Femoral component size in total knee replacement: a retrospective study

Author:

Al Nawaiseh Mohammad1,Al-Ajlouni Jihad M.1,Ibrahim Omar2,Alafeef Nour2,Aburajab Muawia2,Raiq Neven2,Halayqeh Sereen2,Alfayyadh May2,AlSamhori Jehad Feras2,Jabaiti Mohammad S.1,Alshrouf Mohammad Ali2

Affiliation:

1. Division of Orthopaedics, Department of Special Surgery, School of Medicine, The University of Jordan, Amman, Jordan

2. School of Medicine, The University of Jordan, Amman, Jordan

Abstract

Background: Osteoarthrosis is the most common articular disease in the developed world. The number of people affected by knee osteoarthrosis is exponentially increasing, and with total knee replacement (TKA) demand expected to continue growing. The study establishes the relationship between femoral size components used in TKA and patients’ demographics. Methods: A retrospective cohort study of 385 surgeries that underwent TKA at the tertiary hospital over the period between January 2018 and December 2021 was conducted. The patients’ demographics, including height, weight, body mass index, gender, and age, and the implanted component size and type of the component (gender or not) were collected. Results: A significant association was found between the gender of patients and the femoral component size (P<0.001). Furthermore, height and weight had a significant correlation with femoral component size (P=0.399, P<0.001 and P=0.114, P=0.025, respectively); however, body mass index and age were not statistically correlated with femoral component size (P=0.625, 0.138, respectively). Upon ordinal logistic regression analyses, height and gender were significant predictors of the implanted femur component size (P<0.001). However, age and weight were insignificant factors in the model (P=0.640 and P=0.211, respectively). Conclusion: The results described will help with the pre-operative predictions of the total knee replacement femoral component size, which will decrease the operating time and implant supply chain efficiency. Further advancement of the model could be achieved with future multicenter studies with a larger sample.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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