regard to markers predicting patient outcomes and guiding intervention. This systematic review aims to evaluate all IE risk scores currently available.

Methods

Standard methodology (PRISMA guideline) was used. Papers with risk score analysis for IE patients were included, with attention to studies reporting area under the receiver-operating characteristic curve (AUC/ROC). Qualitative analysis was carried out, including assessment of validation processes and comparison of these results to original derivation cohorts where available. Risk-of-bias analysis illustrated according to PROBAST guidelines.

Results

Of 75 articles initially identified, 32 papers were analyzed for a total of 20 proposed scores (range 66-13,000 patients), 14 of which were specific for IE. The number of variables per score ranged from 3 to 14 with only 50% including microbiological variables and 15% including biomarkers. The following scores had good performance (AUC > 0.8) in studies proposing the score (often the derivation cohort); however fared poorly when applied to a new cohort: PALSUSE, DeFeo, ANCLA, RISK-E, EndoSCORE, MELD-XI, COSTA, and SHARPEN. DeFeo score demonstrated the largest discrepancy with initial AUC of 0.88, compared to 0.58 when applied to different cohorts. The inflammatory response in IE has been well documented and CRP has been found to be an independent predictor for worse outcomes. There is ongoing investigation on alternate inflammatory biomarkers which may assist in IE management. Of the scores identified in this review, only three have included a biomarker as a predictor.

Conclusion

Despite the variety of available scores, their development has been limited by small sample size, retrospective collection of data and short-term outcomes, with lack of external validation, limiting their transportability. Future population studies and large comprehensive registries are required to address this unmet clinical need.

Acinetobacter baumannii-induced infective endocarditis: new insights into pathophysiology and antibiotic resistance mechanisms

Infective endocarditis (IE), characterized by inflammation of the endocardial surface of the heart and its valves, results from infections caused by Staphylococcus, Streptococcus and Acinetobacter species and less commonly fungi. Acinetobacter-induced IE is a relatively rare condition with significant morbidity and mortality worldwide. Notably, its mortality rate is greater than that of endocarditis induced by Haemophilus species, Aggregatibacter actinomycetemcomitans, Cardiobacterium hominis, Eikenella corrodens and Kingella kingae. Although it is rare, Acinetobacter-induced IE caused by A. baumannii might bring unique therapeutic challenges such as increased antibiotic resistance. Therefore, it is vital to understand perfectly the possible pathophysiologic and antibiotic resistance mechanisms adopted by A. baumannii during IE. This review discusses the probable underlying pathomechanisms involved in A. baumannii-induced IE and highlights the potential antibiotic resistance mechanisms, suggesting therapeutic targets for A. baumannii-induced IE.

Prognostic Value of Lymphocyte-to-White Blood Cell Ratio for In-Hospital Mortality in Infective Endocarditis Patients

Background

The prognosis of Infective endocarditis (IE) is poor, and we conducted this investigation to evaluate the worth of admission lymphocyte-to-white blood cell ratio (LWR) for prediction of short-term outcome in IE patients.

Methods

We retrospectively assessed the medical records of 147 IE patients from January 2017 to December 2019. Patients were divided into the survivor group and nonsurvivor group. Univariate and multivariate analyses were applied to estimate the independent factors contribution to in-hospital death, and receiver-operator characteristic (ROC) curve was utilized to check the performance.

Results

The levels of LWR (0.17 ± 0.08 vs. 0.10 ± 0.06) were significantly increased among the survivor group compared with the nonsurvivor group (P = 0.001). Multivariate analysis displayed that LWR (hazard ratio (HR): 1.755, 1.304-2.362, P < 0.001) was not interfered by other confounding factors for early death. Moreover, ROC analysis suggested that LWR (cutoff value = 0.10) performed the best among assessed indexes for the forecast of primary outcome (area under curve (AUC) = 0.750, 95% confidence interval (CI) = 0.634-0.867, P < 0.001, sensitivity = 70.0%, specificity = 76.4%), and the proportion of in-hospital mortality was remarkably inferior in patients with LWR > 0.10 than in those with LWR ≤ 0.10. (5.83% vs. 31.8%, P < 0.001).

Conclusions

LMR is an independent, simple, universal, inexpensive, and reliable prognostic parameter to identify high-risk IE patients for in-hospital mortality.

