Use of Japanese big data from electronic medical records to investigate risk factors and identify their high-risk combinations for linezolid-induced thrombocytopenia

Abstract

Purpose: Thrombocytopenia is a major event associated with linezolid (LZD) therapy. Factors affecting LZD-induced thrombocytopenia (LIT) have been reported in previous studies. However, several issues pertaining to LIT have not yet been clarified. In the present study, we used Japanese big data to investigate associated factors and their high-risk combinations that influence LIT.

Methods: Patients administered LZD between May 2006 and October 2020 were included in this study. LIT was defined as either a 30% or more reduction from the baseline platelets or platelet values of < 100,000/µL. We evaluated factors affecting LIT and combinations of factors that alter LIT risk according to a decision tree (DT) analysis, a typical machine learning method.

Results: We successfully enrolled 1399 patients and LIT occurred in 44.7% of the patients (n = 626). We classified the laboratory data on renal function, LZD duration, age, and body weight (BW) into smaller categories. The results of multivariate analysis showed that prolonged LZD therapy, BW < 45 kg, estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m², and dialysis were risk factors for LIT. The DT analysis revealed that the highest risk was a combination of LZD duration ≥ 14 days and eGFR < 30 mL/min/1.73 m².

Conclusions: The present study extracted four risk factors and identified high-risk combinations for LIT. Patients with these risk factors should be closely monitored.

Keywords: Electronic medical record database; Linezolid; Risk factor; Thrombocytopenia; Tree analysis.