WHAT IS TMD? CONNECTIONS TO CHRONIC PAIN Temporomandicular Disorder (TMD) is a chronic pain

disorder defined as extended pain in the orofacial region, particularly in the masticatory muscles or at least one temporomandibular joint.

Ranks second only to headache as the most likely clinical condition to cause craniofacial pain.

Patient treatment is largely restricted to symptomatic care

Other chronic pain disorders include migraine headaches and fibromyalgia, which are comorbid with TMD

Though this study was restricted to studying TMD, our study is relevant to all chronic pain disorders. By constructing a model to identify risk factors of TMD, we hope to identify the biological mechanisms controlling the body’s response to painful stimuli. Such information would ultimately change the way chronic pain is studied and treated.

OROFACIAL PAIN: PROSPECTIVE EVALUATION AND RISK ASSESSMENT (OPPERA) STUDY Purpose: Establish the causal determinants of

TMD pain. Prospective cohort design of 3200 people

who did not have TMD when enrolled as well as a case-control study that enrolled 200 TMD cases

Based on a model with enhanced pain sensitivity as an intermediate phenotypic risk factor for TMD development

Baseline assessment of pain sensitivity from quantitative sensory testing (QST), including thermal pain sensitivity measures

THERMAL PAIN SENSITIVITY TESTING-PROTOCOL

Heightened sensitivity to pain (including thermal pain) is believed to be a risk factor for TMD

Subjects were given a series of heat pulses with a thermode (a small metal block) and asked to report a pain rating between 0 and 100

3 trials with peak temperatures of 46°C, 48°C, 50°C

10 pulses for each stimuli, pulses applied at the same temperature successively

If subject rated their pain as 100, they were given the option to continue or end these tests.

Following the 10th pulse, the subject gave lingering pain rating at intervals of 15 and 30 seconds (referred to as aftersensations)

TEMPORAL SUMMATION AND CHRONIC PAIN

There are two risk factors for pain: overall sensitivity (measured by the first pulse) and temporal summation

The primary reason for the 10 consecutive pulses was to assess temporal summation, sometimes referred to as “windup”.

It is believed there is a difference between cases and controls in the amount of the subjects will increase their pain rating when exposed to repetitive painful stimuli. Subjects with a higher first pulse (overall sensitivity) are also more likely to be cases.

Though a common theory in the pain world, temporal summation as a predictor of chronic pain disorders has been largely unstudied.

This study indicates an optimal predictor of case status combines a measure of general sensitivity to pain and a measure of temporal summation.

These results are intriguing for chronic pain specialists, since temporal summation is believed to be partly responsible for the transition from acute to chronic pain.

MEAN PAIN RATINGS OVER TIME

--- Control

__ Case

There is clearly a difference between the patterns of the cases and controls in this windup data. Our goal is to understand the mechanisms causing these differences from a quantitative standpoint.

EXISTING METHODS FOR ANALYZING WINDUP DATA

First Pulse - Subject’s pain rating at time Higher the first pulse, the higher the risk of TMD Relatively weak association Does not consider an increase or decrease in pain

after the first pulse, so does not provide any information on temporal summation

Area Under the Curve- Plot the pain rating against time, taking the area underneath the curve More strongly associated with case status Thought to better account for any windup since it

uses all 10 ratings Highly correlated with the first pulse statistic

(correlations of at least 0.81 at each temperature)

EXISTING METHODS FOR ANALYZING WINDUP DATA

Delta- Difference between the maximum pain rating over all ten pulses and the first pulse. Only weakly associated with case status Problem: “ceiling effect”

For subjects whose first pulse is very high, it is difficult to obtain a large increase since the maximum possible rating is 100

Since subjects whose first pulse is very high are more likely to be cases, this measure was a poor predictor of case status.

