Cost-effectiveness of paediatric central venous catheters in the UK:A secondary publication from the CATCH clinical trial

Background: Antibiotic-impregnated central venous catheters (CVCs) reduce the risk of bloodstream infections (BSIs) in patients treated in pediatric intensive care units (PICUs). However, it is unclear if they are cost-effective from the perspective of the National Health Service (NHS) in the UK.Methods: Economic evaluation alongside the CATCH trial (ISRCTN34884569) to estimate the incremental cost effectiveness ratio (ICER) of antibiotic-impregnated (rifampicin and minocycline), heparin-bonded and standard polyurethane CVCs. The 6-month costs of CVCs and hospital admissions and visits were determined from administrative hospital data and case report forms.Results: BSIs were detected in 3.59% (18/502) of patients randomized to standard, 1.44% (7/486) to antibiotic and 3.42% (17/497) to heparin CVCs. Lengths of hospital stay did not differ between intervention groups. Total mean costs (95% confidence interval) were: £45,663 (£41,647–£50,009) for antibiotic, £42,065 (£38,322–£46,110) for heparin, and £44,503 (£40,619–£48,666) for standard CVCs. As heparin CVCs were not clinically effective at reducing BSI rate compared to standard CVCs, they were considered not to be cost-effective. The ICER for antibiotic vs. standard CVCs, of £54,057 per BSI avoided, was sensitive to the analytical time horizon.Conclusions: Substituting standard CVCs for antibiotic CVCs in PICUs will result in reduced occurrence of BSI but there is uncertainty as to whether this would be a cost-effective strategy for the NHS

Cost-effectiveness analysis of adalimumab for the treatment of uveitis associated with Juvenile Idiopathic Arthritis

Purpose To investigate the cost effectiveness of adalimumab in combination with methotrexate, compared with methotrexate alone, for the management of uveitis associated with juvenile idiopathic arthritis (JIA). Design A cost-utility analysis based on a clinical trial and decision analytic model. Participants Children and adolescents 2 to 18 years of age with persistently active uveitis associated with JIA, despite optimized methotrexate treatment for at least 12 weeks. Methods The SYCAMORE (Randomised controlled trial of the clinical effectiveness, SafetY and Cost effectiveness of Adalimumab in combination with MethOtRExate for the treatment of juvenile idiopathic arthritis associated uveitis) trial (identifier, ISRCTN10065623) of methotrexate (up to 25 mg weekly) with or without fortnightly administered adalimumab (20 or 40 mg, according to body weight) provided data on resource use (based on patient self-report and electronic records) and health utilities (from the Health Utilities Index questionnaire). Surgical event rates and long-term outcomes were based on data from a 10-year longitudinal cohort. A Markov model was used to extrapolate the effects of treatment based on visual impairment. Main Outcome Measures Medical costs to the National Health Service in the United Kingdom, utility of defined health states, quality-adjusted life-years (QALYs), and incremental cost per QALY. Results Adalimumab in combination with methotrexate resulted in additional costs of £39 316, with a 0.30 QALY gain compared with methotrexate alone, resulting in an incremental cost-effectiveness ratio of £129 025 per QALY gained. The probability of cost effectiveness at a threshold of £30 000 per QALY was less than 1%. Based on a threshold analysis, a price reduction of 84% would be necessary for adalimumab to be cost effective. Conclusions Adalimumab is clinically effective in uveitis associated with JIA; however, its cost effectiveness is not demonstrated compared with methotrexate alone in the United Kingdom setting

Economic evaluation of a behavior-modifying intervention to enhance antiepileptic drug adherence

