Asquini et al. (2022)

Predicting improvement in temporomandibular disorders following manual therapy

Predicting improvement in temporomandibular disorders is possible through assessment of 4 factors

These include pain during mouth opening, central sensitization measured with the CSI, treatment expectations and the number of pain locations

The clinical value of this prediction model should now be validated in an unrelated sample, but gives a good starting point to whether or not opt for manual therapy

Introduction

The aim of this study was to find factors predicting improvement in temporomandibular disorders. Finding factors related to treatment outcomes are relevant as this may guide which treatment should best be opted for. The effectiveness of manual therapy interventions for temporomandibular disorders has previously been demonstrated in several studies. Typically, improvements in pain symptoms may be expected over a course of a month. However beneficial, it is not known what treatments are superior and therefore this study that evaluates predictors for good outcomes following manual therapy may help the decision whether or not to opt for a manual therapy treatment in a certain patient.

 

Methods

To find which factors are predicting improvement in temporomandibular disorders, a prospective observational cohort study was conducted in an Italian dental hospital. Adults from the age of 18 years and on were eligible when they were diagnosed with a temporomandibular joint disorder according to the DC/TMD criteria. They hadn’t received an intervention for their disorder in the previous 6 months. 

A physiotherapist independently assessed each participant at baseline and after one month. During this period, four 20- to 30-minute manual therapy sessions were delivered at a rate of 1 per week. Manual therapy techniques were directed to the temporomandibular joints, temporal muscles, masseter muscles, pterygoid muscles and suprahyoid muscles. These sessions were given by 2 physiotherapists who had more than 5 years of experience with temporomandibular disorders and received special training. 

Pain intensity was the primary outcome of interest and was rated on the VAS for current pain, average pain and worst pain over the previous week. The minimal clinical important difference was 30% and improvements below this level were considered as poor outcomes. 

 

Results

In total, 120 participants were recruited and 90 of them completed the whole study. Two of the dropouts started taking NSAIDs, 1 was transferred for work and 9 had to cancel their participation due to COVID travel restrictions. 

The following factors can be used for predicting improvement in temporomandibular disorders according to the prediction model: pain during mouth opening, central sensitization measured with the CSI, treatment expectations and the number of pain locations. According to the authors, these predictors revealed high explained variance (R2 = 64%) and discrimination (AUC = 0.90).

predicting improvement in temporomandibular disorders
From: Asquini et al.,Musculoskelet Sci Pract (2022)

 

The predictors were used to develop a screening tool and this was constructed as a nomogram. By indicating the results of your baseline assessment, it is possible to calculate the probability of a good outcome in your patient who follows the intervention. 

predicting improvement in temporomandibular disorders
From: Asquini et al.,Musculoskelet Sci Pract (2022)

 

Questions and thoughts

So how can this tool help in predicting improvement in temporomandibular disorders? The following citation explains:

“If a patient reports a positive treatment expectation about MT they would receive 33 points for this predictor. This score is calculated by selecting the corresponding baseline value for the predictor (in this case: “Yes”) and determining the corresponding points on the “Points” line at the top of the plot. The “Total predictor points” value can be obtained if the same process is replicated for each predictor and each score is summed. Then, a vertical line is drawn from the “Total predictor points” line to the “Good outcome probability” line at the bottom of the plot to estimate the probability of good outcome.”

The plot itself is somewhat difficult to read as the line of points is not so clearly displayed. However, it may aid you in your prognostication to give you an idea whether manual therapy may be of value to offer this patient. You can see that a high CSI score (indicating the presence of central sensitization), together with a negative treatment expectation and more mouth opening pain than 2/10 will lead to a good outcome probability of less than 10%. Indeed, in patients with a component of central sensitization, better outcomes may be expected when their biopsychosocial context is considered, rather than focussing on biomechanical issues alone. To get to know more about this, I suggest you consider following Jo Nijs’ course with us! What I found particularly interesting is that rather than giving a standardized treatment, this study tried to personalize treatment and even though this study did not examine treatment effectiveness, I strongly encourage this way of care!

 

Talk nerdy to me

The selection of the possible predictors was based on previous research in the temporomandibular area, but also wider candidate predictors from altered pain modulation in musculoskeletal disorders were chosen. The set of predictors that was considered for the study thus included a wide variety of possible factors in the biopsychosocial environment. This

is especially important as we now know that musculoskeletal complaints do not only have a biomedical cause, and the fact that a wide range of possible factors was chosen is very informative. 

Predicting improvement in temporomandibular disorders is possible using the nomogram provided. However, it should be noted that the studied sample was composed from people attending a dental clinic and may be not directly generalizable to patients referred to physiotherapy. Something to bear in mind when looking at these results is that the factors predicting improvement in temporomandibular disorders had been identified and tested in the same sample. Better would have been that the prediction model was tested in a new sample of patients. This can however still be done, and it would be optimal if this new population consists of people of a wide spectrum of temporomandibular disorders.

The model has withstanded several checks, under which multicollinearity, cross-validation, internal validation. The Hosmer-Lemeshow was non-significant and this shows that this model has a good fit. The model had a relatively good explained variance, which means that the model can largely explain the observed dispersion of the data. But when the internal validation was done, the explained variance dropped to 40%, which is not so positive. I’m curious to see how this prediction model would perform in an unrelated set of people.

 

Take home messages

This cohort study gives an interesting insight into predictors of pain outcome following manual therapy for temporomandibular disorders. Therapy was administered through clinical reasoning, and this represents common practice. A nomogram was developed to find out the probability of success with manual therapy treatment of temporomandibular joint disorders. By assessing pain during mouth opening, central sensitization measured with the CSI, treatment expectations and the number of pain locations, you can get an idea of the probability of a good outcome. This should now be validated in an unrelated sample to determine the clinical value of the prediction model.

 

Reference

Asquini G, Devecchi V, Borromeo G, Viscuso D, Morato F, Locatelli M, Falla D. Predictors of pain reduction following a program of manual therapies for patients with temporomandibular disorders: A prospective observational study. Musculoskelet Sci Pract. 2022 Jul 31;62:102634. doi: 10.1016/j.msksp.2022.102634. Epub ahead of print. PMID: 35939919. https://pubmed.ncbi.nlm.nih.gov/35939919/

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