Clinical, Microbiological, and Imaging Characteristics of Infective Endocarditis in Latin America: A Systematic Review

OBJECTIVES

We aimed to describe the clinical, microbiological, and imaging characteristics of patients with infective endocarditis (IE) in studies from Latin America (LATAM).

METHODS

A systematic search through PubMed, EMBASE, LILACS, and SciELO from inception until February 2021 was conducted. We included observational studies that assessed adults with IE from LATAM and reported data on clinical, microbiological, or imaging characteristics. Data were independently extracted by 2 authors and the risk of bias was evaluated by study design with its respective tool. Findings were summarized using descriptive statistics.

RESULTS

Forty-four studies were included. Most cases were male (68.5%), had a predisposing condition including valve disease (24.3%), or had a prosthetic valve (23.4%). Clinical manifestations included fever (83.9%), malaise (63.2%), or heart murmur (57.7%). A total of 36.4% and 27.1% developed heart failure or embolism, respectively. Blood cultures were negative in 23.9% and S. aureus (18.6%) and the viridans group streptococci (17.8%) were the most common isolates. Most cases were native valve IE (67.3%) affecting mainly left-sided valves. Echocardiographic findings included vegetations (84.3%) and regurgitation (75.9%). In-hospital mortality was 25.1%.

CONCLUSIONS

This is the first systematic review that evaluated the characteristics of IE in LATAM patients. A lack of multicenter studies reflects the need for these studies in LATAM.

Anesthesia Considerations in Infective Endocarditis

The management of infective endocarditis is complex and inherently requires multidisciplinary cooperation. About half of all patients diagnosed with infective endocarditis will meet the criteria to undergo cardiac surgery, which regularly takes place in urgent or emergency settings. The pathophysiology and clinical presentation of infective endocarditis make it a unique disorder within cardiac surgery that warrants a thorough understanding of specific characteristics in the perioperative period. This includes, among others, echocardiography, coagulation, bleeding management, or treatment of organ dysfunction. In this narrative review article, the authors summarize the current knowledge on infective endocarditis relevant for the clinical anesthesiologist in perioperative management of respective patients. Furthermore, the authors advocate for the anesthesiologist to become a structural member of the endocarditis team.

Infective endocarditis in paediatric population

Infective endocarditis is very uncommon in children; however, when it does arise, it can lead to severe consequences. The biggest risk factor for paediatric infective endocarditis today is underlying congenital heart defects. The most common causative organisms are Staphylococcus aureus and the viridans group of streptococci. The spectrum of symptoms varies widely in children and this produces difficulty in the diagnosis of infective endocarditis. Infective endocarditis in children is reliant on the modified Duke criteria. The use of blood cultures remains the most effective microbiological test for pathogen identification. However, in blood culture-negative infective endocarditis, serology testing and IgG titres are more effective for diagnosis. Imaging techniques used include echocardiograms, computed tomography and positron emission tomography. Biomarkers utilised in diagnosis are C-reactive protein, with recent literature reviewing the use of interleukin-15 and C-C motif chemokine ligand for reliable risk prediction. The American Heart Association (AHA) and European Society of Cardiology (ESC) guidelines have been compared to describe the differences in the approach to infective endocarditis in children. Medical intervention involves the use of antimicrobial treatment and surgical interventions include the repair and replacement of cardiac valves. Quality of life is highly likely to improve from surgical intervention.Conclusion: Over the past decades, there have been great advancements in clinical practice to improve outcomes in patients with infective endocarditis. Nonetheless, further work is required to better investigative and manage such high risk cohort. What is Known: • The current diagnostic techniques including 'Duke's criteria' for paediatric infective endocarditis diagnosis • The current management guidelines utilised for paediatric infective endocarditis What is New: • The long-term outcomes of patients that underwent medical and surgical intervention • The quality of life of paediatric patients that underwent medical and surgical intervention.

C-Reactive Protein at Hospital Discharge and 1-Year Mortality in Acute Infective Endocarditis: A Prospective Observational Study

Background: An accurate biomarker at hospital discharge is needed to identify patients with acute infective endocarditis (IE) who are at high risk of mortality. This prospective observational study evaluated the prognostic value of C-reactive protein (CRP).

