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Applied Clinical Trials
Medical imaging is an important source of subject eligibility, drug efficacy, and safety data for many clinical trials.
Medical imaging is an important source of subject eligibility, drug efficacy and safety data for many clinical trials. This month, let’s look at a key image collection and review metric: MCC Imaging Metric v2.0 #17: the percentage of images that the imaging core lab deemed “non-evaluable” due to image quality problems.
Why this metric is important: Imaging is an important part of assessing subject eligibility as well as drug safety and efficacy. When images do not meet quality parameters, the images cannot be analyzed resulting in missing data. Corrective action is required to avoid additional imaging problems with other patients. Additionally, patients with the missing imaging data may need to be removed from the study, depending on protocol requirements.
Definition: To identify image quality problems in a study, look at the percentage of images received that the imaging core lab identifies as non-evaluable images.
How to calculate this metric:
The formula is the (number of non-evaluable images) divided by the (number of images reviewed) multipled by one hundred. Typically, this metric is calculated on a monthly basis.
In this example, we have a study that has 5 non-evaluable images out of 100 total images reviewed for a given month. If we follow the formula, the percentage of non-evaluable images is 5 percent.
10% is a reasonable target for this metric.
What you need in order to measure this: All you need to calculate this metric is the number of non-evaluable images and the number of total images reviewed in a given time frame (i.e. month)
Things that you can do to improve performance: Once you are tracking this metric, if studies are not meeting the target of 10%, you should consider the following:
Other metrics that you should consider in tandem with this metric include: MCC Imaging Metric v2.0 (9) image read process completion in accordance with the agreed upon turnaround time, (16) the proportion of images that have an acquisition parameter deviation but can be evaluated, and (18) baseline images that are non-evaluable.
Linda Sullivan, CEO, Metrics Champion Consortium