噪声所致高频听力损失的个体预测模型研究

Establishment of a nomogram for predicting the high frequency hearing loss of workers exposed to noise

目的探讨噪声作业劳动者高频听力损失的影响因素，建立个体预测模型列线图，并验证其准确度。方法2017年6～10月，对某市46家企业的822名噪声作业劳动者进行纯音听力测试和职业流行病学调查，并收集其岗位噪声强度检测数据；使用单因素筛选和多因素Logistic回归分析高频听力损失的影响因素，使用列线图建立个体预测模型。结果822名噪声作业劳动者中，共检出166例（20．2％）高频听力损失，多因素Logistic回归分析显示年龄增加、男性、戴耳机时间增加、较少佩戴耳塞、岗位高噪声强度是高频听力损失的独立危险因素。列线图模型预测高频听力损失发生风险的一致性指数（C-index）为0．834（95％CI：0．748—0．903），预测能力曲线下面积为0．834（95％CI：0．799～0．869，P〈0．01）。结论年龄、性别、戴耳机时间、佩戴耳塞和岗位噪声强度为成都市噪声作业劳动者高频听力损失的独立影响因素；构建的个体预测模型图，可直观和准确的评价噪声作业劳动者高频听力损失的发生风险。

Objective To explore the related influencing factors of high frequency hearing loss （HFHL） in workers exposed to noise and establish a prediction nomogram for HFHL. Methods A total of 822 workers exposed to noise from 46 enterprises were included. A questionnaire survey and a pure-tone hearing test were conducted for the workers. The data of noise level of the workers exposed was also collected. After single factor analysis of related influencing factors, the multivariate Logistic regression analysis was performed to identify the final independent influencing factors of HFHL. Finally, a nomogram model was established by R software to achieve individual prediction of HFHL. Results Among the 822 workers exposed to noise, 166 （20.2%） workers had HFHL. In multivariate Logistic regression analysis, increasing age, men, increasing wearing earphone time, less wearing earplugs, and high noise level were the independent risk factors for HFHL. The C-index of the nomogram model for predicting HFHL was 0.834 （95%CI：0.748-0.903）. The area under the predictive power curve of nomogram model was 0.834 （95%CI：0.799-0.869 ,P〈0.001 ）. Conclusion Age, sex, wearing earphone time, wearing earplugs, and noise level are independent influence factors for HFHL. The nomogram model is successfully established as a accurate and visible tool for individually predicting the HFHL risk in workers exposed to noise.