EXISTING METHODS FOR ANALYZING WINDUP DATA Regression Slope- Slope of the regression line

that predicts the pain rating based on time for the first three ratings. (both with or without an intercept) With intercept: not significantly associated with

case status Problem of ceiling effect: Subjects with high first

pulse, have little room to increase before reaching the maximum rating, giving regression slope of nearly 0

Highly correlated with delta measure (correlations of 0.8 at each measure)

Without intercept: associated with case status Highly correlated with the first pulse measure

and area under the curve (correlations of 0.94 at each temperature)

ADDITIONAL PREDICTORS

Maximum Maximum pain rating for each temperature Strongly associated with case status at every

temperature Again, correlated with the first pulse rating and

the area under the curve (correlation of 0.7 or higher)

Aftersensation 2 measurements from the raw data 15-second aftersensation rating at 50 degrees

was the strongest predictor observed (compared to classical variables and raw data)

LASSO MODELS

Penalized regression used to determine the optimal combination of variables for a multivariate regression model for predicting case status

Useful when the predictors in a regression model are correlated with one another

Penalizes models with too many nonzero coefficients by minimizing the following:

xij‘s are the predictors

yi is the response variable

bj‘s are the LASSO regression coefficients

λ is a tuning parameter

LAMBDA AND CROSS-VALIDATION

λ is a tuning parameter that specifies how much each coefficient is penalized

To choose our lambda, we use cross-validation1. Data set is split into ten approximately equally-

sized partitions. 2. For each partition, we fit a model using the 90%

of the data that is not in the partition.3. Attempt to predict the values of y for the 10%

that is in the partition. 4. Repeat this procedure for each partition and for

various λ values. 5. Use our predicted values of y to calculate the area

under the receiver operating characteristic (ROC) curve (denoted by AUC) in each left out partition.

CROSS-VALIDATION CURVE WITH CLASSICAL VARIABLES, RAW DATA, AND AFTERSENSATION

This is our cross-validation curve with our raw data values (30 pulses), the derived measures described previously, and the aftersensation ratings. The numbers on the top indicate the approximate number of variables in the model. The minimum lambda gives a model with 2 variables.

LASSO APPROACH 1ALL DERIVED VARIABLES AS WELL AS RAW DATA

Lambda Value

Cross-Validation AUC

Cross-Validation SD Surviving Variables

0.025077711 0.545247937 0.017337447Site ID Maximum rating, 46°

0.022849876 0.566869905 0.020354564Site IDMaximum rating, 46°

0.020819956 0.565731841 0.01961884

Site IDMaximum rating, 46° Pulse 9, 50°

0.009891166 0.563328382 0.02037828

Site IDMaximum rating, 46° Slope no int, 50°Pulse 3, 46° Pulse 9, 50°

Best AUC Surviving Variables: Site ID, Max Rating, 46°

Lambda Value

Cross-Validation AUC

Cross-Validation SD Surviving Variables

0.037664 0.547167 0.018205Site ID

0.034318 0.622338 0.025852

Site IDAftersensation at 15 s, 50°

0.021553 0.619363 0.025145

Site IDMaximum rating, 46°Aftersensation at 15 s, 50°

0.00933 0.607636 0.021236

Site IDMaximum rating, 46° Slope no int, 50° Pulse 3, 46° Pulse 9, 50°Aftersensation at 15 s, 50°

Best AUC Surviving Variables: Site ID, Aftersensation at 15 s, 50°

LASSO APPROACH 2ALL DERIVED VARIABLES, RAW DATA, AFTERSENSATIONS

NOTES ON LASSO

Tempting to speculate that the maximum measurement and aftersensation are more informative than all of the other measures.

However, if there are several equally good predictors that are correlated with one another, LASSO tends to arbitrarily pick one of them, even if an alternative predictor would produce comparable accuracy.

However, LASSO shows that if we know that if we know one variable (in this case maximum and/or aftersensation), we know as much about case status as we would if we included all the variables

FIRST PULSE AND WINDUP RELATIONSHIP

As discussed earlier, overall sensitivity to pain (measured by first pulse) is believed to be distinct from temporal summation (measured by delta)

Maximum rating = First pulse rating + Delta rating Interesting to note that the strongest predictor of

cases status (when aftersensations are excluded) is the sum of these two measures

To better understand the relationship between first pulse and delta, the subjects were separated into ten groups based on first pulse (0-9, 10-19, etc).

