A Nationwide Study on Mitral Valve Repair vs Replacement for Active Endocarditis.

BACKGROUND: Real-world evidence supporting the reproducibility and superiority of valve repair over replacement in active mitral valve infective endocarditis is lacking. METHODS: Data from a prospective nationwide database, including all cardiac surgical procedures in The Netherlands, were used. Adult patients undergoing primary mitral valve intervention who had a diagnosis of active infective endocarditis and who underwent surgery between 2013 and 2020 were included. Survival analysis was performed for the whole follow-up period as well as after applying the landmark of 90 days. RESULTS: Of 715 patients who met the inclusion criteria, 294 (41.1%) underwent valve repair. Mitral valve repair rates decreased slightly over the course of the study. The early mortality rate was 13.0%, and a trend of steadily declining early mortality rates over the course of the study, despite a steady increase in patient complexity, was observed. On risk-adjusted analysis, mitral valve replacement demonstrated inferior results when compared with valve repair (adjusted hazard ratio, 2.216; 95% CI, 1.425-3.448; P < .001), even after a landmark analysis was performed (adjusted hazard ratio 2.489; 95% CI, 1.124-5.516; P = .025). These results were confirmed by a propensity score-adjusted analysis (adjusted hazard ratio 2.251; 95% CI, 1.029-4.21; P = .042). CONCLUSIONS: Contemporary trends in mitral valve surgery for active infective endocarditis suggest growing patient complexity but slightly declining early mortality rates. A trend of decreasing mitral valve repair rates was seen. The results of this study suggest improved late outcomes of valve repair compared with valve replacement.

NL-EVDR: Netherlands-ExtraVascular Device Registry.

Cardiac implantable electronic device (CIED) therapy is an essential element in treating cardiac arrhythmias. Despite their benefits, conventional transvenous CIEDs are associated with a significant risk of mainly pocket- and lead-related complications. To overcome these complications, extravascular devices (EVDs), such as the subcutaneous implantable cardioverter-defibrillator and intracardiac leadless pacemaker, have been developed. In the near future, several other innovative EVDs will become available. However, it is difficult to evaluate EVDs in large studies because of high costs, lack of long-term follow-up, imprecise data or selected patient populations. To improve evaluation of these technologies, real-world, large-scale, long-term data are of utmost importance. A Dutch registry-based study seems to be a unique possibility for this goal due to early involvement of Dutch hospitals in novel CIEDs and an existing quality control infrastructure, the Netherlands Heart Registration (NHR). Therefore, we will soon start the Netherlands-ExtraVascular Device Registry (NL-EVDR), a Dutch nationwide registry with long-term follow-up of EVDs. The NL-EVDR will be incorporated in NHR's device registry. Additional EVD-specific variables will be collected both retrospectively and prospectively. Hence, combining Dutch EVD data will provide highly relevant information on safety and efficacy. As a first step, a pilot project has started in selected centres in October 2022 to optimise data collection.

Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury.

OBJECTIVE: Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac arrest, however interrater-agreement among electroencephalographers is limited. We sought to evaluate the performance of machine learning methods using EEG reactivity data to predict good long-term outcomes in hypoxic-ischemic brain injury. METHODS: We retrospectively reviewed clinical and EEG data of comatose cardiac arrest subjects. Electroencephalogram reactivity was tested within 72 h from cardiac arrest using sound and pain stimuli. A Quantitative EEG (QEEG) reactivity method evaluated changes in QEEG features (EEG spectra, entropy, and frequency features) during the 10 s before and after each stimulation. Good outcome was defined as Cerebral Performance Category of 1-2 at six months. Performance of a random forest classifier was compared against a penalized general linear model (GLM) and expert electroencephalographer review. RESULTS: Fifty subjects were included and sixteen (32%) had good outcome. Both QEEG reactivity methods had comparable performance to expert EEG reactivity assessment for good outcome prediction (mean AUC 0.8 for random forest vs. 0.69 for GLM vs. 0.69 for expert review, respectively; p non-significant). CONCLUSIONS: Machine-learning models utilizing quantitative EEG reactivity data can predict long-term outcome after cardiac arrest. SIGNIFICANCE: A quantitative approach to EEG reactivity assessment may support prognostication in cardiac arrest.