External Validation Demonstrates Limited Clinical Utility of a Preoperative Prognostic Calculator for Periprosthetic Joint Infection

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 (PJI) 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 multivariable prediction models for the treatment of PJI. We searched MEDLINE, EMBASE, Web of Science, and Google Scholar from inception until March 1st, 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 to 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 to 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 if any available models would provide reliable information if used to guide clinical decision-making.

Methodological Challenges in Predicting Periprosthetic Joint Infection Treatment Outcomes: A Narrative Review

The management of periprosthetic joint infection (PJI) generally requires both surgical intervention and targeted antimicrobial therapy. Decisions regarding surgical management–whether it be irrigation and debridement, one-stage revision, or two-stage revision–must take into consideration an array of factors. These include the timing and duration of symptoms, clinical characteristics of the patient, and antimicrobial susceptibilities of the microorganism(s) involved. Moreover, decisions relating to surgical management must consider clinical factors associated with the health of the patient, alongside the patient's preferences. These decisions are further complicated by concerns beyond mere eradication of the infection, such as the level of improvement in quality of life related to management strategies. To better understand the probability of successful surgical treatment of a PJI, several predictive tools have been developed over the past decade. This narrative review provides an overview of available clinical prediction models that aim to guide treatment decisions for patients with periprosthetic joint infection, and highlights key challenges to reliably implementing these tools in clinical practice.