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; R² = 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.

中文翻译：

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来自 PROMIS 系统的高灵敏度国际膝关节文献委员会调查指数：膝关节损伤人群的下一代患者报告结果

背景：

患者报告结果 (PRO) 衡量进展和护理质量。虽然国际膝关节文献委员会 (IKDC) 调查等传统 PRO 已得到充分验证，但冗长的 PRO 会给患者带来时间负担，降低依从性。近年来，诸如患者报告结果测量信息系统 (PROMIS) 身体功能和疼痛干扰调查之类的 PRO 被开发为计算机自适应测试，从而缩短了完成时间。以前的研究已经检查了遗留 PRO 和 PROMIS 之间的相关性；然而，没有研究开发出利用 PROMIS 创建 IKDC 指数的有效预测模型。虽然 IKDC 是标准的膝盖 PRO，但计算机自适应 PRO 提供了许多实际优势。

目的：

开发一个非线性预测模型，利用 PROMIS 物理功能和疼痛干扰来估计 IKDC 调查分数并检查算法的敏感性和有效性。

学习规划：

队列研究（诊断）；证据等级，3。

方法：

MOTION（军事骨科跟踪损伤和结果网络）数据库是前瞻性收集的 PRO 和术中变量的存储库。接受膝关节手术的患者在不同的时间点完成了 IKDC 和 PROMIS 调查。创建了使用高斯分布和 beta 分布的非线性多变量预测模型来建立 IKDC 指数评分，然后使用留一法和最小临床重要差异分析对其进行验证。

结果：

共有1011名患者完成了IKDC和PROMIS身体功能和疼痛干扰，提供了1618个完整的观察结果。高斯分布和 beta 分布的算法经过验证以预测 IKDC（Pearson = 0.84-0.86；R ² = 0.71-0.74；均方根误差 = 9.3-10.0）。

结论：

公开可用的预测模型可以近似 IKDC 分数。通过创建 IKDC 指数分数，结果可用于将 PROMIS 身体功能和疼痛干扰与历史 IKDC 分数进行比较。IKDC 指数的连续使用允许比传统 IKDC 具有更低的最小临床重要差异。PROMIS 可替代以减轻患者负担、提高完成率并生成特定于骨科的调查类似物。