Paravertebral Catheter for Three-Level Injection in Radical Mastectomy: A Randomised Controlled Study

Introduction

Paravertebral block (PVB) is an alternative to general anaesthesia (GA) for breast surgery. However, for extensive surgery multiple punctures are needed increasing the immanent risk of the method. The purpose of this study was to evaluate PVB via catheter and injections at three different levels. Primary outcome was the quality of postoperative analgesia, in particular, the number of patients requiring additional morphine.

Methods

In a randomised single blinded clinical study patients scheduled for breast surgery including axillary approach, were randomly allocated to different anaesthetic techniques, n = 35 each. Patients received either GA with sevoflurane or PVB with catheter at level Th 4. In PVB-patients a 1:2 mixture of bupivacaine 0.5% and lidocaine 2% with adrenaline was injected sequentially 10 ml each at three different levels.

Results

Complication-free catheter insertion was possible in all 35 scheduled patients. The need for postoperative analgesics was higher after GA compared to PVB (22 vs.14 patients); p = 0.056. Postoperative morphine consumption was 1.55 (GA) and 0.26 mg (PVB) respectively (p < 0.001). Visual rating score (VRS) for pain at rest and at movement was higher in GA patients on post anaesthesia care unit (PACU) as well as on the ward at 1 - 6h and 6 - 12h. Readiness for discharge was earlier after PVB (4.96 and 6.52 hours respectively). After GA the incidence and severity of postoperative nausea and vomiting (PONV) was higher, though not significantly. Patients’ satisfaction was comparable in both groups.

Conclusions

Three-level injection PVB via catheter for extensive mastectomy was efficient and well accepted. Using a catheter may enhance safety by avoiding multiple paravertebral punctures when extended spread of analgesia is required.

Trial Registration

www.ClinicalTrial.gov NCT02065947     

optPBN: An Optimisation Toolbox for Probabilistic Boolean Networks

Background

There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only limited quantitative insights into the complexity of biological systems because of the inherited qualitative nature of Boolean networks.

Results

We introduce optPBN, a Matlab-based toolbox for the optimisation of probabilistic Boolean networks (PBN) which operates under the framework of the BN/PBN toolbox. optPBN offers an easy generation of probabilistic Boolean networks from rule-based Boolean model specification and it allows for flexible measurement data integration from multiple experiments. Subsequently, optPBN generates integrated optimisation problems which can be solved by various optimisers.In term of functionalities, optPBN allows for the construction of a probabilistic Boolean network from a given set of potential constitutive Boolean networks by optimising the selection probabilities for these networks so that the resulting PBN fits experimental data. Furthermore, the optPBN pipeline can also be operated on large-scale computational platforms to solve complex optimisation problems. Apart from exemplary case studies which we correctly inferred the original network, we also successfully applied optPBN to study a large-scale Boolean model of apoptosis where it allows identifying the inverse correlation between UVB irradiation, NFκB and Caspase 3 activations, and apoptosis in primary hepatocytes quantitatively. Also, the results from optPBN help elucidating the relevancy of crosstalk interactions in the apoptotic network.

Summary

The optPBN toolbox provides a simple yet comprehensive pipeline for integrated optimisation problem generation in the PBN formalism that can readily be solved by various optimisers on local or grid-based computational platforms. optPBN can be further applied to various biological studies such as the inference of gene regulatory networks or the identification of the interaction''s relevancy in signal transduction networks.