Pain Medicine 2015; 00: 00–00 Wiley Periodicals, Inc.
Original Composite Pain Index: Reliability, Validity, and Sensitivity of a Patient-Reported Outcome for Research
Diana J. Wilkie, PhD, RN, FAAN,*,† Robert E. Molokie, MD,†,‡,§,¶ Marie L. Suarez, PhD,* Miriam O. Ezenwa, PhD, RN,* and Zaijie J. Wang, PhD†,¶ *Department of Biobehavioral Health Science, University of Illinois, Chicago College of Nursing Chicago, Illinois, USA; †University of Illinois Cancer Center, Chicago, Illinois, USA; ‡Division of Hematology/Oncology, University of Illinois, Chicago College of Medicine, Chicago, Illinois, USA; §Jesse Brown Veteran’s Administration Medical Center, Chicago, Illinois; ¶Department of Biopharmaceutical Sciences, University of Illinois, Chicago College of Pharmacy, Chicago, Illinois, USA Reprints requests to: Diana J. Wilkie, PhD, RN, FAAN, Department of Biobehavioral Health Science (MC 802), University of Illinois at Chicago, 845 S. Damen Ave., Room 660, Chicago, IL 60612-7350, USA. Tel: 312.413.5469; Fax: 312.996.1819; E-mail: [email protected]. Funding sources: This research was made possible by Grant Numbers 1R01CA81918 and 2R01CA081918 from the National Institutes of Health, National Cancer Institute. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute. The final peerreviewed manuscript is subject to the National Institutes of Health Public Access Policy. Conflicts of interest: Drs. Molokie and Wilkie are coinvestigators on an unrelated study funded by Pfizer. There are no other conflicts related to this research.
Abstract Objective. A single score that represents the multidimensionality of pain would be an innovation for
patient-reported outcomes. Our aim was to determine the reliability, validity, and sensitivity of the Composite Pain Index (CPI). Design. Methodological analysis of data from a randomized controlled, pretest/post-test educationbased intervention study. Setting. The study was conducted in outpatient oncology clinics. Subjects. The 176 subjects had pain, were 52 6 12.5 years on average, 63% were female, and 46% had stage IV cancers. Methods. We generated the CPI from pain location, intensity, quality, and pattern scores measured with an electronic version of Melzack’s McGill Pain Questionnaire. Results. The internal consistency values for the individual scores comprising the CPI were adequate (0.71 baseline, 0.69 post-test). Principal components analysis extracted one factor with an eigenvalue of 2.17 with explained variance of 54% at baseline and replicated the one factor with an eigenvalue of 2.11 at post-test. The factor loadings for location, intensity, quality, and pattern were 0.65, 0.71, 0.85, and 0.71, respectively (baseline), and 0.59, 0.81, 0.84, and 0.63, respectively (posttest). The CPI was sensitive to an education intervention effect. Conclusions. Findings support the CPI as a score that integrates the multidimensional pain experience in people with cancer. It could be used as a patient-reported outcome measure to quantify the complexity of pain in clinical research and population studies of cancer pain and studied for relevance in other pain populations. Key Words. Pain Management; Interventional 1
Wilkie et al. Introduction Pain is recognized as a multidimensional phenomenon, but few instruments provide one score that integrates its multiple components. Researchers created a variety of pain indices using pain intensity and affective pain components [1], pain quality ranked by intensity, affective and cognitive components [2], and pain behavior [3]. None created an index that includes all four of the self-reported sensory pain components (location, intensity, quality, and pattern) as well as affective and cognitive qualities. A single summary score can replace multiple main scores when all are important [4], as is the case with the multiple pain dimensions. Although many investigators and clinicians focus only on pain intensity, the pain location is critical for determining new pain sites that may represent new or progressive pathology and pain pattern is important for determining the timing of pain therapies. Some pain experts consider pain quality a proxy for pain intensity, but pain quality is an additional pain characteristic that helps to guide selection of the pain therapies (e.g., adjuvant drugs) independent of pain intensity. Availability of a single score that integrates the complex pain experience has statistical benefits for research by preserving degrees of freedom, conserving sample size, and improving the power of statistical tests [4], a desirable characteristic for a pain phenotype or a clinical trial outcome measure. It is also desirable if such a patient-reported pain outcome can be derived from a pain tool that also has research and clinical utility, such as the McGill Pain Questionnaire (MPQ) [2]. Our study purpose was to determine the reliability, validity, and sensitivity of the Composite Pain Index (CPI), a single score that integrates the multidimensionality of cancer pain. The multiple dimensions of cancer pain are well known to include physiologic-sensory, affective, cognitive, and behavioral dimensions [2,5,6]). Commonly, however, investigators use pain tools that quantified one or two dimensions such as pain severity and pain affect [7], or pain impact on the patient’s life and functioning [8]. Only two tools are used often in cancer studies to measure more than one pain dimension: [1] the Brief Pain Inventory [8] measures pain severity and functional interference; and [2] the MPQ [2] measures four components of the sensory dimension of pain, specifically, its location, intensity, quality, and pattern as well as the affective, cognitive, and behavioral dimensions of pain [2,5]. The well-known MPQ pain rating index-total (PRI-T) score integrates the sensory, affective and cognitive pain dimensions, namely the PRIsensory, PRI-miscellaneous, PRI-affective, and PRIevaluative (cognitive) scores. The PRI-T does not integrate the pain location, intensity, or pattern components of sensory pain. To date, the MPQ behavior dimension data (aggravating and alleviating factors) are in qualitative format and not yet amenable to a quantitative score. Based on well-accepted approaches [4], the CPI is an additional MPQ score that could be a robust outcome because it integrates pain location, intensity, quality, and pattern (Figure 1). The CPI score reported by adults with 2
Figure 1 CPI: Pain dimensions and scores included.
sickle cell disease at an outpatient visit was as strong a predictor of their acute care utilization over the subsequent year as average pain intensity (current, least and worst in the previous 24 hours) [9]. Furthermore, when used as a pain phenotype in adults with sickle cell disease, the CPI discriminated a single nucleotide polymorphism that is associated with pain [10]. Although these findings provide encouraging support for the CPI in outpatients with sickle cell disease, additional evidence for the psychometrics of the CPI in another pain population is important for researchers to have confidence in this measure. We report analyses that demonstrate the internal consistency, test-retest reliability, construct validity, and sensitivity of the CPI as a patient-reported pain outcome measure for outpatients with cancer. Materials and Methods Design and Setting We used data from a randomized clinical trial of a tailored, multimedia educational intervention that we conducted in radiation and medical oncology outpatient clinics of a large medical center in the Puget Sound area in Seattle. The Human Subjects Division at the University of Washington and the Institutional Review Board at the University of Illinois at Chicago approved the study. Sample Primary study eligibility criteria required that the subjects had a cancer diagnosis, had pain or fatigue within the week prior to enrollment, spoke and read English, and were 18-years-old or older. Blind patients or those physically and cognitively unable to complete the study were not eligible for the study with a multimedia intervention.
