Intravenous insulin treatment in acute stroke: a systematic review and meta-analysis of randomized controlled trials.

INTRODUCTION: Poststroke hyperglycemia has been associated with unfavorable outcome. Several trials investigated the use of intravenous insulin to control hyperglycemia in acute stroke. This meta-analysis summarizes all available evidence from randomized controlled trials in order to assess its efficacy and safety. METHODS: We searched PubMed until 15/02/2013 for randomized clinical trials using the following search items: 'intravenous insulin' or 'hyperglycemia', and 'stroke'. Eligible studies had to be randomized controlled trials of intravenous insulin in hyperglycemic patients with acute stroke. Analysis was performed on intention-to-treat basis using the Peto fixed-effects method. The efficacy outcomes were mortality and favorable functional outcome. The safety outcomes were mortality, any hypoglycemia (symptomatic or asymptomatic), and symptomatic hypoglycemia. RESULTS: Among 462 potentially eligible articles, nine studies with 1491 patients were included in the meta-analysis. There was no statistically significant difference in mortality between patients who were treated with intravenous insulin and controls (odds ratio: 1.16, 95% confidence interval: 0.89-1.49). Similarly, the rate of favorable functional outcome was not statistically different (odds ratio: 1.01, 95% confidence interval: 0.81-1.26). The rates of any hypoglycemia (odds ratio: 8.19, 95% confidence interval: 5.60-11.98) and of symptomatic hypoglycemia (odds ratio: 6.15, 95% confidence interval: 1.88-20.15) were higher in patients treated with intravenous insulin. There was no heterogeneity across the included trials in any of the outcomes studied. CONCLUSIONS: This meta-analysis of randomized controlled trials does not support the use of intravenous insulin in hyperglycemic stroke patients to improve mortality or functional outcome. The risk of hypoglycemia is increased, however.

Risk factors for survival after lung metastasectomy in colorectal cancer patients: systematic review and meta-analysis.

ABSTRACT  Despite the lack of randomized trials, lung metastasectomy is currently proposed for colorectal cancer patients under certain conditions. Many retrospective studies have reported different prognostic factors of poorer survival, but eligibility for pulmonary metastasectomy remains determined by the complete resection of all pulmonary metastases. The aim of this review is to clarify which pre-operative risk factors reported in systematic reviews or meta-analysis are determinant for survival in colorectal metastatic patients. Different criteria have been now identified to select which patient will really benefit from lung metastasectomy.

Meta-analysis of gene-level associations for rare variants based on single-variant statistics.

Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying "causal" rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available.

Effectiveness of chronic obstructive pulmonary disease-management programs: systematic review and meta-analysis

BACKGROUND: Disease-management programs may enhance the quality of care provided to patients with chronic diseases, such as chronic obstructive pulmonary disease (COPD). The aim of this systematic review was to assess the effectiveness of COPD disease-management programs. METHODS: We conducted a computerized search of MEDLINE, EMBASE, CINAHL, PsychINFO, and the Cochrane Library (CENTRAL) for studies evaluating interventions meeting our operational definition of disease management: patient education, 2 or more different intervention components, 2 or more health care professionals actively involved in patients' care, and intervention lasting 12 months or more. Programs conducted in hospital only and those targeting patients receiving palliative care were excluded. Two reviewers evaluated 12,749 titles and fully reviewed 139 articles; among these, data from 13 studies were included and extracted. Clinical outcomes considered were all-cause mortality, lung function, exercise capacity (walking distance), health-related quality of life, symptoms, COPD exacerbations, and health care use. A meta-analysis of exercise capacity and all-cause mortality was performed using random-effects models. RESULTS: The studies included were 9 randomized controlled trials, 1 controlled trial, and 3 uncontrolled before-after trials. Results indicate that the disease-management programs studied significantly improved exercise capacity (32.2 m, 95% confidence interval [CI], 4.1-60.3), decreased risk of hospitalization, and moderately improved health-related quality of life. All-cause mortality did not differ between groups (pooled odds ratio 0.84, 95% CI, 0.54-1.40). CONCLUSION: COPD disease-management programs modestly improved exercise capacity, health-related quality of life, and hospital admissions, but not all-cause mortality. Future studies should explore the specific elements or characteristics of these programs that bring the greatest benefit.

Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

Diagnostic performance of &supl;⁸F-fluorodeoxyglucose positron emission tomography in giant cell arteritis: a systematic review and meta-analysis.

PURPOSE: The aim of this study was to conduct a systematic review and perform a meta-analysis on the diagnostic performances of (18)F-fluorodeoxyglucose positron emission tomography (FDG PET) for giant cell arteritis (GCA), with or without polymyalgia rheumatica (PMR). METHODS: MEDLINE, Embase and the Cochrane Library were searched for articles in English that evaluated FDG PET in GCA or PMR. All complete studies were reviewed and qualitatively analysed. Studies that fulfilled the three following criteria were included in a meta-analysis: (1) FDG PET used as a diagnostic tool for GCA and PMR; (2) American College of Rheumatology and Healey criteria used as the reference standard for the diagnosis of GCA and PMR, respectively; and (3) the use of a control group. RESULTS: We found 14 complete articles. A smooth linear or long segmental pattern of FDG uptake in the aorta and its main branches seems to be a characteristic pattern of GCA. Vessel uptake that was superior to liver uptake was considered an efficient marker for vasculitis. The meta-analysis of six selected studies (101 vasculitis and 182 controls) provided the following results: sensitivity 0.80 [95% confidence interval (CI) 0.63-0.91], specificity 0.89 (95% CI 0.78-0.94), positive predictive value 0.85 (95% CI 0.62-0.95), negative predictive value 0.88 (95% CI 0.72-0.95), positive likelihood ratio 6.73 (95% CI 3.55-12.77), negative likelihood ratio 0.25 (95% CI 0.13-0.46) and accuracy 0.84 (95% CI 0.76-0.90). CONCLUSION: We found overall valuable diagnostic performances for FDG PET against reference criteria. Standardized FDG uptake criteria are needed to optimize these diagnostic performances.

Electrical neuroimaging of memory discrimination based on single-trial multisensory learning.

Multisensory experiences influence subsequent memory performance and brain responses. Studies have thus far concentrated on semantically congruent pairings, leaving unresolved the influence of stimulus pairing and memory sub-types. Here, we paired images with unique, meaningless sounds during a continuous recognition task to determine if purely episodic, single-trial multisensory experiences can incidentally impact subsequent visual object discrimination. Psychophysics and electrical neuroimaging analyses of visual evoked potentials (VEPs) compared responses to repeated images either paired or not with a meaningless sound during initial encounters. Recognition accuracy was significantly impaired for images initially presented as multisensory pairs and could not be explained in terms of differential attention or transfer of effects from encoding to retrieval. VEP modulations occurred at 100-130ms and 270-310ms and stemmed from topographic differences indicative of network configuration changes within the brain. Distributed source estimations localized the earlier effect to regions of the right posterior temporal gyrus (STG) and the later effect to regions of the middle temporal gyrus (MTG). Responses in these regions were stronger for images previously encountered as multisensory pairs. Only the later effect correlated with performance such that greater MTG activity in response to repeated visual stimuli was linked with greater performance decrements. The present findings suggest that brain networks involved in this discrimination may critically depend on whether multisensory events facilitate or impair later visual memory performance. More generally, the data support models whereby effects of multisensory interactions persist to incidentally affect subsequent behavior as well as visual processing during its initial stages.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.