Identifying Potential Fibromyalgia Patients · PDF fileIdentifying Potential Fibromyalgia Patients ... healthcare system in search of appropriate care an ... in a nursing facility

Identifying Potential Fibromyalgia PatientsTen Predictors of Fibromyalgia Diagnosis Derived from Electronic Health Record (EHR) Data Analysis

CONTENTS:

PART 1: Analysis Background and Results ......................... 2

PART 2: Applying the Results ............................................. 6

Appendix .......................................................................... 14

Companion resource to the PopulationDetect Portal

www.populationdetect.com

http://www.populationdetect.com/

Identifying Potential Fibromyalgia Patients | 2

PART 1: Analysis Background and Results

Fibromyalgia in Context

Understanding the Challenges of Fibromyalgia Diagnosis

Fibromyalgia is a widespread pain condition that affects more than 5 million Americans.1,2 Diagnosing fibromyalgia can be challenging; based on market research, only 39% of fibromyalgia patients are correctly diagnosed.3

Fibromyalgia is associated with a host of comorbidities, such as depression, mood disorders, sleep disorders, and irritable bowel syndrome.4 The fact that fibromyalgia so frequently coincides with other disorders can make diagnosis difficult.5 Patients can cycle through the healthcare system in search of appropriate care an average of 5 years before receiving a fibromyalgia diagnosis.6 Earlier detection may improve treatment and management of the condition and could have an impact on disease-specific healthcare utilization. Additional ways of identifying factors associated with a fibromyalgia diagnosis may help facilitate earlier diagnosis and management.

Enabling Earlier Identification of Potential Fibromyalgia Patients

To address the challenges associated with diagnosing fibromyalgia, Pfizer Inc conducted a retrospective analysis to determine which patient variables could be associated with a fibromyalgia diagnosis. The purpose of this analysis was to identify potential undiagnosed fibromyalgia patients through the development of a predictive model. Objectives of the analysis included the following:

1. Summarize and describe subjects who go on to develop a diagnosis of fibromyalgia and compare them to subjects who do not develop fibromyalgia in a large electronic health record (EHR) database.

2. Develop a predictive model of fibromyalgia diagnosis to potentially facilitate earlier diagnosis and treatment through the use of real-world data. NOTE: To date, this model has not been tested in an external database.

*Data from health care provider and consumer Pfizer market research.

PRE-DIAGNOSIS DIAGNOSIS MANAGEMENT

Go Unidentified

Receive Incorrect Care

Get Incorrect or No Diagnosis

Manage Pain

Visit Health Care Provider or Specialist

Get Symptoms Noticed Fibro-myalgia

Seek Help

Don’t Seek Help

Chronic Pain Sufferers

Get Screened and Diagnosed

100 millionAmerican adults suffer

from chronic pain.7

Symptoms linked to fibromyalgia may include:

• Uncontrolled pain

• Co-morbid anxiety and/or depression

• Irritable bowel syndrome (IBS)

• Chronic fatigue

Patient and health care provider work together to develop a treatment plan which may include:

• Patient education

• Setting treatment goals

• Applying a multimodal treatment approach

• Tracking progress

Over 5 million people suffer from fibromyalgia,2 but only 39% of fibromyalgia patients are correctly diagnosed.3*

Costly CycleOn average, it takes5 years to diagnose

fibromyalgia.6

How might we help ensure that potential fibromyalgia patients are identified?

FIBROMYALGIA PATIENT JOURNEY


Subject Identification

The analysis was conducted using a large EHR database, which included demographics, diagnoses, inpatient and outpatient encounters, medications, procedures, lab results, and vital signs. Records were linked through a unique patient identifier.8,11 Subjects were identified from among the patients in the EHR database using the following criteria:

YES NO

19,726,316 patients in EHR database

CASES:

4,296subjects with

fibromyalgia diagnosis

CONTROLS:

583,665subjects with NO

fibromyalgia diagnosis

587,961subjects*

Did the subject have a fibromyalgia diagnosis

in 2012 (two 729.1 codes ≥ 30 days apart)?

EXCLUSION CRITERIA:

• Patients with cancer • Patients having

undergone a transplantation

• Patients residing in a nursing facility

• Patients who had a fibromyalgia diagnosis prior to 2012

INCLUSION CRITERIA:

• Aged 18 years or older in 2011

• Enrolled in an integrated delivery system

• Had at least one encounter with a health care provider in 2011 and in 2012


Analysis Subjects

Subject Demographics

Subjects had the following characteristics:

CASES CONTROLS

GENDER n % n %

Female 3,379 78.7 282,369 64.5

AGE mean SD mean SD

Age, years 53.3 14.6 52.7 16.3

AGE DISTRIBUTION n % n %

18-49 years 1,651 38.4 229,910 39.4

50-64 years 1,482 34.5 183,414 31.4

≥ 65 years 1,163 27.1 170,341 29.2

RACE n % n %

African American 296 6.9 83,727 14.3

Asian 32 0.7 11,294 1.9

Caucasian 3,778 87.9 429,955 73.7

Other/Unknown 190 4.4 58,689 10.1

REGION n % n %

Midwest 2,540 59.1 375,872 64.4

Northeast 373 8.7 118,146 20.2

South 1,125 26.2 75,414 12.9

West 5 0.1 458 0.1

Other/Unknown 253 5.9 13,775 2.4

COMORBIDITY mean SD mean SD

Charlson Comorbidity Index 0.8 1.3 0.5 1.1

n = Number of subjects% = Percent of subjectsmean = Average of all subjectsSD = Standard deviation

* A training data set of 440,976 subjects and a test data set of 146,985 subjects were sampled for this analysis. See page 4 for more details.


