Clinical prediction models to guide treatment of periprosthetic joint infections: a systematic review and meta-analysis

Background

Several clinical prediction models that aim to guide decisions about the management of periprosthetic joint infections (PJIs) have been developed. While some models have been recommended for use in clinical settings, their suitability remains uncertain.

Methods

We systematically reviewed and critically appraised all multi-variable prediction models for the treatment of PJI. We searched MEDLINE, EMBASE, Web of Science, and Google Scholar from inception until 1^st March 2024 and included studies that developed or validated models that predict the outcome of PJI. We used PROBAST (Prediction model Risk Of Bias ASsessment Tool) to assess the risk of bias and applicability. Model performance estimates were pooled via random effect meta-analysis.

Results

Thirteen predictive models and seven external validations were identified. Methodological issues were identified in all studies. Pooled estimates indicated that the KLIC (Kidney, Liver, Index surgery, Cemented prosthesis, C-reactive protein) score had fair discriminative performance (pooled c-statistic 0.62, 95% CI 0.55–0.69). Both the τ² (0.02) and I² (33.4) estimates indicated that between-study heterogeneity was minimal. Meta-analysis indicated Shohat et al.‘s model had good discriminative performance (pooled c-statistic 0.74, 95% CI 0.57–0.85). Both the τ² (0.0) and I² (0.0) indicated that between study heterogeneity was minimal.

Conclusions

Clinicians should be aware of limitations in the methods used to develop available models to predict outcomes of PJI. As no models have consistently demonstrated adequate performance across external validation studies, it remains unclear whether any available models would provide reliable information if used to guide clinical decision making.