Cost-effectiveness of minimal interventional procedures for chronic mechanical low back pain: design of four randomised controlled trials with an economic evaluation

Background: Minimal interventional procedures are frequently applied in patients with mechanical low back pain which is defined as pain presumably resulting from single sources: facet, disc, sacroiliac joint or a combination of these. Usually, these minimal interventional procedures are an integral part of a multidisciplinary pain programme. A recent systematic review issued by the Dutch Health Insurance Council showed that the effectiveness of these procedures for the total group of patients with chronic low back pain is yet unclear and cost-effectiveness unknown. The aim of the study is to evaluate whether a multidisciplinary pain programme with minimal interventional procedures is cost-effective compared to the multidisciplinary pain programme alone for patients with chronic mechanical low back pain who did not respond to conservative primary care and were referred to a pain clinic. Methods. All patients with chronic low back pain who are referred to one of the 13 participating pain clinics will be asked to participate in an observational study. Patients with a suspected diagnosis of facet, disc or sacroiliac joint problems will receive a diagnostic block to confirm this diagnosis. If confirmed, they will be asked to participate in a Randomized Controlled Trial (RCT). For each single source a separate RCT will be conducted. Patients with a combination of facet, disc or sacroiliac joint problems will be invited for participation in a RCT as well. An economic evaluation from a societal perspective will be performed alongside these four RCTs. Patients will complete questionnaires at baseline, 3 and 6 weeks, 3, 6, 9 and 12 months after start of the treatment

Application of chemometric analysis to infrared spectroscopy for the identification of wood origin

Chemical characteristics of wood are used in this study for plant taxonomy classification based on the current Angiosperm Phylogeny Group classification (APG III System) for the division, class and subclass of woody plants. Infrared spectra contain information about the molecular structure and intermolecular interactions among the components in wood but the understanding of this information requires multivariate techniques for the analysis of highly dense datasets. This article is written with the purposes of specifying the chemical differences among taxonomic groups, and predicting the taxa of unknown samples with a mathematical model. Principal component analysis, t-test, stepwise discriminant analysis and linear discriminant analysis, were some of the chosen multivariate techniques. A procedure to determine the division, class, subclass and order of unknown samples was built with promising implications for future applications of Fourier Transform Infrared spectroscopy in wood taxonomy classification

Effect of medication review and cognitive behaviour treatment by community pharmacists of patients discharged from the hospital on drug related problems and compliance: design of a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Drug related problems (DRPs) are common among elderly patients who are discharged from the hospital and are using several drugs for their chronic diseases. Examples of drug related problems are contra-indications, interactions, adverse drug reactions and inefficacy of treatment. Causes of these problems include prescription errors and non-compliance with treatment. The aim of this study is to examine the effect of <it>medication review </it>and <it>cognitive behaviour therapy </it>of discharged patients by community pharmacists to minimize the occurrence of drug related problems.</p> <p>Methods/Design</p> <p>A randomized controlled trial will be performed. Community pharmacists will be randomized into a control group and an intervention group. 342 Patients, aged over 60 years, discharged from general and academic hospitals, using five or more prescription drugs for their chronic disease will be asked by their pharmacy to participate in the study.</p> <p>Patients randomized to the control group will receive usual care according to the Dutch Pharmacy Standard. The medication of patients randomised to the intervention group will be reviewed by the community pharmacist with use of the national guidelines for the treatment of diseases, when patients are discharged from the hospital. The Pharmaceutical Care network Europe Registration form will be used to record drug related problems. Trained pharmacy technicians will counsel patients at home at baseline and at 1,3,6,9 and 12 months, using Cognitive Behaviour Treatment according to the Theory of Planned Behaviour. The patient's attitude towards medication and patient's adherence will be subject of the cognitive behaviour treatment. The counselling methods that will be used are <it>motivational interviewing </it>and <it>problem solving treatment</it>. Patients adherence towards drug use will be determined with use of the Medication Adherence Report Scale Questionnaire. There will be a follow-up of 12 months.</p> <p>The two primary outcome measures are the difference in occurrence of DRPs between intervention and control group and adherence with drug use. Secondary endpoints are attitude towards drug use, incidence of Re-hospitalisations related to medicines, functional status of the patient, quality of life and the cost-effectiveness of this intervention.</p> <p>Discussion</p> <p>Combining both medication review and Cognitive Behaviour Treatment may decrease DRPs and may result in more compliance with drug use among patients discharged from the hospital and using 5 or more chronic drugs.</p> <p>Trial registration</p> <p>Dutch Trial Register NTR1194</p

Estimating uncertainty in ecosystem budget calculations

© The Authors, 2010. This article is distributed under the terms of the Creative Commons Attribution-Noncommercial License. The definitive version was published in Ecosystems 13 (2010): 239-248, doi:10.1007/s10021-010-9315-8.Ecosystem nutrient budgets often report values for pools and fluxes without any indication of uncertainty, which makes it difficult to evaluate the significance of findings or make comparisons across systems. We present an example, implemented in Excel, of a Monte Carlo approach to estimating error in calculating the N content of vegetation at the Hubbard Brook Experimental Forest in New Hampshire. The total N content of trees was estimated at 847 kg ha−1 with an uncertainty of 8%, expressed as the standard deviation divided by the mean (the coefficient of variation). The individual sources of uncertainty were as follows: uncertainty in allometric equations (5%), uncertainty in tissue N concentrations (3%), uncertainty due to plot variability (6%, based on a sample of 15 plots of 0.05 ha), and uncertainty due to tree diameter measurement error (0.02%). In addition to allowing estimation of uncertainty in budget estimates, this approach can be used to assess which measurements should be improved to reduce uncertainty in the calculated values. This exercise was possible because the uncertainty in the parameters and equations that we used was made available by previous researchers. It is important to provide the error statistics with regression results if they are to be used in later calculations; archiving the data makes resampling analyses possible for future researchers. When conducted using a Monte Carlo framework, the analysis of uncertainty in complex calculations does not have to be difficult and should be standard practice when constructing ecosystem budgets

