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使用最佳筛选工具进行转介和结局预测的疼痛相关痛苦的纵向监测:预测疼痛强度和残疾的减轻。

Longitudinal Monitoring of Pain Associated Distress With the Optimal Screening for Prediction of Referral and Outcome Yellow Flag Tool: Predicting Reduction in Pain Intensity and Disability.

机构信息

Duke Clinical Research Institute, Duke University, Durham, North Carolina; Department of Orthopaedic Surgery, Duke University, Durham, North Carolina.

Biostatistics & Bioinformatics, Duke University, Durham, North Carolina.

出版信息

Arch Phys Med Rehabil. 2020 Oct;101(10):1763-1770. doi: 10.1016/j.apmr.2020.05.025. Epub 2020 Jun 26.

Abstract

OBJECTIVE

To investigate the Optimal Screening for Prediction of Referral and Outcome Yellow Flag (OSPRO-YF) tool for longitudinal monitoring of pain associated distress with the goal of improving prediction of 50% reduction in pain intensity and disability outcomes.

DESIGN

Cohort study with 12-month follow-up after initial care episode.

SETTING

Ambulatory care, participants seeking care from outpatient physical therapy clinics.

PARTICIPANTS

Participants (N=440) were seeking care for primary complaint of neck, low back, knee, or shoulder pain. This secondary analysis included 440 subjects (62.5% female; mean age, 45.1±17y) at baseline with n=279 (63.4%) providing follow-up data at 12 months.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

A 50% reduction (baseline to 12-mo follow-up) in pain intensity and self-reported disability.

RESULTS

Trends for prediction accuracy were similar for all versions of the OSPRO-YF. For predicting 50% reduction in pain intensity, model fit met the statistical criterion for improvement (P<.05) with each additional time point added from baseline. Model discrimination improved statistically when the 6-month to 12-month change was added to the model (area under the curve=0.849, P=.003). For predicting 50% reduction in disability, there was no evidence of improvement in model fit or discrimination from baseline with the addition of 4-week, 6-month, or 12-month changes (P>.05).

CONCLUSIONS

These results suggested that longitudinal monitoring improved prediction accuracy for reduction in pain intensity but not for disability reduction. Differences in OSPRO-YF item sets (10 vs 17 items) or scoring methods (simple summary score vs yellow flag count) did not affect predictive accuracy for pain intensity, providing flexibility for implementing this tool in practice settings.

摘要

目的

研究 Optimal Screening for Prediction of Referral and Outcome Yellow Flag(OSPRO-YF)工具,用于纵向监测与疼痛相关的痛苦,以提高对疼痛强度减轻 50%和残疾结局的预测能力。

设计

初始治疗后进行 12 个月随访的队列研究。

设置

门诊护理,参与者从门诊物理治疗诊所寻求治疗。

参与者

参与者(N=440)因颈部、下背部、膝盖或肩部疼痛的主要抱怨寻求治疗。这项二次分析包括 440 名患者(62.5%为女性;平均年龄 45.1±17 岁),其中 279 名(63.4%)在 12 个月时提供了随访数据。

干预措施

不适用。

主要观察指标

疼痛强度和自我报告残疾的 50%减轻(基线至 12 个月随访)。

结果

对于所有版本的 OSPRO-YF,预测准确性的趋势相似。对于预测疼痛强度减轻 50%,从基线开始增加每个额外的时间点都符合统计学标准(P<.05)。当将 6 个月至 12 个月的变化添加到模型中时,模型的区分度在统计学上得到了提高(曲线下面积=0.849,P=.003)。对于预测残疾减轻 50%,从基线开始增加 4 周、6 个月或 12 个月的变化,模型拟合或区分度均无统计学改善(P>.05)。

结论

这些结果表明,纵向监测可提高疼痛强度减轻的预测准确性,但不能提高残疾减轻的预测准确性。OSPRO-YF 项目集(10 项与 17 项)或评分方法(简单总结评分与黄色标志计数)的差异并不影响疼痛强度的预测准确性,为在实践环境中实施该工具提供了灵活性。

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