Development and Validation of a Risk Prediction Model for Sudden Sensorineural Hearing Loss Based on a Nomogram

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Methods: A retrospective analysis was conducted on 119 patients diagnosed with SSNHL in the Department of Otolaryngology-Head and Neck Surgery at Shaanxi Provincial People’s Hospital between October 1, 2022, and November 1, 2023 (observation group), as well as 70 healthy controls (control group), to create a modeling set. Additionally, 51 patients diagnosed with SSNHL and 30 healthy controls were collected from the same department during the same period as a validation set. Patient information, including gender, age, education level, marital status, occupation, living situation, history of hypertension, fasting blood glucose, triglycerides, rhinitis, pharyngitis, ear fullness, dizziness, headache, VAS scores for auditory hypersensitivity, SAS scores for anxiety, SDS scores for depression, THI scores for tinnitus, tinnitus duration, and sleep quality, were collected. Univariate and multivariate logistic regression analyses were performed to compare the clinical parameters of the modeling and validation sets, and a nomogram for predicting the risk of SSNHL was constructed and evaluated. Results: There were no statistically significant differences in general characteristics between the modeling and validation sets (P>0.05). Using univariate and multivariate logistic regression, six variables were selected for inclusion in the final predictive model: gender, education level, marital status, living situation, pharyngitis (VAS score), and tinnitus duration. A nomogram was constructed based on these variables. The H-L goodness-of-fit tests yielded P values of 0.5349 and 0.6763 for the modeling and validation sets, respectively. The C-index values were 0.963 and 0.980, indicating excellent predictive accuracy. The AUC values of the ROC curves were 0.970 (95% CI: 0.951-0.990) and 0.992 (95% CI: 0.981-1) for the modeling and validation sets, respectively, demonstrating excellent discriminative ability. Conclusion: Male gender, lower education level, unmarried status, living alone, absence of pharyngitis, and presence of persistent tinnitus were identified as independent risk factors for SSNHL in the western part of China. The nomogram based on these risk factors can effectively assess and quantify the risk of SSNHL. sudden sensorineural hearing loss nomogram risk prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Sudden sensorineural hearing loss (SSNHL) is defined as a rapid onset of hearing loss in one or both ears 1 . It is a debilitating condition that significantly impacts daily life and interpersonal communication 2 . SSNHL is considered a common otologic emergency, and some patients may experience accompanying symptoms such as tinnitus, ear fullness, and dizziness. The exact etiology of SSNHL remains unclear, with current theories suggesting viral infection, impaired blood supply, and autoimmune disorders as possible causes 3 – 5 . If left untreated, SSNHL can result in permanent hearing loss 6 . Therefore, early prevention of this condition holds great clinical significance. A nomogram is a visual model that allows for the intuitive assessment of disease risk and helps clinicians predict the individual risk of a patient. It has been widely used in the field of oncology and has gained popularity in other disciplines in recent years 7 . We found that previous studies on nomograms for SSNHL mainly focused on factors influencing prognosis 8 – 12 , with limited research on predictive models for disease occurrence, and most studies included laboratory or diagnostic indicators 13 . To address these research gaps, our study retrospectively analyzed data from relevant populations and included common socio-demographic indicators such as gender, age, occupation, marital status, living situation, and education level, as well as subjective symptom scores. We aimed to establish a nomogram model that could efficiently and quickly identify individuals at high risk for SSNHL. Subjects and Methods 1. Patients This study retrospectively analyzed data from 119 patients diagnosed with SSNHL in the Department of Otolaryngology-Head and Neck Surgery at Shaanxi Provincial People’s Hospital between October 1, 2022, and November 1, 2023 (observation group), as well as 70 healthy