Cluster to Improve Clinical Luster!

In today’s physical therapy world we have so many special tests that can be used for our patients that sometimes it may seem overwhelming.

I was late to my own party as there is already a special test named “Craig’s Test”. BUMMER! Have you ever wondered why there are so many special tests? Seriously, think about how many special tests there are for shoulder impingement (Hawkins Kennedy, Neer, Zaslav Test, Supine impingement, etc..).

It would not be advised to “justify” a potential tissue impairment by simply conducting a single test listed above and saying with confidence that a positive result YIELDS AN IMPAIRMENT. That idea is simply foolish and that is why there are so many of them available for us to use. Not to worry, there are ways to use “clusters of tests and clinical findings” to create a better clinical picture of the potential underlying injury. Clustering allows for more confidence with evidence-based research to objectify your clinical decisions. Prior to proceeding, we must understand some terminology first.

Sensitivity & Specificity Explained

Specificity (Sp) and Sensitivity (Sn) These two words can haunt some people for their entire college curriculum. They can be confusing to understand if you do not fully understand their meaning. So let’s take a look to help create some clarity. Let us start with sensitivity…..

The definition of Sensitivity (Sn) is the proportion of individuals that test positive for a disease who are thought to have the disease. Therefore highly sensitive tests detect TRUE POSITIVE results. If we find a negative result from a highly sensitive test we can have a high level of confidence this patient does not have the disease. When a test result comes back negative in this population we can RULE-OUT. SnOUT = If the negative result on highly sensitive (Sn) test we rule out (OUT). Sensitive tests are used as screening tools.

Specificity (Sp) refers to the proportion of individuals who are not believed to have the disease that tests negative for the disease. Highly specific tests are great at identifying those individuals who do NOT have the disease or people who are determined to be TRULY NEGATIVE. When a positive result comes back in this population we can determine these individuals are NOT NEGATIVE and therefore we can RULE-IN the diagnosis. SpIN = If positive result on highly specific (Sp) test we rule-in (IN). Specific tests are used as diagnostic tools.

What Does This Mean?

This may seem confusing but let us try to put it into a clinical example. A patient came into your clinic after sustaining an injury to their knee. Upon further questioning, it was determined that the mechanism of injury was non-contact compression of the knee with varus loading. The patient heard a clicking sound and immediately felt their knee wanting to give way. As a savvy therapist, you decide to use a cluster from the research to assess this clinical hypothesis. According to a study from Lowery et al, 2006 it was determined that the following cluster of findings can predict meniscal injury potential…

History of joint locking

Positive test result with McMurray Test

Positive for tenderness along the lateral joint line of the knee pain experienced with flexion overpressure of knee

Pain experienced with extension overpressure of the knee according to the study

When 3 of the above are found: 31% Sensitivity —- 90% SpecificityWhen 4 of the above are found: 17% Sensitivity —- 96% Specificity

When all 5 are found: 11% Sensitivity —- 99% SpecificityIn this instance we can say that there is a 99% chance to rule in meniscal tear if our patient above showed all of the symptoms above and tested positive for them. ****Remember SpIN = positive results on a highly specific test (99%) rule in! ****

To Wrap It All Up…

The example above highlights the importance of using evidence-based practice to improve clinical decision-making skills. In fact, a meta-analysis performed in 2007 found that the McMurray test, when used alone only, boasts (Sn) of 70% and (Sp) of 71%. If we solely used the McMurray test, the accuracy of our findings would be inferior to the findings from Lowery et al in 2006.

Always strive to find and utilize clusters of information. These clusters can be used to create optimal evidence-based decision-making in the clinic and support positive patient outcomes. There is a wealth of knowledge to be gained from many sources. Below I have listed a few that have helped me along the way (some free and some paid).