On the Necessity of a Customized Knee Spacer in Peri-Prosthetic Joint Infection Treatment: 3D Numerical Simulation Results.
Marco BalatoCarlo PetrarcaVincenzo de MatteoMarco LenziEnrico FestaAndrea SellittoJessica CampiMauro ZarrelliGiovanni BalatoPublished in: Journal of personalized medicine (2021)
Peri-prosthetic joint infections (PJIs) dramatically affect human health, as they are associated with high morbidity and mortality rates. Two-stage revision arthroplasty is currently the gold standard treatment for PJI and consists of infected implant removal, an accurate debridement, and placement of antimicrobial impregnated poly-methyl-metha-acrylate (PMMA) spacer. The use of antibiotic-loaded PMMA (ALPMMA) spacers have showed a success rate that ranges from 85% to 100%. ALPMMA spacers, currently available on the market, demonstrate a series of disadvantages, closely linked to a low propensity to customize, seen as the ability to adapt to the patients' anatomical characteristics, with consequential increase of surgical complexity, surgery duration, and post-operative complications. Conventionally, ALPMMA spacers are available only in three or four standard sizes, with the impossibility of guaranteeing the perfect matching of ALPMMA spacers with residual bone (no further bone loss) and gap filling. In this paper, a 3D model of an ALPMMA spacer is introduced to evaluate the cause- effect link between the geometric characteristics and the correlated clinical improvements. The result is a multivariable-oriented design able to effectively manage the size, alignment, stability, and the patients' anatomical matching. The preliminary numerical results, obtained by using an "ad hoc" 3D virtual planning simulator, clearly point out that to restore the joint line, the mechanical and rotational alignment and the surgeon's control on the thicknesses (distal and posterior thicknesses) of the ALPMMA spacer is mandatory. The numerical simulations campaign involved nineteen patients grouped in three different scenarios (Case N° 1, Case N° 2 and Case N° 3) whose 3D bone models were obtained through an appropriate data management strategy. Each scenario is characterized by a different incidence rate. In particular, the observed rates of occurrence are, respectively, equal to 17% (Case N° 1), 74% (Case N° 2), and 10% (Case N° 3).
Keyphrases
- end stage renal disease
- ejection fraction
- chronic kidney disease
- newly diagnosed
- risk assessment
- bone loss
- human health
- minimally invasive
- risk factors
- prognostic factors
- staphylococcus aureus
- climate change
- peritoneal dialysis
- machine learning
- bone mineral density
- deep learning
- body composition
- knee osteoarthritis
- postmenopausal women
- artificial intelligence
- high resolution