Predictive model for aminoglycoside induced ototoxicity.

IF 2.7 3区 医学 Q2 CLINICAL NEUROLOGY
Frontiers in Neurology Pub Date : 2024-11-01 eCollection Date: 2024-01-01 DOI:10.3389/fneur.2024.1461823
Adebolajo A Adeyemo, Josephine Adeolu, Joshua O Akinyemi, Olayemi O Omotade, Odunayo M Oluwatosin
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引用次数: 0

Abstract

Background: Irreversible hearing loss is a well-known adverse effect of aminoglycosides, however, inability to accurately predict ototoxicity is a major limitation in clinical care. We addressed this limitation by developing a prediction model for aminoglycoside ototoxicity applicable to the general population.

Methods: We employed a prospective non-drug-resistant tuberculosis (TB), non-HIV/AIDS cohort of 153 adults on Streptomycin based anti-TB therapy. High frequency pure-tone audiometry was done at regular intervals throughout the study. Clinical and audiological predictors of ototoxicity were collated and ototoxic threshold shift from the baseline audiogram computed. The prediction model was developed with logistic regression method by examining multiple predictors of ototoxicity. Series of models were fitted sequentially; the best model was identified using Akaike Information Criterion and likelihood ratio test. Key variables in the final model were used to develop a logit model for ototoxicity prediction.

Results: Ototoxicity occurred in 35% of participants. Age, gender, weight, cumulative Streptomycin dosage, social class, baseline pure tone average (PTA) and prior hearing symptoms were explored as predictors. Multiple logistic regression showed that models with age, cumulative dosage and baseline PTA were best for predicting ototoxicity. Regression parameters for ototoxicity prediction showed that yearly age increment raised ototoxicity risk by 5% (AOR = 1.05; CI, 1.01-1.09), and a gram increase in cumulative dosage increased ototoxicity risk by 7% (AOR = 1.05; CI, 1.05-1.12) while a unit change in baseline log (PTA) was associated 254% higher risk of ototoxicity (AOR = 3.54, CI: 1.25, 10.01). Training and validation models had area under the receiver operating characteristic curve as 0.84 (CI, 0.76-0.92) and 0.79 (CI, 0.62-0.96) respectively, showing the model has discriminatory ability.

Conclusion: This model can predict aminoglycoside ototoxicity in the general population.

氨基糖苷类药物诱发耳毒性的预测模型
背景:众所周知,不可逆听力损失是氨基糖苷类药物的不良反应,然而,无法准确预测耳毒性是临床治疗的一大局限。针对这一局限性,我们开发了适用于普通人群的氨基糖苷类药物耳毒性预测模型:方法:我们采用了一个前瞻性的非耐药结核病(TB)、非艾滋病毒/艾滋病队列,其中包括 153 名正在接受链霉素类抗结核治疗的成年人。在整个研究过程中定期进行高频纯音测听。整理了耳毒性的临床和听力学预测因素,并计算了基线听力图的耳毒性阈值偏移。通过检查耳毒性的多个预测因素,采用逻辑回归法建立了预测模型。依次拟合了一系列模型;使用 Akaike 信息标准和似然比检验确定了最佳模型。最终模型中的关键变量被用于建立耳毒性预测的logit模型:结果:35%的参与者出现耳毒性。年龄、性别、体重、链霉素累积用量、社会阶层、基线纯音平均值(PTA)和先前的听力症状均可作为预测因素。多元逻辑回归结果表明,年龄、累积用量和基线纯音平均值模型最能预测耳毒性。预测耳毒性的回归参数显示,年龄每年增加会使耳毒性风险增加 5%(AOR = 1.05;CI:1.01-1.09),累积剂量每增加一克会使耳毒性风险增加 7%(AOR = 1.05;CI:1.05-1.12),而基线对数(PTA)每变化一个单位会使耳毒性风险增加 254%(AOR = 3.54,CI:1.25-10.01)。训练模型和验证模型的接收者操作特征曲线下面积分别为 0.84 (CI, 0.76-0.92) 和 0.79 (CI, 0.62-0.96) ,表明该模型具有鉴别能力:结论:该模型可预测一般人群的氨基糖苷类药物耳毒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Neurology
Frontiers in Neurology CLINICAL NEUROLOGYNEUROSCIENCES -NEUROSCIENCES
CiteScore
4.90
自引率
8.80%
发文量
2792
审稿时长
14 weeks
期刊介绍: The section Stroke aims to quickly and accurately publish important experimental, translational and clinical studies, and reviews that contribute to the knowledge of stroke, its causes, manifestations, diagnosis, and management.
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