OBJECTIVE To preliminarily assess the horizontal sound localization and its influencing factors in patients with unilateral sudden sensorineural hearing loss during the acute phase.METHODS The azimuth discrimination test and azimuth identification test were completed,with the speech sound(65 dB SPL)as the stimulus.The minimum audible angle(MAA)and root-mean-square error(RMSE)were obtained,and the RMSE of the affected side and the healthy side were calculated respectively.According to the WHO(2021)hearing loss classification criteria,the data were analyzed based on the pure-tone average(PTA)of the affected ear.And the best resident hearing at each frequency of the affected ear was recorded.RESULTS The performance of the unilateral sudden sensorineural hearing loss patients in the sound localization varied greatly.Some performed close to the normal level,while others completely lost the ability to localize sound.The RMSE of the moderate hearing loss group(≥35 dB HL)was significantly higher than that of the normal hearing group(P<0.01),the MAA of the moderate to severe hearing loss group(≥50 dB HL)showed statistically significant differencescompared with normal hearing group(P<0.001).The RMSE of the affected side of patients in the severe and above hearing loss group was significantly larger than that of the healthy side.Regression analysis showed that the best resident hearing at each frequency of the affected ear was the most significant factor affecting MAA(R2=0.572,P<0.001)and RMSE(R2=0.768,P<0.001).CONCLUSION The horizontal sound localization of unilateral sudden sensorineural hearing loss patients in the acute phase varies greatly.When the PTA of the affected side reaches moderate hearing loss,the localization ability is significantly lower than that of normal-hearing individuals.The best resident hearing at each frequency of the affected ear is the key factor affecting the localization ability.

Objective:To investigate the correlation between serum neuron-specific enolase (NSE) levels and poor neurological outcomes in cardiac arrest (CA) patients supported by veno-arterial extracorporeal membrane oxygenation (VA-ECMO).Methods:This retrospective analysis was conducted on adult CA patients treated with VA-ECMO at Hangzhou First People's Hospital Affiliated to Westlake University School of Medicine, and Second Affiliated Hospital Zhejiang University School of Medicine, from December 2018 to February 2024. General clinical data and serial serum NSE levels at 24, 48, and 72 h after ECMO initiation were collected. Based on the Glasgow-Pittsburgh Cerebral Performance Category (CPC) at discharge, patients were divided into poor neurological outcome group (CPC 3-5) and good neurological outcome group (CPC 1-2). Differences in serum NSE levels between the two groups were compared. The accuracy of serum NSE levels at three time points in predicting poor neurological outcomes in CA patients was assessed via receiver operating characteristic curves, and the optimal cut-off values were determined by the Youden index. Multivariate logistic regression analysis was performed to determine the relationship between serum NSE levels and poor neurological outcomes. Subgroup analysis was based on age, sex, location of CA, and extracorporeal cardiopulmonary resuscitation (ECPR).Results:A total of 120 eligible CA patients were included, with 88 patients (73.3%) having poor neurological outcomes at discharge. Serum NSE levels at 24, 48, and 72 h after ECMO initiation were higher in the poor outcome group compared to the good outcome group (all P<0.05). The serum NSE level at 72 h had the highest accuracy in predicting poor outcomes, with an area under the curve (AUC) of 0.91 (95% CI: 0.85-0.96), and a cut-off value of 42.0 μg/L. The AUCs for 24 and 48 h were 0.78 (95% CI: 0.69-0.86) and 0.87 (95% CI: 0.80-0.94), with cut-off values of 70.6 μg/L and 64.5 μg/L, respectively. Multivariate logistic regression analysis suggested that the serum NSE level at 72 h was associated with poor outcomes ( P<0.05), and an NSE level >42.0 μg/L was an independent risk factor for poor outcomes ( OR=20.29, 95% CI: 2.90-92.15). Subgroup analysis showed that serum NSE level at 72 h was an independent risk factor for poor neurological outcomes in CA patients aged<60 years old, male or female, out-of-hospital or in-hospital CA, and whether to perform ECPR (all P<0.05). Conclusion:Elevated serum NSE levels at 72 h after VA-ECMO initiation are associated with poor neurological outcomes in CA patients, with the cut-off value of 42.0 μg/L.

In-hospital cardiac arrest (IHCA) is a critical medical issue threatening the survival and prognosis of hospitalized patients, characterized by high incidence, high mortality and poor prognosis. Early warning and intervention for IHCA are urgently needed. The early warning score (EWS) is developed as a point-of-care warning tool for early identification and intervention of hospitalized patients with deteriorating condition. In recent years, EWS has become one of the important methods for early warning of IHCA, especially EWS based on machine learning (ML) has shown great potential. This review mainly focuses on the traditional EWS and ML-based EWS, discusses the research status of EWS worldwide, and focuses on the research progress of EWS in early warning of IHCA.
Humans ; Heart Arrest/therapy* ; Early Warning Score ; Prognosis ; Machine Learning ; Hospitalization