To perform this analysis, random forest was used to predict a diagnosis of fibromyalgia.12

Random forest is a computationally extensive data mining technique that takes predictor variables as inputs and classifies the outcome using an ensemble of trees. (In this analysis, the outcome classification for each subject was a diagnosis of fibromyalgia or no fibromyalgia.) Random forest has built-in cross-validation techniques and allows for the determination of variables’ importance.9

This method was chosen over traditional methods because of its higher predictive accuracy and lower pre-processing requirements.9,10

The analysis looked at prediction accuracy (sensitivity and specificity) using over 70 predictors as inputs for the model, which include demographic information, clinical characteristics, and healthcare resource utilization.8 The top 10 predictors from the random forest method provide a practical basis for prediction without compromising accuracy.


Analysis Method

THE RANDOM FOREST METHOD

70 predictor variables

440,976 subjects

1

Age (y

ears)

# offic

e visi

ts

# opio

id med

icat

# emerg

ency

dep

# med

icatio

ns

# pain

med

icatio

# mus

culos

kelet

# visi

ts w

# o

32 4 5 6 8 9

Subject 1

Subject 2

Subject 3

Subject 4

Subject 5

Subject 6

7

440,976 randomly sampled

subjects with replacement

Random subset of variables

2

# offic

e visi

ts

# pain

med

icatio

Age (y

ears)

# opio

id

#

16 3 8

Subject 47

Subject 8

Subject 12

Subject 390

Subject 167

Subject 1

Subject 2

Subject 3

Subject 4

Subject 5

PROBABILITY OF FM

Subject 6 0.5700000 FM

0.0026666 NO FM

0.6250000 FM

0.4506666 NO FM

0.3920000 NO FM

0.6799000 FM

Classification of all subjects:FM or NO FM

Variable 1

Variable 2

Variable ...

1,500 datasetswhich were used to build...

2

3 1,500 classifier treesfrom a subset of ten variables. The results of all trees were computed as an ensemble to arrive at...

4 Probability of fibromyalgia diagnosisfor all subjects, which was analyzed to identify the top ten predictors of a fibromyalgia diagnosis*

Original data for all subjectswas used to create...

1

Random forest uses a training data set to build the model that is then validated using the test data set. A test data set (146,985 or ~25% of the 587,961 subjects) was randomly selected from the original data and the remaining 440,976 subjects were placed in the training data set to build the model. The test data set was used to test the strength of predictive relationships and ensure the model works. Both data sets include the full set of 70 predictor variables.

A classifier tree was generated for every data set. Each node in a tree classifies subjects by a randomly chosen variable, either categorical, such as gender, or discrete, such as number of medications (less than or greater than a specified value). Variables are chosen algorithmically to best split the data into larger and purer divisions, thus aiding classification. Subdivision of a tree continues until all subjects are classified FM or NO FM.

The 1,500 classifier trees are used to predict the probability of fibromyalgia for each subject. A variable importance plot is then obtained to provide a graphical view of the relative importance of each predictor to the overall prediction model. See Appendix 2 (page 14) for a description of the variable importance plot.

Each of the 1,500 data sets, or bootstrapped samples, was generated using a random subset of 10 variables and the same number of subjects as the original data set (440,976). Subjects were randomly sampled with replacement, meaning that some subjects were repeated or left out of each bootstrapped data set.

Variable 1

Variable 2

Variable 3

Variable 4

Variable 5

Variable 6

VARIABLE IMPORTANCE PLOT

* Refer to Appendix 2 (page 14) for an explanation of variable importance.



10 Predictors of Fibromyalgia Diagnosis

The following 10 variables contributed to the model as predictors of a fibromyalgia diagnosis. The variables are listed in order of importance, from most predictive to least predictive. Refer to Appendix 2 (page 14) for an explanation of how variable importance was determined and Appendix 3 (pages 15-16) for details on how each variable differs between cases and controls. For details on sensitivity and specificity of the model, refer to the receiver operator characteristic (ROC) curve in Appendix 4 (page 17).

CASES(n = 4,296)

CONTROLS(n = 583,665)

VARIABLE MEASURED IN 2011 TO PREDICT POTENTIAL FIBROMYALGIA IN 2012 MEAN RANGE* MEAN RANGE*

1. Number of visits where lab tests and/or non-imaging diagnostic tests were ordered

3.1 0-59 1.1 0-186

2. Number of other outpatient visits (includes visits where the interaction type is “ambulatory patient services” or “day surgery”)

4.5 0-91 1.6 0-229

3. Age (years) 53.3 18-74 52.7 18-74

4. Number of office visits(physician office visit or other health care provider)

16.8 0-162 11.1 0-312

5. Number of opioid medications (prescriptions written + medications administered + medications ordered)

3.5 0-60 0.9 0-136

6. Number of medications (prescriptions written)

14.4 0-201 7.2 0-217

7. Number of pain medications (excluding opioids) (outpatient prescriptions written + medications administered + medications ordered)

4.5 0-118 1.6 0-232

8. Number of medications administered or ordered (inpatient or outpatient)

15.1 0-566 6.6 0-2,701

9. Number of emergency department visits 0.8 0-51 0.3 0-102

10. Number of musculoskeletal pain conditions (minimum = 0, maximum = 9)

1.6 0-8 0.7 0-9

* Ranges listed here show the minimum and maximum value of all data for each variable. In some cases, outliers in the data set push the maximum value much higher than normal. Inpatient and outpatient data has been included, which also affects the range of values.