Composite Pain Index Of the 800 subjects referred to the original study, 378 met eligibility criteria, 286 enrolled, and 230 of those enrolled completed the study. For this analysis, we included the 176 of the 230 subjects who reported they had pain within the previous week; the other 34 had fatigue but no pain. The average age of the sample was 52 6 12.5 years (ranged from 19 to 84 years). The majority of the 176 subjects were female (n 5 111; 63%). Most subjects were married (63%); 27% were single, 3% were widowed, and 7% were other/ unknown. The ethnic/racial distribution of the sample was 89% Caucasian, 6% Asian, 2% African American, 2% Native American, and 1% Hispanic. Their education levels were: 1% completed 8th grade or less; 27% completed high school; 8% completed some vocational school; 19% had an associate degree; 28% had a baccalaureate degree; 13% had a master’s degree; 2% had a doctoral degree; and 2% had another or unknown level of education. Subjects had the following cancer diagnoses: breast cancer (38%); head and neck (18%); sarcoma (9%); lung cancer (5%); cervical cancer (3%); colon and rectal cancer (4%); prostate (5%); lymphoma (4%); brain cancer (4%); stomach (1%); ovarian cancer (2%); myeloma (2%); other or unspecified (4%); and pancreatic, kidney, bladder, or leukemia (0.6% each). Nearly half (46%) of the sample had stage IV cancers and the rest (54%) had stage I (13%), II (24%), 3 (15%) or unknown stage (2%) cancers.
Procedures We recruited eligible patients who had regularly scheduled outpatient visits in radiation or medical oncology clinics. After obtaining the patient’s written consent, the researcher introduced the patient to a pentablet comR [11] proputer with the Windows-based PAINReportItV gram to assess baseline pain measures. We used a computerized randomization program with assignment in permutated blocks (18–24 subjects per block) to randomly assign (via embedded hidden programming code not accessible to the researchers) all subjects to R (tailored multimedia intervention) or usual PAINUCopeV care with computer games (attention control) groups. About 3–4 weeks after the delivery of the intervention or computer game sessions, all subjects completed R for post-test measures. Primary study PAINReportItV outcomes are under review.
Instruments R described elsewhere [11], is an elecPAINReportIt,V tronic version of the 1970 MPQ version that was first published in 1975 [2] and is a widely accepted valid and reliable measure of pain [12]. The CPI is a single score that integrates the location, intensity, Pain Rating IndexTotal (PRI-T; sensory, affective and evaluative quality dimensions), and pattern of pain, all of which are measR It is comprised of the following ured by PAINReportIt.V four scores:
1. Pain location score (marked on a body outline; scored by counting the number of pain sites) [raw score ranges from 0 to 22]. The number of pain sites have been used in cancer [13–15], and postoperative models of pain [16], this score decreases as pain diminishes with recovery [17]. 2. Total pain intensity score (measured on the 0–10 Pain Intensity Number Scale [18]; scored by the sum of scores for current, least and worst pain in previous 24 hours [mean raw score ranges from 0–10]. The 0–10 scale replaced the MPQ’s verbal descriptor scale of pain intensity because the descriptors demonstrated a lack of equivalent intervals between the descriptors that was particularly extreme for descriptors such as distressing and horrible or excruciating (Unpublished data, under review). 3. Pain quality score (original MPQ PRI-T score [raw score ranges from 0 to 78], which includes sensory, affective, and evaluative pain quality dimensions); and 4. Pain temporal pattern score (derived from the nine pain pattern words on the MPQ; we calculated the temporal pattern score by assigning values to each of the pain pattern descriptors: 3 assigned to constant, steady, continuous; 2 assigned to brief, momentary, transient; 1 assigned to intermittent, periodic, rhythmic; and 0 assigned for no pattern words selected [the ordinal level raw score ranges from 0 to 6 because patients often select words from more than one group]). Previous research supports the pain pattern score in that the number of pain sites, pain intensity scores, and PRI scores all differed significantly by pain temporal pattern groups [13]. Statistical Analysis To construct the CPI value, each of the four raw scores for the pain measures were converted to a proportional score on a 0–100 scale. The converted four proportional scores were summed and averaged to calculate the CPI value with a possible range of 0–100. This approach is similar to that used to create quality of life scores [19]. We calculated the internal consistency of the four newly created proportional pain scores using Cronbach’s alpha. We computed test-retest reliability by the Pearson correlation coefficient of the CPI at baseline and post-test in the control group only because those in the experimental group had received an intervention that significantly reduced the raw pain intensity score at post-test (Unpublished data, under review). We used the entire sample for all other tests. We used principal components analysis with varimax rotation to maximize variance of loadings within factor(s) across the proportional pain scores to examine the construct validity of the values comprising the CPI. We assessed criterion validity of the CPI by evaluating its sensitivity to the intervention outcome using multiple regression analysis; we hypothesized that the significant group effect detected in the primary study (Unpublished data, under review) also would be detected with the CPI as the outcome measure. 3
Wilkie et al.
Table 1 Baseline and retest means, SD, differences and correlations for the individual proportional pain scores, and the CPI in the usual care control group Usual Care Control Group Mean 6 SD (n 5 88)
Location Intensity Quality Pattern CPI
Baseline
3–4 week retest
10.2 6 8.9 30.6 6 21.1 25.0 6 16.2 38.1 6 25.6 26.0 6 13.5
9.8 6 13.6 32.2 6 22.2 24.9617.9 33.3 6 24.1 25.1 6 14.0
Paired t test (P) 0.27 20.69 0.08 1.30 0.64
r* (P)
(ns) (ns) (ns) (ns) (ns)
0.49 0.51 0.54 0.08 0.52
( < 0.001) ( < 0.001) ( < 0.001) ( < 0.47) ( < 0.001)
r: Pearson correlationnns 5 not significant at 0.05 level.
