Classification Tree Analysis Based On Machine Learning for Predicting Linezolid-Induced Thrombocytopenia

Association between gut microbiota and locomotive syndrome risk in healthy Japanese adults: a cross-sectional study

This cross-sectional study examined the association between gut microbiota composition and locomotive syndrome in 568 healthy Japanese adults (36.8% male, median age 58.5 years) using data from the Kanagawa "ME-BYO" Prospective Cohort Study. Locomotive syndrome was assessed using the 5-question Geriatric Locomotive Function Scale (GLFS-5). Linear discriminant analysis effect size showed an enrichment of Actinobacteria and depletion of Firmicutes in GLFS-5 positive individuals. Classification tree analysis identified three terminal nodes as GLFS-5 positive, with one node involving Holdemania. Participants aged ≥70.0 and <78.0 years who did not consume probiotic foods and had ≥0.04% relative abundance of Holdemania were classified as at risk for locomotive syndrome. Our findings suggest a potential association between gut microbiota, particularly higher Holdemania abundance, and locomotive syndrome in older adults. This study provides insights into the complex relationship between gut microbiome composition and musculoskeletal health in aging populations. However, the cross-sectional design limits causal inference.

A nomogram incorporating linezolid and metabolite concentrations for predicting linezolid induced thrombocytopenia in patients with renal impairment

A nomogram to estimate the risk of linezolid-induced thrombocytopenia in patients with renal impairment is not available. The aim of the study is to develop a nomogram for predicting linezolid-induced thrombocytopenia in patients with renal impairment and to investigate the incremental value of PNU-142300 concentration beyond clinical factors and linezolid trough concentration (Cmin) for risk prediction. Logistic regression was used to identify independent risk factors for linezolid-induced thrombocytopenia in patients with renal impairment and nomograms were established. The performance of the nomograms was assessed in terms of area under the receiver operating characteristic curve (AUROC), net reclassification improvement (NRI), integrated discrimination improvement (IDI) , decision curve analysis (DCA) and calibration. Internal validation and external validation of the nomograms were also performed. Four nomograms were created: nomogram A including total bilirubin, creatinine clearance and concomitant mannitol use; nomogram B containing linezolid Cmin additionally; nomogram C containing total bilirubin, concomitant mannitol use, linezolid Cmin, and PNU142300 concentration; nomogram D including total bilirubin, concomitant mannitol use, and PNU142300 concentration. Nomogram C improved the prediction performance than nomogram A (AUROC 0.881 vs. 0.749; NRI 0.290; IDI 0.226) and nomogram B (AUROC 0.881 vs. 0.812; NRI 0.152; IDI 0.130) in the training cohort. DCA analysis showed that nomogram C yielded a greater net benefit. Compared with nomogram A and nomogram B, nomogram C also showed superior discriminatory efficacy, good calibration and clinical usefulness in the external validation cohort. The nomogram containing PNU-142300 concentration and linezolid Cmin had better predictive capability than that containing linezolid Cmin for predicting linezolid-induced thrombocytopenia in patients with renal impairment.

Applications of Artificial Intelligence in Thrombocytopenia

Thrombocytopenia is a medical condition where blood platelet count drops very low. This drop in platelet count can be attributed to many causes including medication, sepsis, viral infections, and autoimmunity. Clinically, the presence of thrombocytopenia might be very dangerous and is associated with poor outcomes of patients due to excessive bleeding if not addressed quickly enough. Hence, early detection and evaluation of thrombocytopenia is essential for rapid and appropriate intervention for these patients. Since artificial intelligence is able to combine and evaluate many linear and nonlinear variables simultaneously, it has shown great potential in its application in the early diagnosis, assessing the prognosis and predicting the distribution of patients with thrombocytopenia. In this review, we conducted a search across four databases and identified a total of 13 original articles that looked at the use of many machine learning algorithms in the diagnosis, prognosis, and distribution of various types of thrombocytopenia. We summarized the methods and findings of each article in this review. The included studies showed that artificial intelligence can potentially enhance the clinical approaches used in the diagnosis, prognosis, and treatment of thrombocytopenia.

Hematological toxicities associated with linezolid therapy in adults: key findings and clinical considerations

INTRODUCTION

Linezolid can cause serious adverse effects including thrombocytopenia and anemia. Here, we focus specifically on linezolid-related hematological toxicity in adult patients requiring prolonged drug treatment.

AREAS COVERED

We review the available evidence on the likelihood of hematological toxicity in adult patients treated with linezolid, with a focus on the main risk factors and strategies to prevent this adverse event. A MEDLINE PubMed search for articles published from January 2000 to May 2022 was completed matching the terms linezolid, hematology, hematological toxicity, anemia, and thrombocytopenia. Moreover, additional studies were identified from the reference lists of retrieved articles.

EXPERT OPINION

Thrombocytopenia is the major concern with administration of linezolid for Gram-positive infections, whereas anemia is more common in patients with tuberculosis. The important clinical risk factors for the development of linezolid-related thrombocytopenia are aging, renal dysfunction, low baseline platelet count, duration of treatment, and linezolid plasma trough concentrations >8 mg/L. Patients receiving linezolid for extended periods of time or patient populations with increased risk of altered drug pharmacokinetics would benefit from therapeutic drug monitoring or from the availability of toxico-dynamic predictive models to optimize linezolid dosing.

Expert consensus statement on therapeutic drug monitoring and individualization of linezolid

Linezolid is an oxazolidinone antibacterial drug, and its therapeutic drug monitoring and individualized treatment have been challenged since its approval. With the in-depth clinical research of linezolid, we have changed our attitude toward its therapeutic drug monitoring and our view of individualized treatment. On the basis of summarizing the existing clinical studies, and based on the practical experience of each expert in their respective professional fields, we have formed this expert consensus. Our team of specialists is a multidisciplinary team that includes pharmacotherapists, clinical pharmacology specialists, critical care medicine specialists, respiratory specialists, infectious disease specialists, emergency medicine specialists and more. We are committed to the safe and effective use of linezolid in patients in need, and the promotion of its therapeutic drug monitoring.

Initially Reduced Linezolid Dosing Regimen to Prevent Thrombocytopenia in Hemodialysis Patients

This retrospective cohort study investigated the effects of an initially reduced linezolid dosing regimen in hemodialysis patients through therapeutic drug monitoring (TDM). Patients were divided into two groups depending on their initial dose of linezolid (standard dose of 600 mg every 12 h or initially reduced dose of 300 mg every 12 h/600 mg every 24 h). The cumulative incidence rates of thrombocytopenia and severe thrombocytopenia were compared between both groups using the Kaplan-Meier method and log-rank test. Eleven episodes of 8 chronic hemodialysis patients were included; 5 were in the initially reduced-dose group. Thrombocytopenia developed in 81.8% of patients. The cumulative incidence rates of thrombocytopenia and severe thrombocytopenia in the initially reduced-dose group were significantly lower than in the standard-dose group (p < 0.05). At the standard dose, the median linezolid trough concentration (C_min) just before hemodialysis was 49.5 mg/L, and C_min at the reduced doses of 300 mg every 12 h and 600 mg every 24 h were 20.6 mg/L and 6.0 mg/L, respectively. All five episodes underwent TDM in the standard-dose group required dose reduction to 600 mg per day. Our findings indicate that initial dose reduction should be implemented to reduce the risk of linezolid-induced thrombocytopenia among hemodialysis patients.