A modeling intervention in heart failure

BACKGROUND: Peak oxygen uptake (VO(2)) testing is commonly used to assess chronic heart failure (CHF) patients' exercise tolerance. The test requires maximal effort; however, many participants have low confidence (self-efficacy) to perform optimally. PURPOSE: This randomized controlled trial examined the effectiveness of a modeling intervention to increase Peak VO(2) (PVO(2)) and self-efficacy in people diagnosed with CHF. METHODS: Twenty participants with a diagnosis of CHF were randomized to either an intervention (modeling DVD) or a control group. Both groups completed a measure of self-efficacy prior to performing two PVO(2) tests, each separated by 7 days. After completing the first test (T1) the intervention group watched a 10-min coping model DVD. All participants returned 1 week later (T2) to complete identical study procedures. RESULTS: Analysis of covariance results showed that compared with the participants in the control group, those assigned to the modeling intervention had higher PVO(2) at T2, F (1, 19) = 4.38, p = 0.05, eta (2) = 0.21 and self-efficacy, F (1, 19) = 5.80, p < 0.05, eta (2) = 0.25. Only partial support was found for change in self-efficacy mediating treatment outcome (PVO(2)). CONCLUSIONS: Watching a modeling video is associated with increased PVO(2) and self-efficacy. These results have implications for testing patients in a clinical setting to maximize exercise tolerance test results.

DeepCare: a deep dynamic memory model for predictive medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.

Intervention-driven predictive framework for modeling healthcare data

Assessing prognostic risk is crucial to clinical care, and critically dependent on both diagnosis and medical interventions. Current methods use this augmented information to build a single prediction rule. But this may not be expressive enough to capture differential effects of interventions on prognosis. To this end, we propose a supervised, Bayesian nonparametric framework that simultaneously discovers the latent intervention groups and builds a separate prediction rule for each intervention group. The prediction rule is learnt using diagnosis data through a Bayesian logistic regression. For inference, we develop an efficient collapsed Gibbs sampler. We demonstrate that our method outperforms baselines in predicting 30-day hospital readmission using two patient cohorts - Acute Myocardial Infarction and Pneumonia. The significance of this model is that it can be applied widely across a broad range of medical prognosis tasks. © 2014 Springer International Publishing.

The Use of Video Self-Modeling as an Intervention to Teach Rules and Procedures to Students with Autism Spectrum Disorder

The Use of Video Self-Modeling as an Intervention to Teach Rules and Procedures to Students with Autism Spectrum Disorder.

Modeling height-diameter relationship in pinus pinaster ait. in the forest intervention zone of Lomba, NE-Portugal

In this work, the relationship between diameter at breast height (d) and total height (h) of individual-tree was modeled with the aim to establish provisory height-diameter (h-d) equations for maritime pine (Pinus pinaster Ait.) stands in the Lomba ZIF, Northeast Portugal. Using data collected locally, several local and generalized h-d equations from the literature were tested and adaptations were also considered. Model fitting was conducted by using usual nonlinear least squares (nls) methods. The best local and generalized models selected, were also tested as mixed models applying a first-order conditional expectation (FOCE) approximation procedure and maximum likelihood methods to estimate fixed and random effects. For the calibration of the mixed models and in order to be consistent with the fitting procedure, the FOCE method was also used to test different sampling designs. The results showed that the local h-d equations with two parameters performed better than the analogous models with three parameters. However a unique set of parameter values for the local model can not be used to all maritime pine stands in Lomba ZIF and thus, a generalized model including covariates from the stand, in addition to d, was necessary to obtain an adequate predictive performance. No evident superiority of the generalized mixed model in comparison to the generalized model with nonlinear least squares parameters estimates was observed. On the other hand, in the case of the local model, the predictive performance greatly improved when random effects were included. The results showed that the mixed model based in the local h-d equation selected is a viable alternative for estimating h if variables from the stand are not available. Moreover, it was observed that it is possible to obtain an adequate calibrated response using only 2 to 5 additional h-d measurements in quantile (or random) trees from the distribution of d in the plot (stand). Balancing sampling effort, accuracy and straightforwardness in practical applications, the generalized model from nls fit is recommended. Examples of applications of the selected generalized equation to the forest management are presented, namely how to use it to complete missing information from forest inventory and also showing how such an equation can be incorporated in a stand-level decision support system that aims to optimize the forest management for the maximization of wood volume production in Lomba ZIF maritime pine stands.