Results Reliability In the complete sample, the proportional scores for the four pain measures represented adequate internal consistency with Cronbach’s alpha of 0.71 for baseline and minimally acceptable internal consistency of 0.69 at post-test [20]. In the control group, there was no significant difference between the mean CPI scores at baseline and post-test (Table 1), which allowed evaluation of the stability of the CPI over time. This 3–4 week testretest reliability was 0.52 in this sample of outpatients with cancer who were receiving usual cancer care. Construct Validity Table 2 shows the convergent validity of how the constructs of the four pain scores were correlated. Using the entire sample for construct validity testing, the principal components analysis extracted one factor with high factor loadings for location, intensity, quality, and pattern at baseline and essentially replicated at posttest (Table 3). The communality estimates in Table 3 show that the common variance among the pain location, intensity, quality, and pattern scores were moderate to large at both baseline and post-test. The one
Table 2
extracted factor had a large eigenvalue (2.17) at baseline. When we replicated the principal components analysis at post-test, the eigenvalue (2.11) for the single extracted factor was large, also. The screen test also clearly indicated that the one factor was the only factor to extract from the four pain measures at both baseline and post-test. The one factor, which we named CPI, explained 54% of the total variance in the four pain scores at baseline and 53% of the variance at post-test. Data were not available for divergent validity testing in this sample. Criterion Validity: Sensitivity of CPI As indicated in Table 4, we conducted multiple regression analysis in which we adjusted for the baseline CPI scores, gender, race, education, cancer stage, and cancer type at post-test. We controlled for these demographic variables because other investigators have shown that cancer pain often varies by them [13]. Results show that the experimental group had a statically lower CPI score (P < 0.04) than the usual care control group. In addition to group, race was a significant predictor (P < 0.01) of the CPI score at post-test. In the control group at baseline and post-test, the mean CPI scores for whites were 25.1 6 13.5 and 23.8 6 13.5, whereas those means for racial minorities were
Correlations among proportional pain scores (0–100 scale) and the CPI scores Baseline (N 5 176)
1 2 3 4 5
Location Intensity Quality Pattern CPI
1
2
1 0.21** 0.44*** 0.32*** 005***
1 0.54*** 0.33*** 0.73***
3
1 0.45*** 0.80***
3–4 week post-test (n 5 174) 4
1 0.80***
1 Location 2 Intensity 3 Quality 4 Pattern 5 CPI
Key: 2-tailed correlation is significant at the level of < 0.05*, < 0.01**, < 0.001***; 1 5 Location; 2 5 Intensity; 3 5 Quality; 4 5 Pattern.
4
1
2
3
4
1 0.30*** 0.36*** 0.18* 0.54***
1 0.58*** 0.36*** 0.80***
1 0.36*** 0.77***
1 0.74***
Composite Pain Index
Table 3 Principal component analysis of the proportional scores for the four pain measures: Factor loadings and communalities at baseline (N 5 176) and post-test (n 5 174) Factor loadings
Communalities
Pain measures Baseline Post-test Baseline Post-test Locations Intensity Quality Pattern
0.65 0.71 0.85 0.71
0.59 0.81 0.84 0.63
0.42 0.51 0.73 0.51
0.35 0.66 0.70 0.40
Note: one component extracted with eigenvalue 5 2.17 accounting for 54% of the variance at baseline, and these findings were replicated at post-test: one component extracted with eigenvalue 5 2.11 accounting for 53% of the variance.
34.3 6 10 and 39.1 6 13.6, respectively. In the experimental group at baseline and post-test, the mean CPI scores for whites were 23.0 6 12.1 and 20.48 6 11.1, whereas those means for racial minorities were 30.1 6 15.1 and 28.6 6 14.9, respectively (Figure 2). The model explained 33% of the total variance in the CPI score at post-test. The multiple regression equation with the predictors (unstandardized b) was: CPI at posttest 5 intercept (19.25) 1 (0.49) * baseline CPI 1 (2.58) * male gender 1 (27.79) * White race 1 (21.55) * cancer stage IV 1 (22.92) * breast cancer 1 (2.28) * at least some college education 1 (23.70) * experimental group.
Table 4 Multivariate regression analysis predicting the CPI score at post-test (F (167) 5 10.99, P < 0.001) t value CPI at baseline 7.29 Group (experimental 22.13† vs. usual care) Gender (male vs. female) 1.16 Race (white vs. 2.74† minority race) Cancer type (breast 21.30 cancer vs. other cancer) Cancer stage 2.87 (stage IV vs. other stage) Education (at least some 1.18 college vs. high school) R2 / Adjusted R2
P
*Beta coefficient
0.49 0.001† 0.04† 23.70 0.27 0.01†
2.58 27.79
0.20
22.92
0.39
21.55
0.24
2.28
0.33/0.30
*Unstandardized Coefficient from multiple linear regression. † P < 0.05.
