Adherence to the medical regime in patients with heart failure

To assess adherence to medical treatment in patients with heart failure (HF) using a specific questionnaire and measurement of the serum concentration of digoxin.

Is computer-assisted telephone triage safe? A prospective surveillance study in walk-in patients with non-life-threatening medical conditions

Background Patients often establish initial contact with healthcare institutions by telephone. During this process they are frequently medically triaged. Purpose To investigate the safety of computer-assisted telephone triage for walk-in patients with non-life-threatening medical conditions at an emergency unit of a Swiss university hospital. Methods This prospective surveillance study compared the urgency assessments of three different types of personnel (call centre nurses, hospital physicians, primary care physicians) who were involved in the patients' care process. Based on the urgency recommendations of the hospital and primary care physicians, cases which could potentially have resulted in an avoidable hazardous situation (AHS) were identified. Subsequently, the records of patients with a potential AHS were assessed for risk to health or life by an expert panel. Results 208 patients were enrolled in the study, of whom 153 were assessed by all three types of personnel. Congruence between the three assessments was low. The weighted κ values were 0.115 (95% CI 0.038 to 0.192) (hospital physicians vs call centre), 0.159 (95% CI 0.073 to 0.242) (primary care physicians vs call centre) and 0.377 (95% CI 0.279 to 0.480) (hospital vs primary care physicians). Seven of 153 cases (4.57%; 95% CI 1.85% to 9.20%) were classified as a potentially AHS. A risk to health or life was adjudged in one case (0.65%; 95% CI 0.02% to 3.58%). Conclusion Medical telephone counselling is a demanding task requiring competent specialists with dedicated training in communication supported by suitable computer technology. Provided these conditions are in place, computer-assisted telephone triage can be considered to be a safe method of assessing the potential clinical risks of patients' medical conditions.

Barriers and facilitators to chemotherapy patients' engagement in medical error prevention

Medical errors are a serious threat to chemotherapy patients. Patients can make contributions to safety but little is known about the acceptability of error-preventing behaviors and its predictors.

Parochialism or self-consciousness? Internationality in medical history journals 1997-2006

Research councils, universities and funding agencies are increasingly asking for tools to measure the quality of research in the humanities. One of their preferred methods is a ranking of journals according to their supposed level of internationality. Our quantitative survey of seventeen major journals of medical history reveals the futility of such an approach. Most journals have a strong national character with a dominance of native language, authors and topics. The most common case is a paper written by a local author in his own language on a national subject regarding the nineteenth or twentieth century. American and British journals are taken notice of internationally but they only rarely mention articles from other history of medicine journals. Continental European journals show a more international review of literature, but are in their turn not noticed globally. Increasing specialisation and fragmentation has changed the role of general medical history journals. They run the risk of losing their function as international platforms of discourse on general and theoretical issues and major trends in historiography, to international collections of papers. Journal editors should therefore force their authors to write a more international report, and authors should be encouraged to submit papers of international interest and from a more general, transnational and methodological point of view.

Sexual activity-related emergency department admissions: eleven years of experience at a Swiss university hospital

PRINCIPALS: Most people enjoy sexual intercourse without complications, but a significant, if small, number need to seek emergency medical help for related health problems. The true incidence of these problems is not known. We therefore assessed all admissions to our emergency department (ED) in direct relation to sexual intercourse. METHODS: All data were collected prospectively and entered into the ED's centralised electronic patient record database (Qualicare, Switzerland) and retrospectively analysed. The database was scanned for the standardised key words: 'sexual intercourse' (German 'Geschlechtsverkehr') or 'coitus' (German 'Koitus'). RESULTS: A total of 445 patients were available for further evaluation; 308 (69.0%) were male, 137 (31.0%) were female. The median age was 32 years (range 16-71) for male subjects and 30 years (range 16-70) for female subjects. Two men had cardiovascular emergencies. 46 (10.3%) of our patients suffered from trauma. Neurological emergencies occurred in 55 (12.4%) patients: the most frequent were headaches in 27 (49.0%), followed by subarachnoid haemorrhage (12, 22.0%) and transient global amnesia (11, 20.0%). 154 (97.0%) of the patients presenting with presumed infection actually had infections of the urogenital tract. The most common infection was urethritis (64, 41.0%), followed by cystitis (21, 13.0%) and epididymitis (19, 12.0%). A sexually transmitted disease (STD) was diagnosed in 43 (16.0%) of all patients presenting with a presumed infection. 118 (43.0%) of the patients with a possible infection requested testing for an STD because of unsafe sexual activity without underlying symptoms. CONCLUSIONS: Sexual activity is mechanically dangerous, potentially infectious and stressful for the cardiovascular system. Because information on ED presentation related to sexual intercourse is scarce, more efforts should be undertaken to document all such complications to improve treatment and preventative strategies.