Methods: Patients with acute IE (n = 343) and hospitalized at 2 university-affiliated medical centers from January 2014 to December 2019 were enrolled. Patients were categorized as having low or high CRP (n = 217 and 126, respectively) at hospital discharge according to the optimal cutoff (CRP = 6.5 mg/L) determined via receiver-operating characteristic curve analysis. The primary endpoint was all-cause death, from hospital discharge to 1 year. The secondary endpoint was the cumulative rate of rehospitalization or paravalvular abscess at 1 year.

Results: At the 12-month follow-up, the mortality rate of the high-CRP group (21.43%) was significantly higher than that of the low-CRP group (2.76%, log-rank P < 0.0001). The multivariate regression analysis indicated that the high-CRP group had a higher excess mortality hazard risk (HR = 4.182; 95% CI: 2.120, 5.211; P < 0.001). The cumulative 1-year incidence of paravalvular abscess of the high-CRP group (11.90%) was significantly higher than that of the low-CRP (5.07%; P = 0.022). The cumulative rate of heart rehospitalizations of the 2 groups were similar (18.25% cf. 14.29%, P = 0.273).

Conclusion: For hospitalized patients with acute IE, a high CRP at discharge suggests a poor prognosis for 1-year mortality and paravalvular abscess.

Prognostic value of D-dimer for adverse outcomes in patients with infective endocarditis: an observational study

BACKGROUND

Increased D-dimer levels have been shown to correlate with adverse outcomes in various clinical conditions. However, few studies with a large sample size have been performed thus far to evaluate the prognostic value of D-dimer in patients with infective endocarditis (IE).

METHODS

613 patients with IE were included in the study and categorized into two groups according to the cut-off of D-dimer determined by receiver operating characteristic (ROC) curve analysis for in-hospital death: > 3.5 mg/L (n = 89) and ≤ 3.5 mg/L (n = 524). Multivariable regression analysis was used to determine the association of D-dimer with in-hospital adverse events and six-month death.

RESULTS

In-hospital death (22.5% vs. 7.3%), embolism (33.7% vs 18.2%), and stroke (29.2% vs 15.8%) were significantly higher in patients with D-dimer > 3.5 mg/L than in those with D-dimer ≤ 3.5 mg/L. Multivariable analysis showed that D-dimer was an independent risk factor for in-hospital adverse events (odds ratio = 1.11, 95% CI 1.03-1.19, P = 0.005). In addition, the Kaplan-Meier curve showed that the cumulative 6-month mortality was significantly higher in patients with D-dimer > 3.5 mg/L than in those with D-dimer ≤ 3.5 mg/L (log-rank test = 39.19, P < 0.0001). Multivariable Cox regression analysis showed that D-dimer remained a significant predictor for six-month death (HR 1.11, 95% CI 1.05-1.18, P < 0.001).

CONCLUSIONS

D-dimer is a reliable prognostic biomarker that independently associated with in-hospital adverse events and six-month mortality in patients with IE.

Current Challenges in the Management of Infective Endocarditis

Infective endocarditis is a relatively rare, but deadly cause of sepsis, with an overall mortality ranging from 20 to 25% in most series. Although the classic clinical classification into syndromes of acute or subacute endocarditis have not completely lost their usefulness, current clinical forms have changed according to the profound epidemiological changes observed in developed countries. In this review, we aim to address the changing epidemiology of endocarditis, several recent advances in the understanding of the pathophysiology of endocarditis and endocarditis-triggered sepsis, new useful diagnostic tools as well as current concepts in the medical and surgical management of this disease. Given its complexity, the management of infective endocarditis requires the close collaboration of multidisciplinary endocarditis teams that must decide on the diagnostic approach; the appropriate initial treatment in the critical phase; the detection of patients needing surgery and the timing of this intervention; and finally the accurate selection of patients for out-of-hospital treatment, either at home hospitalization or with oral antibiotic treatment.