Figure 2 CPI by intervention group and race at baseline and post-test (n 5 174). The regression equation showed the significant group effect on decreasing the CPI score at post-test when controlling the predictors. In addition, race had a significant effect on the post-test CPI score. We conducted a similar analysis of the PRI-T post-test score, but there was no statistically significant difference between the usual care and experimental groups, which indicates that in contrast to the CPI, the PRI-T was not sensitive to the intervention effects. Discussion We were successful in producing the CPI, a patientreported outcome measure for cancer pain that integrates multiple dimensions of the experience. The CPI is an alternate way to derive a score from the MPQ. In this study of 176 patients with cancer, we demonstrated adequate internal consistency and construct validity of items contributing to the CPI and sensitivity of the CPI as a cancer pain outcome measure. In the control group (n 5 90), we demonstrated low testretest reliability over a relatively long time interval. The adequate psychometric properties support the CPI’s potential to integrate the multidimensional nature of cancer pain as a single outcome score that holds much promise for future research. This approach provides a new way of conceptualizing and scoring the MPQ as a patient-reported outcome score for research and complements the other MPQ scores that have been useful for clinical practice and research since 1975 [2]. Melzack [21] suggested nearly 40 years ago that additional research would advance the MPQ. One such advance that did not become widely accepted is an alternate weighted scoring for the quality descriptors [22]. Another advance was a short form tool [23]. We made another advance by converting the MPQ to a computer-based tool [11] and converting scores to a proportional scale (0–100) and summing the location, intensity, quality, and pattern scores to create the CPI, 5
Wilkie et al. a single score representing the multidimensional pain experience. Researchers ultimately will determine the value of the CPI as an additional MPQ score by conducting additional studies that build on the strong evidence from the current investigation. Measuring pain is a challenging task but is needed to determine the pain mechanisms and effectiveness of pain treatment at both the individual and group or population levels for patients with cancer. At the individual level, it is essential to have valid, reproducible measures of the multiple dimensions of the pain that clinicians can easily use to guide therapy decisions and evaluate their effects [8]. The traditional MPQ scores have well-known value to guide clinical therapy decisions and to document changes in pain over time. At the group or population level, it is helpful to have a single summary pain measure that is not only an authentic integration of its multidimensionality, but also can serve as an outcome indicator. We demonstrated that the CPI, a summary indicator of pain location, intensity, PRI-T (sensory, miscellaneous, affective and evaluative quality dimensions), and pattern, is a reliable and valid indicator of pain in outpatients with cancer. Our reliability estimates related to the CPI are adequate for internal consistency but low for stability. The Cronbach’s alphas at two different time points revealed that the CPI components had adequate and reproducible internal consistency prior to being combined as the CPI. The alphas show that the four MPQ pain scores reflected the same concept (pain) but are not redundant indicators. As all the four scores are important for understanding the complexity of pain, it is legitimate to combine them into one index score [4]. Test-retest reliability indicated that there was low stability in CPI scores that were measured at an interval of 3–4 weeks in the usual care control group, which did not receive the study intervention. This finding is consistent with the notion that pain, including cancer pain, varies over time and evaluations of instrument stability should be close in time proximity [14,24]. The 3–4 week interval is relatively long for assessment of stability in a dynamic phenomenon, such as pain. The evidence that the pattern score had the lowest test-retest reliability coefficient provides additional support for the need to conduct additional research regarding the stability of the CPI over time. Our findings are strongly supportive of the construct validity of the CPI as a multidimensional indicator of cancer pain. The one factor solution with a high eigenvalue and moderate to strong loadings indicated that the four pain scores were related closely to the construct we named the CPI. Interestingly, the PRI-T score loaded highest on the CPI factor at both measurement times (baseline and 4-weeks afterwards). Other investigators reported similar findings when they created index scores from some but not all of the pain dimensions that we included. De Conno et al. [25] reported that a single factor emerged from the factor analysis of a combination of pain intensity and pain quality measures (Visual Analogue 6
Scale, Numeric Rating Scale, Verbal Rating Scale, and MPQ PRI-T). Our principal component analysis showed that pain quality as represented by the PRI-T score had a strong correlation and strong common variance with the other pain measures. These findings support the PRIT score as a global measure of pain quality that is related but separate from pain intensity, a finding that has been noted by many researchers [12] ever since Melzack first introduced it in 1975 [2]. The PRI-T, however, is a summary score representing the various sensory pain qualities (e.g., pressure, spatial, temporal, thermal, and others) as well as affective, and evaluative pain qualities. As such, the CPI does not provide insights about the unique contribution of each sensory, affective, and evaluative pain quality to the pain experience [26]. We also demonstrated that the CPI was a sensitive outcome to the multimedia, educational intervention, supporting the criterion-related validity of the CPI. Using the CPI at baseline, gender, race, education, cancer stage, cancer type, and group as predictors, the CPI at posttest demonstrated that this outcome variable was sensitive to the intervention effects. This finding replicates the finding from the primary analysis where pain barrier scores and worst pain were significantly reduced in the experimental group as compared to the usual care control group (Unpublished data, under review). Furthermore, the PRI-T score, which has been available since 1975 [2], was not sensitive to the intervention effect. This finding indicates that the CPI score, which integrates pain dimensions that are not included in the PRI-T score (namely location, intensity over time, and pain pattern), was more robust to intervention sensitivity than the PRI-T, the multidimensional measure that had been available before our creation of the CPI score. Other researchers, including those involved in the IMMPACT recommendations or PROMIS, offer a variety of pain outcome measures, some of which represent pain as a multidimensional construct and others that represent only a single dimension [27–32]. These outcome measures have been used in clinical trials testing the effects of drugs and other therapies. It is unclear at this time how the CPI would compare to these other outcome measures. Additional research is needed to compare the CPI to these other outcome measures. One obvious advantage of the CPI is that the data from multiple dimensions of the MPQ can be used to guide clinical therapies and then the CPI can be used to evaluate the effects of the therapies. To use a single instrument as a diagnostic and outcome tool has important implications for reducing subject burden. For example, when a person with lung cancer draws pain on the MPQ body outline in the distribution of the C6 cervical dermatome, rates it as 7/10 pain intensity, and describes it as constant, burning, and radiating, the clinician may recognize that the pain likely indicates brachial plexopathy and a prescription for amitripyline is likely to improve pain control. Then a CPI score can be calculated from the same data and used as a group-level outcome measure. The relative value of the CPI as an outcome measure compared to pain intensity measures, however,
Composite Pain Index will require additional research. Such research is warranted based on our promising findings. A few limitations detract from the CPI as a summary measure of the multiple dimensions of pain. We were unable to provide evidence of discriminate validity for the CPI in this study because it lacked appropriate measures for testing divergent validity. In future research, it will be particularly important to evaluate the discriminate validity of the CPI. In this study, we also were unable to determine the clinical meaningfulness of the CPI and when the CPI is an appropriate measure. Clearly, we do not expect that the CPI will be the best outcome measure in all studies. As a summary measure, it will not provide evidence of the dimension or dimensions driving an individual CPI score, which often helps clinicians and researchers tailor or personalize therapies. The CPI itself cannot be deconstructed, but since the individual scores from which it derives will always be available, clinicians and researchers can refer to those scores if deconstruction is needed. Additional research studies are needed to clarify and rectify these limitations. In summary, despite these minor limitations, we conclude that the CPI is a reliable and valid composite score that integrates the sensory (location, intensity, quality, and pattern), and affective and cognitive quality dimensions of cancer pain. The CPI could be used as a patient-reported outcome measure to quantify the complexity of cancer pain for the health services research, clinical research, and population studies. As the CPI derives from the MPQ, the individual scores can be used for patient-level decision making and outcomes to guide clinical cancer pain practice. Future research is needed to provide additional evidence of the value of the CPI as another MPQ score in people with cancer pain and other pain conditions. Acknowledgment
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The authors thank Kevin Grandfield for editorial assistance and Veronica Angulo for administrative support.
