The modified-KLICC score: a novel tool to predict outcomes following debridement, antibiotics, and implant retention after early acute periprosthetic hip infection.

Abstract

Two preoperative risk models have been designed to predict debridement, antibiotics, and implant retention (DAIR) failure: KLICC and CRIME-80 scores. However, external validation of both scores is scarce. We aimed to validate these scores in an external cohort and to create a new model with additional risk factors. We retrospectively evaluated 96 patients with early acute periprosthetic hip infection treated with DAIR. At a two-year cut-off, failure was defined as the need for second DAIR, implant removal, or 90-day infection-related death. Association between demographic variables and failures was tested. The model discriminatory performance was measured using the time-dependent receiver operating characteristic (ROC) curve and Harrell concordance index (C-index). The 'calibration in the large' (CITL) was calculated as the logistic regression model intercept. A modified KLICC score was created by adding the variable time from onset of symptoms to DAIR. The 24-month cumulative incidence of failure was 23.96% (95% CI 15.9 to 32.8). KLICC's area under receiver operating characteristic (AUROC) was 0.79 (95% CI 0.67 to 0.90), with a CITL of -0.57 (95% CI -1.16 to -0.01) and a slope of 0.68 (95% CI 0.35 to 1.02). CRIME-80's AUROC was 0.63 (95% CI 0.51 to 0.76), with a CITL of -1.66 (95% CI -2.13 to -1.19) and a slope of 0.35 (95% CI -0.14 to 0.85). The difference between both AUROCs was statistically significant (p = 0.0138), with the KLICC score performing better. As compared with the original KLICC score, the modified-KLICC improved the AUROC to 0.85 and the beta-slope and α intercept to 1.24 and -0.07, respectively (p = 0.020). KLICC was superior to CRIME-80 in predicting DAIR failure. The modified-KLICC score improved the model prediction and could be useful to help indicate alternatives to DAIR when the predictive failure is high.