Division of Pain Medicine, Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, California; Division of Immunology and Rheumatology, Department of Medicine, Stanford University School of Medicine, Stanford, California.
School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada.
J Pain. 2023 Jun;24(6):1094-1103. doi: 10.1016/j.jpain.2023.03.008. Epub 2023 Mar 24.
Over 20 million adults in the United States live with high impact chronic pain (HICP), or chronic pain that limits life or work activities for ≥3 months. It is critically important to differentiate people with HICP from those who sustain normal activities although experiencing chronic pain. Therefore, we aim to help clinicians and researchers identify those with HICP by: 1) developing models that identify factors associated with HICP using the 2016 national health interview survey (NHIS) and 2) evaluating the performances of those models overall and by sociodemographic subgroups (sex, age, and race/ethnicity). Our analysis included 32,980 respondents. We fitted logistic regression models with LASSO (a parametric model) and random forest (a nonparametric model) for predicting HICP using the whole sample. Both models performed well. The most important factors associated with HICP were those related to underlying ill-health (arthritis and rheumatism, hospitalizations, and emergency department visits) and poor psychological well-being. These factors can be used for identifying higher-risk sub-groups for screening for HICP. We will externally validate these findings in future work. We need future studies that longitudinally predict the initiation and maintenance of HICP, then use this information to prevent HICP and direct patients to optimal treatments. PERSPECTIVE: Our study developed models to identify factors associated with high-impact chronic pain (HICP) using the 2016 National Health Interview Survey. There was homogeneity in the factors associated with HICP by gender, age, and race/ethnicity. Understanding these risk factors is crucial to support the identification of populations and individuals at highest risk for developing HICP and improve access to interventions that target these high-risk subgroups.
超过 2000 万美国成年人患有高度影响的慢性疼痛(HICP),或慢性疼痛限制了≥3 个月的生活或工作活动。区分患有 HICP 的人和那些虽然经历慢性疼痛但仍能维持正常活动的人至关重要。因此,我们旨在通过以下方式帮助临床医生和研究人员识别患有 HICP 的人:1)使用 2016 年全国健康访谈调查(NHIS)开发识别与 HICP 相关因素的模型,2)评估这些模型的整体表现和按社会人口统计学亚组(性别、年龄和种族/族裔)的表现。我们的分析包括 32980 名受访者。我们使用全样本拟合了逻辑回归模型,使用 LASSO(参数模型)和随机森林(非参数模型)预测 HICP。这两个模型都表现良好。与 HICP 最相关的重要因素是与潜在健康不良(关节炎和风湿病、住院和急诊就诊)和心理健康状况不佳相关的因素。这些因素可用于识别 HICP 的高风险亚组进行筛查。我们将在未来的工作中对这些发现进行外部验证。我们需要未来的研究来纵向预测 HICP 的开始和维持,然后利用这些信息来预防 HICP,并为患者提供最佳治疗。观点:我们的研究使用 2016 年全国健康访谈调查开发了模型来识别与高度影响的慢性疼痛(HICP)相关的因素。性别、年龄和种族/族裔之间与 HICP 相关的因素具有同质性。了解这些危险因素对于支持识别最有可能发展为 HICP 的人群和个体以及改善针对这些高风险亚组的干预措施的获取至关重要。