controls (control group) as the modeling set. Additionally, 51 patients diagnosed with SSNHL and 30 healthy controls were collected from the same department during the same period as the validation set. The inclusion criteria for SSNHL were as follows: SSNHL within 72 hours and a decrease of at least 30 dB HL in three consecutive frequencies on the pure-tone audiogram. Patients were excluded from the study if they met any of the following exclusion criteria: (1) incomplete clinical data, (2) history of genetic hearing loss, (3) history of head trauma or ear surgery, (4) history of other confirmed autoimmune diseases, (5) history of excessive noise exposure, (6) history of ototoxic drug use, or (7) presence of post-cochlear lesions, such as vestibular schwannoma and stroke. 2. Data Collection Data collected for included patients included gender, age, education level, marital status, occupation, living situation, fasting blood glucose, triglycerides, history of hypertension, rhinitis, pharyngitis, ear fullness, dizziness, headache, auditory hypersensitivity (assessed using the visual analog scale, VAS), self-rating anxiety scale (SAS) scores 14 , self-rating depression scale (SDS) scores 15 , tinnitus handicap inventory (THI) scores 16 , type of persistent tinnitus, and sleep quality. 3. Statistical Methods For continuous variables, mean ± standard deviation or median and interquartile range were used to describe data, depending on whether they followed a normal distribution. For categorical variables, frequencies and percentages were used. The t-test was used for the comparison of continuous variables between groups when the data followed a normal distribution and had homogeneity of variance. The Kruskal-Wallis rank sum test was used for continuous variables that did not follow a normal distribution or had heterogeneity of variance. The chi-square test or Fisher’s exact test was used for categorical variables. Univariate logistic regression analysis was performed to assess the association between the occurrence of SSNHL and clinical parameters. Variables with a P-value <0.1 were selected for inclusion in the multivariate logistic regression analysis to further screen the variables. A P-value <0.05 was considered statistically significant. 4. Development and Validation of the Nomogram The variables with a P-value <0.05 from the multivariate logistic regression analysis were included in the risk prediction model to create the nomogram. Internal and external validation were performed using the bootstrap method with 1,000 resamples. The Hosmer-Lemeshow goodness-of-fit test was used to assess the calibration of the nomogram model, and calibration curves were plotted to compare the predicted probabilities with the observed probabilities. The calibration and discrimination of the nomogram model were evaluated using receiver operating characteristic (ROC) curves. Decision curve analysis (DCA) curves and clinical impact curves (CIC) were used to evaluate the clinical utility of the model. Statistical analysis was performed using R software (version 4.3.2, The R Foundation, https://www.r-project.org/). Results 1. Comparison of General Characteristics between the Modeling and Validation Sets A total of 270 patients were included in this study, with 189 patients (70%) in the modeling group and 81 patients (30%) in the validation group. There were no statistically significant differences in gender, age, education level, marital status, occupation, living situation, history of hypertension, fasting blood glucose, triglycerides, rhinitis, pharyngitis, ear fullness, dizziness, headache, auditory hypersensitivity (VAS scores), anxiety (SAS scores), depression (SDS scores), tinnitus (THI scores), type of persistent tinnitus, and sleep quality between the two groups (P > 0.05). The detailed information were presented in Table 1. Table 1: Comparison of General Characteristics between the Modeling and Validation Sets Variables Total Development Validation p 270 189 81 Gender (%) female 167 (61.85) 117 (61.90) 50 (61.73) 1 male 103 (38.15) 72 (38.10) 31 (38.27) Age (median [IQR]) 54.00 [39.00, 63.00] 53.00 [38.00, 62.00] 55.00 [41.00, 65.00] 0.4 Education level (%) junior high school 126 (46.67) 93 (49.21) 33 (40.74) 0.48 junior college 19 (7.04) 11 (5.82) 8 (9.88) senior high school 67 (24.81) 45 (23.81) 22 (27.16) university 58 (21.48) 40 (21.16) 18 (22.22) Marital status (%) divorced 2 (0.74) 2 (1.06) 0 (0.00) 0.26 married 235 (87.04) 160 (84.66) 75 (92.59) unmarried 30 (11.11) 24 (12.70) 6 (7.41) widowed 3 (1.11) 3 (1.59) 0 (0.00) Occupation (%) both 26 (9.63) 20 (10.58) 6 (7.41) 0.87 brainwork 63 (23.33) 43 (22.75) 20 (24.69) handiwork 62 (22.96) 43 (22.75) 19 (23.46) neither 119 (44.07) 83 (43.92) 36 (44.44) Residence (%) live alone 56 (20.74) 40 (21.16) 16 (19.75) 0.92 live together 214 (79.26) 149 (78.84) 65 (80.25) Hypertension (%) no 185 (68.52) 132 (69.84) 53 (65.43) 0.57 yes 85 (31.48) 57 (30.16) 28 (34.57) Triglyceride (median [IQR]) 1.26 [1.08, 1.50] 1.28 [1.09, 1.50] 1.24 [1.07, 1.50] 0.53 Fasting blood glucose (median [IQR]) 5.40 [4.90, 5.97] 5.40 [4.90, 6.00] 5.40 [4.80, 5.90] 0.48 Rhinitis (VAS score) (median [IQR]) 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.83 Pharyngitis (VAS score) (median [IQR]) 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.94 Ear stuffy (VAS score) (median [IQR]) 0.00 [0.00, 2.00] 0.00 [0.00, 2.00] 0.00 [0.00, 2.00] 0.43 Vertigo (VAS score) (median [IQR]) 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.51 Headache (VAS score) (median [IQR]) 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.34 Auditory sensitivity (VAS score) (median [IQR]) 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.17 Tinnitus THI score (median [IQR]) 7.00 [0.00, 30.00] 8.00 [0.00, 30.00] 6.00 [0.00, 28.00] 0.59 Persistent type of tinnitus (%) continuous 111 (41.11) 81 (42.86) 30 (37.04) 0.64 none 127 (47.04) 87 (46.03) 40 (49.38) Intermittent 32 (11.85) 21 (11.11) 11 (13.58) Anxiety SAS score (median [IQR]) 50.00 [12.00, 56.00] 51.00 [12.00, 56.00] 50.00 [12.00, 56.00] 0.73 Depressed SDS score (median [IQR]) 33.00 [11.00, 44.00] 32.00 [11.00, 43.00] 34.00 [13.00, 46.00] 0.38 Sleep disorder (%) mildly 42 (15.56) 32 (16.93) 10 (12.35) 0.25 moderately 11 (4.07) 10 (5.29) 1 (1.23) none 216 (80.00) 146 (77.25) 70 (86.42) severe 1 (0.37) 1 (0.53) 0 (0.00) Note: Data presented as n (%) or mean ± standard deviation. 2. Factor Selection for Risk Prediction Model in the Modeling Set Univariate and multivariate logistic regression analyses were conducted to analyze the data of the modeling group patients (Table 2). The results of the univariate logistic regression analysis indicated that gender, education level, marital status, living situation, triglycerides, pharyngitis (VAS score), dizziness (VAS score), tinnitus (THI score), and type of persistent tinnitus had P-values < 0.1. These variables were included in the multivariate logistic regression analysis. Ultimately, gender, education level, marital status, living situation, pharyngitis (VAS score), and type of persistent tinnitus were selected as the variables for the predictive model construction and are presented in the form of a nomogram (Figure 1). Table 2: Univariate and Multivariate Logistic Regression Analysis for the Occurrence of Sudden Sensorineural Hearing Loss Univariate regression multivariate regression Characteristics OR CI P OR CI P gender 0.26 0.13-0.52 <0.0001 0.064 0.014-0.237 <0.001 age 1 0.99-1.02 0.69 Education level 0.79 0.62-1.01 0.06 0.427 0.217-0.753 0.006 Marital status 0.41 0.19-0.9 0.03 0.031 0.003-0.21 0.001 occupation 1.09 0.82-1.45 0.56 residence 0.29 0.12-0.7 0.01 0.083 0.008-0.567 0.019 hypertension 0.6 0.32-1.12 0.11 triglyceride 0.53 0.27-1.02 0.06 0.408 0.082-1.655 0.239 Fasting blood glucose 1.19 0.92-1.53 0.18 Rhinitis (VAS score) 1.14 0.9-1.45 0.27 Pharyngitis (VAS score) 0.82 0.67-1 0.05 0.468 0.254-0.798 0.008 Ear stuffy (VAS score) 12475762 0-Inf 0.98 Vertigo (VAS score) 1.87 1.08-3.23 0.03 2.245 0.934-9.242 0.168 headache (VAS score) 1.04 0.75-1.44 0.83 Auditory sensitivity (VAS score) 3338131 0-Inf 0.99 Tinnitus THI score 1.16 1.1-1.22 <0.0001 1.03 0.955-1.127 0.489 Persistent type of tinnitus 16.95 7.16-40.13 <0.0001 24.104 5.089-194.78 <0.001 Anxiety (SAS score) 237.53 0-Inf 1 Depressed (SDS score) 86.3 0-Inf 1 Sleep disorder 1.17 0.7-1.98 0.54 Inf=infimum Note: Variables with P-values less than 0.1 in the univariate logistic regression analysis were included in the multivariate logistic regression analysis. Triglycerides, dizziness (VAS score), and tinnitus (THI score) were excluded from the multivariate analysis. 