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Original Composite Pain Index: Reliability, Validity, and Sensitivity of a Patient-Reported Outcome for Research
Diana J. Wilkie, PhD, RN, FAAN,*,† Robert E. Molokie, MD,†,‡,§,¶ Marie L. Suarez, PhD,* Miriam O. Ezenwa, PhD, RN,* and Zaijie J. Wang, PhD†,¶ *Department of Biobehavioral Health Science, University of Illinois, Chicago College of Nursing Chicago, Illinois, USA; †University of Illinois Cancer Center, Chicago, Illinois, USA; ‡Division of Hematology/Oncology, University of Illinois, Chicago College of Medicine, Chicago, Illinois, USA; §Jesse Brown Veteran’s Administration Medical Center, Chicago, Illinois; ¶Department of Biopharmaceutical Sciences, University of Illinois, Chicago College of Pharmacy, Chicago, Illinois, USA Reprints requests to: Diana J. Wilkie, PhD, RN, FAAN, Department of Biobehavioral Health Science (MC 802), University of Illinois at Chicago, 845 S. Damen Ave., Room 660, Chicago, IL 60612-7350, USA. Tel: 312.413.5469; Fax: 312.996.1819; E-mail: [email protected]. Funding sources: This research was made possible by Grant Numbers 1R01CA81918 and 2R01CA081918 from the National Institutes of Health, National Cancer Institute. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute. The final peerreviewed manuscript is subject to the National Institutes of Health Public Access Policy. Conflicts of interest: Drs. Molokie and Wilkie are coinvestigators on an unrelated study funded by Pfizer. There are no other conflicts related to this research.
Abstract Objective. A single score that represents the multidimensionality of pain would be an innovation for
patient-reported outcomes. Our aim was to determine the reliability, validity, and sensitivity of the Composite Pain Index (CPI). Design. Methodological analysis of data from a randomized controlled, pretest/post-test educationbased intervention study. Setting. The study was conducted in outpatient oncology clinics. Subjects. The 176 subjects had pain, were 52 6 12.5 years on average, 63% were female, and 46% had stage IV cancers. Methods. We generated the CPI from pain location, intensity, quality, and pattern scores measured with an electronic version of Melzack’s McGill Pain Questionnaire. Results. The internal consistency values for the individual scores comprising the CPI were adequate (0.71 baseline, 0.69 post-test). Principal components analysis extracted one factor with an eigenvalue of 2.17 with explained variance of 54% at baseline and replicated the one factor with an eigenvalue of 2.11 at post-test. The factor loadings for location, intensity, quality, and pattern were 0.65, 0.71, 0.85, and 0.71, respectively (baseline), and 0.59, 0.81, 0.84, and 0.63, respectively (posttest). The CPI was sensitive to an education intervention effect. Conclusions. Findings support the CPI as a score that integrates the multidimensional pain experience in people with cancer. It could be used as a patient-reported outcome measure to quantify the complexity of pain in clinical research and population studies of cancer pain and studied for relevance in other pain populations. Key Words. Pain Management; Interventional 1
Wilkie et al. Introduction Pain is recognized as a multidimensional phenomenon, but few instruments provide one score that integrates its multiple components. Researchers created a variety of pain indices using pain intensity and affective pain components [1], pain quality ranked by intensity, affective and cognitive components [2], and pain behavior [3]. None created an index that includes all four of the self-reported sensory pain components (location, intensity, quality, and pattern) as well as affective and cognitive qualities. A single summary score can replace multiple main scores when all are important [4], as is the case with the multiple pain dimensions. Although many investigators and clinicians focus only on pain intensity, the pain location is critical for determining new pain sites that may represent new or progressive pathology and pain pattern is important for determining the timing of pain therapies. Some pain experts consider pain quality a proxy for pain intensity, but pain quality is an additional pain characteristic that helps to guide selection of the pain therapies (e.g., adjuvant drugs) independent of pain intensity. Availability of a single score that integrates the complex pain experience has statistical benefits for research by preserving degrees of freedom, conserving sample size, and improving the power of statistical tests [4], a desirable characteristic for a pain phenotype or a clinical trial outcome measure. It is also desirable if such a patient-reported pain outcome can be derived from a pain tool that also has research and clinical utility, such as the McGill Pain Questionnaire (MPQ) [2]. Our study purpose was to determine the reliability, validity, and sensitivity of the Composite Pain Index (CPI), a single score that integrates the multidimensionality of cancer pain. The multiple dimensions of cancer pain are well known to include physiologic-sensory, affective, cognitive, and behavioral dimensions [2,5,6]). Commonly, however, investigators use pain tools that quantified one or two dimensions such as pain severity and pain affect [7], or pain impact on the patient’s life and functioning [8]. Only two tools are used often in cancer studies to measure more than one pain dimension: [1] the Brief Pain Inventory [8] measures pain severity and functional interference; and [2] the MPQ [2] measures four components of the sensory dimension of pain, specifically, its location, intensity, quality, and pattern as well as the affective, cognitive, and behavioral dimensions of pain [2,5]. The well-known MPQ pain rating index-total (PRI-T) score integrates the sensory, affective and cognitive pain dimensions, namely the PRIsensory, PRI-miscellaneous, PRI-affective, and PRIevaluative (cognitive) scores. The PRI-T does not integrate the pain location, intensity, or pattern components of sensory pain. To date, the MPQ behavior dimension data (aggravating and alleviating factors) are in qualitative format and not yet amenable to a quantitative score. Based on well-accepted approaches [4], the CPI is an additional MPQ score that could be a robust outcome because it integrates pain location, intensity, quality, and pattern (Figure 1). The CPI score reported by adults with 2
Figure 1 CPI: Pain dimensions and scores included.
sickle cell disease at an outpatient visit was as strong a predictor of their acute care utilization over the subsequent year as average pain intensity (current, least and worst in the previous 24 hours) [9]. Furthermore, when used as a pain phenotype in adults with sickle cell disease, the CPI discriminated a single nucleotide polymorphism that is associated with pain [10]. Although these findings provide encouraging support for the CPI in outpatients with sickle cell disease, additional evidence for the psychometrics of the CPI in another pain population is important for researchers to have confidence in this measure. We report analyses that demonstrate the internal consistency, test-retest reliability, construct validity, and sensitivity of the CPI as a patient-reported pain outcome measure for outpatients with cancer. Materials and Methods Design and Setting We used data from a randomized clinical trial of a tailored, multimedia educational intervention that we conducted in radiation and medical oncology outpatient clinics of a large medical center in the Puget Sound area in Seattle. The Human Subjects Division at the University of Washington and the Institutional Review Board at the University of Illinois at Chicago approved the study. Sample Primary study eligibility criteria required that the subjects had a cancer diagnosis, had pain or fatigue within the week prior to enrollment, spoke and read English, and were 18-years-old or older. Blind patients or those physically and cognitively unable to complete the study were not eligible for the study with a multimedia intervention.
