A Monte Carlo Simulation Framework to Quantify Platelet Dose Variability in Platelet-Rich Plasma Therapies

Le résumé fourni par la source

Platelet-rich plasma (PRP) therapies are increasingly used in musculoskeletal and regenerative medicine; however, substantial variability in reported outcomes persists even when similar preparation protocols are employed. In quantitative terms, PRP preparation can be interpreted as a stochastic process in which uncertainty propagates through multiple biological and technical inputs. Herein we propose a probabilistic framework to quantify variability in the platelet dose delivered (PDD) using Monte Carlo simulations. The platelet dose was formulated as a random variable defined by a multiplicative model involving the platelet count (modeled as a normal distribution), concentration factor (log-normal), injected volume (uniform), and processing efficiency (beta). Input parameters were represented by probability distributions derived from ranges reported in the literature, and uncertainty propagation was explored through 100,000 Monte Carlo iterations. The resulting simulations revealed a wide dispersion in PDD, characterized by a right-skewed distribution with a median of 3.1 × 109 platelets and an interquartile range of 1.9 × 109 platelets, yielding a coefficient of variation exceeding 50%. Sensitivity analysis based on variance-based global sensitivity measures (Sobol indices) identified the injected volume and concentration factor as the dominant contributors to output variability, with substantial interaction effects between these parameters accounting for a considerable portion of total variance. The baseline platelet count and processing efficiency had comparatively smaller effects. These results demonstrate how probabilistic modeling can clarify the sources of variability in PRP preparation and provide a generalizable framework for uncertainty quantification in multiplicative biomedical systems.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.