Johns Hopkins University School of Nursing, Baltimore, Maryland.
The Research Education Advocacy Community Health Initiative, Johns Hopkins University School of Nursing, Baltimore, Maryland.
Clin Infect Dis. 2020 Feb 14;70(5):917-924. doi: 10.1093/cid/ciz289.
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.
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.
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).
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.
在资源有限的环境中,接受抗结核药物治疗的耐多药结核病(DR-TB)患者经常会出现永久性听力损失,但目前尚无实用方法来识别高危人群。我们旨在开发一种预测南非 DR-TB 患者接受氨基糖苷类(AGs)治疗后发生 AG 诱导性听力损失的临床预测模型。
我们利用南非一项队列研究中的嵌套前瞻性数据,该研究纳入了 379 例接受基于 AG 的方案治疗确诊的 DR-TB 成人患者。我们使用多变量逻辑回归开发了该预测模型。在 DR-TB 治疗开始时,通过临床、听力和实验室评估收集预测因素。AG 诱导性听力损失的结果通过与基线相比,在 6 个月强化期内听力阈值恶化来确定。
63%的参与者(n = 238)出现了任何程度的听力损失。预测频率为 250 至 8000Hz 的听力损失模型包括每周 AG 剂量、人类免疫缺陷病毒状态和 CD4 计数、年龄、血清白蛋白、体重指数和既往听力损失。该模型具有较好的区分度(接受者操作特征曲线下面积 [AUC] = 0.71)和校准度(χ2[8] = 6.10,P =.636)。使用听力损失预测概率为 80%的截断值,该模型的阳性预测值为 83%,阴性预测值为 40%。超高频率听力损失(频率>9000Hz;AUC = 0.81)的模型区分度相似,而临床确定的听力损失(AUC = 0.60)的模型区分度较弱。
该模型可能识别出 DR-TB 患者中发生 AG 诱导性耳毒性风险最高的患者,并有助于在获得有限的临床环境中为 AG 节约方案确定优先级。