3. Construction of a column line chart prediction model Based on the results of multivariate logistic regression, a nomogram model combining six factors is constructed (Figure 1). The sum of scores for each factor corresponds to the predicted probability of occurrence of SSNHL, which represents the risk value. A higher total score on the nomogram represented a higher risk of SSNHL occurrence. 4. Performance evaluation of the nomogram prediction model The chi-square values for the goodness-of-fit test (Hosmer-Lemeshow test) for the modeling set and validation set were 1.2512 and 0.1744, respectively, with corresponding p-values of 0.5349 and 0.6763. The C-index for the model was 0.963 for the modeling set and 0.980 for the validation set (Figure 2), indicating excellent predictive accuracy of the column line chart model. The area under the ROC curve (AUC) was 0.970 (95% CI: 0.951-0.990) for the modeling set and 0.992 (95% CI: 0.981-1) for the validation set (Figure 3), indicating excellent discrimination ability of the nomogram model. The decision curve analysis (DCA) curve (Figure 4) and clinical impact curve (CIC) (Figure 5) both demonstrate the wide clinical utility of this prediction model. Discussion SSNHL is a common sensory disorder that can significantly impact the quality of life of patients 17 . The diagnosis of SSNHL is primarily based on audiograms, but relying solely on subjective audiograms may lead to inaccurate diagnosis and potential misjudgment. In this study, we established a reliable nomogram model for diagnosing SSNHL using univariate and multivariate logistic regression, and the results showed excellent predictive performance of the model. The nomogram model that we developed includes six indicators, including gender, education level, marital status, living situation, pharyngitis (VAS score), and type of tinnitus duration. All of these indicators can be obtained without the requirement for examination or testing (most of them are sociological indicators), making them highly practical in clinical application and easier to implement compared to previous research findings. Among them, the unmarried status has a score of 100 in the nomogram model, indicating a significant influence on the results. We know that living alone is not equivalent to being unmarried, but to some extent, the two are strongly related. Research has shown that living alone can affect dietary habits, and a lack of companionship can lead to a monotonous diet and nutrient deficiencies 18 . If poor dietary habits persist for a long time, it may lead to the occurrence of vascular diseases and even immune deficiency, thereby promoting the occurrence of sudden deafness 19 , 20 . Additionally, individuals who live alone often experience higher psychological stress, anxiety, and depression as well as higher sensitivity to the environment than the general population 21 . Therefore, it is not difficult to understand why the risk of sudden deafness is higher in unmarried and single individuals. Nakamura et al. found that alcohol intake is an independent risk factor for sudden deafness, especially in individuals with severe hearing loss who often have higher alcohol intake 22 . Based on this, we speculate that men may be more prone to sudden deafness compared to women, possibly due to higher alcohol intake. Individuals with lower education levels often engage in more outdoor work. Tsai et al., in an 11-year follow-up study, found that individuals with long-term exposure to polluted air are more likely to develop sudden deafness 23 . The influence of outdoor noise pollution cannot be ruled out. Besides, our nomogram also includes an indicator for the absence of pharyngitis. Although sudden deafness and pharyngitis are both common otolaryngologic diseases, no studies have reported their correlation, and further exploration is needed. At the same time, we also recognize the limitations of this study. For example, this study is a single-center retrospective study, and the results may only be applicable to specific populations in certain regions. It is unclear whether the nomogram can be applied in other centers. The indicators included in the study still do not cover all sociological behaviors, such as smoking, alcohol intake, staying up late, dietary habits, etc., leaving potential for further research. Declarations Author contributions All authors contributed to study conceptualization, data collection and analysis, and manuscript writing. All