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(343) The pain burden inventory: a brief measure of pain interference
S60
Abstracts
The Journal of Pain
(340) Automatic pain intensity estimation using multimodal data from wearable sensors D Lopez Martinez, O Rudovic, D Doughty, J Anand Subramony, and R Picard; Harvard-MIT Division of Health Science and Technology, Cambridge, MA
Patients’ self-report is the most common method for pain assessment. However, while self-reports are often convenient and useful, they have a number of limitations, including being highly subjective, inconsistent and cumbersome to obtain in long-term large-scale studies. Furthermore, they cannot be obtained reliably from the mentally impaired and other vulnerable populations, such as children and elderly people. Therefore, there is an ever-growing need for reliable automatic pain assessment methods as the means for detecting pain, and for evaluating and comparing the effectiveness of different pain reduction strategies. In this work, we present a novel pain intensity estimation method that uses multi-modal data from a wrist-worn sensor. Our algorithm combines different autonomic activity metrics derived from electrodermal activity and plethysmogram wrist signals to estimate the intensity of nociceptive stimuli. We verify the robustness of our algorithm in a single-center, comparative, randomized, crossover, clinical study that evaluates the impact of different injection parameters on subcutaneous injection pain tolerance.
(341) Comparing the assay sensitivity of average pain intensity and worst pain intensity: an ACTTION metaanalysis S Smith, M Jensen, H He, R Kitt, J Koch, M McDermott, D Turk, and R Dworkin; University of Rochester, Rochester, NY Research on pain assessment has focused on the reliability and validity of different pain rating methods (e.g., 0-10 NRS vs. 10cm VAS rating pain over 24 hours vs. the past week retrospective vs. real-time). Less attention has been given to the effect of pain assessment characteristics on a clinical trial’s ability to detect a treatment effect if one exists (i.e., assay sensitivity). In this systematic review and meta-analysis, we compared the assay sensitivity of ratings of ‘‘average pain intensity’’ (API) and ‘‘worst pain intensity’’ (WPI) reported in pain treatment trials published in the peer-reviewed literature. To be eligible, trials had to: (1) be published between 1980 to 2015 (2) compare 1 or more active, efficacious pain treatment(s) (based on FDA labeling and available treatment guidelines) to placebo (3) evaluate a treatment for chronic low back pain, osteoarthritis, painful diabetic peripheral neuropathy, postherpetic neuralgia, or fibromyalgia and (4) provide data for API and WPI for all treatment groups. A sample of 24 articles (28 treatment vs. placebo comparisons) met these criteria. We calculated the standardized effect sizes (SES) for API and WPI in each trial. Based on a meta-analysis, mean API SES was statistically significantly greater than mean WPI SES (estimated mean difference = -0.033, P = 0.026). This finding indicates that among published trials of efficacious pain treatments, API detected a significantly larger difference between treatment and placebo (i.e., greater assay sensitivity) than did WPI. Pain intensity is typically utilized as the primary outcome measure in pain treatment trials, and the current results suggest that using API as the primary outcome, rather than WPI, could increase the ability to demonstrate a treatment’s analgesic effect. Support was provided by ACTTION, which has received revenue from FDA, pharmaceutical and device companies, and other sources.