Composite Pain Index Of the 800 subjects referred to the original study, 378 met eligibility criteria, 286 enrolled, and 230 of those enrolled completed the study. For this analysis, we included the 176 of the 230 subjects who reported they had pain within the previous week; the other 34 had fatigue but no pain. The average age of the sample was 52 6 12.5 years (ranged from 19 to 84 years). The majority of the 176 subjects were female (n 5 111; 63%). Most subjects were married (63%); 27% were single, 3% were widowed, and 7% were other/ unknown. The ethnic/racial distribution of the sample was 89% Caucasian, 6% Asian, 2% African American, 2% Native American, and 1% Hispanic. Their education levels were: 1% completed 8th grade or less; 27% completed high school; 8% completed some vocational school; 19% had an associate degree; 28% had a baccalaureate degree; 13% had a master’s degree; 2% had a doctoral degree; and 2% had another or unknown level of education. Subjects had the following cancer diagnoses: breast cancer (38%); head and neck (18%); sarcoma (9%); lung cancer (5%); cervical cancer (3%); colon and rectal cancer (4%); prostate (5%); lymphoma (4%); brain cancer (4%); stomach (1%); ovarian cancer (2%); myeloma (2%); other or unspecified (4%); and pancreatic, kidney, bladder, or leukemia (0.6% each). Nearly half (46%) of the sample had stage IV cancers and the rest (54%) had stage I (13%), II (24%), 3 (15%) or unknown stage (2%) cancers.
Procedures We recruited eligible patients who had regularly scheduled outpatient visits in radiation or medical oncology clinics. After obtaining the patient’s written consent, the researcher introduced the patient to a pentablet comR [11] proputer with the Windows-based PAINReportItV gram to assess baseline pain measures. We used a computerized randomization program with assignment in permutated blocks (18–24 subjects per block) to randomly assign (via embedded hidden programming code not accessible to the researchers) all subjects to R (tailored multimedia intervention) or usual PAINUCopeV care with computer games (attention control) groups. About 3–4 weeks after the delivery of the intervention or computer game sessions, all subjects completed R for post-test measures. Primary study PAINReportItV outcomes are under review.
Instruments R described elsewhere [11], is an elecPAINReportIt,V tronic version of the 1970 MPQ version that was first published in 1975 [2] and is a widely accepted valid and reliable measure of pain [12]. The CPI is a single score that integrates the location, intensity, Pain Rating IndexTotal (PRI-T; sensory, affective and evaluative quality dimensions), and pattern of pain, all of which are measR It is comprised of the following ured by PAINReportIt.V four scores:
1. Pain location score (marked on a body outline; scored by counting the number of pain sites) [raw score ranges from 0 to 22]. The number of pain sites have been used in cancer [13–15], and postoperative models of pain [16], this score decreases as pain diminishes with recovery [17]. 2. Total pain intensity score (measured on the 0–10 Pain Intensity Number Scale [18]; scored by the sum of scores for current, least and worst pain in previous 24 hours [mean raw score ranges from 0–10]. The 0–10 scale replaced the MPQ’s verbal descriptor scale of pain intensity because the descriptors demonstrated a lack of equivalent intervals between the descriptors that was particularly extreme for descriptors such as distressing and horrible or excruciating (Unpublished data, under review). 3. Pain quality score (original MPQ PRI-T score [raw score ranges from 0 to 78], which includes sensory, affective, and evaluative pain quality dimensions); and 4. Pain temporal pattern score (derived from the nine pain pattern words on the MPQ; we calculated the temporal pattern score by assigning values to each of the pain pattern descriptors: 3 assigned to constant, steady, continuous; 2 assigned to brief, momentary, transient; 1 assigned to intermittent, periodic, rhythmic; and 0 assigned for no pattern words selected [the ordinal level raw score ranges from 0 to 6 because patients often select words from more than one group]). Previous research supports the pain pattern score in that the number of pain sites, pain intensity scores, and PRI scores all differed significantly by pain temporal pattern groups [13]. Statistical Analysis To construct the CPI value, each of the four raw scores for the pain measures were converted to a proportional score on a 0–100 scale. The converted four proportional scores were summed and averaged to calculate the CPI value with a possible range of 0–100. This approach is similar to that used to create quality of life scores [19]. We calculated the internal consistency of the four newly created proportional pain scores using Cronbach’s alpha. We computed test-retest reliability by the Pearson correlation coefficient of the CPI at baseline and post-test in the control group only because those in the experimental group had received an intervention that significantly reduced the raw pain intensity score at post-test (Unpublished data, under review). We used the entire sample for all other tests. We used principal components analysis with varimax rotation to maximize variance of loadings within factor(s) across the proportional pain scores to examine the construct validity of the values comprising the CPI. We assessed criterion validity of the CPI by evaluating its sensitivity to the intervention outcome using multiple regression analysis; we hypothesized that the significant group effect detected in the primary study (Unpublished data, under review) also would be detected with the CPI as the outcome measure. 3
Wilkie et al.
Table 1 Baseline and retest means, SD, differences and correlations for the individual proportional pain scores, and the CPI in the usual care control group Usual Care Control Group Mean 6 SD (n 5 88)
Location Intensity Quality Pattern CPI
Baseline
3–4 week retest
10.2 6 8.9 30.6 6 21.1 25.0 6 16.2 38.1 6 25.6 26.0 6 13.5
9.8 6 13.6 32.2 6 22.2 24.9617.9 33.3 6 24.1 25.1 6 14.0
Paired t test (P) 0.27 20.69 0.08 1.30 0.64
r* (P)
(ns) (ns) (ns) (ns) (ns)
0.49 0.51 0.54 0.08 0.52
( < 0.001) ( < 0.001) ( < 0.001) ( < 0.47) ( < 0.001)
r: Pearson correlationnns 5 not significant at 0.05 level.