authors read and approved the final manuscript. Funding This work was supported by the science and technology talent support plan of Shaanxi Provincial People's Hospital(SPPHPR2022050). Data availability The datasets analyzed during the current study are not publicly available due to ethical considerations, but are available from the corresponding author on reasonable request. Ethics approval Participants have reviewed and signed a consent form to indicate their agreement to take part in the research. The study received approval from the Institutional Review Board (IRB) at the corresponding author’s hospital (SPPH-LLBG-17-3-2). All methods and studies were carried out in accordance with relevant guidelines and regulations. Consent for publication Participants have reviewed and signed a consent form to indicate their agreement to allow for the use of their shared insights for research and publication. No personal information or identifiable photos are used in this publication. Competing interests The authors declare no competing interests. References Chandrasekhar SS, Tsai Do BS, Schwartz SR, et al. Clinical Practice Guideline: Sudden Hearing Loss (Update). Otolaryngol Head Neck Surg. 2019;161(1suppl):S1–45. 10.1177/0194599819859885 . Weiss D, Böcker AJ, Koopmann M, Savvas E, Borowski M, Rudack C. Predictors of hearing recovery in patients with severe sudden sensorineural hearing loss. J Otolaryngol Head Neck Surg. 2017;46(1):27. 10.1186/s40463-017-0207-1 . Merchant SN, Adams JC, Nadol JB. Pathology and pathophysiology of idiopathic sudden sensorineural hearing loss. Otol Neurotol. 2005;26(2):151–60. 10.1097/00129492-200503000-00004 . Lindsay JR, Davey PR, Ward PH. Inner ear pathology in deafness due to mumps. Ann Otol Rhinol Laryngol. 1960;69:918–35. 10.1177/000348946006900318 . McCabe BF. Autoimmune sensorineural hearing loss. Ann Otol Rhinol Laryngol. 1979;88(5 Pt 1):585–9. 10.1177/000348947908800501 . Ng B, Crowson MG, Lin V. Management of sudden sensorineural hearing loss among primary care physicians in Canada: a survey study. J Otolaryngol Head Neck Surg. 2021;50(1):22. 10.1186/s40463-021-00498-x . Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26(8):1364–70. 10.1200/JCO.2007.12.9791 . Zhang Z, Yu C, Wang X, et al. The construction and validation of prognostic prediction model for sudden sensorineural hearing loss in middle-aged and elderly people. Auris Nasus Larynx. 2023;51(2):276–85. 10.1016/j.anl.2023.10.001 . Zhang S, Li P, Fan F, et al. Nomogram for predicting the prognosis of sudden sensorineural hearing loss patients based on clinical characteristics: a retrospective cohort study. Ann Transl Med. 2023;11(2):104. 10.21037/atm-22-5647 . Zhou W, Yuan H, Yang Y, Liu S, Huang J, Zhang H. Nomogram for predicting the prognostic role in idiopathic sudden sensorineural hearing loss. Am J Otolaryngol. 2023;44(2):103736. 10.1016/j.amjoto.2022.103736 . Huang GJ, Luo MS, Lu BQ, Li SH. Noninvasive prognostic factors and web predictive tools for idiopathic sudden sensorineural hearing loss. Am J Otolaryngol. 2023;44(6):103965. 10.1016/j.amjoto.2023.103965 . Wu H, Wan W, Jiang H, Xiong Y. Prognosis of Idiopathic Sudden Sensorineural Hearing Loss: The Nomogram Perspective. Ann Otol Rhinol Laryngol. 2023;132(1):5–12. 10.1177/00034894221075114 . Zeng C, Yang Y, Huang S, et al. Development and validation for multifactor prediction model of sudden sensorineural hearing loss. Front Neurol. 2023;14:1134564. 10.3389/fneur.2023.1134564 . Zung WW. A rating instrument for anxiety disorders. Psychosomatics. 1971;12(6):371–9. 10.1016/S0033-3182(71)71479-0 . Zung WW, A SELF-RATING DEPRESSION SCALE. Arch Gen Psychiatry. 1965;12:63–70. 10.1001/archpsyc.1965.01720310065008 . Newman CW, Jacobson GP, Spitzer JB. Development of the Tinnitus Handicap Inventory. Arch Otolaryngol Head Neck Surg. 1996;122(2):143–8. 10.1001/archotol.1996.01890140029007 . Koo M, Hwang JH. Risk of sudden sensorineural hearing loss in patients with common preexisting sensorineural hearing impairment: a population-based study in Taiwan. PLoS ONE. 2015;10(3):e0121190. 10.1371/journal.pone.0121190 . Sandri E, Pérez-Bermejo M, Cabo A, Cerdá-Olmedo G. Living Alone: Associations with Diet and Health in the Spanish Young Adult Population. Nutrients. 2023;15(11):2516. 