Nicolas S. Piuzzi, MD, Receives Kappa Delta Young Investigator Award

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 in total hip and knee arthroplasty (THA, TKA). Utilizing patient-reported outcome measures (PROMs) can help clinicians identify risk factors and predict outcomes more accurately, allowing for tailored interventions at the patient level.

“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 associate staff and director of research at the Cleveland Clinic Adult Reconstruction Research (CCARR) program and codirector of the Cleveland Clinic Musculoskeletal Research Center. “Despite affecting over one-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.”

Comprehensive data collection
In 2015, Cleveland Clinic developed the Orthopaedic Minimal Data Set Episode of Care database as a comprehensive PROMs data-collection platform specifically for total joint arthroplasty (TJA). The database collects patient demographics, general health and joint-specific PROMs, and disease severity and treatment details from patients and surgeons at specific points in time following surgery. Integrating PROMs collection into routine clinical workflow 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 that patients are reached. If patients do not respond, telephone calls and personalized mailed letters are sent. A study published by the research team shows 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.

An individualized approach
Preoperative PROMs phenotypes that incorporate pain, function, and mental health provide a more accurate representation of each patient’s unique needs and risk factors. 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 (HOOS-PS), 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 the phenotype representing below-median scores across all three PROMs had the highest odds of dissatisfaction compared to the reference phenotype. By identifying patients with high-risk phenotypes, clinicians can develop targeted interventions to optimize patients’ preoperative condition and manage expectations.

Integrating PROMs and relevant patient characteristics into a comprehensive predictive model allows for the development of a personalized outcome-prediction tool that can estimate the likelihood of improved pain, function, and quality of life after surgery for individuals. 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 sub-scores). These models include a range of patient factors—demographics, comorbidities, baseline PROMs, and laboratory values—and allow the predictive tool to consider modifiable risk factors.

The personalized outcome-prediction tool demonstrated high accuracy in predicting outcomes for new patients. This demonstrates the potential for using data-driven models to provide patients and surgeons with individualized estimates of expected outcomes based on preoperative characteristics and PROMs.

References

1. American College of Rheumatology: Joint Replacement Surgery. Available at: https://rheumatology.org/patients/joint-replacement-surgery. Accessed Dec. 18, 2024.
2. Orr MN, Klika AK, Emara AK, et al; Cleveland Clinic Arthroplasty Group: Dissatisfaction after total hip arthroplasty associated with preoperative patient-reported outcome phenotypes. J Arthroplasty 2022;37(7S):S498-509.
3. McConaghy K, Klika AK, Apte SS, et al: A call to action for musculoskeletal research funding: the growing economic and disease burden of musculoskeletal conditions in the United States is not reflected in musculoskeletal research funding. J Bone Joint Surg Am 2023;105(6):492-8.
4. OME Cleveland Clinic Orthopaedics: Implementing a scientifically valid, cost-effective, and scalable data collection system at point of care: the Cleveland Clinic OME cohort. J Bone Joint Surg Am 2019;101(5):458-64.
5. Rullán PJ, Pasqualini I, Zhang C, et al; 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.