# Novel Strategies for Personalized Clinical Decisions in Knee Arthroplasty

> **NIH AHRQ R01** · UNIVERSITY OF COLORADO DENVER · 2020 · $375,780

## Abstract

Abstract
This proposal seeks to address two major problems in the delivery of care surrounding Total Knee
Arthroplasty (TKA): 1) patients are under-informed regarding the trajectory and timing of postoperative
recovery following this elective procedure, and 2) post-acute care is typically delivered according to one-
size-fits-all protocols, which are inadequate for decision-making with individual patients. Our work explores
a “patients-like-me” approach to guide decision-making surrounding TKA. Briefly, for any new patient
considering TKA, outcomes data from similar historical patients can be used to create a personalized
reference chart (PRC) to describe the anticipated recovery profile of the new patient. The method for
selecting patients-like-me is a novel extension of multiple imputation (predictive mean matching). With the
PRC, clinicians and patients can more precisely judge whether recovery is proceeding better than, worse
than, or just as expected, compared to a patient's peers. Deviations from the expected trajectory can be
rapidly detected and addressed. Patients can be better informed regarding prognosis, and resources can be
more efficiently allocated: more visits for those whose recovery is lagging; fewer visits for those excelling.
 With this proposal, we propose to bring the PRC innovation into practice. First (Aim 1), we will
develop procedures for optimizing and validating PRC algorithms for 3 important functional outcomes
following TKA. Second (Aim 2), we will incorporate these algorithms into a software application capable of
producing PRCs at the point of care in routine practice. Finally (Aim 3), we will test preliminary efficacy of
PRCs in improving functional outcomes following TKA as well as the quality of shared decision making and
other outcomes such as post-acute care utilization. Following completion of this work, we will be positioned
to 1) conduct a larger cluster-randomized trial to formally test the effectiveness of PRC-informed care
pathway, and 2) make a web-based PRC application available for widespread use. Ultimately, we foresee
PRC methodology as a means of advancing personalized medicine for a number of diverse patient
populations. Our team includes clinical experts (Dr. Dawn Waugh, PT, and Dr. Michael Dayton, MD),
experts in analytics (Dr. Kathryn Colborn, PhD and Dr. Stef van Buuren, PhD), an expert in shared decision
making and implementation science (Dr. Daniel Matlock, MD, MPH) and experts in clinical research for TKA
(Dr. Jennifer Stevens-Lapsley, PT, PhD and Dr. Andrew Kittelson, PT, PhD).
 This research responds to AHRQ priorities. It advances care for a major health condition (TKA), in a
priority population (older adults), by providing a novel framework for shared decision-making. PRCs
represent a paradigm shift from traditional one-size-fits-all approaches, with opportunities for higher quality,
tailored care for individuals and more efficient resource allocation in a learning healthcare system.

## Key facts

- **NIH application ID:** 9965906
- **Project number:** 5R01HS025692-03
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Jennifer E. Stevens-Lapsley
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $375,780
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9965906

## Citation

> US National Institutes of Health, RePORTER application 9965906, Novel Strategies for Personalized Clinical Decisions in Knee Arthroplasty (5R01HS025692-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9965906. Licensed CC0.

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