Patients with intracardiac vegetations identified on transesophageal echocardiogram can safely undergo complete device extraction using standard percutaneous lead extraction techniques. Permanent devices can safely be reimplanted provided blood cultures remain sterile. The presence of intracardiac vegetations identifies a subset of patients at increased risk for complications and early mortality from systemic infection despite device extraction and appropriate antimicrobial therapy.
Background: We compared the relationship between the third heart sound (S3) measured by an implantable cardiac device (devS3) and auscultation (ausS3) and evaluated their prognostic powers for predicting heart failure events (HFEs). Methods and Results: In the MultiSENSE study, devS3 was measured daily with continuous values, whereas ausS3 was assessed at study visits with discrete grades. They were compared among patients with and without HFEs at baseline and against each other directly. Cox proportional hazard models were developed between follow-up visits and over the whole study. Simulations were performed on devS3 to match the limitations of auscultation. We studied 900 patients, of whom 106 patients experienced 192 HFEs. Two S3 sensing modalities correlated with each other, but at baseline, only devS3 differentiated patients with or without HFEs (P < 0.0001). The prognostic power of devS3 was superior to that of ausS3 both between follow-up visits (HR = 5.7, P < 0.0001, and 1.7, P = 0.047, respectively) and over the whole study (HR = 2.9, P < 0.0001, and 1.4, P = 0.216, respectively). Simulation results suggested this superiority may be attributed to continuous monitoring and to subaudible measuring capability. Conclusions: S3 measured by implantable cardiac devices has stronger prognostic power to predict episodes of future HFEs than that of auscultation.
Pharmacotherapy of neonates is complex and marked to a large extent of off-label use. The implementation of the Paediatric Regulation (2007) gave hope for a change in the safety and efficacy for drugs used in neonatal intensive care units (NICU). This study investigates drug utilisation patterns and off-label use in a German neonatal intensive care unit (NICU) in 2014. A 12-months retrospective, observational cohort study was performed at the NICU of the University Children’s Hospital Erlangen, Germany. Licensing status was determined using the Summary of Product Characteristics (SmPC). Results are compared with a similar study conducted 10 years earlier. The study included 204 patients (57.8% male) (2004: 183) and 2274 drug prescriptions were recorded (2004: 1978). The drugs that were mostly prescribed were drugs for the nervous system (2004: 22.6%; 2014: 26.9%) and anti-infectives for systemic use (2004: 26.0%; 2014: 24.9%);34.3% (2004) and 39.2% (2014) of all prescriptions were off-label;62.7% of all patients received at least one off-label or unlicensed drug (2004: 70%). For 13 drugs, the licensing status changed either from off-label to label (n = 9) or vice versa (n = 4). Overall, there was no significant change neither in terms of the drugs used nor regarding their licensing status. Further studies are needed to validate these findings in a European context.
Objective: Adverse drug events (ADEs) in the outpatient pediatric pharmacotherapy can be serious and lead to inpatient admissions. Recent research only focused on ADE identification during hospitalization. The aim of the present study was to develop an algorithm to identify drugrelated hospital admissions in pediatrics.Methods: A systematic literature research was performed, and a pediatric trigger tool for identifying drug-related inpatient admissions was built. The initial version was tested in a sample of 292 patients admitted to a German university children's hospital. Subsequently, the tool was further improved by combining different modules as a novel approach.
Results:The obtained algorithm with 39 triggers in 5 modules identified drug-related inpatient admissions at a sensitivity of 95.5% (95% confidence interval [CI], 89.3%-100%) and a specificity of 16.5% (95% CI, 11.9%-21.2%), respectively. After modifications including trigger activation requiring a combination of different modules, specificity increased to 56.9% (95% CI, 50.7%-63.0%). Identifying 36 of 44 ADEs leading to admission, sensitivity remained high (81.8% [95% CI, 70.4%-93.2%]). The overall positive predictive value was 25.2% (95% CI, 18.1%-32.3%).
Conclusions:The algorithm is the first trigger tool to identify ambulant acquired ADEs leading to hospital admission in pediatrics. However, the underlying patient sample is small.Using a larger population for refinement will allow further specifications and reduction in the total amount of triggers and thus signals.
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