Modeling the impact of potential vaccines on epidemics of sexually transmitted chlamydia trachomatis infection

Background. We investigated the likely impact of vaccines on the prevalence of and morbidity due to Chlamydia trachomatis (chlamydia) infections in heterosexual populations. Methods.An individual‐based mathematical model of chlamydia transmission was developed and linked to the infection course in chlamydia‐infected individuals. The model describes the impact of a vaccine through its effect on the chlamydial load required to infect susceptible individuals (the “critical load”), the load in infected individuals, and their subsequent infectiousness. The model was calibrated using behavioral, biological, and clinical data. Results.A fully protective chlamydia vaccine administered before sexual debut can theoretically eliminate chlamydia epidemics within 20 years. Partially effective vaccines can still greatly reduce the incidence of chlamydia infection. Vaccines should aim primarily to increase the critical load in susceptible individuals and secondarily to decrease the peak load and/or the duration of infection in vaccinated individuals who become infected. Vaccinating both sexes has a beneficial impact on chlamydia‐related morbidity, but targeting women is more effective than targeting men. Conclusions.Our findings can be used in laboratory settings to evaluate vaccine candidates in animal models, by regulatory bodies in the promotion of candidates for clinical trials, and by public health authorities in deciding on optimal intervention strategies.

Identifying parent perspectives to guide active travel intervention with small populations : case study in Denver, Colorado

This paper investigates a strategy for guiding school-based active travel intervention. School-based active travel programs address the travel behaviors and perceptions of small target populations (i.e., at individual schools) so they can encourage people to walk or bike. Thus, planners need to know as much as possible about the behaviors and perceptions of their target populations. However, existing strategies for modeling travel behavior and segmenting audiences typically work with larger populations and may not capture the attitudinal diversity of smaller groups. This case study used Q technique to identify salient travel-related attitude types among parents at an elementary school in Denver, Colorado; 161 parents presented their perspectives about school travel by rank-ordering 36 statements from strongly disagree to strongly agree in a normalized distribution, single centered around no opinion. Thirty-nine respondents' cases were selected for case-wise cluster analysis in SPSS according to criteria that made them most likely to walk: proximity to school, grade, and bus service. Analysis revealed five core perspectives that were then correlated with the larger respondent pool: optimistic walkers, fair-weather walkers, drivers of necessity, determined drivers, and fence sitters. Core perspectives are presented—characterized by parents' opinions, personal characteristics, and reported travel behaviors—and recommendations are made for possible intervention approaches. The study concludes that Q technique provides a fine-grained assessment of travel behavior for small populations, which would benefit small-scale behavioral interventions

Nutritional intervention incorporating expedited 10g protein counter (EP-10) to improve the albumin and transferrin of chronic hemodialysis patients

Objective: The expedited 10g protein counter (EP-10) is a quick and valid clinical tool for dietary protein quantification. This study aims to assess the clinical effectiveness of the EP-10 in improving serum albumin and transferrin in chronic hemodialysis patients. Methods: Forty-five patients with low serum albumin (< 38 g /L) were enrolled in this study. Parameters measured included dry weight, height, dietary intake, and levels of serum albumin, transferrin, potassium, phosphate and kinetic modeling (Kt/v). The nutritional intervention incorporated the EP-10 in two ways (1)lto quantify protein intake of patients and (2)ito educate patients to meet their protein requirements. Mean values of the nutritional parameters before and after intervention were compared using paired t-test. Results: Three months after nutritional intervention, mean albumin levels increased significantly from 32.2+4.8g/L to 37.0+3.2g/L (p<0.001). Thirty-eight (84%) patients showed an increase in albumin levels while two (4%) maintained their levels. Of the thirty-six (80%) patients with low transferrin levels (<200 mg/dL), 28 (78%) had an increase and two maintained their levels post-intervention. Mean transferrin levels increased significantly from 169.4+39.9mg/dL to 180.9+38.1mg/dL (p< 0.05). Conclusion: Nutritional intervention incorporating the EP-10 method is able to make significant improvements to albumin and transferrin levels of chronic hemodialysis patients.

Current trends in modeling hostpathogen interactions

The rapid emergence of infectious diseases calls for immediate attention to determine practical solutions for intervention strategies. To this end, it becomes necessary to obtain a holistic view of the complex hostpathogen interactome. Advances in omics and related technology have resulted in massive generation of data for the interacting systems at unprecedented levels of detail. Systems-level studies with the aid of mathematical tools contribute to a deeper understanding of biological systems, where intuitive reasoning alone does not suffice. In this review, we discuss different aspects of hostpathogen interactions (HPIs) and the available data resources and tools used to study them. We discuss in detail models of HPIs at various levels of abstraction, along with their applications and limitations. We also enlist a few case studies, which incorporate different modeling approaches, providing significant insights into disease. (c) 2013 Wiley Periodicals, Inc.

