Analysis of risk factors and nomogram prediction model of adverse reactions in patients with periprosthetic joint infections administered with vancomycin

Abstract

Background This study aims to investigate the risk factors for adverse reactions when vancomycin is administered to patients with periprosthetic joint infection (PJI) and construct its nomogram prediction model for the occurrence of vancomycin-related adverse reactions (VRAR). Methods This retrospective case-control study analyzed the clinical data of 203 patients with PJI intravenously treated with vancomycin and admitted to the Department of Joint Surgery of the First Affiliated Hospital of Xinjiang Medical University between January 2015 and May 2022. The patients were divided into an adverse reaction group (n = 67) and a non-adverse reaction group (n = 136) based on whether they developed vancomycin-related adverse reactions (VRAR). Clinical data from patients in both groups were used to establish the risk factors for the occurrence of VRAR by lasso-logistic regression models. The R Programming language was used to construct a nomogram prediction model for the occurrence of VRAR in patients with PJI. Further, we plotted the ROC curves and calibration curves to confirm the accuracy of the model. Results The predictive factors included age, obesity (BMI ≥ 28 kg/m2), hypertension, treatment course (≥ 2 weeks), and vancomycin combined with other anti-infective drugs. Internal validation of the model revealed a C-index of 0.863 (95% CI: 0.809–0.916), indicating good discrimination of the model. All the calibration curves were extremely close to the standard curve, indicating good calibration of the model. Conclusions In summary, this study constructed a nomogram prediction model for the occurrence of VRAR in patients with PJI. Consequently, we noted that the established nomogram prediction model has good discrimination and accuracy. The model provides an intuitive and individualized analysis of VRAR risk in patients with PJI, screens the high-risk group, and helps improve the capacity of clinicians to detect VRAR early in patients with PJI. Trial registration Retrospectively registered.