[The expression level of cytokine genes in the cases of native heart valves in infectious endocarditis]

The expression level of IL1B, IL6, IL8, IL10, IL12A, IL12B, IL18, IL23, IL33, CCL2, and IL1RL1 has been investigated using biopsies of native mitral, aortic, and tricuspid valves obtained during surgical correction of acquired defect from 25 patients with infectious endocarditis. Biopsies of native mitral and aortic valve cusps from 12 patients who underwent surgical correction of acquired heart disease of non-infectious etiology were used as control. We used quantitative PCR with fluorescent dye SYBR Green for determination of the cytokine gene expression level. This study revealed that genes could be subdivided into three groups: (i) genes with increased expression (IL1B, IL6, and IL8); (ii) genes with reduced expression (IL33 and IL1RL1); (iii) genes with unchanged expression (IL12A, IL18, IL23, and CCL2). The IL8 gene expression was characterized by the most pronounced increase (9.83 times versus control), while the IL1RL1 gene demonstrated the most pronounced decrease in its expression (4.17 times). Expression of IL10 and IL12B genes was negligible in all samples.

Clinical Prognosis of Right-Sided Infective Endocarditis not Associated with Cardiac Devices or Intravenous Drug use: a Cohort Study and Meta-Analysis

Right-sided infective endocarditis (RSIE), classically associated with intravenous drug use or intracardiac devices, is considered a good-prognosis infective endocarditis (IE) form. However, predisposing factors and prognosis for "NODID" RSIE (NOt associated with cardiac Devices or Intravenous Drug use) remain unclear. The aim of this study was to evaluate predisposing factors and prognosis of NODID RSIE compared to other RSIE forms. A retrospective cohort study (January 2008-January 2019) was conducted in a reference center on 300 patients diagnosed with IE. Endocarditis-related events were defined as related to IE in mortality or open-heart surgery during follow-up. A review and meta-analysis of associated literature (January 2008-January 2019) were also performed. Fifty-seven patients presented RSIE (19%), 22 of which were NODID RSIE (39%). Use of intravascular catheters (23% vs 3%; p = 0.027) and congenital heart diseases (18% vs 0%; p = 0.019) were associated with NODID RSIE. This group had a higher in-hospital mortality (23% vs 3%; p = 0.027) and endocarditis-related event rates (41% vs 6%; p = 0.001) than non-NODID RSIE. Furthermore, NODID RSIE was independently associated with in-hospital endocarditis-related events (OR = 19.29; 95%CI:2.23-167.16; p = 0.007). Our meta-analysis evaluated four studies and identified 96 cases (30%) of NODID RSIE from 320 total RSIE cases. NODID RSIE patients demonstrated higher in-hospital mortality (RR = 2.81; 95%CI:1.61-4.90; p < 0.001; I2 = 0.0%) and necessity of open-heart surgery (RR = 13.89; 95%CI:4.14-46.60; p < 0.001; I2 = 0.0%) than non-NODID RSIE cases. Our study suggests that NODID RSIE has the highest endocarditis-related event rate and in-hospital mortality among RSIE cases and therefore should not be considered a good-prognosis IE.

Risk stratification biomarkers for Staphylococcus aureus bacteraemia

Objectives

To identify risk stratification biomarkers to enrich for the subset of Staphylococcus aureus bacteraemia patients who develop deep‐seated tissue infections with high morbidity and mortality to guide clinical trial enrolment and clinical management.

Methods

We evaluated the prognostic value of eight biomarkers for persistent bacteraemia, mortality and endovascular infection foci in a validation cohort of 160 patients with S. aureus bacteraemia enrolled consecutively over 3 years.

Results

High levels of IL‐17A, IL‐10 or soluble E‐selectin at bacteraemia diagnosis correlated with the duration of positive blood cultures. When thresholds defined in an independent cohort were applied, these biomarkers were robust predictors of persistent bacteraemia or endovascular infection. High serum levels of IL‐17A and IL‐10 often preceded the radiographic diagnosis of infective endocarditis, suggesting potential utility for prioritising diagnostic radiographic imaging. High IL‐8 was prognostic for all‐cause mortality, while IL‐17A and IL‐10 were superior to clinical metrics in discriminating between attributable mortality and non‐attributable mortality. High IL‐17A and IL‐10 identified more patients who developed microbiological failure or mortality than were identified by infective endocarditis diagnosis.

Conclusion

These biomarkers offer potential utility to identify patients at risk of persistent bacteraemia to guide diagnostic imaging and clinical management. Low biomarker levels could be used to rule out the need for more invasive TEE imaging in patients at lower risk of infective endocarditis. These biomarkers could enable clinical trials by enriching for patients with the greatest need for novel therapies.