Support for Seasonal Influenza Vaccination Requirements among US Healthcare Personnel.

Objective. To measure support for seasonal influenza vaccination requirements among US healthcare personnel (HCP) and its associations with attitudes regarding influenza and influenza vaccination and self-reported coverage by existing vaccination requirements. Design. Between June 1 and June 30, 2010, we surveyed a sample of US HCP ([Formula: see text]) recruited using an existing probability-based online research panel of participants representing the US general population as a sampling frame. Setting. General community. Participants. Eligible HCP who (1) reported having worked as medical doctors, health technologists, healthcare support staff, or other health practitioners or who (2) reported having worked in hospitals, ambulatory care facilities, long-term care facilities, or other health-related settings. Methods. We analyzed support for seasonal influenza vaccination requirements for HCP using proportion estimation and multivariable probit models. Results. A total of 57.4% (95% confidence interval, 53.3%-61.5%) of US HCP agreed that HCP should be required to be vaccinated for seasonal influenza. Support for mandatory vaccination was statistically significantly higher among HCP who were subject to employer-based influenza vaccination requirements, who considered influenza to be a serious disease, and who agreed that influenza vaccine was safe and effective. Conclusions. A majority of HCP support influenza vaccination requirements. Moreover, providing HCP with information about the safety of influenza vaccination and communicating that immunization of HCP is a patient safety issue may be important for generating staff support for influenza vaccination requirements.

Neurally Adjusted Ventilatory Assist (NAVA) improves the matching of diaphragmatic electrical activity and tidal volume in comparison to pressure support (PS) under non invasive ventilation

INTRODUCTION. Patient-ventilator asynchrony is a frequent issue in non invasivemechanical ventilation (NIV) and leaks at the patient-mask interface play a major role in itspathogenesis. NIV algorithms alleviate the deleterious impact of leaks and improve patient-ventilator interaction. Neurally adusted ventilatory assist (NAVA), a neurally triggered modethat avoids interferences between leaks and the usual pneumatic trigger, could further improvepatient-ventilator interaction in NIV patients.OBJECTIVES. To evaluate the feasibility ofNAVAin patients receiving a prophylactic postextubationNIV and to compare the respective impact ofPSVandNAVAwith and withoutNIValgorithm on patient-ventilator interaction.METHODS. Prospective study conducted in 16 beds adult critical care unit (ICU) in a tertiaryuniversity hospital. Over a 2 months period, were included 17 adult medical ICU patientsextubated for less than 2 h and in whom a prophylactic post-extubation NIV was indicated.Patients were randomly mechanically ventilated for 10 min with: PSV without NIV algorithm(PSV-NIV-), PSV with NIV algorithm (PSV-NIV+),NAVAwithout NIV algorithm (NAVANIV-)and NAVA with NIV algorithm (NAVA-NIV+). Breathing pattern descriptors, diaphragmelectrical activity, leaks volume, inspiratory trigger delay (Tdinsp), inspiratory time inexcess (Tiexcess) and the five main asynchronies were quantified. Asynchrony index (AI) andasynchrony index influenced by leaks (AIleaks) were computed.RESULTS. Peak inspiratory pressure and diaphragm electrical activity were similar in thefour conditions. With both PSV and NAVA, NIV algorithm significantly reduced the level ofleak (p\0.01). Tdinsp was not affected by NIV algorithm but was shorter in NAVA than inPSV (p\0.01). Tiexcess was shorter in NAVA and PSV-NIV+ than in PSV-NIV- (p\0.05).The prevalence of double triggering was significantly lower in PSV-NIV+ than in NAVANIV+.As compared to PSV,NAVAsignificantly reduced the prevalence of premature cyclingand late cycling while NIV algorithm did not influenced premature cycling. AI was not affectedby NIV algorithm but was significantly lower in NAVA than in PSV (p\0.05). AIleaks wasquasi null with NAVA and significantly lower than in PSV (p\0.05).CONCLUSIONS. NAVA is feasible in patients receiving a post-extubation prophylacticNIV. NAVA and NIV improve patient-ventilator synchrony in different manners. NAVANIV+offers the best patient-ventilator interaction. Clinical studies are required to assess thepotential clinical benefit of NAVA in patients receiving NIV.

