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镇痛依从模式可预测癌症疼痛门诊患者的医疗保健利用率。

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

作者信息

Meghani Salimah H, Knafl George J

机构信息

Department of Biobehavioral Health Sciences, NewCourtland Center of Transitions and Health, School of Nursing, University of Pennsylvania, Philadelphia, PA, USA.

School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

出版信息

Patient Prefer Adherence. 2016 Jan 27;10:81-98. doi: 10.2147/PPA.S93726. eCollection 2016.

Abstract

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.

摘要

背景

在慢性非癌性疼痛环境中的研究发现,使用阿片类药物会增加医疗保健利用率。尽管镇痛药,特别是阿片类药物,在癌症疼痛治疗中起着关键作用,但据我们所知,尚无关于癌症疼痛患者对规定的全天候(ATC)镇痛药的依从性与急性医疗保健利用率(住院)之间关系的文献。

目的

确定服用ATC镇痛药治疗疼痛的癌症患者随时间推移的依从模式,将这些模式聚类为依从类型,将这些类型合并为住院的依从风险因素,确定其他住院风险因素,并确定镇痛依从性不一致的风险因素。

材料与方法

收集了一项为期3个月的前瞻性观察性研究的数据,该研究对象为诊断患有实体瘤或多发性骨髓瘤、患有癌症相关疼痛且至少有一张口服ATC镇痛药处方的患者。使用药物事件监测系统以电子方式收集依从性数据。分析采用基于启发式搜索的自适应建模方法,通过似然交叉验证分数控制的替代模型进行。

结果

确定了六种依从类型,并将其合并为随时间推移依从性不一致与一致的住院风险因素。还确定了其他20个单独显著的住院风险因素,但镇痛依从性不一致是这些预测因素中最强的(即产生最大的似然交叉验证分数)。这些风险因素基于六个具有出色辨别力的成对相互作用风险因素(即受试者操作特征曲线下面积为0.91)被自适应合并为一个住院模型。患者具有零至五个这些风险因素,此类风险因素数量每增加一个单位,住院的优势比为5.44(95%置信区间3.09 - 9.58)。

结论

对规定的ATC镇痛药依从性不一致,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一个强风险因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4c9/4734825/e1db9ec8ded2/ppa-10-081Fig1.jpg

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