A Decision Support Framework for Periprosthetic Joint Infection Treatment: A Cost-Effectiveness Analysis Using Two Modeling Approaches

Leonenko, Vasiliy N.; Kaliberda, Yulia; Muravyova, Yulia V.; Artyukh, Vasiliy A.

doi:10.3390/jpm12081216

Cited by 2 publications

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Application of a metatranscriptomics technology, CSI-Dx, for the detection of pathogens associated with prosthetic joint infections

Wright,

Chen See,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Preoperative identification of causal organism(s) is crucial for effective prosthetic joint infection treatment. Herein, we explore the clinical application of a novel metatranscriptomic (MT) workflow, CSI-Dx, to detect pathogens associated with prosthetic joint infection. MT provides insight into transcriptionally active microbes, overcoming limitations of culture-based and available molecular methods. This study included 340 human synovial fluid specimens subjected to CSI-Dx and traditional culture-based methods. Exploratory analyses were conducted to determine sensitivity and specificity of CSI-Dx for detecting clinically-relevant taxa. Our findings provide insights into the active microbial community composition of synovial fluid from arthroplasty patients and demonstrate the potential clinical utility of CSI-Dx for aiding prosthetic joint infection diagnosis. This approach offers potential for improved sensitivity and acceptable specificity compared to synovial fluid culture, enabling detection of culturable and non-culturable microorganisms. Furthermore, CSI-Dx provides valuable information on antimicrobial resistance gene expression. While further optimization is needed, integrating metatranscriptomic technologies like CSI-Dx into routine clinical practice can revolutionize prosthetic joint infection diagnosis by offering a comprehensive and active snapshot of associated pathogens. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-74375-8.

show abstract

Application of a metatranscriptomics technology, CSI-Dx, for the detection of pathogens associated with prosthetic joint infections

Wright,

Chen See,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

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

Xue

Maimatiaimaier

Guo

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

A Decision Support Framework for Periprosthetic Joint Infection Treatment: A Cost-Effectiveness Analysis Using Two Modeling Approaches

Cited by 2 publications

References 34 publications

Application of a metatranscriptomics technology, CSI-Dx, for the detection of pathogens associated with prosthetic joint infections

Application of a metatranscriptomics technology, CSI-Dx, for the detection of pathogens associated with prosthetic joint infections

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

Contact Info

Product

Resources

About