Decision-support tools to manage drug incompatibilities: evaluation by nurses

Introduction Preventing drug incompatibilities has a high impact onthe safety of drug therapy. Although there are no internationalguidelines to manage drug incompatibilities, different decision-supporttools such as handbooks, cross-tables and databases are available.In a previous study, two decision-support tools have been pre-selectedby pharmacists as fitting nurses' needs on the wards1. The objective ofthis study was to have these both tools evaluated by nurses todetermine which would be the most suitable for their daily practice.Materials & Methods Evaluated tools were:1. Cross-table of drug pairs (http://files.chuv.ch/internet-docs/pha/medicaments/pha_phatab_compatibilitessip.pdf)2. Colour-table (a colour for each drug according to the pH: red =acid; blue = basic; yellow = neutral; black = to be infused alone)2Tools were assessed by 48 nurses in 5 units (PICU, adult andgeriatric intensive care, surgery, onco-hematology) using a standardizedform1. The scientific accuracy of the tools was evaluated bydetermining the compatibility of five drugs pairs (rate of correctanswers according to the Trissel's Handbook on Injectable Drugs,chi-square test). Their ergonomics, design, reliability and applicabilitywere estimated using visual analogue scales (VAS 0-10; 0 =null, 10 = excellent). Results are expressed as the median and interquartilerange (IQR) for 25% and 75% (Wilcoxon rank sum test).Results The rate of correct answers was above 90% for both tools(cross-table 96.2% vs colour-table 92.5%, p[0.05).The ergonomics and the applicability were higher for the crosstable[7.1 (IQR25 4.0, IQR75 8.0) vs 5.0 (IQR25 2.7, IQR75 7.0), p =0.025 resp. 8.3 (IQR25 7.4, IQR75 9.2) vs 7.6 (IQR25 5.9, IQR75 8.8)p = 0.047].The design of the colour-table was judged better [4.6 (IQR25 2.9,IQR75 7.1) vs 7.1 (IQR25 5.4, IQR75 8.4) p = 0.002].No difference was observed in terms of reliability [7.3 (IQR25 6.5,IQR75 8.4) vs 6.7 (IQR25 5.0, IQR758.6) p[0.05].The cross-table was globally preferred by 65% of the nurses (27%colour-table, 8% undetermined) and 68% would like to have thisdecision-support tool available for their daily practice.Discussion & Conclusion Both tools showed the same accuracy toassess drug compatibility. In terms of ergonomics and applicabilitythe cross-table was better than the colour-table, and was preferred bythe nurses for their daily practice. The cross-table will be implementedin our hospital as decision-support tool to help nurses tomanage drug incompatibilities.

Body fluid and tissue analysis using filter paper sampling support prior to LC-MS/MS: application to fatal overdose with colchicine

Because of the various matrices available for forensic investigations, the development of versatile analytical approaches allowing the simultaneous determination of drugs is challenging. The aim of this work was to assess a liquid chromatography-tandem mass spectrometry (LC-MS/MS) platform allowing the rapid quantification of colchicine in body fluids and tissues collected in the context of a fatal overdose. For this purpose, filter paper was used as a sampling support and was associated with an automated 96-well plate extraction performed by the LC autosampler itself. The developed method features a 7-min total run time including automated filter paper extraction (2 min) and chromatographic separation (5 min). The sample preparation was reduced to a minimum regardless of the matrix analyzed. This platform was fully validated for dried blood spots (DBS) in the toxic concentration range of colchicine. The DBS calibration curve was applied successfully to quantification in all other matrices (body fluids and tissues) except for bile, where an excessive matrix effect was found. The distribution of colchicine for a fatal overdose case was reported as follows: peripheral blood, 29 ng/ml; urine, 94 ng/ml; vitreous humour and cerebrospinal fluid, < 5 ng/ml; pericardial fluid, 14 ng/ml; brain, < 5 pg/mg; heart, 121 pg/mg; kidney, 245 pg/mg; and liver, 143 pg/mg. Although filter paper is usually employed for DBS, we report here the extension of this alternative sampling support to the analysis of other body fluids and tissues. The developed platform represents a rapid and versatile approach for drug determination in multiple forensic media.

