Concept: Decision analysis
While associations between specific risk factors and subsequent suicidal thoughts or behaviours have been widely examined, there is limited understanding of the interplay between risk factors in the development of suicide risk. This study used a decision tree approach to develop individual models of suicide risk and identify the risk factors for suicidality that are important for different subpopulations.
Epilepsy is a global disease with considerable incidence due to recurrent unprovoked seizures. These seizures can be noninvasively diagnosed using electroencephalogram (EEG), a measure of neuronal electrical activity in brain recorded along scalp. EEG is highly nonlinear, nonstationary and non-Gaussian in nature. Nonlinear adaptive models such as empirical mode decomposition (EMD) provide intuitive understanding of information present in these signals. In this study a novel methodology is proposed to automatically classify EEG of normal, inter-ictal and ictal subjects using EMD decomposition. EEG decomposition using EMD yields few intrinsic mode functions (IMF), which are amplitude and frequency modulated (AM and FM) waves. Hilbert transform of these IMF provides AM and FM frequencies. Features such as spectral peaks, spectral entropy and spectral energy in each IMF are extracted and fed to decision tree classifier for automated diagnosis. In this work, we have compared the performance of classification using two types of decision trees (i) classification and regression tree (CART) and (ii) C4.5. We have obtained the highest average accuracy of 95.33%, average sensitivity of 98%, and average specificity of 97% using C4.5 decision tree classifier. The developed methodology is ready for clinical validation on large databases and can be deployed for mass screening.
BACKGROUND: The advent of endoscopic sphenopalatine artery ligation (ESPAL) for the control of posterior epistaxis provides an effective, low-morbidity treatment option. In the current practice algorithm, ESPAL is pursued after failure of posterior packing. Given the morbidity and limited effectiveness of posterior packing, we sought to determine the cost-effectiveness of first-line ESPAL compared to the current practice model. METHODS: A standard decision analysis model was constructed comparing first-line ESPAL and current practice algorithms. A literature search was performed to determine event probabilities and published Medicare data largely provided cost parameters. The primary outcomes were cost of treatment and resolution of epistaxis. One-way sensitivity analysis was performed for key parameters. RESULTS: Costs for the first-line ESPAL arm and the current practice arm were $6450 and $8246, respectively. One-way sensitivity analyses were performed for key variables including duration of packing. The baseline difference of $1796 in favor of the first-line ESPAL arm was increased to $6263 when the duration of nasal packing was increased from 3 to 5 days. Current practice was favored (cost savings of $437 per patient) if posterior packing duration was decreased from 3 to 2 days. CONCLUSION: This study demonstrates that ESPAL is cost-saving as first-line therapy for posterior epistaxis. Given the improved effectiveness and patient comfort of ESPAL compared to posterior packing, ESPAL should be offered as an initial treatment option for medically stable patients with posterior epistaxis.
The impending public health impact of Alzheimer’s disease is tremendous. Physical activity is a promising intervention for preventing and managing Alzheimer’s disease. However, there is a lack of evidence-based public health messaging to support this position. This paper describes the application of the Appraisal of Guidelines Research and Evaluation II (AGREE-II) principles to formulate an evidence-based message to promote physical activity for the purposes of preventing and managing Alzheimer’s disease.
Escalating drug prices have catalysed the generation of numerous “value frameworks” with the aim of informing payers, clinicians and patients on the assessment and appraisal process of new medicines for the purpose of coverage and treatment selection decisions. Although this is an important step towards a more inclusive Value Based Assessment (VBA) approach, aspects of these frameworks are based on weak methodologies and could potentially result in misleading recommendations or decisions. In this paper, a Multiple Criteria Decision Analysis (MCDA) methodological process, based on Multi Attribute Value Theory (MAVT), is adopted for building a multi-criteria evaluation model. A five-stage model-building process is followed, using a top-down “value-focused thinking” approach, involving literature reviews and expert consultations. A generic value tree is structured capturing decision-makers' concerns for assessing the value of new medicines in the context of Health Technology Assessment (HTA) and in alignment with decision theory. The resulting value tree (Advance Value Tree) consists of three levels of criteria (top level criteria clusters, mid-level criteria, bottom level sub-criteria or attributes) relating to five key domains that can be explicitly measured and assessed: (a) burden of disease, (b) therapeutic impact, © safety profile (d) innovation level and (e) socioeconomic impact. A number of MAVT modelling techniques are introduced for operationalising (i.e. estimating) the model, for scoring the alternative treatment options, assigning relative weights of importance to the criteria, and combining scores and weights. Overall, the combination of these MCDA modelling techniques for the elicitation and construction of value preferences across the generic value tree provides a new value framework (Advance Value Framework) enabling the comprehensive measurement of value in a structured and transparent way. Given its flexibility to meet diverse requirements and become readily adaptable across different settings, the Advance Value Framework could be offered as a decision-support tool for evaluators and payers to aid coverage and reimbursement of new medicines.
The present paper describes the results of a rating study performed by a group of European Union (EU) drug experts using the multi-criteria decision analysis model for evaluating drug harms.
