Do self-reported intentions predict clinicians behaviour: a systematic review.

Background: Implementation research is the scientific study of methods to promote the systematic uptake of clinical research findings into routine clinical practice. Several interventions have been shown to be effective in changing health care professionals' behaviour, but heterogeneity within interventions, targeted behaviours, and study settings make generalisation difficult. Therefore, it is necessary to identify the 'active ingredients' in professional behaviour change strategies. Theories of human behaviour that feature an individual's "intention" to do something as the most immediate predictor of their behaviour have proved to be useful in non-clinical populations. As clinical practice is a form of human behaviour such theories may offer a basis for developing a scientific rationale for the choice of intervention to use in the implementation of new practice. The aim of this review was to explore the relationship between intention and behaviour in clinicians and how this compares to the intention-behaviour relationship in studies of non-clinicians. Methods: We searched: PsycINFO, MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, Science/Social science citation index, Current contents (social & behavioural med/clinical med), ISI conference proceedings, and Index to Theses. The reference lists of all included papers were checked manually. Studies were eligible for inclusion if they had: examined a clinical behaviour within a clinical context, included measures of both intention and behaviour, measured behaviour after intention, and explored this relationship quantitatively. All titles and abstracts retrieved by electronic searching were screened independently by two reviewers, with disagreements resolved by discussion. Discussion: Ten studies were found that examined the relationship between intention and clinical behaviours in 1623 health professionals. The proportion of variance in behaviour explained by intention was of a similar magnitude to that found in the literature relating to non-health professionals. This was more consistently the case for studies in which intention-behaviour correspondence was good and behaviour was self-reported. Though firm conclusions are limited by a smaller literature, our findings are consistent with that of the non-health professional literature. This review, viewed in the context of the larger populations of studies, provides encouragement for the contention that there is a predictable relationship between the intentions of a health professional and their subsequent behaviour. However, there remain significant methodological challenges

Minimum sample size for external validation of a clinical prediction model with a binary outcome

In prediction model research, external validation is needed to examine an existing model's performance using data independent to that for model development. Current external validation studies often suffer from small sample sizes and consequently imprecise predictive performance estimates. To address this, we propose how to determine the minimum sample size needed for a new external validation study of a prediction model for a binary outcome. Our calculations aim to precisely estimate calibration (Observed/Expected and calibration slope), discrimination (C-statistic), and clinical utility (net benefit). For each measure, we propose closed-form and iterative solutions for calculating the minimum sample size required. These require specifying: (i) target SEs (confidence interval widths) for each estimate of interest, (ii) the anticipated outcome event proportion in the validation population, (iii) the prediction model's anticipated (mis)calibration and variance of linear predictor values in the validation population, and (iv) potential risk thresholds for clinical decision-making. The calculations can also be used to inform whether the sample size of an existing (already collected) dataset is adequate for external validation. We illustrate our proposal for external validation of a prediction model for mechanical heart valve failure with an expected outcome event proportion of 0.018. Calculations suggest at least 9835 participants (177 events) are required to precisely estimate the calibration and discrimination measures, with this number driven by the calibration slope criterion, which we anticipate will often be the case. Also, 6443 participants (116 events) are required to precisely estimate net benefit at a risk threshold of 8%. Software code is provided.</p

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

<p>Abstract</p> <p>Background</p> <p>Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications.</p> <p>Methods</p> <p>Models based on Bayes rule, <it>k-</it>nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view.</p> <p>Results</p> <p>Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. <it>k</it>-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical.</p> <p>Conclusion</p> <p>Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU.</p

