Meghani Salimah H, Knafl George J
Salimah H Meghani, Department of Biobehavioral Health Sciences, NewCourtland Center for Transitions and Health, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA 19104-4217, United States.
World J Clin Oncol. 2017 Feb 10;8(1):75-85. doi: 10.5306/wjco.v8.i1.75.
To identify unique clusters of patients based on their concerns in using analgesia for cancer pain and predictors of the cluster membership.
This was a 3-mo prospective observational study ( = 207). Patients were included if they were adults (≥ 18 years), diagnosed with solid tumors or multiple myelomas, and had at least one prescription of around-the-clock pain medication for cancer or cancer-treatment-related pain. Patients were recruited from two outpatient medical oncology clinics within a large health system in Philadelphia. A choice-based conjoint (CBC) analysis experiment was used to elicit analgesic treatment preferences (utilities). Patients employed trade-offs based on five analgesic attributes (percent relief from analgesics, type of analgesic, type of side-effects, severity of side-effects, out of pocket cost). Patients were clustered based on CBC utilities using novel adaptive statistical methods. Multiple logistic regression was used to identify predictors of cluster membership.
The analyses found 4 unique clusters: Most patients made trade-offs based on the expectation of pain relief (cluster 1, 41%). For a subset, the main underlying concern was type of analgesic prescribed, ., opioid non-opioid (cluster 2, 11%) and type of analgesic side effects (cluster 4, 21%), respectively. About one in four made trade-offs based on multiple concerns simultaneously including pain relief, type of side effects, and severity of side effects (cluster 3, 28%). In multivariable analysis, to identify predictors of cluster membership, clinical and socioeconomic factors (education, health literacy, income, social support) rather than analgesic attitudes and beliefs were found important; only the belief, ., pain medications can mask changes in health or keep you from knowing what is going on in your body was found significant in predicting two of the four clusters [cluster 1 (-); cluster 4 (+)].
Most patients appear to be driven by a single salient concern in using analgesia for cancer pain. Addressing these concerns, perhaps through real time clinical assessments, may improve patients' analgesic adherence patterns and cancer pain outcomes.
根据癌症疼痛患者使用镇痛药时的担忧因素以及聚类成员的预测因素,识别出独特的患者聚类。
这是一项为期3个月的前瞻性观察性研究(n = 207)。纳入标准为成年患者(≥18岁),诊断为实体瘤或多发性骨髓瘤,且至少有一张用于治疗癌症或癌症治疗相关疼痛的全天候止痛药物处方。患者从费城一个大型医疗系统内的两家门诊肿瘤诊所招募。采用基于选择的联合分析(CBC)实验来引出镇痛治疗偏好(效用)。患者根据五种镇痛属性(镇痛药缓解疼痛的百分比、镇痛药类型、副作用类型、副作用严重程度、自付费用)进行权衡。使用新颖的自适应统计方法根据CBC效用对患者进行聚类。采用多因素逻辑回归来识别聚类成员的预测因素。
分析发现了4个独特的聚类:大多数患者根据疼痛缓解的期望进行权衡(聚类1,41%)。对于一部分患者,主要潜在担忧分别是所开具的镇痛药类型,即阿片类与非阿片类(聚类2,11%)以及镇痛药副作用类型(聚类4,21%)。约四分之一的患者同时基于多种担忧进行权衡,包括疼痛缓解、副作用类型和副作用严重程度(聚类3,28%)。在多变量分析中,为了识别聚类成员的预测因素,发现临床和社会经济因素(教育程度、健康素养、收入、社会支持)而非镇痛态度和信念很重要;仅有一种信念,即疼痛药物会掩盖健康状况的变化或使您无法了解身体内部的情况,在预测四个聚类中的两个时具有显著意义[聚类1(-);聚类4(+)]。
大多数患者在使用镇痛药治疗癌症疼痛时似乎受单一突出担忧因素驱动。通过实时临床评估等方式解决这些担忧,可能会改善患者的镇痛依从模式和癌症疼痛治疗效果。