Prediction modelling and public health communication with Swiss National Joint Registry (SIRIS) data — a mixed-methods population research study

PD Dr. Cesar Hincapié  (Department of Epidemiology, Epidemiology, Biostatistics and Prevention Institute [EBPI], Balgrist University Hospital – University Spine Centre Zurich [UWZH])
Prof. Thomas Friemel  (Department of Communication and Media Research)

Collaborators: Dr. Léonie Hofstetter, Nathalie Schweyckart, Dr. Christian Brand, Prof. Mazda Farshad, Prof. Milo Puhan, Prof. Laura Rosella
 
The challenge
Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are standard surgical procedures in Switzerland. The Swiss National Joint Registry (SIRIS) captures data on over 95% of these surgeries annually—approximately 22,000 THAs and 18,000 TKAs per year. Despite the typical lifespan of joint replacements being 25 years on average, a small proportion requires premature revision surgery imposing significant challenges for both patients and the healthcare system. Clinical prediction modeling and effective public communication of surgical outcomes is paramount for decision-making, encompassing perspectives from patients, surgeons, hospitals, and the health system. While clinical prediction models are proliferating, the optimal practices for communicating their results to the public and other stakeholders remain unclear.
 
The opportunity
This project presents a unique opportunity to leverage clinical prediction models for improved decision-making in total joint arthroplasty. These models may offer valuable insights into the risks and probable outcomes of THA and TKA, enhancing preoperative shared decision-making for all stakeholders. The incorporation of Bayesian belief networks may hold promise in identifying causal links between presurgical factors and outcomes, potentially serving as decision support tools to enhance communication between patients and surgeons. By incorporating end-user perspectives and facilitating public communication of these decision support tools, our project aims to foster transparency, build trust, and empower individuals to actively engage in their healthcare decisions.
 
Overall aim
The overarching aim of our project is to advance understanding of prediction modeling and public health communication concerning patient-reported outcomes and revision surgery after THA and TKA, using population-based SIRIS data and qualitative approaches.
 
Specific objectives
1.   To develop and validate prediction models for patient outcomes (health-related quality of life, pain intensity, and satisfaction) one year after THA and TKA.
2.   To develop and validate prediction models for premature revision surgery within 5 years after THA and TKA.
3.   To identify predictors of patient outcomes and revision surgery after THA and TKA using Bayesian belief networks as a clinical decision support tool.
4.   To qualitatively describe and assess the effects of public health communication related to prediction modelling on patients, surgeons, hospitals, and the health system.