Clinical predictors of nephrotoxicity associated with teicoplanin: Meta‐analysis and meta‐regression

Hirai, Toshinori; Hosohata, Keiko; Ogawa, Yukari; Iwamoto, Takuya

doi:10.1111/bcpt.13679

Cited by 14 publications

(8 citation statements)

References 35 publications

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“…In this study, the incidence of AKIs during teicoplanin treatment was 4.3%, which was slightly lower compared to previous reports [13,20,29]. The events occurred shortly after teicoplanin commencement.…”

Section: Discussioncontrasting

confidence: 72%

“…A meta-analysis published recently ascertained that albumin level is negatively correlated with the development of teicoplanin-associated nephrotoxicity, and no correlation was found between teicoplanin trough level and nephrotoxicity development. [29] Regarding teicoplanin's pharmacokinetic characteristics, serum albumin levels can affect the volume of distribution and clearance of teicoplanin, which suggests that patients with hypoalbuminemia require a high dose of teicoplanin to attain the desired target concentration. On the other hand, hypoalbuminemia also increases the fraction of unbound teicoplanin in blood.…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, more drugs might accumulate in the kidney and directly contribute to kidney damage [29,30].…”

Section: Discussionmentioning

confidence: 99%

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Early Therapeutic Drug Monitoring Optimizes Teicoplanin Use in Patients with Hematological Malignancies

Wang,

Hou,

Lin

et al. 2024

Preprint

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Purpose Teicoplanin is a time-dependent glycopeptide antibiotic. The trough concentration (Cmin) ≥ 15–20 mg/L between the fourth and sixth day has been suggested for severe infections or the management of febrile neutropenia (FN). Owing to no reports discussing the impact of early target attainment on treatment outcomes, this study aimed to evaluate the dose–Cmin relationship and clinical outcome and estimate the optimal early target Cmin for FN in patients with hematological malignancies Methods This single-center, prospective study enrolled hematological malignancy patients who were treated with teicoplanin either as an empirical antibiotic for FN or as definitive treatment for Gram-positive bacteria. Blood samples were collected on day three (48 hours) post-loading doses, day five (96 hours), and day eight (when applicable) and determined by ultra high pressure liquid chromatography-triple quadruple mass spectrometry. A two-tailed α value of 0.05 was considered as statistical significance. Results A total of 117 samples from 47 FN patients were consecutively analyzed. The mean Cmin at 48 hours, 96 hours, and on day eight were 23.4 mg/L, 21.4 mg/L, and 27.8 mg/L, respectively. The patients achieving Cmin ≥ 20 mg/L at 48 hours had a higher likelihood of treatment success. The areas under the receiver operating characteristic curves were 0.71 for clinical efficacy and the cut-off value of Cmin at 48 hours was 18.85 mg/L (95% confidence interval; 0.55–0.87; P = 0.018). Conclusions The Cmin of teicoplanin after completion of loading doses could predict the treatment response, with a target concentration ≥ 18.85 mg/L.

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Section: Discussioncontrasting

confidence: 72%

Section: Discussionmentioning

confidence: 99%

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Early Therapeutic Drug Monitoring Optimizes Teicoplanin Use in Patients with Hematological Malignancies

Wang,

Hou,

Lin

et al. 2024

Preprint

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“…Nephrotoxic events are the most feared of these adverse events because they occur with a high risk in older patients [ 101 ]. A recent meta-analysis by Hirai et al highlighted that among 634 patients across eight studies treated with teicoplanin, the overall incidence was 11.0% (95% CI 8.0–13.0), with a higher risk in patients aged > 65 years [ 178 ]. Chinzowu et al analyzed the risk of acute kidney injury among older adults in treatment with glycopeptides across eight studies (total of 23,431 participants), finding an overall absolute risk of 19.1% (95% CI 15.4–22.7%) [ 174 ].…”

Section: Adverse Events From Antimicrobial Agents Among Older Patientsmentioning

confidence: 99%

Safety and Tolerability of Antimicrobial Agents in the Older Patient

et al. 2023

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Older patients are at high risk of infections, which often present atypically and are associated with high morbidity and mortality. Antimicrobial treatment in older individuals with infectious diseases represents a clinical challenge, causing an increasing burden on worldwide healthcare systems; immunosenescence and the coexistence of multiple comorbidities determine complex polypharmacy regimens with an increase in drug–drug interactions and spread of multidrug-resistance infections. Aging-induced pharmacokinetic and pharmacodynamic changes can additionally increase the risk of inappropriate drug dosing, with underexposure that is associated with antimicrobial resistance and overexposure that may lead to adverse effects and poor adherence because of low tolerability. These issues need to be considered when starting antimicrobial prescriptions. National and international efforts have been made towards the implementation of antimicrobial stewardship (AMS) interventions to help clinicians improve the appropriateness and safety of antimicrobial prescriptions in both acute and long-term care settings. AMS programs were shown to decrease consumption of antimicrobials and to improve safety in hospitalized patients and older nursing home residents. With the abundance of antimicrobial prescriptions and the recent emergence of multidrug resistant pathogens, an in-depth review of antimicrobial prescriptions in geriatric clinical practice is needed. This review will discuss the special considerations for older individuals needing antimicrobials, including risk factors that shape risk profiles in geriatric populations as well as an evidence-based description of antimicrobial-induced adverse events in this patient population. It will highlight agents of concern for this age group and discuss interventions to mitigate the effects of inappropriate antimicrobial prescribing.

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“…Teicoplanin is a glycopeptide antibiotic for the treatment of severe infections caused by Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus (MRSA) ( 1 ). As an alternative to vancomycin, teicoplanin shows comparable clinical outcomes with fewer occurrences of nephrotoxicity, ototoxicity, and red man syndrome ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning

Liu

et al. 2022

Front. Med.

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ObjectiveTo establish an optimal model to predict the teicoplanin trough concentrations by machine learning, and explain the feature importance in the prediction model using the SHapley Additive exPlanation (SHAP) method.MethodsA retrospective study was performed on 279 therapeutic drug monitoring (TDM) measurements obtained from 192 patients who were treated with teicoplanin intravenously at the First Affiliated Hospital of Army Medical University from November 2017 to July 2021. This study included 27 variables, and the teicoplanin trough concentrations were considered as the target variable. The whole dataset was divided into a training group and testing group at the ratio of 8:2, and predictive performance was compared among six different algorithms. Algorithms with higher model performance (top 3) were selected to establish the ensemble prediction model and SHAP was employed to interpret the model.ResultsThree algorithms (SVR, GBRT, and RF) with high R2 scores (0.676, 0.670, and 0.656, respectively) were selected to construct the ensemble model at the ratio of 6:3:1. The model with R2 = 0.720, MAE = 3.628, MSE = 22.571, absolute accuracy of 83.93%, and relative accuracy of 60.71% was obtained, which performed better in model fitting and had better prediction accuracy than any single algorithm. The feature importance and direction of each variable were visually demonstrated by SHAP values, in which teicoplanin administration and renal function were the most important factors.ConclusionWe firstly adopted a machine learning approach to predict the teicoplanin trough concentration, and interpreted the prediction model by the SHAP method, which is of great significance and value for the clinical medication guidance.

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Clinical predictors of nephrotoxicity associated with teicoplanin: Meta‐analysis and meta‐regression

Cited by 14 publications

References 35 publications

Early Therapeutic Drug Monitoring Optimizes Teicoplanin Use in Patients with Hematological Malignancies

Early Therapeutic Drug Monitoring Optimizes Teicoplanin Use in Patients with Hematological Malignancies

Safety and Tolerability of Antimicrobial Agents in the Older Patient

Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning

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