From Support to Control : A Configurational Perspective on Conjugal Quality

The topic of conjugal quality provides an empirical illustration of the relevance of the configurational perspective on families. Based on a longitudinal sample of 1,534 couples living in Switzerland drawn from the study "Social Stratification, Cohesion and Conflict in Contemporary Families", we show that various types of interdependencies with relatives and friends promote distinct conflict management strategies for couples as well as unequal levels of conjugal quality. We find that configurations characterized by supportive and non-interfering relationships with relatives and friends for both partners are associated with higher conjugal quality, while configurations characterized by interference are associated with lower conjugal quality.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

Detection of Optimal Models in Parameter Space with Support Vector Machines

The paper proposes an approach aimed at detecting optimal model parameter combinations to achieve the most representative description of uncertainty in the model performance. A classification problem is posed to find the regions of good fitting models according to the values of a cost function. Support Vector Machine (SVM) classification in the parameter space is applied to decide if a forward model simulation is to be computed for a particular generated model. SVM is particularly designed to tackle classification problems in high-dimensional space in a non-parametric and non-linear way. SVM decision boundaries determine the regions that are subject to the largest uncertainty in the cost function classification, and, therefore, provide guidelines for further iterative exploration of the model space. The proposed approach is illustrated by a synthetic example of fluid flow through porous media, which features highly variable response due to the parameter values' combination.

Opioid antagonist detoxification under anaesthesia versus traditional clonidine detoxification combined with an additional week of psychosocial support: a randomised clinical trial.

BACKGROUND: While detoxification under anaesthesia accelerates the detoxification procedure, there is a lack of randomised clinical trials evaluating its effectiveness compared to traditional detoxification procedures, and a lack of data on long-term abstinence. METHODS: Prospective randomised clinical trial. Analysis by intention to treat and per protocol. Setting: Specialised substance abuse unit in a psychiatric teaching hospital and an intensive care unit of a general hospital. Participants: Seventy patients with opiate mono-dependence requesting detoxification: 36 randomised to RODA (treatment as allocated received by 26) and 34 randomised to classical clonidine detoxification (treatment as allocated received by 21). Main outcome measures: Successful detoxification, safety and self-reported abstinence at 3, 6 and 12 months after detoxification. RESULTS: Socio-demographics were similar in both groups at baseline. No complications were reported during or after anaesthesia. According to the intention to treat analysis, 28/36 (78%) RODA patients and 21/34 (62%) of the clonidine group successfully completed the detoxification process (p=0.14). In the intention to treat analysis, 30% of RODA patients were abstinent after 3 months compared to 14% in the clonidine group (p=0.11). No difference was found at 6 and 12 months (both groups showed less than 5% abstinence after 12 months). The per-protocol analysis showed similar results with no statistical differences either for ASI mean scores or for the SF36 questionnaire. CONCLUSION: Although the detoxification success rate and abstinence after 3 months were slightly better for the RODA procedure compared to clonidine treatment, these differences were not statistically significant and disappeared completely after 6 and 12 months.

Assistance circulatoire chronique: le point en 2013 [Long-term mechanical circulatory support: where do we stand in 2013?].

With the advent of new technologies, experience with long-term mechanical circulatory support (MCS) is rapidly growing. Candidates to MCS are selected based on concepts, strategies and classifications that are specific to this indication. As results drastically improve, supported by stronger scientific evidence, the trend is towards earlier implantation. An adequate pre-implant follow-up is mandatory in order to avoid missing the best window of opportunity for implantation. While on chronic support, the hemodynamic profile of patients with continuous-flow ventricular assist devices is unique and remarkably influenced by the hydration status. Optimal management of these patients from the pre-implant phase to the long-term support phase requires a multidisciplinary approach that is similar to that already long validated for organ transplantation.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI.

BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.