Results Reliability In the complete sample, the proportional scores for the four pain measures represented adequate internal consistency with Cronbach’s alpha of 0.71 for baseline and minimally acceptable internal consistency of 0.69 at post-test [20]. In the control group, there was no significant difference between the mean CPI scores at baseline and post-test (Table 1), which allowed evaluation of the stability of the CPI over time. This 3–4 week testretest reliability was 0.52 in this sample of outpatients with cancer who were receiving usual cancer care. Construct Validity Table 2 shows the convergent validity of how the constructs of the four pain scores were correlated. Using the entire sample for construct validity testing, the principal components analysis extracted one factor with high factor loadings for location, intensity, quality, and pattern at baseline and essentially replicated at posttest (Table 3). The communality estimates in Table 3 show that the common variance among the pain location, intensity, quality, and pattern scores were moderate to large at both baseline and post-test. The one
Table 2
extracted factor had a large eigenvalue (2.17) at baseline. When we replicated the principal components analysis at post-test, the eigenvalue (2.11) for the single extracted factor was large, also. The screen test also clearly indicated that the one factor was the only factor to extract from the four pain measures at both baseline and post-test. The one factor, which we named CPI, explained 54% of the total variance in the four pain scores at baseline and 53% of the variance at post-test. Data were not available for divergent validity testing in this sample. Criterion Validity: Sensitivity of CPI As indicated in Table 4, we conducted multiple regression analysis in which we adjusted for the baseline CPI scores, gender, race, education, cancer stage, and cancer type at post-test. We controlled for these demographic variables because other investigators have shown that cancer pain often varies by them [13]. Results show that the experimental group had a statically lower CPI score (P < 0.04) than the usual care control group. In addition to group, race was a significant predictor (P < 0.01) of the CPI score at post-test. In the control group at baseline and post-test, the mean CPI scores for whites were 25.1 6 13.5 and 23.8 6 13.5, whereas those means for racial minorities were
Correlations among proportional pain scores (0–100 scale) and the CPI scores Baseline (N 5 176)
1 2 3 4 5
Location Intensity Quality Pattern CPI
1
2
1 0.21** 0.44*** 0.32*** 005***
1 0.54*** 0.33*** 0.73***
3
1 0.45*** 0.80***
3–4 week post-test (n 5 174) 4
1 0.80***
1 Location 2 Intensity 3 Quality 4 Pattern 5 CPI
Key: 2-tailed correlation is significant at the level of < 0.05*, < 0.01**, < 0.001***; 1 5 Location; 2 5 Intensity; 3 5 Quality; 4 5 Pattern.
4
1
2
3
4
1 0.30*** 0.36*** 0.18* 0.54***
1 0.58*** 0.36*** 0.80***
1 0.36*** 0.77***
1 0.74***
Composite Pain Index
Table 3 Principal component analysis of the proportional scores for the four pain measures: Factor loadings and communalities at baseline (N 5 176) and post-test (n 5 174) Factor loadings
Communalities
Pain measures Baseline Post-test Baseline Post-test Locations Intensity Quality Pattern
0.65 0.71 0.85 0.71
0.59 0.81 0.84 0.63
0.42 0.51 0.73 0.51
0.35 0.66 0.70 0.40
Note: one component extracted with eigenvalue 5 2.17 accounting for 54% of the variance at baseline, and these findings were replicated at post-test: one component extracted with eigenvalue 5 2.11 accounting for 53% of the variance.
34.3 6 10 and 39.1 6 13.6, respectively. In the experimental group at baseline and post-test, the mean CPI scores for whites were 23.0 6 12.1 and 20.48 6 11.1, whereas those means for racial minorities were 30.1 6 15.1 and 28.6 6 14.9, respectively (Figure 2). The model explained 33% of the total variance in the CPI score at post-test. The multiple regression equation with the predictors (unstandardized b) was: CPI at posttest 5 intercept (19.25) 1 (0.49) * baseline CPI 1 (2.58) * male gender 1 (27.79) * White race 1 (21.55) * cancer stage IV 1 (22.92) * breast cancer 1 (2.28) * at least some college education 1 (23.70) * experimental group.
Table 4 Multivariate regression analysis predicting the CPI score at post-test (F (167) 5 10.99, P < 0.001) t value CPI at baseline 7.29 Group (experimental 22.13† vs. usual care) Gender (male vs. female) 1.16 Race (white vs. 2.74† minority race) Cancer type (breast 21.30 cancer vs. other cancer) Cancer stage 2.87 (stage IV vs. other stage) Education (at least some 1.18 college vs. high school) R2 / Adjusted R2
P
*Beta coefficient
0.49 0.001† 0.04† 23.70 0.27 0.01†
2.58 27.79
0.20
22.92
0.39
21.55
0.24
2.28
0.33/0.30
*Unstandardized Coefficient from multiple linear regression. † P < 0.05.
