A Modeling Framework for Decision Support in Periprosthetic Joint Infection Treatment

Area of Science:

• Orthopedics
• Medical Informatics
• Health Economics

Background:

• Periprosthetic joint infection (PJI) is a severe complication following joint arthroplasty.
• Choosing the optimal treatment strategy for PJI is complex and impacts patient outcomes and healthcare costs.
• Existing decision-making tools for PJI treatment may lack comprehensive modeling capabilities.

Purpose of the Study:

• To develop and present a novel framework to assist in selecting the best treatment strategies for periprosthetic joint infection (PJI).
• To incorporate both detailed and general modeling approaches for patient treatment pathways.
• To provide a tool for cost-effectiveness analysis and individual treatment outcome forecasting in PJI.

Main Methods:

• Development of a detailed non-Markovian model utilizing a decision tree approach.
• Construction of a general Markov model to represent essential patient treatment states.
• Application and demonstration of the framework using a dataset of PJI patients from the Russian Scientific Research Institute of Traumatology and Orthopedics.

Main Results:

• The framework effectively models patient treatment pathways for PJI.
• Demonstrated application on a real-world dataset of post-arthroplasty PJI cases.
• The study discusses methods for cost-effectiveness analysis and outcome prediction based on chosen strategies.

Conclusions:

• The proposed framework facilitates informed decision-making for PJI treatment strategies.
• The integrated modeling approach allows for comprehensive analysis of treatment options.
• This framework supports personalized medicine by forecasting individual treatment outcomes and evaluating cost-effectiveness.