Using the variables identified through this analysis may be helpful to identify potential fibromyalgia patients who could be cycling through your health system. Identification of potential fibromyalgia patients would precede traditional screening and diagnosis. It is important to instruct health care providers to administer screening and perform a diagnostic exam to confirm a fibromyalgia diagnosis as these variables do NOT guarantee a confirmed fibromyalgia diagnosis. To date, this model has not been tested in an external database. These results are intended to potentially help identify undiagnosed fibromyalgia patients.

The instructions outlined below and in the pages that follow are intended to provide general guidelines for appropriate use of the variables. These are not meant to be comprehensive and may not apply to all health care settings.

As an alternative to the method described here, you may consider using the PopulationDetect Portal (www.populationdetect.com) to identify potential fibromyalgia patients. This web-based tool enables calculation of the predicted probability of fibromyalgia for individual patients or populations of patients given data for the ten predictor variables. Results for populations of patients can be printed or downloaded.

OVERVIEW WHERE TO LOOK

STEP 1:

Select variables and cut-off values

Once you have familiarized yourself with the list of predictor variables, you will need to choose the appropriate method that can help you identify potential fibromyalgia patients in your health system. The first step will be to select predictor variables and cut-off values in order to generate a pursuit list.

Page 5Pages 7-8Appendix 2, page 14Appendix 3, pages 15-16

STEP 2:

Generate pursuit list in EHR

After you have selected the variables by which to search for potential undiagnosed fibromyalgia patients, you will need to generate a pursuit list, or list of patients possessing these characteristics within your EHR system.

Pages 9-12Appendix 1, page 14Appendix 5, page 18

STEP 3:

Contact patients for screening

Follow-up with potential fibromyalgia patients will be an important next step, as is the choice of resources to activate and educate patients.

Page 13

STEP 4:

Ensure appropriate care

In order to provide appropriate care for identified potential fibromyalgia patients, it will also be important to determine what process changes are appropriate for your healthcare organization, including referrals and health care provider/specialist communication.

Page 13

PART 2: Applying the Results

Appropriate Use of Variables



Selecting Variables and Cut-off Values

Knowing which variables to use first depends on what data you are currently capturing in your EHR system. From the list of 10 predictor variables on page 5, identify those for which measures exist. Once you have a set of actionable variables, you can refine your selection using one of the methods provided below:

OPTION 1:

Using the Variable Importance Plot and Cumulative Distribution Function (CDF) Graphs

This method enables more active decision-making in the selection of variables and cut-offs, based on available data and the desired degree of customization of the pursuit list.

1. Select variables: Use the Variable Importance Plot (Appendix 2, page 14) to help you decide which variables to use. As the plot shows, variables with higher importance to the predictive model may have greater predictive capability than others. Similarly, using all 10 variables (rather than a small subset) may increase the likelihood of identifying potential fibromyalgia patients. The overall goal in deciding how many variables to use is to have as many as can be measured but as few as possible. Another important decision to make is the timeframe of data you wish to use.*

2. Determine cut-off values for variables: After you select variables, review your data to determine what the cut-off values should be based on existing ranges. Another option is to use the Cumulative Distribution Function (CDF) graphs (Appendix 3, page 15-16) to define cut-offs. Values where separation is greatest between the graphed lines for cases and controls provide a good starting point for setting minimum and maximum cut-offs. For reference, you may wish to graph the distribution of your data and compare with the CDF graphs provided here.

3. Generate a pursuit list: Once you have defined cut-offs for each variable, you can then generate a pursuit list in your EHR using the instructions on pages 9-12.

OPTION 2:

Using C5.0 Rules

This method provides more direct guidance in the selection of variables and cut-offs using an algorithmically defined set of rules derived from a technique called “C5.0.”10

C5.0 RULES VALUE

1. Number of office visits ≤ 9

2. Number of other outpatient visits ≥ 1

3. Number of visits where diagnostics and/or lab tests were ordered ≥ 1

4. Number of emergency department visits ≥ 1

5. Number of opioid prescriptions ≥ 1

6. Number of total pain medications (excluding opioids) ≥ 3

7. Number of total medications administered or ordered ≤ 3

8. Number of musculoskeletal pain conditions ≥ 1

1. Confirm data availability for each variable: In order to use this method, data must be available in your EHR system for these 8 specific variables. The effectiveness of this method is driven by the combination of the variables; therefore, patients must meet all criteria, not a subset. You may also want to consider which timeframe of data to use, given the available data.*

2. Generate a pursuit list: Once you have confirmed that data exists for all variables, use the full list of 8 rules above to generate a pursuit list in your EHR system using the instructions on pages 9-12.