10.3390/nu15112516 . Delgado-Lista J, Alcala-Diaz JF, Torres-Peña JD, et al. Long-term secondary prevention of cardiovascular disease with a Mediterranean diet and a low-fat diet (CORDIOPREV): a randomised controlled trial. Lancet. 2022;399(10338):1876–85. 10.1016/S0140-6736(22)00122-2 . Nakamura M, Whitlock G, Aoki N, et al. Japanese and Western diet and risk of idiopathic sudden deafness: a case-control study using pooled controls. Int J Epidemiol. 2001;30(3):608–15. 10.1093/ije/30.3.608 . Wilmoth JM, Chen PC. Immigrant status, living arrangements, and depressive symptoms among middle-aged and older adults. J Gerontol B Psychol Sci Soc Sci. 2003;58(5):S305–313. 10.1093/geronb/58.5.s305 . Nakamura M, Aoki N, Nakashima T, et al. Smoking, alcohol, sleep and risk of idiopathic sudden deafness: a case-control study using pooled controls. J Epidemiol. 2001;11(2):81–6. 10.2188/jea.11.81 . Tsai SCS, Hsu YC, Lai JN, et al. Long-term exposure to air pollution and the risk of developing sudden sensorineural hearing loss. J Transl Med. 2021;19(1):424. 10.1186/s12967-021-03095-8 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 05 May, 2025 Editor invited by journal 09 Apr, 2025 Editor assigned by journal 08 Apr, 2025 Submission checks completed at journal 08 Apr, 2025 First submitted to journal 26 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6312681","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452394884,"identity":"7543b3c4-9c16-43e0-a18b-8e6a30fffab7","order_by":0,"name":"Xin Wang","email":"","orcid":"","institution":"Shaanxi Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":452394885,"identity":"09d3c6e3-9a22-43c7-b510-b1f08fd29866","order_by":1,"name":"Yong-gang Yun","email":"","orcid":"","institution":"Shaanxi Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yong-gang","middleName":"","lastName":"Yun","suffix":""},{"id":452394886,"identity":"f6fcd437-535f-4112-93b2-aaf983466334","order_by":2,"name":"Hui Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACPmYGNgYGAwYeNmb+hw8SKv7JsbG3H8CrhQ2qRYaPvYfZ4MOZA8Z8PGcS8GsBIwYGGzmeM2ySM9sOJM6TcDDAr4Wdx+wxTwHQYRK5B6R52O6kt0kwJDD8qNiGx2E85sY8IL9I5CUY8/A8y22TbjzA2HPmNj4tZtIQLQkGyTwSzLltMgcSmBnbiNRymMeAOR3EIFILzxnDxhkJhxOI0MJWJjkHpIW9LZnhw4E0wzZgIB/E5xd+/sPbJN78YbCXb2Y+/iPxn428fHv7wQc/KnBrgYL/qNwDhNSPglEwCkbBKMAPAE87R0sDEkPWAAAAAElFTkSuQmCC","orcid":"","institution":"Shaanxi Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-03-26 13:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6312681/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6312681/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82559441,"identity":"fcd83145-f12b-4059-bf2a-a4bc92601517","added_by":"auto","created_at":"2025-05-13 01:28:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64501,"visible":true,"origin":"","legend":"\u003cp\u003eA nomogram for predicting the risk of sudden deafness. The axes represent gender, education level, marital status, living situation, pharyngitis (VAS score), and duration of tinnitus. The total score on each axis represents the sum of these six factors and can predict the risk of SSNHL occurrence.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6312681/v1/517a2a07f18782424d40b5e0.jpg"},{"id":82559443,"identity":"771be84c-0f07-422b-b0f2-92318abfe843","added_by":"auto","created_at":"2025-05-13 01:28:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64632,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for the modeling set (2A) and validation set (2B) of the prediction model. Internal and external validation were performed using 1000 iterations of bootstrap resampling. The calibration curves for the training and validation sets both show good alignment between the predicted risk of sudden deafness occurrence and the actual observed risk, indicating good consistency between the nomogram predicted risk and the actual risk.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6312681/v1/5e35378777d4c7b33eee70f7.jpg"},{"id":82559442,"identity":"efcdb7b0-bb4b-4641-8192-85ac3e799480","added_by":"auto","created_at":"2025-05-13 01:28:07","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56904,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for the modeling set (3A) and validation set (3B) of the prediction model. Based on receiver operating characteristic (ROC) analysis, the AUC area under the ROC curve for the modeling set and validation set were 0.970 (95% CI: 0.951-0.990) and 0.992 (95% CI: 0.981-1), respectively, indicating good discrimination ability of the model in both the modeling and validation sets.