BAYESIAN MODELING OF FLUID FLOW EFFECTS IN DRUG-RELEASING ORTHOPEDIC IMPLANTS

Abstract

Drug-releasing orthopedic implants are increasingly used to enhance localized therapy and reduce post-operative infections; however, drug transport at the implant-tissue interface is strongly influenced by physiological fluid flow and patient-specific variability. Conventional deterministic models are widely used to describe drug transport mechanisms and provide mechanistic insight under fixed and/or initial conditions; however, they are limited in their ability to quantify physiological and patient-specific uncertainties, resulting in reduced predictive reliability of therapeutic outcomes. This research study proposes a Bayesian Belief Network (BBN) framework to probabilistically model fluid flow effects in drug-releasing orthopedic implants. Nine root variables, five intermediate variables, and one outcome variable (therapeutic efficacy) were identified and structured within Bayesian directed acyclic graph. Prior and conditional probabilities were estimated using a combination of published literature, expert knowledge, and clinical data. Four types of Bayesian reasonings were carried out; namely diagnostic reasoning, patient-specific reasoning, fluid dynamic reasoning and implant design reasoning to identify critical determinants of drug-releasing orthopedic implant’s therapeutic performance. Bayesian causal inference predicted a 58.6% probability of effective therapeutic efficacy. The analysis revealed that high fluid velocity reduces drug retention time, while coating thickness and implant material significantly influence release kinetics. Patient-specific factors, including joint type, bone porosity, and inflammation level, were also found to exert substantial effects on drug diffusion, infection risk, and implant failure probability. The proposed BBN framework provides a robust decision-support tool that captures physiological and implant design uncertainties previously under-quantified in deterministic models. By quantifying uncertainty through probabilistic representations rather than single-point estimates, this approach enables patient-specific optimization of implant design and drug-release strategies, thereby improving predictive accuracy, reducing clinical risk, and advancing personalized orthopedic implant development.