* Data for the variables used in the prediction model were gathered over one year (2011), so all values of predictor variables are assumed to be yearly rates. One year of data is the recommended timeframe. Six months is a second choice. If less than one year is used, then values of the variables need to be prorated to a yearly rate.



Selecting Variables and Cut-off Values continued

After reviewing the list of 10 variables and determining which data is captured in your EHR system, you may find that not all 10 variables are available for an individual patient or a patient population. Depending on how many variables are missing, you may use one of the methods below to address gaps in data:

If ONE or at most TWO variables are unavailable:

1. Use the mean value for controls for the variables not available: Refer to the list of 10 predictor variables (page 5) for mean values for controls.

2. Generate a pursuit list: Once you have the mean values for controls for variables not available, you can then generate a pursuit list in your EHR using the instructions on pages 9-12.

If THREE OR MORE variables are unavailable:

Use the following strategy based on C5.0 rules (page 7) to identify patients.

1. Confirm data availability for three variables and conditions: Listed below are the variables and conditions required for this approach.

C5.0 RULES VALUE

2. Number of other outpatient visits > 0

7. Number of prescriptions administered ≤ 3

8. Number of musculoskeletal pain conditions > 0

2. Choose at least two other variables and conditions: Refer to the list below.

C5.0 RULES VALUE

1. Number of office visits ≤ 9

3. Number of visits where lab/non-imaging tests were ordered > 0

4. Number of emergency room visits > 0

5. Number of opioid prescriptions > 0

6. Number of total pain medications (excluding opioids) > 2

3. Generate a pursuit list: Use the 5-7 variables derived from this approach to generate a pursuit list in your EHR system using the instructions on pages 9-12.


Use the EHR instructions below as a guide to identify potential undiagnosed fibromyalgia patients. They are intended to aid in the generation of pursuit lists and may not reflect your system’s current software or updates. Your organization’s HIT department may be able to assist with generating the pursuit list.

Before you begin, it will be important to verify that all variables you selected are structured/discrete fields within your EHR system. When specifying cut-off values in your EHR, consider setting the upper limit for a given variable to the maximum value allowed in your system. After you generate this list, you also have the option to create a registry and associated registry alert.

The registry allows practices to run queries and narrow their patient database search to return results for each subset. Follow the instructions below:

1. Click the Registry band in the left navigation pane.

2. Click the Registry icon. The system opens the Registry window.

3. Click the Demographics tab.

4. Use selected variables.

Steps 5–12 are OPTIONAL:

5. Insert 18 in Age Range (18 – _ _).

6. Click Run New.

7. Click the appropriate tabs to display the filters.

8. Select ICD-9* codes and variables.

9. Run Subset [NOT] ICD 9 729.1 to exclude existing FM patients. Steps 10-12 are optional if an opioid variable is selected.

10. Enter drug class (opiate agonists, partial agonists)

11. Enter drug category (opioid).

12. Run Subset.

13. SAVE the query for future use.

14. You can now export to Excel or PDF or use the Patient Recall functionality in the Registry band.

A new Epic registry would be a tool for generating a list of potential fibromyalgia patients. This will require work by the IT Department for initial setup. Another option is a Clarity Report, which can be configured by the Clarity reporting team. The steps below are for the IT Department to complete setup of the registry.

1. Create a diagnosis grouper (VCG) for fibromyalgia by going to Management Console and clicking Grouper Record Editor (VCG).

2. Create a new diagnosis group for fibromyalgia (if doesn’t exist already).

a. Master file: EDGb. Type: Conceptc. Relationship Type: Concept Hierarchyd. Concepts: Primary fibromyalgia syndrome

3. Create a registry inclusion rule: click Epic button > Tools > Rule Editor Tools > Rule Editor.

4. Create a new rule of context: Registry Inclusion Criteriaa. Add the ‘Is Diagnosis in Encounter Diagnoses Data’

property.

i. Diagnosis Grouper ID: grouper created in step 2ii. Operator: <>

iii. Value: 1b. Add ‘Patient Status’ property.

i. Operator: =ii. Value: Alive

c. Search for applicable properties according to the variables selected from the predictive model and add as criteria.

5. Create applicable registry metric rules: click Epic button > Tools > Rule Editor Tools > Rule Editor.

6. Create a new rule of context: Registry Metrics.

a. Add in the appropriate property for the metric (e.g. Number of office visits).

7. Repeat steps 5 and 6 for all variables from the predictive model as needed.

8. Create the registry: click Epic button > Report Management > Analytics > Registry Editor.

9. Create a new analytics registry.

a. Go to Metrics and Rules.

b. Add your Registry Inclusion rule.

c. Add any Registry Metric rules.

d. Go to General and set Registry Status to Active.


Generating a Pursuit List

* Refer to Appendix 1 (page 14) for a list of suggested ICD-9 codes.


Cerner’s Business Objects Reporting Tool, PowerInsight, may be used to create a custom report using the variables that are chosen from the predictive model. Organizations with the ability to ensure that all variables are included in the Universe may find that PowerInsight is their best choice for creating this report. Should all variables not be included in the Universe, custom SQL can be written to obtain the data. For organizations without access to PowerInsight, one or more custom reports based on the variables would need to be generated using Cerner Command Language (CCL).