782 consecutive construction work accidents: who is at risk? A 10-year analysis from a Swiss university hospital trauma unit

BACKGROUND: Mortality and morbidity are particularly high in the building industry. The annual rate of non-fatal occupational accidents in Switzerland is 1,133 per 100,000 inhabitants. METHODS: Retrospective analysis of the electronic database of a university emergency centre. Between 2001 and 2011, 782 occupational accidents to construction workers were recorded and analysed using specific demographic and medical keywords. RESULTS: Most patients were aged 30-39 (30.4%). 66.4% of the injured workers were foreigners. This is almost twice as high as the overall proportion of foreigners in Switzerland or in the Swiss labour market. 16% of the Swiss construction workers and 8% of the foreign construction workers suffered a severe injury with ISS >15. There was a trend for workers aged 60 and above to suffer an accident with a high ISS (p = 0.089). CONCLUSIONS: As in other European countries, most patients were in their thirties. Older construction workers suffered fewer injuries, although these tended to be more severe. The injuries were evenly distributed through the working days of the week. A special effort should be made that current health and safety measures are understood and applied by foreign and older construction workers.

Additive Hazards Models with Latent Treatment Effectiveness Lag Time

In many clinical trials to evaluate treatment efficacy, it is believed that there may exist latent treatment effectiveness lag times after which medical procedure or chemical compound would be in full effect. In this article, semiparametric regression models are proposed and studied to estimate the treatment effect accounting for such latent lag times. The new models take advantage of the invariance property of the additive hazards model in marginalizing over random effects, so parameters in the models are easy to be estimated and interpreted, while the flexibility without specifying baseline hazard function is kept. Monte Carlo simulation studies demonstrate the appropriateness of the proposed semiparametric estimation procedure. Data collected in the actual randomized clinical trial, which evaluates the effectiveness of biodegradable carmustine polymers for treatment of recurrent brain tumors, are analyzed.

MODELING DIFFERENTIATED TREATMENT EFFECTS FOR MULTIPLE OUTCOMES DATA

Multiple outcomes data are commonly used to characterize treatment effects in medical research, for instance, multiple symptoms to characterize potential remission of a psychiatric disorder. Often either a global, i.e. symptom-invariant, treatment effect is evaluated. Such a treatment effect may over generalize the effect across the outcomes. On the other hand individual treatment effects, varying across all outcomes, are complicated to interpret, and their estimation may lose precision relative to a global summary. An effective compromise to summarize the treatment effect may be through patterns of the treatment effects, i.e. "differentiated effects." In this paper we propose a two-category model to differentiate treatment effects into two groups. A model fitting algorithm and simulation study are presented, and several methods are developed to analyze heterogeneity presenting in the treatment effects. The method is illustrated using an analysis of schizophrenia symptom data.

LEARNING FROM NEAR MISSES IN MEDICATION ERRORS: A BAYESIAN APPROACH

Medical errors originating in health care facilities are a significant source of preventable morbidity, mortality, and healthcare costs. Voluntary error report systems that collect information on the causes and contributing factors of medi- cal errors regardless of the resulting harm may be useful for developing effective harm prevention strategies. Some patient safety experts question the utility of data from errors that did not lead to harm to the patient, also called near misses. A near miss (a.k.a. close call) is an unplanned event that did not result in injury to the patient. Only a fortunate break in the chain of events prevented injury. We use data from a large voluntary reporting system of 836,174 medication errors from 1999 to 2005 to provide evidence that the causes and contributing factors of errors that result in harm are similar to the causes and contributing factors of near misses. We develop Bayesian hierarchical models for estimating the log odds of selecting a given cause (or contributing factor) of error given harm has occurred and the log odds of selecting the same cause given that harm did not occur. The posterior distribution of the correlation between these two vectors of log-odds is used as a measure of the evidence supporting the use of data from near misses and their causes and contributing factors to prevent medical errors. In addition, we identify the causes and contributing factors that have the highest or lowest log-odds ratio of harm versus no harm. These causes and contributing factors should also be a focus in the design of prevention strategies. This paper provides important evidence on the utility of data from near misses, which constitute the vast majority of errors in our data.