Figure 2 CPI by intervention group and race at baseline and post-test (n 5 174). The regression equation showed the significant group effect on decreasing the CPI score at post-test when controlling the predictors. In addition, race had a significant effect on the post-test CPI score. We conducted a similar analysis of the PRI-T post-test score, but there was no statistically significant difference between the usual care and experimental groups, which indicates that in contrast to the CPI, the PRI-T was not sensitive to the intervention effects. Discussion We were successful in producing the CPI, a patientreported outcome measure for cancer pain that integrates multiple dimensions of the experience. The CPI is an alternate way to derive a score from the MPQ. In this study of 176 patients with cancer, we demonstrated adequate internal consistency and construct validity of items contributing to the CPI and sensitivity of the CPI as a cancer pain outcome measure. In the control group (n 5 90), we demonstrated low testretest reliability over a relatively long time interval. The adequate psychometric properties support the CPI’s potential to integrate the multidimensional nature of cancer pain as a single outcome score that holds much promise for future research. This approach provides a new way of conceptualizing and scoring the MPQ as a patient-reported outcome score for research and complements the other MPQ scores that have been useful for clinical practice and research since 1975 [2]. Melzack [21] suggested nearly 40 years ago that additional research would advance the MPQ. One such advance that did not become widely accepted is an alternate weighted scoring for the quality descriptors [22]. Another advance was a short form tool [23]. We made another advance by converting the MPQ to a computer-based tool [11] and converting scores to a proportional scale (0–100) and summing the location, intensity, quality, and pattern scores to create the CPI, 5
Wilkie et al. a single score representing the multidimensional pain experience. Researchers ultimately will determine the value of the CPI as an additional MPQ score by conducting additional studies that build on the strong evidence from the current investigation. Measuring pain is a challenging task but is needed to determine the pain mechanisms and effectiveness of pain treatment at both the individual and group or population levels for patients with cancer. At the individual level, it is essential to have valid, reproducible measures of the multiple dimensions of the pain that clinicians can easily use to guide therapy decisions and evaluate their effects [8]. The traditional MPQ scores have well-known value to guide clinical therapy decisions and to document changes in pain over time. At the group or population level, it is helpful to have a single summary pain measure that is not only an authentic integration of its multidimensionality, but also can serve as an outcome indicator. We demonstrated that the CPI, a summary indicator of pain location, intensity, PRI-T (sensory, miscellaneous, affective and evaluative quality dimensions), and pattern, is a reliable and valid indicator of pain in outpatients with cancer. Our reliability estimates related to the CPI are adequate for internal consistency but low for stability. The Cronbach’s alphas at two different time points revealed that the CPI components had adequate and reproducible internal consistency prior to being combined as the CPI. The alphas show that the four MPQ pain scores reflected the same concept (pain) but are not redundant indicators. As all the four scores are important for understanding the complexity of pain, it is legitimate to combine them into one index score [4]. Test-retest reliability indicated that there was low stability in CPI scores that were measured at an interval of 3–4 weeks in the usual care control group, which did not receive the study intervention. This finding is consistent with the notion that pain, including cancer pain, varies over time and evaluations of instrument stability should be close in time proximity [14,24]. The 3–4 week interval is relatively long for assessment of stability in a dynamic phenomenon, such as pain. The evidence that the pattern score had the lowest test-retest reliability coefficient provides additional support for the need to conduct additional research regarding the stability of the CPI over time. Our findings are strongly supportive of the construct validity of the CPI as a multidimensional indicator of cancer pain. The one factor solution with a high eigenvalue and moderate to strong loadings indicated that the four pain scores were related closely to the construct we named the CPI. Interestingly, the PRI-T score loaded highest on the CPI factor at both measurement times (baseline and 4-weeks afterwards). Other investigators reported similar findings when they created index scores from some but not all of the pain dimensions that we included. De Conno et al. [25] reported that a single factor emerged from the factor analysis of a combination of pain intensity and pain quality measures (Visual Analogue 6
Scale, Numeric Rating Scale, Verbal Rating Scale, and MPQ PRI-T). Our principal component analysis showed that pain quality as represented by the PRI-T score had a strong correlation and strong common variance with the other pain measures. These findings support the PRIT score as a global measure of pain quality that is related but separate from pain intensity, a finding that has been noted by many researchers [12] ever since Melzack first introduced it in 1975 [2]. The PRI-T, however, is a summary score representing the various sensory pain qualities (e.g., pressure, spatial, temporal, thermal, and others) as well as affective, and evaluative pain qualities. As such, the CPI does not provide insights about the unique contribution of each sensory, affective, and evaluative pain quality to the pain experience [26]. We also demonstrated that the CPI was a sensitive outcome to the multimedia, educational intervention, supporting the criterion-related validity of the CPI. Using the CPI at baseline, gender, race, education, cancer stage, cancer type, and group as predictors, the CPI at posttest demonstrated that this outcome variable was sensitive to the intervention effects. This finding replicates the finding from the primary analysis where pain barrier scores and worst pain were significantly reduced in the experimental group as compared to the usual care control group (Unpublished data, under review). Furthermore, the PRI-T score, which has been available since 1975 [2], was not sensitive to the intervention effect. This finding indicates that the CPI score, which integrates pain dimensions that are not included in the PRI-T score (namely location, intensity over time, and pain pattern), was more robust to intervention sensitivity than the PRI-T, the multidimensional measure that had been available before our creation of the CPI score. Other researchers, including those involved in the IMMPACT recommendations or PROMIS, offer a variety of pain outcome measures, some of which represent pain as a multidimensional construct and others that represent only a single dimension [27–32]. These outcome measures have been used in clinical trials testing the effects of drugs and other therapies. It is unclear at this time how the CPI would compare to these other outcome measures. Additional research is needed to compare the CPI to these other outcome measures. One obvious advantage of the CPI is that the data from multiple dimensions of the MPQ can be used to guide clinical therapies and then the CPI can be used to evaluate the effects of the therapies. To use a single instrument as a diagnostic and outcome tool has important implications for reducing subject burden. For example, when a person with lung cancer draws pain on the MPQ body outline in the distribution of the C6 cervical dermatome, rates it as 7/10 pain intensity, and describes it as constant, burning, and radiating, the clinician may recognize that the pain likely indicates brachial plexopathy and a prescription for amitripyline is likely to improve pain control. Then a CPI score can be calculated from the same data and used as a group-level outcome measure. The relative value of the CPI as an outcome measure compared to pain intensity measures, however,
Composite Pain Index will require additional research. Such research is warranted based on our promising findings. A few limitations detract from the CPI as a summary measure of the multiple dimensions of pain. We were unable to provide evidence of discriminate validity for the CPI in this study because it lacked appropriate measures for testing divergent validity. In future research, it will be particularly important to evaluate the discriminate validity of the CPI. In this study, we also were unable to determine the clinical meaningfulness of the CPI and when the CPI is an appropriate measure. Clearly, we do not expect that the CPI will be the best outcome measure in all studies. As a summary measure, it will not provide evidence of the dimension or dimensions driving an individual CPI score, which often helps clinicians and researchers tailor or personalize therapies. The CPI itself cannot be deconstructed, but since the individual scores from which it derives will always be available, clinicians and researchers can refer to those scores if deconstruction is needed. Additional research studies are needed to clarify and rectify these limitations. In summary, despite these minor limitations, we conclude that the CPI is a reliable and valid composite score that integrates the sensory (location, intensity, quality, and pattern), and affective and cognitive quality dimensions of cancer pain. The CPI could be used as a patient-reported outcome measure to quantify the complexity of cancer pain for the health services research, clinical research, and population studies. As the CPI derives from the MPQ, the individual scores can be used for patient-level decision making and outcomes to guide clinical cancer pain practice. Future research is needed to provide additional evidence of the value of the CPI as another MPQ score in people with cancer pain and other pain conditions. Acknowledgment
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The authors thank Kevin Grandfield for editorial assistance and Veronica Angulo for administrative support.
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