Allscripts reporting tool, Allscripts Analytics, may be used to create a custom report using a variety of inputs. Organizations with the ability to ensure that all variables from the predictive model are included may find that Allscripts Analytics is the best choice for creating the report. The major tables to use would be: AHS_Encounter, AHS_Patient, AHS_MedicationAction, and AHS_Problem. This would allow the analyst to do counts by visit type, medications tied to encounter, and diagnoses. The keys to join on would be encounterid and patientid between the tables.

These instructions are valid for NextGen Ambulatory.

1. Select Reports > Generate Report > By Enterprise.

2. Under the appropriate tab, select the button with 3 dots (…).

3. Use selected variables. On the new screen, select ICD-9* codes or variables. Add selected codes for the report, then click OK.

4. To exclude existing FM patients, select 729.1 > [NOT].

Steps 5–7 are OPTIONAL:

5. Under the Medication tab, if the opioid variable is chosen, select the appropriate medications or medication classes and then click OK.

6. Enter drug category.

7. Under the Demographics tab, select Age 18 and older.

8. Under the Column tab, select what to include in the report or to export to Excel.

9. Click Save and then OK.


Generating a Pursuit List continued

* Refer to Appendix 1 (page 14) for a list of suggested ICD-9 codes.




The information provided below for each variable may help you select and specify variables in your EHR system as you generate your pursuit list.

VARIABLE HOW TO OBTAIN THE VARIABLE COMMENTS


Count the number of visits with an interaction type of “labs” or where labs are ordered.

Use the overall group/categories (i.e. all lab tests vs. one or a combination of lab tests). Refer to Appendix 5 (page 18) for further details.

2. Number of other outpatient visits (includes visits where the interaction type is “ambulatory patient services” or “day surgery”)

Count the number of outpatient visits where the interaction type is not listed as “office.”

May not be captured in some EHR systems. Refer to Appendix 5 (page 18) for further details.

3. Age (years) — Generally captured and easy to extract.

4. Number of office visits(physician office visit or other health care provider)

Count the number of outpatient visits where the interaction type is listed as “office.”

or

Count visits with a CPT code for office visit (see table on page 12).

The type of office visit is based on CPT codes (see table on page 12). Refer to Appendix 5 (page 18) for further details.

5. Number of opioid medications (prescriptions written + medications administered + medications ordered)

Count the number of prescriptions written for opioids. Each record of an opioid administration or an opioid ordered for later administration is added to the number of prescriptions written to define this variable.

Select entire drug category/class of opioids. Refer to Appendix 5 (page 18) for further details.

6. Number of medications (prescriptions written)

Count the number of prescriptions written.

Select entire drug category.

(Continued on next page)


VARIABLE HOW TO OBTAIN THE VARIABLE COMMENTS

7. Number of pain medications (excluding opioids) (outpatient prescriptions written + medications administered + medications ordered)

Count the number of prescriptions written for pain medications (excluding opioids). Also count the number of pain medications (excluding opioids) resulting from provider interaction types that list medications that are administered or ordered. Each record of a pain medication (excluding opioids) administration or a pain medication (excluding opioids) ordered for later administration is added to the number of prescriptions written to define this variable.

Select entire drug category and exclude opioids. Refer to Appendix 5 (page 18) for further details.


Search interaction types where medications are administered or ordered and then count each record of a medication administration or a medication ordered for later administration.

Select entire drug category.

9. Number of emergency department visits

Count the number of visits with an emergency room interaction type. The count should include emergency department visits that do not result in hospitalization in addition to emergency department visits that result in hospitalization.

Sometimes not routinely captured. Consult with your HIT department to obtain ED utilization data.

10. Number of musculoskeletal pain conditions (minimum = 0, maximum = 9)

Search ICD-9 codes for each of the 9 musculoskeletal pain conditions used in this analysis and count the number of musculoskeletal pain conditions found.

Refer to Appendix 1 (page 14) for a list of ICD-9 codes.

CPT CODES FOR OFFICE VISITS

E/M LEVELS MINIMAL PROBLEM FOCUSED

EXPANDED

PROBLEM FOCUSED DETAILED COMPREHENSIVECOMPREHENSIVE / HIGH

New Patient — 99201 99202 99203 99204 99205

Established Patient Consult

99211 9921299241 / 99251

9921399242 / 99252

9921499243 / 99253

*9921599244 / 99254

*9921599245 / 99255




You may consider taking one of several actions given the list of potential fibromyalgia patients. Depending on your EHR system, you may flag patients for screening on their next visit. You may also reach out directly to patients, asking them to discuss any pain-related symptoms they may be experiencing with their health care provider. You also have an option to create clinical decision support based on the variables and cut-offs selected. This will help identify potential fibromyalgia patients on a more routine basis after you’ve created the initial list.

RESOURCES AVAILABLE FROM PFIZER


Contacting Patients for Screening


Ensuring Appropriate Care

Ensuring appropriate and efficient fibromyalgia screening, diagnosis, treatment, and management is essential once identified patients come in for their office visit. You may need to assess gaps in care within your health system and determine areas for further improvement, such as clinical processes and health care provider education.