(342) Measurement of affective, sensory and evaluative dimensions of pain using a Qualities-of-Pain Index (QPI) B Schachtel, S Daniels, K Patrick, S Richardson, S Royall, E Schachtel, M Lorton, S Cho, and B Zhang; Charleston Laboratories, Inc., Jupiter, FL
Patients express painful experiences using affective and sensory descriptors, not just evaluative terms. Previously, we demonstrated the sensitivity of a qualities-of-pain index comparing the effects of 1 dose of a standard analgesic, hydrocodone 7.5 mg/acetaminophen 325 mg (HC/APAP) to placebo.1 To evaluate changes in these qualitative dimensions of pain over 48 hours, we augmented the design of an analgesic study2 with measurements of these other pain descriptors. After primary unilateral metatarsal bunionectomy, 302 qualifying patients aged $18 years with moderate-to-severe pain (on a numerical rating scale, PI-NRS) were randomized to CL-108 (HC/ APAP with rapid-release promethazine 12.5mg) or placebo under double-blind conditions, 1 dose every 4–6 hours (up to 6 doses/24
hours). In addition to regularly-scheduled measurements on the PI-NRS, at baseline and 6, 12, 24, and 48 hours after treatment, patients used a separate 0–10 Likert scale in the QPI to rate each quality of pain: affective (annoying, agonizing) sensory (throbbing, heavy, swollen, tight, sharp, hot, pulling, stabbing, pressing, stinging, radiating) and evaluative (aching, hurting). Changes in QPI scores (the sum of all quality-of-pain scales) were compared for all patients regardless of baseline values. QPI scores for CL-108–treated patients were reduced compared with placebo over 24 hours ( 45.8% vs 16.2%) and 48 hours ( 55.0% vs 35.0%, both p#0.004), correlating with results on the PI-NRS (both r>0.80, p<0.001). Compared with placebo, CL-108 also reduced the affective ( 46.9% vs 14.0%), sensory ( 43.4% vs 12.5%), and evaluative ( 44.0% vs 17.3%) QPI sub-scores over 24 hours (all p<0.001). We conclude that the QPI is a sensitive indicator of changes in qualities of pain over 6–48 hours and that this instrument can be used to demonstrate the acute analgesic efficacy of opioid medications like CL-108. Funded by Charleston Laboratories, Inc., and Daiichi Sankyo, Inc. (1. Schachtel, J Pain, 2015 2. Richardson, J Pain, 2016)
(343) The pain burden inventory: a brief measure of pain interference M Santos, J Santanelli, and W Zempsky; Connecticut Children’s Medical Center, Hartford, CT
Brief self-report measures of pain interference are necessary for youth with chronic pain. We report the reliability and validity of the Pain Burden Inventory-Youth (PBI-Y), a 7-item measure of the impact of chronic pain in youth. The PBI-Y was initially validated in youth with sickle cell disease. This study expands on our original work by focusing on a broader population of youth with chronic pain conditions. The charts of 130 consecutive youth presenting for an initial pediatric chronic pain clinic evaluation were examined with 98 (mean age = 14.49; 66% female) comprising this final sample. A wide array of pain diagnoses was seen including abdominal pain, headaches, back pain, and amplified musculoskeletal pain. The PBI-Y demonstrated strong internal reliability (a = .792) and strong cross-informant concordance (r = .822; p = .000). Good construct validity was seen where higher scores on the PBI-Y were correlated with higher scores of (1) functional disability (r = .689; p = .000), (2) pain catastrophizing (r = .494; p = .000), (3) pain frequency days (r = .526; p = .000), (4) usual pain intensity (r = .467; p = .000) and (5) the worst pain intensity (r = .485; p = .000). Similar results were seen with caregiver assessments. Neither caregiver nor youth reports differed based on the gender or age of the child. The PBI-Y is a brief measure of pain interference that may have great utility for clinicians caring for youth with chronic pain.
(344) The relationship between the Pain Resilience Scale and perceived disability, pain interference, and depression in chronic pain B Ankawi, P Slepian, and C France; Ohio University, Athens, OH The Pain Resilience Scale (PRS) is a measure of pain resilience, or the ability to effectively function emotionally and physically when experiencing pain. The subscales of the PRS, Behavioral Perseverance and Cognitive/Affective Positivity, have shown differing relationships to acute pain outcomes. This study explored the relationships between the PRS subscales and theoretically-related PROMIS outcome measures for chronic pain (i.e., perceived disability, pain interference, depression). It was hypothesized that Behavioral Perseverance would be negatively related to perceived disability and pain interference, whereas Cognitive/Affective Positivity would be negatively related to depression. Two samples of participants with self-reported chronic pain (Sample 1 N = 682, Sample 2 N = 819) completed an online battery of measures using Amazon’s Mechanical Turk. Hierarchical linear regressions were conducted independently for each sample, with pain intensity and psychosocial factors entered into the first block of each analysis and the target subscale entered into the second block. Consistent with our hypotheses, higher Behavioral Perseverance scores were associated with less perceived disability (Sample 1: DF = 30.031, b = -0.175, p < 0.01, DR2 = 0.028; Sample 2: DF = 31.310, b = -0.159, p < 0.01, DR2 = 0.023) and pain interference (Sample 1: DF = 8.942, b = -0.088, p < 0.01, DR2 = 0.007; Sample 2: DF = 7.808, b = -0.077, p < 0.01, DR2 = 0.005) and higher Cognitive/Affective Positivity scores were associated with less depression (Sample 1:
Abstracts
The Journal of Pain
(340) Automatic pain intensity estimation using multimodal data from wearable sensors D Lopez Martinez, O Rudovic, D Doughty, J Anand Subramony, and R Picard; Harvard-MIT Division of Health Science and Technology, Cambridge, MA
Patients’ self-report is the most common method for pain assessment. However, while self-reports are often convenient and useful, they have a number of limitations, including being highly subjective, inconsistent and cumbersome to obtain in long-term large-scale studies. Furthermore, they cannot be obtained reliably from the mentally impaired and other vulnerable populations, such as children and elderly people. Therefore, there is an ever-growing need for reliable automatic pain assessment methods as the means for detecting pain, and for evaluating and comparing the effectiveness of different pain reduction strategies. In this work, we present a novel pain intensity estimation method that uses multi-modal data from a wrist-worn sensor. Our algorithm combines different autonomic activity metrics derived from electrodermal activity and plethysmogram wrist signals to estimate the intensity of nociceptive stimuli. We verify the robustness of our algorithm in a single-center, comparative, randomized, crossover, clinical study that evaluates the impact of different injection parameters on subcutaneous injection pain tolerance.
