A High-Sensitivity International Knee Documentation Committee Survey Index From the PROMIS System: The...

Area of Science:

• Orthopaedic surgery outcomes research
• Biostatistics and predictive modeling
• Patient-reported outcome measures (PROMs) development

Background:

• Patient-reported outcomes (PROs) are crucial for assessing care quality and disease progression.
• Legacy PROs like the International Knee Documentation Committee (IKDC) survey are validated but time-consuming, impacting patient adherence.
• Modern PROs, such as the Patient-Reported Outcomes Measurement Information System (PROMIS) surveys, utilize computer adaptive testing to reduce patient burden.

Purpose of the Study:

• To develop a nonlinear predictive model using PROMIS Physical Function and Pain Interference scores to estimate IKDC survey scores.
• To evaluate the sensitivity and validity of the developed predictive algorithms.

Main Methods:

• Utilized the MOTION database, a prospectively collected repository of PROs and intraoperative variables.
• Developed nonlinear multivariable predictive models with Gaussian and beta distributions to create an IKDC index score.
• Validated the models using leave-one-out techniques and minimal clinically important difference analysis.

Main Results:

• 1618 complete observations from 1011 patients were analyzed.
• Validated algorithms demonstrated strong predictive performance for IKDC scores (Pearson correlation 0.84-0.86, R² 0.71-0.74).
• Achieved a root mean square error of 9.3-10.0, indicating good model accuracy.

Conclusions:

• Publicly available models can approximate IKDC scores using PROMIS data, enabling comparisons with historical IKDC data.
• The IKDC index derived from PROMIS allows for a lower minimal clinically important difference compared to the conventional IKDC.
• PROMIS substitution can decrease patient burden, enhance completion rates, and generate orthopaedic-specific survey analogs.