RESOURCES AVAILABLE FROM PFIZER

If you have widespread muscle pain and your body is painful to the touch, it’s important to tell your doctor about it. Some people with this type of pain may have a condition known as fibromyalgia.1 The information below may help you describe your pain and lead to a better discussion with your doctor.

How can fibromyalgia impact your daily life?Pain and tiredness caused by fibromyalgia may limit activities such as household chores, work, and hobbies.4 It may also affect emotional well-being, as some people with fibromyalgia say they feel alone.4

What can you do about fibromyalgia?Speak to your doctor to find out if the pain you’re feeling may be fibromyalgia. Although fibromyalgia can’t be cured, there are management options available.3 Your doctor may be able to recommend exercise, other non-medical options, and treatments to help reduce the pain.3

Make sure to consult your doctor before starting any treatment.

What is fibromyalgia?Fibromyalgia is a chronic widespread pain disorder, which means the pain lasts more than 3 months.2 People usually feel pain on both their left and right sides, and above and below their waist.2 In addition, the pain level may vary from day to night.3 People with fibromyalgia may feel3:

• Chronic pain• Deep ache• All-over bodily pain• Tender painOther symptoms may include tiredness and low energy, interrupted sleep, problems with memory and thinking clearly, and depression or anxiety.2

For more information on fibromyalgia and how your doctor may diagnose the condition, visit fibrocenter.com.

Fill out the questions on the next page to help you talk to your doctor about your pain symptoms.

Are you living with chronic widespread pain and tenderness?

The Fibromyalgia Patient Activation Questionnaire is meant to help patients determine if their symptoms are consistent with those associated with fibromyalgia. The questionnaire is sent as an attachment with the Fibromyalgia Patient Activation E-mail, which introduces the resource and prompts discussion with the patient’s health care provider.

Fibromyalgia Patient Activation Questionnaire & E-mail

What Is Fibromyalgia?Fibromyalgia is a common chronic pain disorder affecting over 5 million people in the United States. The hallmark symptom of fibromyalgia is chronic widespread pain and tenderness lasting longer than 3 months, and located above and below the waist on the left and right sides of the body. The pain may migrate to all parts of the body, and vary in intensity from day to day. Patients may also exhibit a range of other symptoms, including fatigue, sleep disturbances, depression/ anxiety and concentration/memory problems (sometimes called “fibro fog”).

You Can Actively Work to Improve Fibromyalgia Symptoms.Lifestyle changes may sometimes play an important role in helping to relieve fibromyalgia pain. Options such as exercise, sleeping better, support groups, and medication can all make a difference in how you feel. Work closely with a healthcare professional who is knowledgeable about the diagnosis and treatment of fibromyalgia.

Exercise

Studies show that in addition to medication, the actions most likely to help with fibromyalgia symptoms are light aerobic exercises and strength training. Always check with your doctor before starting any exercise program.

These exercise tips may help you get started:

■ Start slowly. If you’re moving more today than yesterday, that’s progress.

■ Listen closely to your body. Don’t overdo it or increase your activity too quickly.

■ Start with just a few minutes of gentle exercise a day. Walking is a great form of exercise.

■ Track your progress. Note how you feel, both during and after exercising.

■ Stretch your muscles before and after exercise.

■ Post-exercise soreness will decrease over time. But respond to your body’s signals and pace yourself.

Sleep

Sleep problems are common with fibromyalgia. Fortunately, there are things you can do to help:

■ Stick to a sleep schedule, even on weekends.

■ Keep your bedroom cool.

■ Nap if you need to, but be brief. Set an alarm for 20 minutes.

■ As evening approaches, cut out caffeine, including coffee, tea, cola, and chocolate.

■ Avoid alcohol before bed.

■ Don’t exercise before bedtime.

■ A white-noise machine or fan may help you fall asleep to a soothing sound.

■ Develop a relaxing bedtime routine, such as reading or listening to soft music.

A GUIDE TO LIVING WITH FIBROMYALGIA

More information about the chronic widespread pain of fibromyalgia can be found at Fibrocenter.com

The Fibromyalgia Patient Education resource provides patients with disease state information about fibromyalgia and its symptoms. It is available as a printable PDF for health care providers to discuss with their patients.

Fibromyalgia Patient Education

PBP565300-01 © 2013 Pfizer Inc. All rights reserved. May 2013

Clinical Workflowfor Undiagnosed Fibromyalgia Patients

The Clinical Workflow for Undiagnosed Fibromyalgia Patients, was designed to provide a framework for fibromyalgia care among your health care providers. It includes recommendations to improve identification and management of patients with fibromyalgia.

Pfizer Inc offers a number of resources for health care provider education. These resources are focused on improving understanding of fibromyalgia, providing guidance on appropriate diagnosis, and enabling appropriate management of fibromyalgia patients. Examples include the Fibromyalgia Tender Point Exam poster, shown here.

Health Care Provider Education

Clinical Workflow for Undiagnosed Fibromyalgia Patients


Appendix 2

Variable Importance Plot

The variable importance plot shows the relative importance of each variable in the model. Variables were ordered by the loss of performance resulting from omission of the variable in the prediction model. The variable with the largest loss in predicting performance by its omission in the model is normalized to 100%. The plot below shows the top twenty variables, with the top ten in blue.

