Diagnostic computed tomography (CT) scans provide numerous opportunities for body composition analysis including quantification of abdominal circumference, abdominal adipose tissues (subcutaneous, visceral and intermuscular) and skeletal muscle (SM). CT scans are commonly performed for diagnostic purposes in clinical settings and methods for estimating abdominal circumference and whole-body SM mass from them have been reported. A supine abdominal circumference is a valid measure of waist circumference (WC). The valid correlation between a single cross sectional CT image (slice) at third lumbar (L3) for abdominal SM and whole body SM is also well established. Sarcopenia refers to the age-associated decreased in muscle mass and function. A single dimensional definition of sarcopenia using CT images that includes only assessment of low whole body SM has been validated in clinical populations and significantly associated with negative outcomes. However, despite the availability and precision of SM data from CT scans and the relationship between these measurements and clinical outcomes, they have not become a routine component of clinical nutrition assessment. Lack of time, training, and expense are potential barriers that prevent clinicians from fully embracing this technique. This tutorial presents a systematic, step-by-step guide to quickly quantify abdominal circumference as a proxy for WC and SM using a cross-sectional CT image from a regional diagnostic CT scan for clinical identification of sarcopenia. Multiple software options are available, however this tutorial utilizes ImageJ, a free public domain software developed by the National Institutes of Health (NIH).
The evaluation of the cost and health implications of agreeing to cover a new health technology is best accomplished using a model that mathematically combines inputs from various sources, together with assumptions about how these fit together and what might happen in reality. This need to make assumptions, the complexity of the resulting framework, the technical knowledge required, as well as funding by interested parties have led many decision makers to distrust the results of models. To assist stakeholders reviewing a model's report, questions pertaining to the credibility of a model were developed. Because credibility is insufficient, questions regarding relevance of the model results were also created. The questions are formulated such that they are readily answered and they are supplemented by helper questions that provide additional detail. Some responses indicate strongly that a model should not be used for decision making: these trigger a "fatal flaw" indicator. It is hoped that the use of this questionnaire, along with the three others in the series, will help disseminate what to look for in comparative effectiveness evidence, improve practices by researchers supplying these data, and ultimately facilitate their use by health care decision makers.
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