MODELING FUNCTIONAL DATA WITH SPATIALLY HETEROGENEOUS SHAPE CHARACTERISTICS

We propose a novel class of models for functional data exhibiting skewness or other shape characteristics that vary with spatial or temporal location. We use copulas so that the marginal distributions and the dependence structure can be modeled independently. Dependence is modeled with a Gaussian or t-copula, so that there is an underlying latent Gaussian process. We model the marginal distributions using the skew t family. The mean, variance, and shape parameters are modeled nonparametrically as functions of location. A computationally tractable inferential framework for estimating heterogeneous asymmetric or heavy-tailed marginal distributions is introduced. This framework provides a new set of tools for increasingly complex data collected in medical and public health studies. Our methods were motivated by and are illustrated with a state-of-the-art study of neuronal tracts in multiple sclerosis patients and healthy controls. Using the tools we have developed, we were able to find those locations along the tract most affected by the disease. However, our methods are general and highly relevant to many functional data sets. In addition to the application to one-dimensional tract profiles illustrated here, higher-dimensional extensions of the methodology could have direct applications to other biological data including functional and structural MRI.

A BAYESIAN HIERARCHICAL FRAMEWORK FOR SPATIAL MODELING OF fMRI DATA

Functional neuroimaging techniques enable investigations into the neural basis of human cognition, emotions, and behaviors. In practice, applications of functional magnetic resonance imaging (fMRI) have provided novel insights into the neuropathophysiology of major psychiatric,neurological, and substance abuse disorders, as well as into the neural responses to their treatments. Modern activation studies often compare localized task-induced changes in brain activity between experimental groups. One may also extend voxel-level analyses by simultaneously considering the ensemble of voxels constituting an anatomically defined region of interest (ROI) or by considering means or quantiles of the ROI. In this work we present a Bayesian extension of voxel-level analyses that offers several notable benefits. First, it combines whole-brain voxel-by-voxel modeling and ROI analyses within a unified framework. Secondly, an unstructured variance/covariance for regional mean parameters allows for the study of inter-regional functional connectivity, provided enough subjects are available to allow for accurate estimation. Finally, an exchangeable correlation structure within regions allows for the consideration of intra-regional functional connectivity. We perform estimation for our model using Markov Chain Monte Carlo (MCMC) techniques implemented via Gibbs sampling which, despite the high throughput nature of the data, can be executed quickly (less than 30 minutes). We apply our Bayesian hierarchical model to two novel fMRI data sets: one considering inhibitory control in cocaine-dependent men and the second considering verbal memory in subjects at high risk for Alzheimer’s disease. The unifying hierarchical model presented in this manuscript is shown to enhance the interpretation content of these data sets.

COVARIATE-ADJUSTED NONPARAMETRIC ANALYSIS OF MAGNETIC RESONANCE IMAGES USING MARKOV CHAIN MONTE CARLO

Permutation tests are useful for drawing inferences from imaging data because of their flexibility and ability to capture features of the brain that are difficult to capture parametrically. However, most implementations of permutation tests ignore important confounding covariates. To employ covariate control in a nonparametric setting we have developed a Markov chain Monte Carlo (MCMC) algorithm for conditional permutation testing using propensity scores. We present the first use of this methodology for imaging data. Our MCMC algorithm is an extension of algorithms developed to approximate exact conditional probabilities in contingency tables, logit, and log-linear models. An application of our non-parametric method to remove potential bias due to the observed covariates is presented.

NONLINEAR TUBE-FITTING FOR THE ANALYSIS OF ANATOMICAL AND FUNCTIONAL STRUCTURES

We are concerned with the estimation of the exterior surface of tube-shaped anatomical structures. This interest is motivated by two distinct scientific goals, one dealing with the distribution of HIV microbicide in the colon and the other with measuring degradation in white-matter tracts in the brain. Our problem is posed as the estimation of the support of a distribution in three dimensions from a sample from that distribution, possibly measured with error. We propose a novel tube-fitting algorithm to construct such estimators. Further, we conduct a simulation study to aid in the choice of a key parameter of the algorithm, and we test our algorithm with validation study tailored to the motivating data sets. Finally, we apply the tube-fitting algorithm to a colon image produced by single photon emission computed tomography (SPECT)and to a white-matter tract image produced using diffusion tensor `imaging (DTI).

Analyzing Bivariate Survival Data with Interval Sampling and Application to Cancer Epidemiology

In medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. In cancer studies interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme where the first failure event (cancer onset) is identified within a calendar time interval, the time of the initiating event (birth) can be retrospectively confirmed, and the occurrence of the second event (death) is observed sub ject to right censoring. To analyze this type of bivariate failure time data, it is important to recognize the presence of bias arising due to interval sampling. In this paper, nonparametric and semiparametric methods are developed to analyze the bivariate survival data with interval sampling under stationary and semi-stationary conditions. Numerical studies demonstrate the proposed estimating approaches perform well with practical sample sizes in different simulated models. We apply the proposed methods to SEER ovarian cancer registry data for illustration of the methods and theory.