Objective: To compare the hearing screening results of two-step transient evoked otoacoustic emissions (TEOAE) and one-step automatic auditory brainstem response (AABR) in non-risk newborns, and to explore a more suitable hearing screening protocol for infants discharged within 48 h after birth in remote areas of China.Methods: To analyze the age effect on pass rate for hearing screening, 2005 newborns were divided into three groups according to screening time after birth: <24, 24–48, and 48–72 h. All subjects received TEOAE + AABR test as first hearing screen, and those who failed in any test were rescreened with TEOAE + AABR at 6 weeks after birth. The first screening results of AABR and TEOAE were compared among the three groups. The results of two-step TEOAE screening and one-step AABR screening were compared for newborns who were discharged within 48 h. The time spent on screening was recorded for TEOAE and AABR.Results: The pass rate of TEOAE and AABR increased significantly with the increase of first screening time (P < 0.05), and the false positive rate decreased significantly with the increase of first screening time (P < 0.05). The failure rate of first screening of AABR within 48 h was 7.31%, which was significantly lower than that of TEOAE (9.93%) (P < 0.05). The average time spent on AABR was 12.51 ± 6.36 min, which was significantly higher than that of TEOAE (4.05 ± 1.56 min, P < 0.05). The failure rate of TEOAE two-step screening was 1.59%, which was significantly lower than one-step AABR.Conclusions: Compared with TEOAE, AABR screening within 48 h after birth can reduce the failure rate and false positive rate of first screening. However, compared with TEOAE two-step screening, one-step AABR screening has higher referral rate for audiological diagnosis. In remote areas of China, especially in hospitals with high delivery rate, one-step AABR screening is not feasible, and two-step TEOAE screening protocol is still applicable to UNHS screening as more and more infants discharged within 48 h after birth.

Background: The definition of notched audiogram for noise-induced hearing loss (NIHL) is presently based on clinical experience, but audiometric phenotypes of NIHL are highly heterogeneous. The data-driven clustering of subtypes could provide refined characteristics of NIHL, and help identify individuals with typical NIHL at diagnosis.Methods: This cross-sectional study initially recruited 12,218 occupational noise-exposed employees aged 18–60 years from two factories of a shipyard in Eastern China. Of these, 10,307 subjects with no history of otological injurie or disease, family history of hearing loss, or history of ototoxic drug use were eventually enrolled. All these subjects completed health behavior questionnaires, cumulative noise exposure (CNE) measurement, and pure-tone audiometry. We did data-driven cluster analysis (k-means clustering) in subjects with hearing loss audiograms (n = 6,599) consist of two independent datasets (n = 4,461 and n = 2,138). Multinomial logistic regression was performed to analyze the relevant characteristics of subjects with different audiometric phenotypes compared to those subjects with normal hearing audiograms (n = 3,708).Results: A total of 10,307 subjects (9,165 males [88.9%], mean age 34.5 [8.8] years, mean CNE 91.2 [22.7] dB[A]) were included, 3,708 (36.0%) of them had completely normal hearing, the other 6,599 (64.0%) with hearing loss audiograms were clustered into four audiometric phenotypes, which were replicable in two distinct datasets. We named the four clusters as the 4–6 kHz sharp-notched, 4–6 kHz flat-notched, 3–8 kHz notched, and 1–8 kHz notched audiogram. Among them, except for the 4–6 kHz flat-notched audiogram which was not significantly related to NIHL, the other three phenotypes with different relevant characteristics were strongly associated with noise exposure. In particular, the 4–6 kHz sharp-notched audiogram might be a typical subtype of NIHL.Conclusions: By data-driven cluster analysis of the large-scale noise-exposed population, we identified three audiometric phenotypes associated with distinct NIHL subtypes. Data-driven sub-stratification of audiograms might eventually contribute to the precise diagnosis and treatment of NIHL.

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