Effects of a 1-Week Inpatient Course Including Information, Physical Activity, and Group Sessions for Prostate Cancer Patients

This study aims to explore the effects of a 1-week inpatient course including information, physical activity (PA), and group sessions on physical and mental health-related outcomes for prostate cancer (PCa) patients. Further to assess the patients’ satisfaction with the course. PCa patients completed a questionnaire assessing PA, fatigue, mental distress, and quality of life 1 month before (T0) and 3 months after (T1) the course. Total fatigue, physical fatigue, and PSA anxiety decreased significantly from T0 to T1. No significant changes were observed in the other measures. The majority of the participants were satisfied with the course. In spite of minor reductions in fatigue and PSA anxiety and satisfied patients, the findings indicate that a 1-week inpatient course does not influence substantially on most of the health-related outcomes in PCa patients 3 months after the course

A bootstrap approach for assessing the uncertainty of outcome probabilities when using a scoring system

Background: Scoring systems are a very attractive family of clinical predictive models, because the patient score can be calculated without using any data processing system. Their weakness lies in the difficulty of associating a reliable prognostic probability with each score. In this study a bootstrap approach for estimating confidence intervals of outcome probabilities is described and applied to design and optimize the performance of a scoring system for morbidity in intensive care units after heart surgery. Methods: The bias-corrected and accelerated bootstrap method was used to estimate the 95% confidence intervals of outcome probabilities associated with a scoring system. These confidence intervals were calculated for each score and each step of the scoring-system design by means of one thousand bootstrapped samples. 1090 consecutive adult patients who underwent coronary artery bypass graft were assigned at random to two groups of equal size, so as to define random training and testing sets with equal percentage morbidities. A collection of 78 preoperative, intraoperative and postoperative variables were considered as likely morbidity predictors. Results: Several competing scoring systems were compared on the basis of discrimination, generalization and uncertainty associated with the prognostic probabilities. The results showed that confidence intervals corresponding to different scores often overlapped, making it convenient to unite and thus reduce the score classes. After uniting two adjacent classes, a model with six score groups not only gave a satisfactory trade-off between discrimination and generalization, but also enabled patients to be allocated to classes, most of which were characterized by well separated confidence intervals of prognostic probabilities. Conclusions: Scoring systems are often designed solely on the basis of discrimination and generalization characteristics, to the detriment of prediction of a trustworthy outcome probability. The present example demonstrates that using a bootstrap method for the estimation of outcome-probability confidence intervals provides useful additional information about score-class statistics, guiding physicians towards the most convenient model for predicting morbidity outcomes in their clinical context

Antipsychotic monotherapy and polypharmacy in the naturalistic treatment of schizophrenia with atypical antipsychotics

BACKGROUND: Antipsychotic monotherapy is recognized as the treatment of choice for patients with schizophrenia. Simultaneous treatment with multiple antipsychotics (polypharmacy) is suggested by some expert consensus guidelines as the last resort after exhausting monotherapy alternatives. This study assessed the annual rate and duration of antipsychotic monotherapy and its inverse, antipsychotic polypharmacy, among schizophrenia patients initiated on commonly used atypical antipsychotic medications. METHODS: Data were drawn from a large prospective naturalistic study of patients treated for schizophrenia-spectrum disorders, conducted 7/1997–9/2003. Analyses focused on patients (N = 796) who were initiated during the study on olanzapine (N = 405), quetiapine (N = 115), or risperidone (N = 276). The percentage of patients with monotherapy on the index antipsychotic over the 1-year post initiation, and the cumulative number of days on monotherapy were calculated for all patients and for each of the 3 atypical antipsychotic treatment groups. Analyses employed repeated measures generalized linear models and non-parametric bootstrap re-sampling, controlling for patient characteristics. RESULTS: During the 1-year period, only a third (35.7%) of the patients were treated predominately with monotherapy (>300 days). Most patients (57.7%) had at least one prolonged period of antipsychotic polypharmacy (>60 consecutive days). Patients averaged 195.5 days on monotherapy, 155.7 days on polypharmacy, and 13.9 days without antipsychotic therapy. Olanzapine-initiated patients were significantly more likely to be on monotherapy with the initiating antipsychotic during the 1-year post initiation compared to risperidone (p = .043) or quetiapine (p = .002). The number of monotherapy days was significantly greater for olanzapine than quetiapine (p < .001), but not for olanzapine versus risperidone, or for risperidone versus quetiapine-initiated patients. CONCLUSION: Despite guidelines recommending the use of polypharmacy only as a last resort, the use of antipsychotic polypharmacy for prolonged periods is very common during the treatment of schizophrenia patients in usual care settings. In addition, in this non-randomized naturalistic observational study, the most commonly used atypical antipsychotics significantly differed on the rate and duration of antipsychotic monotherapy. Reasons for and the impact of the predominant use of polypharmacy will require further study

A July Spike in Fatal Medication Errors: A Possible Effect of New Medical Residents

residencies and acquire increased responsibility for patient care. Many have suggested that these new medical residents may produce errors and worsen patient outcomes—the so-called “July Effect; ” however, we have found no U.S. evidence documenting this effect. OBJECTIVE: Determine whether fatal medication errors spike in July

A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context

<p>Abstract</p> <p>Background</p> <p>Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.</p> <p>Results</p> <p>PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets.</p> <p>Conclusions</p> <p>The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics.</p