Between 35% and 50% of patients with epilepsy are reported to be not fully adherent to their medication schedule. We aimed to conduct an economic evaluation of strategies for improving adherence to antiepileptic drugs. Based on the findings of a systematic review, we identified an implementation intention intervention (specifying when, where, and how to act) which was tested in a trial that closely resembled current clinical management of patients with epilepsy and which measured adherence with an objective and least biased method. Using patient-level data, trial patients were matched with those recruited for the Standard and New Antiepileptic Drugs trial according to their clinical characteristics and adherence. Generalized linear models were used to adjust cost and utility in order to estimate the incremental cost per quality-adjusted life-year (QALY) gained from the perspective of the National Health Service in the UK. The mean cost of the intervention group, £1340 (95% CI: £1132, £1688), was marginally lower than that of the control group representing standard care, £1352 (95% CI: £1132, £1727). Quality-adjusted life-year values in the intervention group were higher than those in the control group, i.e., 0.75 (95% CI: 0.70, 0.79) compared with 0.74 (95% CI: 0.68, 0.79), resulting in a cost saving of £12 (€15, US$19) and with the intervention being dominant. The probability that the intervention is cost-effective at a threshold of £20,000 per QALY is 94%. Our analysis lends support to the cost-effectiveness of a self-directed, implementation intention intervention for improving adherence to antiepileptic drugs. However, as with any modeling dependent on limited data on efficacy, there is considerable uncertainty surrounding the clinical effectiveness of the intervention which would require a substantive trial for a more definitive conclusion

Random Subspace Ensembles for fMRI Classification

Abstract—Classification of brain images obtained through functional magnetic resonance imaging (fMRI) poses a serious challenge to pattern recognition and machine learning due to the extremely large feature-to-instance ratio. This calls for revision and adaptation of the current state-of-the-art classification methods. We investigate the suitability of the random subspace (RS) ensemble method for fMRI classification. RS samples from the original feature set and builds one (base) classifier on each subset. The ensemble assigns a class label by either majority voting or averaging of output probabilities. Looking for guidelines for setting the two parameters of the method—ensemble size and feature sample size—we introduce three criteria calculated through these parameters: usability of the selected feature sets, coverage of the set of “important ” features, and feature set diversity. Optimized together, these criteria work toward producing accurate and diverse individual classifiers. RS was tested on three fMRI datasets from single-subject experiments: the Haxby et al. data (Haxby, 2001.) and two datasets collected in-house. We found that RS with support vector machines (SVM) as the base classifier outperformed single classifiers as well as some of the most widely used classifier ensembles such as bagging, AdaBoost, random forest, and rotation forest. The closest rivals were the single SVM and bagging of SVM classifiers. We use kappa-error diagrams to understand the success of RS. Index Terms—Classifier ensembles, functional magnetic resonance imaging (fMRI) data analysis, multivariate methods, pattern recognition, random subspace (RS) method. I

Random subspace ensembles for FMRI classification,

Abstract-Classification of brain images obtained through functional magnetic resonance imaging (fMRI) poses a serious challenge to pattern recognition and machine learning due to the extremely large feature-to-instance ratio. This calls for revision and adaptation of the current state-of-the-art classification methods. We investigate the suitability of the random subspace (RS) ensemble method for fMRI classification. RS samples from the original feature set and builds one (base) classifier on each subset. The ensemble assigns a class label by either majority voting or averaging of output probabilities. Looking for guidelines for setting the two parameters of the method-ensemble size and feature sample size-we introduce three criteria calculated through these parameters: usability of the selected feature sets, coverage of the set of "important" features, and feature set diversity. Optimized together, these criteria work toward producing accurate and diverse individual classifiers. RS was tested on three fMRI datasets from single-subject experiments: the Haxby et al. data (Haxby, 2001.) and two datasets collected in-house. We found that RS with support vector machines (SVM) as the base classifier outperformed single classifiers as well as some of the most widely used classifier ensembles such as bagging, AdaBoost, random forest, and rotation forest. The closest rivals were the single SVM and bagging of SVM classifiers. We use kappa-error diagrams to understand the success of RS. Index Terms-Classifier ensembles, functional magnetic resonance imaging (fMRI) data analysis, multivariate methods, pattern recognition, random subspace (RS) method

6.走査型トンネル顕微鏡の試作(上智大学大学院理工学研究科物理学専攻,修士論文題目・アブストラクト(1989年度))

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