The spine journal : official journal of the North American Spine Society · 2019
Exploring the prevalence and construct validity of high-impact chronic pain across chronic low-back pain study samples.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Herman Patricia M, Broten Nicholas, Lavelle Tara A, Sorbero Melony E, Coulter Ian D
Аннотация
BACKGROUND CONTEXT: The US National Pain Strategy focused attention on high-impact chronic pain and its restrictions. Although many interventions have been studied for chronic low-back pain, results are typically reported for heterogeneous samples. To better understand chronic pain and target interventions to those who most need care, more granular classifications recognizing chronic pain's impact are needed.
PURPOSE: To test whether chronic pain impact levels can be identified in chronic low-back pain clinical trial samples, examine the baseline patient mix across studies, and evaluate the construct validity of high-impact chronic pain.
STUDY DESIGN/SETTING: Descriptive analyses using 12 large study datasets.
PATIENT SAMPLES: Chronic low-back pain patients in nonsurgical, nonpharmacologic trials in the US, Canada, and UK.
OUTCOME MEASURES: Preference-based health utilities from the SF-6D and EQ-5D, employment status and absenteeism.
METHODS: We used two logistic regression models to predict whether patients had high-impact chronic pain and whether the remainder had low- or moderate-impact chronic pain. We developed these models using two datasets. Models with the best predictive power were used to impute impact levels for six other datasets. Stratified by these estimated chronic pain impact levels, we characterized the case mix of patients at baseline in each dataset, and summarized their health-utilities and work productivity. This study was funded by a National Center for Complementary and Integrative Medicine grant. The authors have no potential conflicts of interest.
RESULTS: The logistic models had excellent predictive power to identify those with high-impact chronic pain. Although studies were all of chronic low-back pain patients, the baseline mix of patients varied widely. Across all datasets, utilities, and productivity were similar for those with high-impact chronic pain and worsened as chronic pain impact increased.
CONCLUSIONS: There is a need to better categorize chronic pain patients to allow the targeting of optimal interventions for those with each level of chronic pain impact.
PURPOSE: To test whether chronic pain impact levels can be identified in chronic low-back pain clinical trial samples, examine the baseline patient mix across studies, and evaluate the construct validity of high-impact chronic pain.
STUDY DESIGN/SETTING: Descriptive analyses using 12 large study datasets.
PATIENT SAMPLES: Chronic low-back pain patients in nonsurgical, nonpharmacologic trials in the US, Canada, and UK.
OUTCOME MEASURES: Preference-based health utilities from the SF-6D and EQ-5D, employment status and absenteeism.
METHODS: We used two logistic regression models to predict whether patients had high-impact chronic pain and whether the remainder had low- or moderate-impact chronic pain. We developed these models using two datasets. Models with the best predictive power were used to impute impact levels for six other datasets. Stratified by these estimated chronic pain impact levels, we characterized the case mix of patients at baseline in each dataset, and summarized their health-utilities and work productivity. This study was funded by a National Center for Complementary and Integrative Medicine grant. The authors have no potential conflicts of interest.
RESULTS: The logistic models had excellent predictive power to identify those with high-impact chronic pain. Although studies were all of chronic low-back pain patients, the baseline mix of patients varied widely. Across all datasets, utilities, and productivity were similar for those with high-impact chronic pain and worsened as chronic pain impact increased.
CONCLUSIONS: There is a need to better categorize chronic pain patients to allow the targeting of optimal interventions for those with each level of chronic pain impact.