Current trends in modeling hostpathogen interactions

The rapid emergence of infectious diseases calls for immediate attention to determine practical solutions for intervention strategies. To this end, it becomes necessary to obtain a holistic view of the complex hostpathogen interactome. Advances in omics and related technology have resulted in massive generation of data for the interacting systems at unprecedented levels of detail. Systems-level studies with the aid of mathematical tools contribute to a deeper understanding of biological systems, where intuitive reasoning alone does not suffice. In this review, we discuss different aspects of hostpathogen interactions (HPIs) and the available data resources and tools used to study them. We discuss in detail models of HPIs at various levels of abstraction, along with their applications and limitations. We also enlist a few case studies, which incorporate different modeling approaches, providing significant insights into disease. (c) 2013 Wiley Periodicals, Inc.

Development of a Complex Intervention for Secondary Prevention of Coronary Heart Disease in Primary Care Using the UK Medical Research Council Framework

Objective: To apply the UK Medical Research Council (MRC) framework for development and evaluation of trials of complex interventions to a primary healthcare intervention to promote secondary prevention of coronary heart disease. Study Design: Case report of intervention development. Methods: First, literature relating to secondary prevention and lifestyle change was reviewed. Second, a preliminary intervention was modeled, based on literature findings and focus group interviews with patients (n = 23) and staff (n = 29) from 4 general practices. Participants’ experiences of and attitudes toward key intervention components were explored. Third, the preliminary intervention was pilot-tested in 4 general practices. After delivery of the pilot intervention, practitioners evaluated the training sessions, and qualitative data relating to experiences of the intervention were collected using semistructured interviews with staff (n = 10) and patient focus groups (n = 17). Results: Literature review identified 3 intervention components: a structured recall system, practitioner training, and patient information. Initial qualitative data identified variations in recall system design, training requirements (medication prescribing, facilitating behavior change), and information appropriate to the prospective study participants. Identifying detailed structures within intervention components clarified how the intervention could be tailored to individual practice, practitioner, and patient needs while preserving the theoretical functions of the components. Findings from the pilot phase informed further modeling of the intervention, reducing administrative time, increasing practical content of training, and omitting unhelpful patient information. Conclusion: Application of the MRC framework helped to determine the feasibility and development of a complex intervention for primary care research.

Developing the specifications of an open angle glaucoma screening intervention in the United Kingdom: a Delphi approach.

Abstract
BACKGROUND:
Glaucoma is a leading cause of blindness. Early detection is advocated but there is insufficient evidence from randomized controlled trials (RCTs) to inform health policy on population screening. Primarily, there is no agreed screening intervention. For a screening programme, agreement is required on the screening tests to be used, either individually or in combination, the person to deliver the test and the location where testing should take place. This study aimed to use ophthalmologists (who were experienced glaucoma subspecialists), optometrists, ophthalmic nurses and patients to develop a reduced set of potential screening tests and testing arrangements that could then be explored in depth in a further study of their feasibility for evaluation in a glaucoma screening RCT.
METHODS:
A two-round Delphi survey involving 38 participants was conducted. Materials were developed from a prior evidence synthesis. For round one, after some initial priming questions in four domains, specialists were asked to nominate three screening interventions, the intervention being a combination of the four domains; target population, (age and higher risk groups), site, screening test and test operator (provider). More than 250 screening interventions were identified. For round two, responses were condensed into 72 interventions and each was rated by participants on a 0-10 scale in terms of feasibility.
RESULTS:
Using a cut-off of a median rating of feasibility of =5.5 as evidence of agreement of intervention feasibility, six interventions were identified from round 2. These were initiating screening at age 50, with a combination of two or three screening tests (varying combinations of tonometry/measures of visual function/optic nerve damage) organized in a community setting with an ophthalmic trained technical assistant delivering the tests. An alternative intervention was a 'glaucoma risk score' ascertained by questionnaire. The advisory panel recommended that further exploration of the feasibility of screening higher risk populations and detailed specification of the screening tests was required.
CONCLUSIONS:
With systematic use of expert opinions, a shortlist of potential screening interventions was identified. Views of users, service providers and cost-effectiveness modeling are now required to identify a feasible intervention to evaluate in a future glaucoma screening trial.