A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay

<p>Abstract</p> <p>Background</p> <p>Patients with a prolonged intensive care unit (ICU) length of stay account for a disproportionate amount of resource use. Early identification of patients at risk for a prolonged length of stay can lead to quality enhancements that reduce ICU stay. This study developed and validated a model that identifies patients at risk for a prolonged ICU stay.</p> <p>Methods</p> <p>We performed a retrospective cohort study of 343,555 admissions to 83 ICUs in 31 U.S. hospitals from 2002-2007. We examined the distribution of ICU length of stay to identify a threshold where clinicians might be concerned about a prolonged stay; this resulted in choosing a 5-day cut-point. From patients remaining in the ICU on day 5 we developed a multivariable regression model that predicted remaining ICU stay. Predictor variables included information gathered at admission, day 1, and ICU day 5. Data from 12,640 admissions during 2002-2005 were used to develop the model, and the remaining 12,904 admissions to internally validate the model. Finally, we used data on 11,903 admissions during 2006-2007 to externally validate the model.</p> <p>Results</p> <p>The variables that had the greatest impact on remaining ICU length of stay were those measured on day 5, not at admission or during day 1. Mechanical ventilation, PaO₂: FiO₂ratio, other physiologic components, and sedation on day 5 accounted for 81.6% of the variation in predicted remaining ICU stay. In the external validation set observed ICU stay was 11.99 days and predicted total ICU stay (5 days + day 5 predicted remaining stay) was 11.62 days, a difference of 8.7 hours. For the same patients, the difference between mean observed and mean predicted ICU stay using the APACHE day 1 model was 149.3 hours. The new model's r²was 20.2% across individuals and 44.3% across units.</p> <p>Conclusions</p> <p>A model that uses patient data from ICU days 1 and 5 accurately predicts a prolonged ICU stay. These predictions are more accurate than those based on ICU day 1 data alone. The model can be used to benchmark ICU performance and to alert physicians to explore care alternatives aimed at reducing ICU stay.</p

An Assessment of the Effectiveness of High Definition Cameras as Remote Monitoring Tools for Dolphin Ecology Studies.

Research involving marine mammals often requires costly field programs. This paper assessed whether the benefits of using cameras outweighs the implications of having personnel performing marine mammal detection in the field. The efficacy of video and still cameras to detect Indo-Pacific bottlenose dolphins (Tursiops aduncus) in the Fremantle Harbour (Western Australia) was evaluated, with consideration on how environmental conditions affect detectability. The cameras were set on a tower in the Fremantle Port channel and videos were perused at 1.75 times the normal speed. Images from the cameras were used to estimate position of dolphins at the water’s surface. Dolphin detections ranged from 5.6 m to 463.3 m for the video camera, and from 10.8 m to 347.8 m for the still camera. Detection range showed to be satisfactory when compared to distances at which dolphins would be detected by field observers. The relative effect of environmental conditions on detectability was considered by fitting a Generalised Estimation Equations (GEEs) model with Beaufort, level of glare and their interactions as predictors and a temporal auto-correlation structure. The best fit model indicated level of glare had an effect, with more intense periods of glare corresponding to lower occurrences of observed dolphins. However this effect was not large (-0.264) and the parameter estimate was associated with a large standard error (0.113).The limited field of view was the main restraint in that cameras can be only applied to detections of animals observed rather than counts of individuals. However, the use of cameras was effective for long term monitoring of occurrence of dolphins, outweighing the costs and reducing the health and safety risks to field personal. This study showed that cameras could be effectively implemented onshore for research such as studying changes in habitat use in response to development and construction activities

Differential neuromuscular training effects onACL injury risk factors in"high-risk" versus "low-risk" athletes