(341) Comparing the assay sensitivity of average pain intensity and worst pain intensity: an ACTTION metaanalysis S Smith, M Jensen, H He, R Kitt, J Koch, M McDermott, D Turk, and R Dworkin; University of Rochester, Rochester, NY Research on pain assessment has focused on the reliability and validity of different pain rating methods (e.g., 0-10 NRS vs. 10cm VAS rating pain over 24 hours vs. the past week retrospective vs. real-time). Less attention has been given to the effect of pain assessment characteristics on a clinical trial’s ability to detect a treatment effect if one exists (i.e., assay sensitivity). In this systematic review and meta-analysis, we compared the assay sensitivity of ratings of ‘‘average pain intensity’’ (API) and ‘‘worst pain intensity’’ (WPI) reported in pain treatment trials published in the peer-reviewed literature. To be eligible, trials had to: (1) be published between 1980 to 2015 (2) compare 1 or more active, efficacious pain treatment(s) (based on FDA labeling and available treatment guidelines) to placebo (3) evaluate a treatment for chronic low back pain, osteoarthritis, painful diabetic peripheral neuropathy, postherpetic neuralgia, or fibromyalgia and (4) provide data for API and WPI for all treatment groups. A sample of 24 articles (28 treatment vs. placebo comparisons) met these criteria. We calculated the standardized effect sizes (SES) for API and WPI in each trial. Based on a meta-analysis, mean API SES was statistically significantly greater than mean WPI SES (estimated mean difference = -0.033, P = 0.026). This finding indicates that among published trials of efficacious pain treatments, API detected a significantly larger difference between treatment and placebo (i.e., greater assay sensitivity) than did WPI. Pain intensity is typically utilized as the primary outcome measure in pain treatment trials, and the current results suggest that using API as the primary outcome, rather than WPI, could increase the ability to demonstrate a treatment’s analgesic effect. Support was provided by ACTTION, which has received revenue from FDA, pharmaceutical and device companies, and other sources.
