Risk factors for thrombocytopenia in patients receiving linezolid therapy: a systematic review and meta-analysis

Zhang, Dan; Yan, Xu; Wang, Xiang; Hou, Ling; Xing, Min; Xu, Shumin; Guo, Rui; Luo, Ying

doi:10.1007/s00228-023-03542-z

Cited by 5 publications

(1 citation statement)

References 64 publications

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“…It has been reported that patients with thrombocytopenia have significantly lower baseline platelet counts than those without thrombocytopenia, and patients with low baseline platelet counts should be closely monitored ( Choi et al, 2019 ; Qin et al, 2022 ). Studies have shown that patients receiving human serum albumin therapy during Linezolid treatment have a low incidence of thrombocytopenia, and there is a significant negative correlation between serum albumin concentration and Linezolid-induced thrombocytopenia ( Zhang et al, 2023 ). Chen et al ( Chen et al, 2012 )found a high correlation between serum albumin concentration ≤33.5 g/L and thrombocytopenia, which is consistent with the results of our study with the mean albumin concentration of 33.48 g/L in the thrombocytopenia group.…”

Section: Discussionmentioning

confidence: 99%

Prediction of risk factors for linezolid-induced thrombocytopenia based on neural network model

Zhao,

Peng,

et al. 2024

Front. Pharmacol.

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Background: Based on real-world medical data, the artificial neural network model was used to predict the risk factors of linezolid-induced thrombocytopenia to provide a reference for better clinical use of this drug and achieve the timely prevention of adverse reactions.Methods: The artificial neural network algorithm was used to construct the prediction model of the risk factors of linezolid-induced thrombocytopenia and further evaluate the effectiveness of the artificial neural network model compared with the traditional Logistic regression model.Results: A total of 1,837 patients receiving linezolid treatment in a hospital in Xi ‘an, Shaanxi Province from 1 January 2011 to 1 January 2021 were recruited. According to the exclusion criteria, 1,273 cases that did not meet the requirements of the study were excluded. A total of 564 valid cases were included in the study, with 89 (15.78%) having thrombocytopenia. The prediction accuracy of the artificial neural network model was 96.32%, and the AUROC was 0.944, which was significantly higher than that of the Logistic regression model, which was 86.14%, and the AUROC was 0.796. In the artificial neural network model, urea, platelet baseline value and serum albumin were among the top three important risk factors.Conclusion: The predictive performance of the artificial neural network model is better than that of the traditional Logistic regression model, and it can well predict the risk factors of linezolid-induced thrombocytopenia.

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Section: Discussionmentioning

confidence: 99%

Prediction of risk factors for linezolid-induced thrombocytopenia based on neural network model

Zhao,

Peng,

et al. 2024

Front. Pharmacol.

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Thinking Three‐Dimensionally: A Self‐ and Externally‐Controlled Approach to Screening for Drug–Drug–Drug Interactions Among High‐Risk Populations

Acton,

Hennessy,

Gelfand

et al. 2024

Clin Pharma and Therapeutics

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The global rise in polypharmacy has increased both the necessity and complexity of drug–drug interaction (DDI) assessments, given the growing potential for interactions involving more than two drugs. Leveraging large‐scale healthcare claims data, we piloted a semi‐automated, high‐throughput case‐crossover‐based approach for drug–drug–drug interaction (3DI) screening. Cases were direct‐acting oral anticoagulant (DOAC) users with either a major bleeding event during ongoing dispensings for potentially interacting, enzyme‐inhibiting antihypertensive drugs (AHDs) (Study 1), or a thromboembolic event during ongoing dispensings for potentially interacting, enzyme‐inducing antiseizure medications (ASMs) (Study 2). 3DI detection was based on screening for additional drug exposures that served as acute outcome triggers. To mitigate direct effects and confounding by concomitant drugs, self‐controlled estimates were adjusted using negative cases (external “control” DOAC users with the same outcomes but co‐dispensings for non‐interacting AHDs or ASMs). Signal thresholds were set based on P‐values and false discovery rate q‐values to address multiple comparisons. Study 1: 285 drugs were examined among 3,306 episodes. Self‐controlled assessments with q‐value thresholds yielded 9 3DI signals (cases) and 40 DDI signals (negative cases). External adjustment generated 10 3DI signals from the P‐value threshold and no signals from the q‐value threshold. Study 2: 126 drugs were examined among 604 episodes. Assessments with P‐value thresholds yielded 3 3DI and 26 DDI signals following self‐control, as well as 4 3DI signals following adjustment. No 3DI signals met the q‐value threshold. The presented self‐ and externally‐controlled approach aimed to advance paradigms for real‐world higher order drug interaction screening among high‐susceptibility populations with pre‐existent DDI risk.

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Exploring the impact of baseline platelet count on linezolid-induced thrombocytopenia: a retrospective single-center observation study

Inoue,

Kashiwagi,

Sato

et al. 2024

Int J Clin Pharm

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Risk factors for thrombocytopenia in patients receiving linezolid therapy: a systematic review and meta-analysis

Cited by 5 publications

References 64 publications

Prediction of risk factors for linezolid-induced thrombocytopenia based on neural network model

Prediction of risk factors for linezolid-induced thrombocytopenia based on neural network model

Thinking Three‐Dimensionally: A Self‐ and Externally‐Controlled Approach to Screening for Drug–Drug–Drug Interactions Among High‐Risk Populations

Exploring the impact of baseline platelet count on linezolid-induced thrombocytopenia: a retrospective single-center observation study

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