Background: Although therapeutic drug monitoring (TDM) maintains serum teicoplanin (TEIC) concentration between 15 and 30 μg/mL, TEIC-induced liver injury may still occur. The albumin-bilirubin (ALBI)-fibrosis-4 (FIB-4) score may be useful for predicting TEIC-induced liver injury in patients undergoing TDM.

Objective: This pilot study aimed to investigate whether the ALBI-FIB4 score can predict TEIC-induced abnormal liver enzyme levels in patients undergoing TDM.

Methods: The multicenter retrospective cohort study included 140 patients undergoing TDM of TEIC at steady state. The primary outcome was TEIC-induced abnormal liver enzyme levels. Cut-off values for the alanine aminotransferase (ALT), ALBI score, FIB-4 index, and ALBI-FIB4 score were detected using receiver-operating characteristic curves. The cumulative risk of TEIC-induced abnormal liver enzyme levels was evaluated using Kaplan-Meier curves analyzed log-rank test. Subgroup analysis was performed to examine cumulative risk in patients with serum TEIC concentration of <30 μg/mL.

Results: The incidence of TEIC-induced abnormal liver enzyme levels was 14.3% (20/140). Cut-off values were 24 IU/L for ALT (sensitivity: 0.800; specificity: 0.692; area under the curve [AUC]: 0.753), -1.33 for the ALBI score (sensitivity: 0.550; specificity: 0.617; AUC: 0.539), 2.73 for the FIB-4 index (sensitivity: 0.700; specificity: 0.475; AUC: 0.550), and -0.85 for the ALBI-FIB4 score (sensitivity: 0.800; specificity: 0.467; AUC: 0.572). The cumulative risk of TEIC-induced abnormal liver enzyme levels was significantly higher for patients with ALT ≥24 IU/L ( < 0.01) and ALBI-FIB4 score ≥-0.85 ( = 0.042). Patients with a serum TEIC concentration of <30 µg/mL exhibited a similar trend, with a higher cumulative risk for patients with ALBI-FIB4 score ≥-0.85 ( = 0.058).

Conclusion And Relevance: An ALBI-FIB4 score ≥-0.85 may serve as a potential predictor for TEIC-induced abnormal liver enzyme levels in patients undergoing TDM. However, evidence supporting this threshold requires further statistical validation using a larger dataset.

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http://dx.doi.org/10.1177/10600280251319519DOI Listing

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