Revisiones sistemáticas de la literatura

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Abstract

Las Revisiones Sistemáticas son un diseño de investigación observacional y retrospectivo, que sintetiza los resultados de múltiples investigaciones primarias. Son parte esencial de la medicina basada en la evidencia por su rigurosa metodología, identificando los estudios relevantes para responder preguntas específicas de la práctica clínica. El término metanálisis se reserva para la combinación numérica de los datos. No todas las revisiones sistemáticas lo incluyen. Las revisiones sistemáticas son tan populares que han tenido un crecimiento vertiginoso en los últimos años. Este artículo revisará detenidamente la definición, las diferencias con las revisiones narrativas y el proceso para desarrollar e interpretar una revisión sistemática, incluyendo sus limitaciones.

Translated abstract

Systematic Review is a retrospective method of research and it has become increasing accepted. Systematic Reviews are an essential part of evidence-based medicine as rigorous methodology can identify all relevant papers that address specific research questions. Metanalysis is a statistical technique for combining the findings from independent studies to provide a summary effect estimate. Systematic Review has become popular with a dramatic rise in papers over the last years. This paper will review the process has led to the development a systematic review.

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Most cited references 34

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Does the inclusion of grey literature influence estimates of intervention effectiveness reported in meta-analyses?

Ngoc B. Pham, L. McAuley, P Tugwell … (2000)

The inclusion of only a subset of all available evidence in a meta-analysis may introduce biases and threaten its validity; this is particularly likely if the subset of included studies differ from those not included, which may be the case for published and grey literature (unpublished studies, with limited distribution). We set out to examine whether exclusion of grey literature, compared with its inclusion in meta-analysis, provides different estimates of the effectiveness of interventions assessed in randomised trials. From a random sample of 135 meta-analyses, we identified and retrieved 33 publications that included both grey and published primary studies. The 33 publications contributed 41 separate meta-analyses from several disease areas. General characteristics of the meta-analyses and associated studies and outcome data at the trial level were collected. We explored the effects of the inclusion of grey literature on the quantitative results using logistic-regression analyses. 33% of the meta-analyses were found to include some form of grey literature. The grey literature, when included, accounts for between 4.5% and 75% of the studies in a meta-analysis. On average, published work, compared with grey literature, yielded significantly larger estimates of the intervention effect by 15% (ratio of odds ratios=1.15 [95% CI 1.04-1.28]). Excluding abstracts from the analysis further compounded the exaggeration (1.33 [1.10-1.60]). The exclusion of grey literature from meta-analyses can lead to exaggerated estimates of intervention effectiveness. In general, meta-analysts should attempt to identify, retrieve, and include all reports, grey and published, that meet predefined inclusion criteria.

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Using numerical results from systematic reviews in clinical practice.

H J McQuay, R Moore (1997)

Systematic reviews summarize large amounts of information and are more likely than individual trials to describe the true clinical effect of an intervention. Traditional statistical outputs from systematic reviews cannot immediately be applied to clinical practice. The number needed to treat (NNT) has that clinical immediacy. This number can be calculated easily from raw data or from statistical outputs, and the principle involved in its calculation can be applied to different outcomes: treatment efficacy, adverse events (harm), or other end points. The NNT defines the treatment-specific effect of an intervention, and we suggest it as a currency for making decisions about individual patients. Knowing the NNT for different interventions that have the same outcome for the same disorder can help shape individual and institutional practice. Knowing or estimating the number needed to harm is also an important part of the equation. Knowing or estimating an individual patient's risk can, with the NNT, be a guide to the overall or net value of a prophylactic intervention. We advocate an approach to systematic reviews that distills information into, in effect, one number: the NNT. This is simple to remember and directly supports efforts to work with patients to make the best possible clinical decisions for their care.