Patient-reported outcomes in a large community-based pain medicine practice: evaluation for use in phenotype modeling

Abstract

Background: An academic, community medicine partnership was established to build a phenotype-to-outcome model targeting chronic pain. This model will be used to drive clinical decision support for pain medicine in the community setting. The first step in this effort is an examination of the electronic health records (EHR) from clinics that treat chronic pain. The biopsychosocial components provided by both patients and care providers must be of sufficient scope to populate the spectrum of patient types, treatment modalities, and possible outcomes.

Methods: The patient health records from a large Midwest pain medicine practice (Michigan Pain Consultants, PC) contains physician notes, administrative codes, and patient-reported outcomes (PRO) on over 30,000 patients during the study period spanning 2010 to mid-2014. The PRO consists of a regularly administered Pain Health Assessment (PHA), a biopsychosocial, demographic, and symptomology questionnaire containing 163 items, which is completed approximately every six months with a compliance rate of over 95%. The biopsychosocial items (74 items with Likert scales of 0-10) were examined by exploratory factor analysis and descriptive statistics to determine the number of independent constructs available for phenotypes and outcomes. Pain outcomes were examined both in the aggregate and the mean of longitudinal changes in each patient.

Results: Exploratory factor analysis of the intake PHA revealed 15 orthogonal factors representing pain levels; physical, social, and emotional functions; the effects of pain on these functions; vitality and health; and measures of outcomes and satisfaction. Seven items were independent of the factors, offering unique information. As an exemplar of outcomes from the follow-up PHAs, patients reported approximately 60% relief in their pain. When examined in the aggregate, patients showed both a decrease in pain levels and an increase in coping skills with an increased number of visits. When examined individually, 80-85% of patients presenting with the highest pain levels reported improvement by approximately two points on an 11-point pain scale.

Conclusions: We conclude that the data available in a community practice can be a rich source of biopsychosocial information relevant to the phenotypes of chronic pain. It is anticipated that phenotype linkages to best treatments and outcomes can be constructed from this set of records.

Fig. 1

Exploratory factor analysis Scree plot. Scree plot for the exploratory factor analysis of the iPHA data set and for a data set of the same dimensions composed of random answers

Fig. 2

Frequency distribution of the correlation matrix. Frequency distribution of the correlation matrix values for the scales derived from the 15 factors shown in Table 3. All possible combinations of the scales generate 105 correlation values

Fig. 3

Responses to Question 1. Responses to Ques.1 from 61,161 patients who have completed the cPHA

Fig. 4

Responses to Questions 2–4. Responses to questions Ques.2, Ques.3, and Ques.4 for intake and continuing PHA patient surveys. a The 11-point pain scale was consolidated into three ranges (low pain: 0 to 3; medium pain: 4 to 6; and high pain: 7–10). Ques.2 was used for the intake PHA, while the outcome Ques.3 was used for all the cPHA. The number of patients that have completed the respective surveys are shown at the top of the figure as “Actual #”. The possible number who could have completed 1 or more cPHAs is shown at the top of the figure as “Possible #”. The nominal times between the iPHA and subsequent cPHAs are shown below each column. b The 11-point coping scale for Ques.4 was consolidated into three ranges (high coping: 0 to 3; medium coping: 4 to 6; and low coping: 7–10). The number of patients that have completed the respective surveys are shown at the top of the figure as “Actual #”. The possible number who could have completed one or more cPHAs is shown at the top of the figure as “Possible #”. The nominal times between the iPHA and subsequent cPHAs are shown below each column

Fig. 5

Intra-patient changes in responses to questions of pain levels. Intra-patient changes in responses to questions of pain levels. a Patients with starting pain levels (Ques.2) in the range 1–9 are evaluated for mean pain level change as determined by answers to Ques.2 in the cPHA surveys. b Patients with starting pain levels (Ques.2) in the range 1–9 are evaluated for mean pain level change as determined by answers to Ques.3 in the cPHA surveys. c Patients with starting pain levels (Ques.2) in the range 7–9 are evaluated for mean pain level change as determined by answers to Ques.2 in the cPHA surveys. d Patients with starting pain levels (Ques.2) in the range 7–9 are evaluated for mean pain level change as determined by answers to Ques.3 in the cPHA surveys. Characteristics of the distributions are given in Table 4