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Patterns of analgesic adherence predict health care utilization among outpatients with cancer pain

机译：镇痛依从性模式可预测癌症疼痛门诊患者的医疗保健利用情况

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Background: Studies in chronic noncancer pain settings have found that opioid use increases health care utilization. Despite the key role of analgesics, specifically opioids, in the setting of cancer pain, there is no literature to our knowledge about the relationship between adherence to prescribed around-the-clock (ATC) analgesics and acute health care utilization (hospitalization) among patients with cancer pain. Purpose: To identify adherence patterns over time for cancer patients taking ATC analgesics for pain, cluster these patterns into adherence types, combine the types into an adherence risk factor for hospitalization, identify other risk factors for hospitalization, and identify risk factors for inconsistent analgesic adherence. Materials and methods: Data from a 3-month prospective observational study of patients diagnosed with solid tumors or multiple myeloma, having cancer-related pain, and having at least one prescription of oral ATC analgesics were collected. Adherence data were collected electronically using the medication event-monitoring system. Analyses were conducted using adaptive modeling methods based on heuristic search through alternative models controlled by likelihood cross-validation scores. Results: Six adherence types were identified and combined into the risk factor for hospitalization of inconsistent versus consistent adherence over time. Twenty other individually significant risk factors for hospitalization were identified, but inconsistent analgesic adherence was the strongest of these predictors (ie, generating the largest likelihood cross-validation score). These risk factors were adaptively combined into a model for hospitalization based on six pairwise interaction risk factors with exceptional discrimination (ie, area under the receiver-operating-characteristic curve of 0.91). Patients had from zero to five of these risk factors, with an odds ratio of 5.44 (95% confidence interval 3.09–9.58) for hospitalization, with a unit increase in the number of such risk factors. Conclusion: Inconsistent adherence to prescribed ATC analgesics, specifically the interaction of strong opioids and inconsistent adherence, is a strong risk factor for hospitalization among cancer outpatients with pain.
机译：背景：关于慢性非癌性疼痛的研究发现，使用阿片类药物可提高医疗保健利用率。尽管止痛药（尤其是阿片类药物）在癌症疼痛的发生中起着关键作用，但据我们所知，尚无文献报道患者遵守规定的全天候（ATC）止痛药与急性医疗保健（住院）之间的关系患有癌症。目的：确定长期使用ATC镇痛药的癌症患者的依从性模式，将这些模式分组为依从性类型，将这些类型组合为住院的依从性危险因素，确定其他住院危险因素，并确定镇痛性依从性不一致的危险因素。材料和方法：收集来自3个月的前瞻性观察性研究数据，这些数据来自被诊断患有实体瘤或多发性骨髓瘤，患有与癌症相关的疼痛并且至少有一种口服ATC镇痛药的患者。使用药物事件监测系统以电子方式收集依从性数据。使用基于启发式搜索的自适应建模方法，通过由可能性交叉验证分数控制的替代模型进行分析。结果：确定了六种依从性类型，并将其纳入随时间推移不一致依从性和一致依从性住院的危险因素。确定了另外20个单独的住院重要危险因素，但镇痛依从性不一致是这些预测因素中最强的（即，产生最大的交叉验证分数）。这些风险因素基于六个成对互动的风险因素（具有出色的辨别力（即，接收者-操作人员特征曲线下的面积为0.91））自适应地组合到住院模型中。患者的危险因素为零至五个，住院的几率为5.44（95％置信区间3.09–9.58），并且这些危险因素的数量单位增加。结论：对处方的ATC镇痛药的依从性不一致，特别是强阿片类药物与依从性不一致的相互作用，是癌症疼痛门诊患者住院的重要危险因素。

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