Aminoglycoside-induced Hearing Loss Among Patients Being Treated for Drug-resistant Tuberculosis in South Africa: A Prediction Model.

Journal Article (Journal Article)

BACKGROUND: Individuals treated for drug-resistant tuberculosis (DR-TB) with aminoglycosides (AGs) in resource-limited settings often experience permanent hearing loss, yet there is no practical method to identify those at higher risk. We sought to develop a clinical prediction model of AG-induced hearing loss among patients initiating DR-TB treatment in South Africa. METHODS: Using nested, prospective data from a cohort of 379 South African adults being treated for confirmed DR-TB with AG-based regimens we developed the prediction model using multiple logistic regression. Predictors were collected from clinical, audiological, and laboratory evaluations conducted at the initiation of DR-TB treatment. The outcome of AG-induced hearing loss was identified from audiometric and clinical evaluation by a worsened hearing threshold compared with baseline during the 6-month intensive phase. RESULTS: Sixty-three percent of participants (n = 238) developed any level of hearing loss. The model predicting hearing loss at frequencies from 250 to 8000 Hz included weekly AG dose, human immunodeficiency virus status with CD4 count, age, serum albumin, body mass index, and pre-existing hearing loss. This model demonstrated reasonable discrimination (area under the receiver operating characteristic curve [AUC] = 0.71) and calibration (χ2[8] = 6.10, P = .636). Using a cutoff of 80% predicted probability of hearing loss, the positive predictive value of this model was 83% and negative predictive value was 40%. Model discrimination was similar for ultrahigh-frequency hearing loss (frequencies >9000 Hz; AUC = 0.81) but weaker for clinically determined hearing loss (AUC = 0.60). CONCLUSIONS: This model may identify patients with DR-TB who are at highest risk of developing AG-induced ototoxicity and may help prioritize patients for AG-sparing regimens in clinical settings where access is limited.

Full Text

Duke Authors

Cited Authors

  • Hong, H; Dowdy, DW; Dooley, KE; Francis, HW; Budhathoki, C; Han, H-R; Farley, JE

Published Date

  • February 14, 2020

Published In

Volume / Issue

  • 70 / 5

Start / End Page

  • 917 - 924

PubMed ID

  • 30963176

Pubmed Central ID

  • PMC7456344

Electronic International Standard Serial Number (EISSN)

  • 1537-6591

Digital Object Identifier (DOI)

  • 10.1093/cid/ciz289


  • eng

Conference Location

  • United States