Nicolas S. Piuzzi, MD, Wins Kappa Delta Young Investigator Award for Research Pertaining to Total Hip and Knee Arthroplasty

Nicolas S. Piuzzi, MD, was recognized as the 2025 Kappa Delta Young Investigator Award winner for research showing how leveraging advanced analytics with personalized outcome-prediction tools can optimize outcomes and satisfaction among patients who have total hip or knee arthroplasty (THA, TKA). Patient-reported outcome measures (PROMs) can help clinicians identify risk factors and predict outcomes more accurately, allowing for tailored interventions at the patient level. This award recognizes outstanding clinical research related to musculoskeletal disease or injury by investigators younger than 40 years or no more than seven years beyond training.

Each year, there are approximately 790,000 TKAs and 544,000 THAs, and the numbers are expected to grow due to an aging population in the United States. PROMs have become increasingly important in total joint arthroplasty (TJA) as healthcare systems shift toward a value-based model, especially in light of new Centers for Medicare & Medicaid Services (CMS) guidelines for 2025 that require hospitals to submit pre- and postoperative PROMs data for THA and TKA to receive full CMS reimbursement. PROMs provide insights into patients’ perspectives on their pain, function, and quality of life before and after surgery.

However, even with successful surgical outcomes following THA and TKA, 10 to 20 percent of patients report persistent pain, functional limitations, or unmet expectations 1 year after surgery. Dr. Piuzzi and his colleagues from the Cleveland Clinic Adult Reconstruction Research (CCARR) Program theorized that incorporating data analytics and PROMs could help practitioners identify patients who may be at a higher risk for poor outcomes, potentially closing the gap between surgical outcomes and patient satisfaction.

“Musculoskeletal conditions, such as osteoarthritis, represent the leading cause of disability and impose a growing economic and health burden on our society,” said Dr. Piuzzi, who is director of CCARR and codirector of the Cleveland Clinic Musculoskeletal Research Center. “Despite affecting over a third of the U.S. population and accounting for hundreds of billions in annual healthcare costs, these conditions remain underfunded in research, highlighting an urgent need for action to advance innovative treatments and improve patient outcomes. Specifically, hip and knee arthroplasties can be very costly for our healthcare system and patients. While these procedures have been performed successfully for many years, we asked ourselves how we could incorporate the same level of data that other industries use to give patients a detailed assessment of what they can expect with joint replacement and what risk factors need to be included in the modeling.”

Creating a comprehensive data-collection system
In 2015, the Cleveland Clinic developed the Orthopaedic Minimal Data Set Episode of Care database as a comprehensive, TJA-specific PROMs data-collection platform. Patient demographics, general health PROMs, joint-specific PROMs, and details about disease severity and treatment are captured from patients and surgeons at specific points in time after surgery. Integrating PROMs collection into the routine clinical workflow has achieved a high baseline completion rate (>97 percent) for TJA procedures. Passive and active follow-up methods, including automated email reminders, text messages, and electronic health record messages, are used to ensure patients are reached. If patients do not respond, telephone calls are made and personalized letters are mailed. A study published by the research team showed that passive measures captured 1-year PROMs for 38 percent of the THA cohort and 40 percent of the TKA cohort. A significant portion of patients—40 percent for THA and 41 percent for TKA—required more active follow-up to complete their postoperative PROMs. The study showed the need for a multi-modal approach to patient follow-up to collect PROMs data.

Taking an individualized approach
Using preoperative PROMs phenotypes that incorporate pain, function, and mental health provides for a more accurate representation of each patient’s unique needs and risk factors, allowing surgeons to individualize the approach and better predict outcomes after TJA.

In a study of 4,034 primary THA patients, Dr. Piuzzi and the research team defined eight distinct phenotypes based on combinations of above or below median scores for Hip Disability and Osteoarthritis Outcome Score (HOOS) pain, HOOS–Physical Function Shortform, and Veterans RAND 12-Item Health Survey–Mental Health Summary Measure (VR-12 MCS). The study found that phenotypes characterized by lower-than-median VR-12 MCS scores were significantly associated with increased dissatisfaction at 1 year, regardless of pain or function scores. Patients with below-median scores across all three PROMs had the highest odds of dissatisfaction compared to the reference phenotype.

“The variation between the different phenotypes is a 9 to 10 percent risk of failure to a 25 percent risk of failure related to patient satisfaction and perception of improvement,” Dr. Piuzzi said. “This gives practitioners a very powerful tool to counsel patients and address some of the risk factors for each patient. If you have a patient in the high-risk group who is having mental health issues or poor function, clinicians need to set expectations and address some of the issues to mitigate risk factors. It is very applicable as it is readily available to everyone, but we need to ensure the data are collected, analyzed, and implemented.”

Developing a predictive model
With value-based healthcare becoming more important, there is a need for a data-driven, standardized approach to guide the shared decision-making process in TJA. PROMs and relevant patient characteristics can be utilized to develop a personalized outcome-prediction tool to estimate the likelihood of improved pain, function, and quality of life after surgery.

The Cleveland Clinic research team built a tool for TKA that incorporated separate models for predicting length of stay, 90-day readmission, and 1-year improvements in Knee Injury and Osteoarthritis Outcome Score, function, and quality-of-life subscores (Fig. 1). These models include a range of patient factors—demographics, comorbidities, baseline PROMs, and laboratory values—and consider modifiable risk factors. The personalized tool demonstrated high accuracy in predicting outcomes for new patients.

Now, the challenge and opportunity lie in scaling this approach to THA and other surgical procedures, thereby extending its predictive capabilities across additional episodes of care. This expansion will not only broaden the tool’s utility but also deepen its impact by integrating personalized medicine and evidence-based surgery into the decision-making process. As Dr. Piuzzi explained, “By broadening our predictive modeling beyond TKA, we can revolutionize patient care and set new benchmarks in surgical outcomes.”

Molly Todd Rudy is a freelance writer for AAOS Now.

References

1. American College of Rheumatology: Joint Replacement Surgery. Available at: https://rheumatology.org/patients/joint-replacement-surgery. Accessed Dec. 18, 2024.
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5. Rullán PJ, Pasqualini I, Zhang C, et al: The Cleveland Clinic OME Arthroplasty Group: how to raise the bar in the capture of patient-reported outcome measures in total joint arthroplasty: results from active and passive follow-up measures. J Bone Joint Surg Am 2024;106(10):879-90.
6. Emara AK, Orr MN, Klika AK, et al: When is surgery performed? Trends, demographic associations, and phenotypical characterization of baseline patient-reported outcomes before total hip arthroplasty. J Arthroplasty 2022;37(6):1083-91.e3.