<p>Abstract</p> <p>Background</p> <p>Neuromuscular training may reduce risk factors that contribute to ACL injury incidence in female athletes. Multi-component, ACL injury prevention training programs can be time and labor intensive, which may ultimately limit training program utilization or compliance. The purpose of this study was to determine the effect of neuromuscular training on those classified as "high-risk" compared to those classified as "low-risk." The hypothesis was that high-risk athletes would decrease knee abduction moments while low-risk and control athletes would not show measurable changes.</p> <p>Methods</p> <p>Eighteen high school female athletes participated in neuromuscular training 3×/week over a 7-week period. Knee kinematics and kinetics were measured during a drop vertical jump (DVJ) test at pre/post training. External knee abduction moments were calculated using inverse dynamics. Logistic regression indicated maximal sensitivity and specificity for prediction of ACL injury risk using external knee abduction (25.25 Nm cutoff) during a DVJ. Based on these data, 12 study subjects (and 4 controls) were grouped into the high-risk (knee abduction moment >25.25 Nm) and 6 subjects (and 7 controls) were grouped into the low-risk (knee abduction <25.25 Nm) categories using mean right and left leg knee abduction moments. A mixed design repeated measures ANOVA was used to determine differences between athletes categorized as high or low-risk.</p> <p>Results</p> <p>Athletes classified as high-risk decreased their knee abduction moments by 13% following training (Dominant pre: 39.9 ± 15.8 Nm to 34.6 ± 9.6 Nm; Non-dominant pre: 37.1 ± 9.2 to 32.4 ± 10.7 Nm; p = 0.033 training X risk factor interaction). Athletes grouped into the low-risk category did not change their abduction moments following training (p > 0.05). Control subjects classified as either high or low-risk also did not significantly change from pre to post-testing.</p> <p>Conclusion</p> <p>These results indicate that "high-risk" female athletes decreased the magnitude of the previously identified risk factor to ACL injury following neuromuscular training. However, the mean values for the high-risk subjects were not reduced to levels similar to low-risk group following training. Targeting female athletes who demonstrate high-risk knee abduction loads during dynamic tasks may improve efficacy of neuromuscular training. Yet, increased training volume or more specific techniques may be necessary for high-risk athletes to substantially decrease ACL injury risk.</p

DNA hypermethylation markers of poor outcome in laryngeal cancer

This study examined molecular (DNA hypermethylation), clinical, histopathological, demographical, smoking, and alcohol variables to assess diagnosis (early versus late stage) and prognosis (survival) outcomes in a retrospective primary laryngeal squamous cell carcinoma (LSCC) cohort. The study cohort of 79 primary LSCC was drawn from a multi-ethnic (37% African American), primary care patient population, diagnosed by surgical biopsies in the Henry Ford Health System from 1991 to 2004 and followed from 5 to 18 years (through 2009). Of the 41 variables, univariate risk factors of p < 0.10 were tested in multivariate models (logistic regression (diagnosis) and Cox (survival) models (p < 0.05)). Aberrant methylation of estrogen receptor 1 (ESR1; p = 0.01), race as African American (p = 0.04), and tumor necrosis (extensive; p = 0.02) were independent predictors of late stage LSCC. Independent predictors of poor survival included presence of vascular invasion (p = 0.0009), late stage disease (p = 0.03), and methylation of the hypermethylated in cancer 1 (HIC1) gene (p = 0.0002). Aberrant methylation of ESR1 and HIC1 signified independent markers of poorer outcome. In this multi-ethnic, primary LSCC cohort, race remained a predictor of late stage disease supporting disparate diagnosis outcomes for African American patients with LSCC

The IGNITE (investigation to guide new insight into translational effectiveness) trial: Protocol for a translational study of an evidenced-based wellness program in fire departments

<p>Abstract</p> <p>Background</p> <p>Worksites are important locations for interventions to promote health. However, occupational programs with documented efficacy often are not used, and those being implemented have not been studied. The research in this report was funded through the American Reinvestment and Recovery Act Challenge Topic 'Pathways for Translational Research,' to define and prioritize determinants that enable and hinder translation of evidenced-based health interventions in well-defined settings.</p> <p>Methods</p> <p>The IGNITE (investigation to guide new insights for translational effectiveness) trial is a prospective cohort study of a worksite wellness and injury reduction program from adoption to final outcomes among 12 fire departments. It will employ a mixed methods strategy to define a translational model. We will assess decision to adopt, installation, use, and outcomes (reach, individual outcomes, and economic effects) using onsite measurements, surveys, focus groups, and key informant interviews. Quantitative data will be used to define the model and conduct mediation analysis of each translational phase. Qualitative data will expand on, challenge, and confirm survey findings and allow a more thorough understanding and convergent validity by overcoming biases in qualitative and quantitative methods used alone.</p> <p>Discussion</p> <p>Findings will inform worksite wellness in fire departments. The resultant prioritized influences and model of effective translation can be validated and manipulated in these and other settings to more efficiently move science to service.</p