Importance

Depression20.

Chronic fatigue syndrome19.

Number of hospitalizations18.

MI, CHF, PVD, CVD, hypertension17.

Arthritis/other arthropathies16.

Race (Caucasian, non-Caucasian)

15.

Back and neck pain (excluding lower back pain)

14.

GERD / Gas / Duo / Other gastrointestinal (GI) condition

13.

Low back pain12.

Gender (Male, Female)11.

10.

9.

8.

7.

6.

5.

4.

3.

2.

1.

20% 40% 60% 80% 100%

Number of visits where lab tests and/or non-imaging diagnostic tests were ordered

Number of other outpatient visits

Age (years)

Number of office visits

Number of opioid medications

Number of medications(prescriptions written)

Number of pain medications (excluding opioids) Number of medications administered or ordered (inpatient or outpatient)Number of emergency department visits

Number of musculoskeletal pain conditions

Appendix 1

ICD-9 Codes

This list of ICD-9 codes for musculoskeletal pain conditions is for guidance only. Please refer to a public source for further information.

CONDITION CODES

Lupus 710

Diffuse diseases of connective tissue

710.1710.2710.3710.4710.5710.8710.9

Arthritis and arthropathies

711.XX712.XX713.X714.4X714.8X714.9X716.XX717.XX718.XX719.XX

Rheumatoid arthritis

714.0714.1714.2

Osteoarthritis 715.XX

Low back pain 720.0720.1720.2721.3722.10722.32722.5722.83722.93724.00724.02724.2724.5724.6724.70724.71724.79738.4739.3739.4756.11756.12805.4 805.6846.0846.1846.2846.3846.8846.9 847.2847.3847.4

CONDITION CODES

Back and neck pain, other than low back pain

720.81720.89720.9721.0721.2721.5721.6721.7721.8721.90722.11722.30722.31722.39722.4722.6722.80722.81722.82722.90722.91722.92723.X(except 723.4)724.01724.1724.8724.9737.10737.11737.12737.19737.20737.21737.22737.29737.30756.10756.13756.14756.15756.16756.17756.19805.8847.9

Rheumatism 725-728.9729.3-729.9

Other musculoskeletal pain conditions

730.00-739.X


Appendix 3

Cumulative Distribution Function (CDF) Graphs for Variables

A cumulative distribution function (CDF) gives the probability that a random variable is less than or equal to the independent variable of the function. The ranges of values for each variable are discrete random variables and are graphically represented as a step function. In this analysis, CDFs help enhance interpretation of cohort differences in outcomes and can be used to define ranges for each variable when running a pursuit list (see the example in Figure 1).

The graphs below show the range of values for each of the top ten variables and depict the percentage of subjects having a variable less than or equal to a particular value. For example, Figure 5 shows 64% of cases have less than or equal to 2 opioids prescribed, ordered, or administered during 2011 compared to close to 90% of controls. Therefore, 36% of cases had more than 2 opioids compared to approximately 10% of controls. Values where separation between cohorts is small can also be identified as in Figure 3, which shows that approximately 50% of both cases and controls are 55 years of age or younger. The largest difference between cohorts can be seen in Figures 1, 5, 6, and 7.

Cum

ulat

ive

Per

cent

0 2 4 6 8 10 12 14

Number of visits where diagnostics/lab tests were ordered during 2011

Controls

Cases

0

20

40

60

80

100

0 10 20 30 40 50 60 70 80

Age in 2011

0

20

40

60

80

100

Cum

ulat

ive

Per

cent

Controls

Cases

0 2 4 6 8 10 12 14 16 18 20

Number of other outpatient visits during 2011

Cum

ulat

ive

Per

cent

0

20

40

60

80

100

Controls

Cases

0 10 20 30 40 50 60 70

Number of office visits during 2011

0

20

40

60

80

100

Cum

ulat

ive

Per

cent

Controls

Cases


3. Age

2. Number of other outpatient visits

4. Number of office visits

Approximately 50% of both cases and controls are 55 years of age or younger.

EXAMPLE: One way to select variable ranges is to look where separation is greatest between cases and controls. Here, a range of 0–3 visits might provide a good starting point for generating a pursuit list.


Appendix 3

Cumulative Distribution Function (CDF) Graphs for Variables continued

5. Number of opioid medications 6. Number of medications

7. Number of pain medications (excluding opioids)

9. Number of emergency department visits


10. Number of musculoskeletal pain conditions

0 2 4 6 8 10 12 14 16

Number of total pain medication prescriptions (excluding opioids) during 2011

0

20

40

60

80

100

Cum

ulat

ive

Per

cent

Controls

Cases

0 1 2 3 4

Number of emergency department visits during 2011

0

20

40

60

80

100

Cum

ulat

ive

Per

cent

Controls

Cases

0 10 20 30 40 50 60 70 80 90 100 110

Number of total prescriptions administered (ordered) during 2011

0

20

40

60

80

100

Cum

ulat

ive

Per

cent

Controls

Cases

0 1 2 3 4 5 6 7 8 9 10

Number of Musculoskeletal Pain Conditions during 2011

0

20

40

60

80

100

Cum

ulat

ive

Per

cent

Controls

Cases

0 2 4 6 8 10 12 14

Number of opioid prescriptions during 2011

0

20

40

60

80

100

Cum

ulat

ive

Per

cent

Controls

Cases

0 5 10 15 20 25 30 35 40 45

Number of total prescriptions written during 2011

0

20

40

60

80

100

Cum

ulat

ive

Per

cent

Controls

Cases

64% of cases had less than or equal to 2 opioids prescribed, ordered, or administered during 2011, compared to close to 90% of controls.


