Patent foramen ovale. Correct route for implantation of a biventricular permanent pacemaker?

INTRODUCTION

Intentional or unintentional placement of a pacemaker lead into the left ventricle is an uncommon clinical entity that is associated with a high risk for systemic embolization and enormous difficulties in case of explantation. Unintentional implantation through a patent foramen ovale via the mitral valve is the usual pathway for this malposition.

METHODS

We report a case where a pacemaker lead was placed intentionally into the left ventricle via a patent foramen ovale for biventricular pacing for resynchronization therapy. Later, the patient developed life-threatening pacemaker lead-associated endocarditis with sepsis. Emergency open heart surgery for lead removal was necessary in the form of a reoperation after bypass graft surgery a number of years earlier.

CONCLUSION

Although it is technically feasible to implant the pacemaker lead into the left ventricle via a patent foramen ovale, we consider this option to be obsolete for use with a biventricular pacemaker, due to the multitude of risks, which can, in part, be life-threatening for the patient.

[Right heart failure resulting from pacemaker lead-induced tricuspid valve regurgitation]

Pacemaker lead-induced tricuspid valve regurgitation is a severe and often underdiagnosed complication due to the widely variable time interval between implantation and the development of severe tricuspid valve insufficiency with ensuing right heart failure. Complete explantation of inactive pacemaker leads is necessary to avoid permanent damage to right heart structures. If performed in a timely fashion, regression of tricuspid insufficiency can be achieved without additional cardiac procedures.

Rho-kinase inhibitors as therapeutics: from pan inhibition to isoform selectivity

The emerging critical implications of Rho/Rho-kinase (ROCK) signaling in neurodegenerative diseases, glaucoma, renoprotection, diabetes and cancer have sparked growing interest in the pharmacological potential of ROCK inhibitors beyond their current application in cardiovascular disease. This article discusses the therapeutic benefits of novel ROCK inhibitors in development, and highlights the recent advances in the current understanding of disease-dependent and isoform-specific functions of ROCK and their potential impact on future therapeutic strategies.

How wrong can we get? A review of machine learning approaches and error bars

A large number of different machine learning methods can potentially be used for ligand-based virtual screening. In our contribution, we focus on three specific nonlinear methods, namely support vector regression, Gaussian process models, and decision trees. For each of these methods, we provide a short and intuitive introduction. In particular, we will also discuss how confidence estimates (error bars) can be obtained from these methods. We continue with important aspects for model building and evaluation, such as methodologies for model selection, evaluation, performance criteria, and how the quality of error bar estimates can be verified. Besides an introduction to the respective methods, we will also point to available implementations, and discuss important issues for the practical application.

Machine Learning Models for Lipophilicity and Their Domain of Applicability

Unfavorable lipophilicity and water solubility cause many drug failures; therefore these properties have to be taken into account early on in lead discovery. Commercial tools for predicting lipophilicity usually have been trained on small and neutral molecules, and are thus often unable to accurately predict in-house data. Using a modern Bayesian machine learning algorithm--a Gaussian process model--this study constructs a log D7 model based on 14,556 drug discovery compounds of Bayer Schering Pharma. Performance is compared with support vector machines, decision trees, ridge regression, and four commercial tools. In a blind test on 7013 new measurements from the last months (including compounds from new projects) 81% were predicted correctly within 1 log unit, compared to only 44% achieved by commercial software. Additional evaluations using public data are presented. We consider error bars for each method (model based error bars, ensemble based, and distance based approaches), and investigate how well they quantify the domain of applicability of each model.

Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach

Accurate in silico models for predicting aqueous solubility are needed in drug design and discovery and many other areas of chemical research. We present a statistical modeling of aqueous solubility based on measured data, using a Gaussian Process nonlinear regression model (GPsol). We compare our results with those of 14 scientific studies and 6 commercial tools. This shows that the developed model achieves much higher accuracy than available commercial tools for the prediction of solubility of electrolytes. On top of the high accuracy, the proposed machine learning model also provides error bars for each individual prediction.