Development and validation for multifactor prediction model of sudden sensorineural hearing loss

Chaojun Zeng; Yunhua Yang; Shuna Huang; Wenjuan He; Zhang Cai; Dongdong Huang; Chang Lin; Junying Chen

doi:10.3389/fneur.2023.1134564

Development and validation for multifactor prediction model of sudden sensorineural hearing loss

Front Neurol. 2023 May 18:14:1134564. doi: 10.3389/fneur.2023.1134564. eCollection 2023.

Authors

Chaojun Zeng^{1

2

3}, Yunhua Yang⁴, Shuna Huang^{2

5}, Wenjuan He⁶, Zhang Cai³, Dongdong Huang³, Chang Lin^{1

2}, Junying Chen^{2

7}

Affiliations

¹ Department of Otorhinolaryngology Head and Neck Surgery, Fujian Institute of Otorhinolaryngology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
² National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
³ Department of Otorhinolaryngology Head and Neck Surgery, First Hospital of Putian City, Putian, Fujian, China.
⁴ Department of Otolaryngology, Fujian Provincial Geriatric Hospital, Fuzhou, China.
⁵ Department of Clinical Research and Translation Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
⁶ Clinical Laboratory, Fujian Provincial Hospital South Branch, Fuzhou, China.
⁷ Central Laboratory, Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.

Abstract

Background: Sudden sensorineural hearing loss (SSNHL) is a global problem threatening human health. Early and rapid diagnosis contributes to effective treatment. However, there is a lack of effective SSNHL prediction models.

Methods: A retrospective study of SSNHL patients from Fujian Geriatric Hospital (the development cohort with 77 participants) was conducted and data from First Hospital of Putian City (the validation cohort with 57 participants) from January 2018 to December 2021 were validated. Basic characteristics and the results of the conventional coagulation test (CCT) and the blood routine test (BRT) were then evaluated. Binary logistic regression was used to develop a prediction model to identify variables significantly associated with SSNHL, which were then included in the nomogram. The discrimination and calibration ability of the nomogram was evaluated by receiver operating characteristic (ROC), calibration plot, and decision curve analysis both in the development and validation cohorts. Delong's test was used to calculate the difference in ROC curves between the two cohorts.

Results: Thrombin time (TT), red blood cell (RBC), and granulocyte-lymphocyte ratio (GLR) were found to be associated with the diagnosis of SSNHL. A prediction nomogram was constructed using these three predictors. The AUC in the development and validation cohorts was 0.871 (95% CI: 0.789-0.953) and 0.759 (95% CI: 0.635-0.883), respectively. Delong's test showed no significant difference in the ROC curves between the two groups (D = 1.482, p = 0.141).

Conclusion: In this study, a multifactor prediction model for SSNHL was established and validated. The factors included in the model could be easily and quickly accessed, which could help physicians make early diagnosis and clinical treatment decisions.

Keywords: granulocyte lymphocyte ratio; nomogram; prediction; red blood cell; sudden sensorineural hearing loss; thrombin time.