Appendix 4

Receiver Operator Characteristic (ROC) Curve

A receiver operator characteristic (ROC) curve is used to evaluate the performance of a statistical model that classifies subjects into one of two categories (in the case of this analysis, fibromyalgia or no fibromyalgia). It plots two values for the different possible cut-offs of a diagnostic test:

• The true positive rate, or sensitivity, which is how effective the model is at identifying patients with a disease or condition

• The false positive rate, or specificity, which is how effective the model is at identifying patients who do not have a disease or condition

The area under the curve measures the accuracy of the model, or ability of the model to classify patients. An area of 1 means perfect accuracy, while an area of 0.5 means very poor accuracy. The closer the curve is to the upper left corner, the higher the accuracy of the model.

The prediction model applied to the test data set resulted in a ROC curve (shown at right) with good predictive accuracy for a fibromyalgia diagnosis (area under the curve of 0.810). A cut-off value of 0.500 used on the test data to declare a subject to be a fibromyalgia case resulted in a sensitivity of 0.641 and a specificity of 0.794. A cut-off value of 0.446 resulted in a better balance, with a sensitivity of 0.721 and a specificity of 0.740.

1 − Specificity

Sen

sitiv

ity

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

�

�

Cut-off: 0.446Specificity: 0.740Sensitivity: 0.721

Area under curve:0.810

Cut-off: 0.500Specificity: 0.794Sensitivity: 0.641


Appendix 5

Supporting Information for Variable Selection and Specification

Below are example options available within the “Interaction Type” EHR field and the corresponding selections for three variables used in this analysis:

Refer to the information below when making selections for these two variables in your EHR:

5. Number of opioid medications

For a list of National Drug Code (NDC) numbers for opioid medications used in the analysis, please speak with your Pfizer Medical Outcomes Specialist.

7. Number of medications from the classes below to manage pain (excluding opioids)

DRUG CATEGORY

Acetaminophen

Anti-anxiety drugs

Antiepileptic drugs (AEDs)

Benzodiazepines

Hypnotics

Muscle relaxants

Nonsteroidal anti-inflammatory drugs (NSAIDs)

Selective serotonin re-uptake inhibitors (SSRIs)

Serotonin-norepinephrine reuptake inhibitors (SNRIs)

Systemic corticosteroids (exclude topicals)

Tricyclic antidepressants (TCAs)


INTERACTION TYPES

Ambulatory patient services

Day surgery patient

Emergency patient

Home visits

Imaging

Inpatient

Labs and diagnostics.nonimaging

Not recorded

Nursing home inpatient

Observation patient

Office or clinic patient

Other patient type

Prescriptions and refills

2. Number of other outpatient visits

INTERACTION TYPES


Day surgery patient

Emergency patient

Home visits

Imaging

Inpatient


Not recorded


Observation patient


Other patient type


4. Number of office visits

INTERACTION TYPES


Day surgery patient

Emergency patient

Home visits

Imaging

Inpatient


Not recorded


Observation patient


Other patient type


PBP761616-01 © 2015 Pfizer Inc. All rights reserved. July 2015

References

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2. Lawrence RC, Felson DT, Helmick CG, et al. Estimates of the prevalence of arthritis and other rheumatic conditions in the United States. Part II. Arthritis Rheum. 2008;58(1):26-35.

3. Data on file. Pfizer Inc, New York, NY.

4. Arnold LM, Clauw JC, McCarberg BH; FibroCollaborative. Improving the recognition and diagnosis of fibromyalgia. Mayo Clin Proc. 2011;86(5):457-464.

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6. Brown TM, Garg S, Chandran AB, McNett M, Silverman SL, Hadker N. The impact of ‘best-practice’ patient care in fibromyalgia on practice economics. J Eval Clin Prac. 2011;18:793-798.

7. Institute of Medicine. Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research. Washington, DC, June 29, 2011.

8. Masters ET, Mardekian J, Rajicic N, et al. Fibromyalgia retrospective electronic medical record analysis – Phase II. Pfizer protocol ID A0081329, Version 1.0. October 3, 2013.

9. Breiman L. Random forests. Mach Learn. 2001;45(1): 5-32.

10. Kuhn M, Johnson K. Chapter 14: Classification trees and rule-based models. In: Kuhn M, Johnson K. Applied Predictive Modeling. New York, NY: Springer Verlag; 2013:369-413.

11. Masters ET, Mardekian J, Emir B, Clair A, Kuhn M, Silverman SL. Electronic medical record data to identify variables associated with a fibromyalgia diagnosis: importance of health care resource utilization. J Pain Res. 2015;8:131-138.

12. Emir B, Masters ET, Mardekian J, Clair A, Kuhn M, Silverman SL. Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records. J Pain Res. 2015;8:277-288.

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