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糖尿病性周围神经病理性疼痛患者改良简明疼痛问卷中最严重疼痛程度项目的临床重要差异评估

An Assessment of Clinically Important Differences on the Worst Pain Severity Item of the Modified Brief Pain Inventory in Patients with Diabetic Peripheral Neuropathic Pain.

作者信息

Marcus James, Lasch Kathryn, Wan Yin, Yang Mei, Hsu Ching, Merante Domenico

机构信息

Pharmerit International, Bethesda, MD, USA.

Pharmerit International, Boston, MA, USA.

出版信息

Pain Res Manag. 2018 Jul 22;2018:2140420. doi: 10.1155/2018/2140420. eCollection 2018.

Abstract

OBJECTIVES

Using patient global impression of change (PGIC) as an anchor, an approximately 30% reduction on an 11-point numeric pain intensity rating scale (PI-NRS) is considered a clinically important difference (CID) in pain. Our objective was to define the CID for another pain measure, the worst pain severity (WPS) item of the modified Brief Pain Inventory (m-BPI).

METHODS

In this post hoc analysis of a double-blind, placebo-controlled, phase 2 study, 452 randomized patients with diabetic peripheral neuropathic pain (DPNP) were followed over 5 weeks, with m-BPI data collected weekly and PGIC at treatment conclusion. Receiver operating characteristic (ROC) curves (via logistic regression) were used to determine the changes in the m-BPI-WPS score that best predicted ordinal clinical improvement thresholds (i.e., "minimally improved" or better) on the PGIC.

RESULTS

Similar to the PI-NRS, a change of -3 (raw) or -33.3% from the baseline on the m-BPI-WPS optimized prediction for the "much improved" or better PGIC threshold and represents a CID. There was a high correspondence between observed and predicted PGIC categories at each PGIC threshold (ROC AUCs were 0.78-0.82).

CONCLUSIONS

Worst pain on the m-BPI may be used to assess clinically important improvements in DPNP studies. Findings require validation in larger studies.

摘要

目的

以患者总体印象变化(PGIC)为锚定指标,在11点数字疼痛强度评分量表(PI-NRS)上约30%的降低被视为疼痛方面具有临床意义的差异(CID)。我们的目的是确定另一种疼痛测量指标——改良简明疼痛问卷(m-BPI)中最严重疼痛程度(WPS)项目的CID。

方法

在这项对一项双盲、安慰剂对照的2期研究的事后分析中,452名随机分组的糖尿病性周围神经病理性疼痛(DPNP)患者被随访5周,每周收集m-BPI数据,并在治疗结束时收集PGIC数据。采用受试者工作特征(ROC)曲线(通过逻辑回归)来确定m-BPI-WPS评分的变化,该变化能最佳预测PGIC上的有序临床改善阈值(即“稍有改善”或更好)。

结果

与PI-NRS类似,m-BPI-WPS相对于基线的变化为-3(原始分)或-33.3%时,能优化对“明显改善”或更好的PGIC阈值的预测,代表一个CID。在每个PGIC阈值下,观察到的和预测的PGIC类别之间具有高度一致性(ROC曲线下面积为0.78 - 0.82)。

结论

m-BPI中的最严重疼痛可用于评估DPNP研究中具有临床意义的改善情况。研究结果需要在更大规模的研究中进行验证。

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