Integrating Genome-Wide Polygenic Risk Scores With Nongenetic Models to Predict Surgical Site Infection After Total Knee Arthroplasty Using United Kingdom Biobank Data

Abstract

BackgroundPrediction 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 nongenetic risk factors without incorporating genetic risk factors. To address this issue, we performed a genome-wide association study (GWAS) using the UK Biobank database. MethodsAdult patients who underwent primary TKA (n = 19,767) were analyzed and divided into SSI (n = 269) and non-SSI (n = 19,498) cohorts. Nongenetic 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 curve. ResultsThere were 4 variants (rs117896641, rs111686424, rs8101598, and rs74648298) achieving genome-wide significance that were identified. The logistic regression analysis revealed 7 significant risk factors: increasing zPRS, decreasing age, men, chronic obstructive pulmonary disease, diabetes mellitus, rheumatoid arthritis, and peripheral vascular disease. The areas under the receiver operating characteristic curve were 0.628 and 0.708 when zPRS (model 1) and nongenetic covariates (model 2) were used as predictors, respectively. The areas under the receiver operating characteristic curve increased to 0.76 when both zPRS and nongenetic 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 2 covariates that contributed to the risk, with younger age and higher zPRS associated with higher risks. ConclusionsOur GWAS identified 4 novel variants that were significantly associated with susceptibility to SSI following TKA. Integrating genome-wide zPRS with nongenetic risk factors improved the performance of the model in predicting SSI.