氨基糖苷类药物所致耳毒性的预测模型

Predictive model for aminoglycoside induced ototoxicity.

作者信息

Adeyemo Adebolajo A, Adeolu Josephine, Akinyemi Joshua O, Omotade Olayemi O, Oluwatosin Odunayo M

机构信息

Institute of Child Health, College of Medicine, University of Ibadan, Ibadan, Nigeria.

Department of Otolaryngology, University College Hospital, Ibadan, Nigeria.

出版信息

Front Neurol. 2024 Nov 1;15:1461823. doi: 10.3389/fneur.2024.1461823. eCollection 2024.

DOI:10.3389/fneur.2024.1461823

PMID:39555479

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11563990/

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.

摘要

背景

不可逆性听力损失是氨基糖苷类药物众所周知的不良反应，然而，无法准确预测耳毒性是临床护理中的一个主要限制。我们通过开发一种适用于普通人群的氨基糖苷类耳毒性预测模型来解决这一限制。

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153名成年人接受基于链霉素的抗结核治疗。在整个研究过程中定期进行高频纯音听力测定。整理耳毒性的临床和听力学预测因素，并计算相对于基线听力图的耳毒性阈值变化。通过检查耳毒性的多个预测因素，采用逻辑回归方法建立预测模型。依次拟合一系列模型；使用赤池信息准则和似然比检验确定最佳模型。最终模型中的关键变量用于建立耳毒性预测的逻辑模型。

结果

35%的参与者出现耳毒性。研究了年龄、性别、体重、链霉素累积剂量、社会阶层、基线纯音平均听阈（PTA）和既往听力症状作为预测因素。多元逻辑回归显示，包含年龄、累积剂量和基线PTA的模型最适合预测耳毒性。耳毒性预测的回归参数显示，年龄每年增加会使耳毒性风险提高5%（比值比[AOR]=1.05；置信区间[CI]，1.01-1.09），累积剂量每增加1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，耳毒性风险会高出254%（AOR=3.54，CI：1.25，10.01）。训练模型和验证模型的受试者工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。