(342) Measurement of affective, sensory and evaluative dimensions of pain using a Qualities-of-Pain Index (QPI) B Schachtel, S Daniels, K Patrick, S Richardson, S Royall, E Schachtel, M Lorton, S Cho, and B Zhang; Charleston Laboratories, Inc., Jupiter, FL
Patients express painful experiences using affective and sensory descriptors, not just evaluative terms. Previously, we demonstrated the sensitivity of a qualities-of-pain index comparing the effects of 1 dose of a standard analgesic, hydrocodone 7.5 mg/acetaminophen 325 mg (HC/APAP) to placebo.1 To evaluate changes in these qualitative dimensions of pain over 48 hours, we augmented the design of an analgesic study2 with measurements of these other pain descriptors. After primary unilateral metatarsal bunionectomy, 302 qualifying patients aged $18 years with moderate-to-severe pain (on a numerical rating scale, PI-NRS) were randomized to CL-108 (HC/ APAP with rapid-release promethazine 12.5mg) or placebo under double-blind conditions, 1 dose every 4–6 hours (up to 6 doses/24
hours). In addition to regularly-scheduled measurements on the PI-NRS, at baseline and 6, 12, 24, and 48 hours after treatment, patients used a separate 0–10 Likert scale in the QPI to rate each quality of pain: affective (annoying, agonizing) sensory (throbbing, heavy, swollen, tight, sharp, hot, pulling, stabbing, pressing, stinging, radiating) and evaluative (aching, hurting). Changes in QPI scores (the sum of all quality-of-pain scales) were compared for all patients regardless of baseline values. QPI scores for CL-108–treated patients were reduced compared with placebo over 24 hours ( 45.8% vs 16.2%) and 48 hours ( 55.0% vs 35.0%, both p#0.004), correlating with results on the PI-NRS (both r>0.80, p<0.001). Compared with placebo, CL-108 also reduced the affective ( 46.9% vs 14.0%), sensory ( 43.4% vs 12.5%), and evaluative ( 44.0% vs 17.3%) QPI sub-scores over 24 hours (all p<0.001). We conclude that the QPI is a sensitive indicator of changes in qualities of pain over 6–48 hours and that this instrument can be used to demonstrate the acute analgesic efficacy of opioid medications like CL-108. Funded by Charleston Laboratories, Inc., and Daiichi Sankyo, Inc. (1. Schachtel, J Pain, 2015 2. Richardson, J Pain, 2016)
(343) The pain burden inventory: a brief measure of pain interference M Santos, J Santanelli, and W Zempsky; Connecticut Children’s Medical Center, Hartford, CT
Brief self-report measures of pain interference are necessary for youth with chronic pain. We report the reliability and validity of the Pain Burden Inventory-Youth (PBI-Y), a 7-item measure of the impact of chronic pain in youth. The PBI-Y was initially validated in youth with sickle cell disease. This study expands on our original work by focusing on a broader population of youth with chronic pain conditions. The charts of 130 consecutive youth presenting for an initial pediatric chronic pain clinic evaluation were examined with 98 (mean age = 14.49; 66% female) comprising this final sample. A wide array of pain diagnoses was seen including abdominal pain, headaches, back pain, and amplified musculoskeletal pain. The PBI-Y demonstrated strong internal reliability (a = .792) and strong cross-informant concordance (r = .822; p = .000). Good construct validity was seen where higher scores on the PBI-Y were correlated with higher scores of (1) functional disability (r = .689; p = .000), (2) pain catastrophizing (r = .494; p = .000), (3) pain frequency days (r = .526; p = .000), (4) usual pain intensity (r = .467; p = .000) and (5) the worst pain intensity (r = .485; p = .000). Similar results were seen with caregiver assessments. Neither caregiver nor youth reports differed based on the gender or age of the child. The PBI-Y is a brief measure of pain interference that may have great utility for clinicians caring for youth with chronic pain.
(344) The relationship between the Pain Resilience Scale and perceived disability, pain interference, and depression in chronic pain B Ankawi, P Slepian, and C France; Ohio University, Athens, OH The Pain Resilience Scale (PRS) is a measure of pain resilience, or the ability to effectively function emotionally and physically when experiencing pain. The subscales of the PRS, Behavioral Perseverance and Cognitive/Affective Positivity, have shown differing relationships to acute pain outcomes. This study explored the relationships between the PRS subscales and theoretically-related PROMIS outcome measures for chronic pain (i.e., perceived disability, pain interference, depression). It was hypothesized that Behavioral Perseverance would be negatively related to perceived disability and pain interference, whereas Cognitive/Affective Positivity would be negatively related to depression. Two samples of participants with self-reported chronic pain (Sample 1 N = 682, Sample 2 N = 819) completed an online battery of measures using Amazon’s Mechanical Turk. Hierarchical linear regressions were conducted independently for each sample, with pain intensity and psychosocial factors entered into the first block of each analysis and the target subscale entered into the second block. Consistent with our hypotheses, higher Behavioral Perseverance scores were associated with less perceived disability (Sample 1: DF = 30.031, b = -0.175, p < 0.01, DR2 = 0.028; Sample 2: DF = 31.310, b = -0.159, p < 0.01, DR2 = 0.023) and pain interference (Sample 1: DF = 8.942, b = -0.088, p < 0.01, DR2 = 0.007; Sample 2: DF = 7.808, b = -0.077, p < 0.01, DR2 = 0.005) and higher Cognitive/Affective Positivity scores were associated with less depression (Sample 1: