Integrating Genome-wide Polygenic Risk Scores with Non-genetic Models to Predict Surgical Site Infection after Total Knee Arthroplasty Using United Kingdom Biobank Data

Pei-Yu Chen; Shu-Hui Wen

doi:10.1016/j.arth.2024.05.022

Integrating Genome-wide Polygenic Risk Scores with Non-genetic Models to Predict Surgical Site Infection after Total Knee Arthroplasty Using United Kingdom Biobank Data

J Arthroplasty. 2024 May 10:S0883-5403(24)00455-8. doi: 10.1016/j.arth.2024.05.022. Online ahead of print.

Authors

Pei-Yu Chen¹, Shu-Hui Wen²

Affiliations

¹ Tzu Chi University Center for Health and Welfare Data Science, Ministry of Health and Welfare, Hualien City, Taiwan; Institute of Medical Sciences, Tzu Chi University, Hualien City, Taiwan.
² Institute of Medical Sciences, Tzu Chi University, Hualien City, Taiwan; Department of Public Health, College of Medicine, Tzu Chi University, Hualien City, Taiwan. Electronic address: shwen@gms.tcu.edu.tw.

PMID: 38735551
DOI: 10.1016/j.arth.2024.05.022

Abstract

Background: Prediction of the risk of developing surgical site infection (SSI) in patients following total knee arthroplasty (TKA) is of clinical importance. Genetic susceptibility is involved in developing TKA-related SSI. Previously reported models for predicting SSI were constructed using non-genetic risk factors without incorporating genetic risk factors. To address this issue, we performed a genome-wide association study (GWAS) using the UK Biobank database.

Methods: Adult patients who underwent primary TKA (n = 19,767) were analyzed and divided into SSI (n = 269) and non-SSI (n = 19,498) cohorts. Non-genetic covariates, including demographic data and preoperative comorbidities, were recorded. Genetic variants associated with SSI were identified by GWAS and included to obtain standardized polygenic risk scores (zPRS, an estimate of genetic risk). Prediction models were established through analyses of multivariable logistic regression and the receiver operating characteristic (ROC) curve.

Results: There were four variants (rs117896641, rs111686424, rs8101598, and rs74648298) achieving genome-wide significance that were identified. The logistic regression analysis revealed seven significant risk factors: increasing zPRS, decreasing age, men, chronic obstructive pulmonary disease, diabetes mellitus, rheumatoid arthritis, and peripheral vascular disease. The areas under the ROC curve (AUC) were 0.628 and 0.708 when zPRS (model 1) and non-genetic covariates (model 2) were used as predictors, respectively. The AUC increased to 0.76 when both zPRS and non-genetic covariates (model 3) were used as predictors. A risk-prediction nomogram was constructed based on model 3 to visualize the relative effect of statistically significant covariates on the risk of SSI and predict the probability of developing SSI. Age and zPRS were the top two covariates that contributed to the risk, with younger age and higher zPRS associated with higher risks.

Conclusion: Our GWAS identified four novel variants that were significantly associated with susceptibility to SSI following TKA. Integrating genome-wide zPRS with non-genetic risk factors improved the performance of the model in predicting SSI.

Keywords: GWAS; polygenic risk scores; prediction model; risk factors; surgical site infection; total knee arthroplasty.