Predictive model for aminoglycoside induced ototoxicity

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

doi:10.3389/fneur.2024.1461823

Predictive model for aminoglycoside induced ototoxicity

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

Authors

Adebolajo A Adeyemo^{1

2}, Josephine Adeolu¹, Joshua O Akinyemi³, Olayemi O Omotade¹, Odunayo M Oluwatosin⁴

Affiliations

¹ Institute of Child Health, College of Medicine, University of Ibadan, Ibadan, Nigeria.
² Department of Otolaryngology, University College Hospital, Ibadan, Nigeria.
³ Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria.
⁴ Department of Surgery, College of Medicine, University of Ibadan, Ibadan, Nigeria.

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.

Keywords: aminoglycosides; model validation; ototoxicity; predictive model; tuberculosis.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Consortium for Advanced Research Training in Africa (CARTA). CARTA is jointly led by the African Population and Health Research Center and the University of the Witwatersrand and funded by the Carnegie Corporation of New York (Grant No--B 8606.R02), SIDA (Grant No:54100113), the DELTAS Africa Initiative (Grant No: 107768/Z/15/Z) and Deutscher Akademischer Austauschdienst (DAAD). The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS)‘s Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africa’s Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust (UK) and the UK government. In addition, AA also received support from the Robert McNamara World Bank Fellowship. The statements made and views expressed are solely the responsibility of the authors.