Modified Rankin Score-9Q (mRS-9Q)

Why did you develop the mRS-9Q? Was there a particular clinical experience or patient encounter that inspired you to create this tool for clinicians?

The modified Rankin Scale has become the standard way to measure clinical outcomes after a neurological event such as acute stroke, but determination of the mRS has long been plagued by issues with interobserver reliability and the need to use experts trained in its administration to obtain valid results.

We developed and validated the mRS-9Q as a simple way to improve the inter-observer reliability of the modified Rankin Scale (mRS) and to make it possible to determine without the need for trained experts.

The mRS-9Q can be used by medical office personnel, without the need for medically trained experts, and it also has been validated for telephone use.

What tips do you have for users of this tool? Do you know of cases when it has been applied, interpreted, or used inappropriately?

While the mRS-9Q has been found to determine the mRS with a high degree of interobserver reliability (https://pubmed.ncbi.nlm.nih.gov/22843133/), it should be understood that level of function after neurological events like stroke or traumatic brain injury can change over time, so the level function on the mRS determined at one point in time might change if reassessed later.

Are there any adjustments or updates you would make to the score based on new data or practice changes?

It is important to ask the questions in a language that the patient or their caregiver is comfortable with. Several translations have been added by research groups over the years to make the mRS-9Q more widely applicable.

Do you use this tool in your own clinical practice? Can you give an example of a scenario in which you use it?

The mRS-9Q is used to determine level of function after endovascular stroke treatment in the Comprehensive Stroke Centers in Kaiser Permanente Northern California, and many clinical trials have used the mRS-9Q for outcome determination (such as the TTM2 trial of hypothermia after cardiac arrest – https://www.nejm.org/doi/full/10.1056/NEJMoa2100591; https://www.resuscitationjournal.com/article/S0300-9572(20)30113-1/fulltext).

Alexander Flint, MD, PhD, is a neurologist and stroke specialist who practices via telemedicine in Northern California. His research focuses on stroke, neurological critical illness, and the use of ‘big data’ and machine learning in medicine. He was a co-founder of the medical imaging startup image32 (acquired by Citrix in 2015) and is currently working on novel technologies that enable machine learning and search directly on securely encoded datasets.