Prediction of Acute-phase Complications in Patients with Infectious Endocarditis

Risk of embolic events before and after antibiotic treatment initiation among patients with left-side infective endocarditis

PURPOSE

Embolic events (EEs) are a common complication of left-side infective endocarditis (IE). The aim of the present study was to identify risk factors for the occurrence of EEs before or after antibiotic treatment instauration among patients with definite or possible IE.

METHODS

This retro-prospective study was conducted at the Lausanne University Hospital, Lausanne, Switzerland, from January 2014 to June 2022. EEs and IE were defined according to modified Duke criteria.

RESULTS

A total of 441 left-side IE episodes were included (334: 76% were definite and 107; 24% possible IE). EE were diagnosed in 260 (59%) episodes; in 190 (43%) before antibiotic treatment initiation and 148 (34%) after. Central nervous system (184; 42%) was the most common site of EE. Multivariable analysis identified S. aureus (P 0.022), immunological phenomena (P < 0.001), sepsis (P 0.027), vegetation size ≥ 10 mm (P 0.003) and intracardiac abscess (P 0.022) as predictors of EEs before antibiotic treatment initiation. For EEs after antibiotic treatment initiation, multivariable analysis revealed vegetation size ≥ 10 mm (P < 0.001), intracardiac abscess (P 0.035) and prior EE (P 0.042), as independent predictors of EEs, while valve surgery (P < 0.001) was associated with lower risk for EEs.

CONCLUSIONS

We reported a high percentage of EEs among patients with left-side IE; vegetation size, intracardiac abscess, S. aureus and sepsis were independently associated with the occurrence of EEs. In addition to antibiotic treatment, early surgery led to further decrease in EEs incidence.

A Bayesian network-based approach for identifying risk factors and predicting ischemic stroke in infective endocarditis patients

Objective

This study aimed to seek the risk factors and develop a predictive model for ischemic stroke (IS) in patients with infective endocarditis (IE) utilizing a Bayesian network (BN) approach.

Methods

Data were obtained from the electronic medical records of all adult patients at three hospitals between 1 January 2018, and 31 December 2022. Two predictive models, logistic regression and BN, were used. Patients were randomly assigned to the training and test sets in a 7:3 ratio. We established a BN model with the training dataset and validated it with the testing dataset. The Bayesian network model was built by using the Tabu search algorithm. The areas under the receiver operating characteristic curve (AUCs), calibration curve, and decision curve were used to evaluate the prediction performance between the BN and logistic models.

Results

A total of 542 patients [mean (SD) age, 49.6 (15.3) years; 137 (25.3%) female] were enrolled, including 151 (27.9%) with IS and 391 (72.1%) without IS. Hyperlipidemia, hypertension, age, vegetation size (>10 mm), S. aureus infection, and early prosthetic valve IE were closely correlated with IS. The BN models outperformed the logistic regression in training and testing sets, with accuracies of 76.06% and 74.1%, AUC of 0.744 and 0.703, sensitivities of 25.93% and 20.93%, and specificities of 96.27% and 90.24%, respectively.

Conclusion

The BN model is more efficient than the logistic regression model. Therefore, BN models may be suitable for the early diagnosis and prevention of IS in IE patients.

Infective endocarditis complicated by embolic events: Pathogenesis and predictors

BACKGROUND

Infective endocarditis (IE) continues to be associated with great challenges. Embolic events (EE) are frequent and life-threatening complications in IE patients. It remains challenging to predict and assess the embolic risk in individual patients with IE accurately.

HYPOTHESIS

Accurate prediction of embolization is critical in the early identification and treatment of risky and potentially embolic lesions in patients with IE.

METHODS

We searched the PubMed, Web of Science, and Google Scholar databases using a range of related search terms, and reviewed the literatures about the pathogenesis and embolic predictors of IE.

RESULTS

The development of IE and its complications is widely accepted as the result of complex interactions between microorganisms, valve endothelium, and host immune responses. The predictive value of echocardiographic characteristics is the most powerful for EE. In addition, both easily obtained blood biomarkers such as C-reactive protein, mean platelet volume, neutrophil-to-lymphocyte ratio, anti-β2-glycoprotein I antibodies, D-Dimer, troponin I, matrix metalloproteinases, and several microbiological or clinical characteristics might be promising as potential predictors of EE.

CONCLUSION

Our review provides a synthesis of current knowledge regarding the pathogenesis and predictors of embolism in IE along with a review of potentially emerging biomarkers.