L'intervention psychosociale dans le sport pour développer des habiletés de vie chez des jeunes à risque de délinquance

Implanté en 1999 dans le quartier Centre-Sud à Montréal, le programme d'intervention psychosociale « Bien dans mes baskets » (BdmB) utilise le basketball comme outil pour entrer en contact avec des jeunes à risque de délinquance. Ce programme se distingue par le fait que ses entraîneurs-travailleurs-sociaux (ETS) interviennent tant dans le développement d'habiletés de vie que d’habiletés sportives. La présente étude vise à comprendre comment les expériences vécues au sein de BdmB ont pu contribuer au développement d’habiletés de vie chez les adolescents qui ont participé au programme lorsqu’ils étaient à l’école secondaire. Une méthodologie rétrospective qualitative a été utilisée pour cette étude. Des entretiens semi-dirigés ont été effectués auprès de 14 anciens athlètes-étudiants (AÉ) masculins qui ont participé à BdmB pendant leurs études secondaires. Le verbatim des entretiens enregistrés a été transcrit afin de procéder à une analyse de contenu par thématique. Les résultats suggèrent que quatre principaux facteurs semblent contribuer au développement d’habiletés de vie chez certains participants : le modelage de l’ETS a permis aux AÉ de reproduire les comportements de leur entraîneur; le développement d’un sentiment d’attachement entre les joueurs a engendré la création d’un nouveau réseau social dans lequel les AÉ ont pu trouver du soutien social; une culture d’équipe influencée par la philosophie de BdmB semble avoir été intériorisée par les joueurs; et BdmB a agi dans certains cas comme facteur de protection contre des influences externes négatives. Les résultats suggèrent que le sport collectif combiné à des interventions psychosociales favoriserait le développement d’habiletés de vie.

Modeling the interplay between land use, bioenergy production and climate change

Land use has become a force of global importance, considering that 34% of the Earth’s ice-free surface was covered by croplands or pastures in 2000. The expected increase in global human population together with eminent climate change and associated search for energy sources other than fossil fuels can, through land-use and land-cover changes (LUCC), increase the pressure on nature’s resources, further degrade ecosystem services, and disrupt other planetary systems of key importance to humanity. This thesis presents four modeling studies on the interplay between LUCC, increased production of biofuels and climate change in four selected world regions. In the first study case two new crop types (sugarcane and jatropha) are parameterized in the LPJ for managed Lands dynamic global vegetation model for calculation of their potential productivity. Country-wide spatial variation in the yields of sugarcane and jatropha incurs into substantially different land requirements to meet the biofuel production targets for 2015 in Brazil and India, depending on the location of plantations. Particularly the average land requirements for jatropha in India are considerably higher than previously estimated. These findings indicate that crop zoning is important to avoid excessive LUCC. In the second study case the LandSHIFT model of land-use and land-cover changes is combined with life cycle assessments to investigate the occurrence and extent of biofuel-driven indirect land-use changes (ILUC) in Brazil by 2020. The results show that Brazilian biofuels can indeed cause considerable ILUC, especially by pushing the rangeland frontier into the Amazonian forests. The carbon debt caused by such ILUC would result in no carbon savings (from using plant-based ethanol and biodiesel instead of fossil fuels) before 44 years for sugarcane ethanol and 246 years for soybean biodiesel. The intensification of livestock grazing could avoid such ILUC. We argue that such an intensification of livestock should be supported by the Brazilian biofuel sector, based on the sector’s own interest in minimizing carbon emissions. In the third study there is the development of a new method for crop allocation in LandSHIFT, as influenced by the occurrence and capacity of specific infrastructure units. The method is exemplarily applied in a first assessment of the potential availability of land for biogas production in Germany. The results indicate that Germany has enough land to fulfill virtually all (90 to 98%) its current biogas plant capacity with only cultivated feedstocks. Biogas plants located in South and Southwestern (North and Northeastern) Germany might face more (less) difficulties to fulfill their capacities with cultivated feedstocks, considering that feedstock transport distance to plants is a crucial issue for biogas production. In the fourth study an adapted version of LandSHIFT is used to assess the impacts of contrasting scenarios of climate change and conservation targets on land use in the Brazilian Amazon. Model results show that severe climate change in some regions by 2050 can shift the deforestation frontier to areas that would experience low levels of human intervention under mild climate change (such as the western Amazon forests or parts of the Cerrado savannas). Halting deforestation of the Amazon and of the Brazilian Cerrado would require either a reduction in the production of meat or an intensification of livestock grazing in the region. Such findings point out the need for an integrated/multicisciplinary plan for adaptation to climate change in the Amazon. The overall conclusions of this thesis are that (i) biofuels must be analyzed and planned carefully in order to effectively reduce carbon emissions; (ii) climate change can have considerable impacts on the location and extent of LUCC; and (iii) intensification of grazing livestock represents a promising venue for minimizing the impacts of future land-use and land-cover changes in Brazil.