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PROMIS 系统下的国际膝关节文献委员会高敏度调查指标:膝关节损伤人群的下一代患者报告结局。

A High-Sensitivity International Knee Documentation Committee Survey Index From the PROMIS System: The Next-Generation Patient-Reported Outcome for a Knee Injury Population.

机构信息

Defense Healthcare Management Systems, Virginia, USA.

Optimum Performance Analytics Associates, North Carolina, USA.

出版信息

Am J Sports Med. 2021 Nov;49(13):3561-3568. doi: 10.1177/03635465211041593. Epub 2021 Oct 6.

DOI:10.1177/03635465211041593
PMID:34612705
Abstract

BACKGROUND

Patient-reported outcomes (PROs) measure progression and quality of care. While legacy PROs such as the International Knee Documentation Committee (IKDC) survey are well-validated, a lengthy PRO creates a time burden on patients, decreasing adherence. In recent years, PROs such as the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function and Pain Interference surveys were developed as computer adaptive tests, reducing time to completion. Previous studies have examined correlation between legacy PROs and PROMIS; however, no studies have developed effective prediction models utilizing PROMIS to create an IKDC index. While the IKDC is the standard knee PRO, computer adaptive PROs offer numerous practical advantages.

PURPOSE

To develop a nonlinear predictive model utilizing PROMIS Physical Function and Pain Interference to estimate IKDC survey scores and examine algorithm sensitivity and validity.

STUDY DESIGN

Cohort study (diagnosis); Level of evidence, 3.

METHODS

The MOTION (Military Orthopaedics Tracking Injuries and Outcomes Network) database is a prospectively collected repository of PROs and intraoperative variables. Patients undergoing knee surgery completed the IKDC and PROMIS surveys at varying time points. Nonlinear multivariable predictive models using Gaussian and beta distributions were created to establish an IKDC index score, which was then validated using leave-one-out techniques and minimal clinically important difference analysis.

RESULTS

A total of 1011 patients completed the IKDC and PROMIS Physical Function and Pain Interference, providing 1618 complete observations. The algorithms for the Gaussian and beta distribution were validated to predict the IKDC (Pearson = 0.84-0.86; = 0.71-0.74; root mean square error = 9.3-10.0).

CONCLUSION

The publicly available predictive models can approximate the IKDC score. The results can be used to compare PROMIS Physical Function and Pain Interference against historical IKDC scores by creating an IKDC index score. Serial use of the IKDC index allows for a lower minimal clinically important difference than the conventional IKDC. PROMIS can be substituted to reduce patient burden, increase completion rates, and produce orthopaedic-specific survey analogs.

摘要

背景

患者报告的结果(PROs)可衡量疾病进展和医疗质量。国际膝关节文献委员会(IKDC)评分等传统 PRO 已得到充分验证,但冗长的 PRO 会增加患者负担,降低患者依从性。近年来,患者报告结局测量信息系统(PROMIS)躯体功能和疼痛干扰等 PRO 已被开发为计算机自适应测试,从而减少了完成时间。先前的研究已经检验了传统 PRO 与 PROMIS 之间的相关性;然而,尚无研究利用 PROMIS 开发出有效预测模型来创建 IKDC 指数。虽然 IKDC 是膝关节 PRO 的标准,但计算机自适应 PRO 具有许多实际优势。

目的

利用 PROMIS 躯体功能和疼痛干扰开发非线性预测模型,以估算 IKDC 评分,并检验算法的敏感性和有效性。

研究设计

队列研究(诊断);证据等级,3 级。

方法

MOTION(军事骨科追踪损伤和结局网络)数据库是一个前瞻性收集 PRO 和术中变量的数据库。接受膝关节手术的患者在不同时间点完成 IKDC 和 PROMIS 调查。使用高斯和贝塔分布创建非线性多变量预测模型,以建立 IKDC 指数评分,然后使用留一法技术和最小临床重要差异分析进行验证。

结果

共有 1011 例患者完成了 IKDC 和 PROMIS 躯体功能和疼痛干扰调查,提供了 1618 个完整观察值。验证了高斯和贝塔分布算法以预测 IKDC(Pearson = 0.84-0.86; = 0.71-0.74;均方根误差=9.3-10.0)。

结论

可公开获得的预测模型可近似 IKDC 评分。通过创建 IKDC 指数评分,可将 PROMIS 躯体功能和疼痛干扰与历史 IKDC 评分进行比较。连续使用 IKDC 指数可产生比传统 IKDC 更低的最小临床重要差异。替代 PROMIS 可减少患者负担,提高完成率,并生成特定于矫形外科的调查模拟量表。

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