Delta Grouped by First Pulse

The plots indicate that subjects with very low first pulse generally had very low delta values as well. Subjects in the 20-60 range had much higher deltas. Finally, subjects with very high first pulses had low delta values. One might expect this last group had low delta values as a result of the ceiling effect. However, this is clearly not the case since the medians in each group are well below 100.

BOOTSTRAP ANALYSIS To strengthen this analysis, it was necessary to

compare the delta values in each first pulse grouping. Typically, one would take the mean of the three

groups and test the null hypothesis that they are all equal.

However, this comparison was problematic because the mean delta values in each first pulse grouping could be restricted by the ceiling effect. There may be some people in the first pulse group over 60

who stopped at the 100 rating who actually would have continued to an even higher rating.

The true mean of the deltas of this first pulse group might be higher than the mean we actually calculate.

Solution: use the median. As long as the median is less than 100, it is unaffected by this ceiling effect.

BOOTSTRAP RESULTS

In order to compare medians, we need to use bootstrap analysis, as out statistical distribution is unknown.

We created 3 bands in each trial based on first pulse ratings (0-19, 20-59, 60-99).

We then used bootstrapping to find confidence intervals for the median delta in each band and to test the hypothesis that the medians are the same in all the bands.

Null hypothesis: the median deltas in all the bands were equal.

Found that the middle band had significantly higher median deltas than the lowest and highest bands (bootstrap confidence intervals never overlap)

Confidence Intervals from Bootstrap Analysis

At each temperature, the 95% confidence intervals of groups 1 and 3 never overlap with group 2. Clearly there is a difference between the median deltas in these groups.

Mean Pain Ratings Over Time

There are distinct different patterns occurring between first pulse groups, temperatures, cases, and controls. While we have found several strong predictors of TMD case status, the story of temporal summation is not nearly complete. Temporal summation is only a predictor of case status for a certain subset or the population.

Number of Members in Each Subgroup

Temperature First Pulse Rating

Number of Members in Group

Case Status Number of Members

Proportion of Cases

46 Degrees

0-19 593Case 43

0.0725Control 550

20-59 624Case 68

0.109Control 556

60-100 390Case 53

0.136Control 337

48 Degrees

0-19 393Case 26

0.0662Control 367

20-59 629Case 70

0.111Control 559

60-100 548Case 68

0.124Control 480

50 Degrees

0-19 246Case 12

0.0488Control 234

20-59 508Case 56

0.110Control 452

60-100 835Case 94

0.113Control 741

DELTA AS A PREDICTOR IN EACH SUBGROUP

TemperatureFirst Pulse Rating Delta P-Value SOR1 95% Lower CI 95% Upper CI

46 Degrees 0-19 0.003131 1.377217 1.113764 1.702988

46 Degrees 20-59 0.841465 0.974414 0.755866 1.256153

46 Degrees 60-100 0.453408 0.883381 0.638807 1.221592

48 Degrees 0-19 0.085068 1.132512 0.87278 1.469538

48 Degrees 20-59 0.23776 1.164056 0.91586 1.479512

48 Degrees 60-100 0.703617 0.924867 0.692039 1.236027

50 Degrees 0-19 0.200849 0.874548 0.643513 1.188528

50 Degrees 20-59 0.065336 1.235455 0.950629 1.605621

50 Degrees 60-100 0.58626 1.129605 0.88158 1.447409

1-Standardized Odds Ratio (SOR)- the predictor was transformed to a unit-normal deviate prior to fitting the logistic regression model. This transformation means that odds ratios can be interpreted as the relative increase in odds of TMD for each standard deviation increase in the variable.

CONCLUSIONS Our results suggest possible improvements for

the thermal testing protocol It may not be necessary to repeat the experiment

three times at different temperatures, since the results do not vary greatly with respect to temperature

Higher temperatures may be better when measuring aftersensations

Our results also increase our understanding of chronic pain Overall thermal sensitivity and temporal summation

are related, but the relationship is complex Better understanding of temporal summation may

lead to novel methods to treat and prevent chronic pain