Role of Reduced-Intensity Conditioning Allogeneic Hematopoietic Stem-Cell Transplantation in Older Patients With De Novo Myelodysplastic Syndromes: An International Collaborative Decision Analysis
- Journal of clinical oncology : official journal of the American Society of Clinical Oncology
- Published about 7 years ago
PURPOSEMyelodysplastic syndromes (MDS) are clonal hematopoietic disorders that are more common in patients aged ≥ 60 years and are incurable with conventional therapies. Reduced-intensity conditioning (RIC) allogeneic hematopoietic stem-cell transplantation is potentially curative but has additional mortality risk. We evaluated RIC transplantation versus nontransplantation therapies in older patients with MDS stratified by International Prognostic Scoring System (IPSS) risk. PATIENTS AND METHODSA Markov decision model with quality-of-life utility estimates for different MDS and transplantation states was assessed. Outcomes were life expectancy (LE) and quality-adjusted life expectancy (QALE). A total of 514 patients with de novo MDS aged 60 to 70 years were evaluated. Chronic myelomonocytic leukemia, isolated 5q- syndrome, unclassifiable, and therapy-related MDS were excluded. Transplantation using T-cell depletion or HLA-mismatched or umbilical cord donors was also excluded. RIC transplantation (n = 132) stratified by IPSS risk was compared with best supportive care for patients with nonanemic low/intermediate-1 IPSS (n = 123), hematopoietic growth factors for patients with anemic low/intermediate-1 IPSS (n = 94), and hypomethylating agents for patients with intermediate-2/high IPSS (n = 165).ResultsFor patients with low/intermediate-1 IPSS MDS, RIC transplantation LE was 38 months versus 77 months with nontransplantation approaches. QALE and sensitivity analysis did not favor RIC transplantation across plausible utility estimates. For intermediate-2/high IPSS MDS, RIC transplantation LE was 36 months versus 28 months for nontransplantation therapies. QALE and sensitivity analysis favored RIC transplantation across plausible utility estimates. CONCLUSIONFor patients with de novo MDS aged 60 to 70 years, favored treatments vary with IPSS risk. For low/intermediate-1 IPSS, nontransplantation approaches are preferred. For intermediate-2/high IPSS, RIC transplantation offers overall and quality-adjusted survival benefit.
Motivation deficits, such as apathy, are pervasive in both neurological and psychiatric diseases. Even when they are not the core symptom, they reduce quality of life, compromise functional outcome and increase the burden for caregivers. They are currently assessed with clinical scales that do not give any mechanistic insight susceptible to guide therapeutic intervention. Here, we present another approach that consists of phenotyping the behaviour of patients in motivation tests, using computational models. These formal models impose a precise and operational definition of motivation that is embedded in decision theory. Motivation can be defined as the function that orients and activates the behaviour according to two attributes: a content (the goal) and a quantity (the goal value). Decision theory offers a way to quantify motivation, as the cost that patients would accept to endure in order to get the benefit of achieving their goal. We then review basic and clinical studies that have investigated the trade-off between the expected cost entailed by potential actions and the expected benefit associated with potential rewards. These studies have shown that the trade-off between effort and reward involves specific cortical, subcortical and neuromodulatory systems, such that it may be shifted in particular clinical conditions, and reinstated by appropriate treatments. Finally, we emphasize the promises of computational phenotyping for clinical purposes. Ideally, there would be a one-to-one mapping between specific neural components and distinct computational variables and processes of the decision model. Thus, fitting computational models to patients' behaviour would allow inferring of the dysfunctional mechanism in both cognitive terms (e.g. hyposensitivity to reward) and neural terms (e.g. lack of dopamine). This computational approach may therefore not only give insight into the motivation deficit but also help personalize treatment.
- International journal of environmental research and public health
- Published almost 3 years ago
Few studies of walkability include both perceived and audited walkability measures. We examined perceived walkability (Neighborhood Environment Walkability Scale-Abbreviated, NEWS-A) and audited walkability (Irvine-Minnesota Inventory, IMI) measures for residents living within 2 km of a “complete street”-one renovated with light rail, bike lanes, and sidewalks. For perceived walkability, we found some differences but substantial similarity between our final scales and those in a prior published confirmatory factor analysis. Perceived walkability, in interaction with distance, was related to complete street active transportation. Residents were likely to have active transportation on the street when they lived nearby and perceived good aesthetics, crime safety, and traffic safety. Audited walkability, analyzed with decision trees, showed three general clusters of walkability areas, with 12 specific subtypes. A subset of walkability items (n = 11), including sidewalks, zebra-striped crosswalks, decorative sidewalks, pedestrian signals, and blank walls combined to cluster street segments. The 12 subtypes yielded 81% correct classification of residents' active transportation. Both perceived and audited walkability were important predictors of active transportation. For audited walkability, we recommend more exploration of decision tree approaches, given their predictive utility and ease of translation into walkability interventions.
Demonstrate the application of decision trees-classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs)-to understand structure in missing data.