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6312681/v1/334e778e1483ddfc3860ea10.jpg"},{"id":82562290,"identity":"3bdc08ff-4da2-4808-98b7-8d1aa8f631ee","added_by":"auto","created_at":"2025-05-13 01:44:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":48658,"visible":true,"origin":"","legend":"\u003cp\u003eDCA curves for the modeling set (4A) and validation set (4B) of the prediction model. The net benefit of the “intervention” strategy or the “no intervention” strategy is higher for patients across the entire range of risk threshold probabilities compared to the “treat all” strategy, indicating the wide clinical utility of the model.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6312681/v1/4c3e47f11391d9898bd23a17.jpg"},{"id":82560575,"identity":"d6ebecbc-4858-48f1-9e93-1851d0eb1785","added_by":"auto","created_at":"2025-05-13 01:36:08","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":54845,"visible":true,"origin":"","legend":"\u003cp\u003eCIC curves for the modeling set (5A) and validation set (5B) of the prediction model. The curves show the number of true positive cases of sudden deafness risk at different threshold probabilities, as well as the number of cases predicted by the model. The close alignment of the two curves indicates the high clinical utility of the model.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6312681/v1/636d1b6da648cc2ebadbc861.jpg"},{"id":82562292,"identity":"ab416e75-4c39-4b3b-9af9-c03de0a81dd1","added_by":"auto","created_at":"2025-05-13 01:44:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1039778,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6312681/v1/27856ebf-e916-4fad-84f5-fc1a63f705d1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Risk Prediction Model for Sudden Sensorineural Hearing Loss Based on a Nomogram","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSudden sensorineural hearing loss (SSNHL) is defined as a rapid onset of hearing loss in one or both ears\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It is a debilitating condition that significantly impacts daily life and interpersonal communication\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. SSNHL is considered a common otologic emergency, and some patients may experience accompanying symptoms such as tinnitus, ear fullness, and dizziness. The exact etiology of SSNHL remains unclear, with current theories suggesting viral infection, impaired blood supply, and autoimmune disorders as possible causes\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. If left untreated, SSNHL can result in permanent hearing loss\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Therefore, early prevention of this condition holds great clinical significance.\u003c/p\u003e \u003cp\u003eA nomogram is a visual model that allows for the intuitive assessment of disease risk and helps clinicians predict the individual risk of a patient. It has been widely used in the field of oncology and has gained popularity in other disciplines in recent years\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. We found that previous studies on nomograms for SSNHL mainly focused on factors influencing prognosis\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, with limited research on predictive models for disease occurrence, and most studies included laboratory or diagnostic indicators\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo address these research gaps, our study retrospectively analyzed data from relevant populations and included common socio-demographic indicators such as gender, age, occupation, marital status, living situation, and education level, as well as subjective symptom scores. We aimed to establish a nomogram model that could efficiently and quickly identify individuals at high risk for SSNHL.\u003c/p\u003e"},{"header":"Subjects and Methods","content":"\u003cp\u003e1.\u0026nbsp;\u003cstrong\u003ePatients\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study retrospectively analyzed data from 119 patients diagnosed with SSNHL in the Department of Otolaryngology-Head and Neck Surgery at Shaanxi Provincial People\u0026rsquo;s Hospital between October 1, 2022, and November 1, 2023 (observation group), as well as 70 healthy controls (control group) as the modeling set. Additionally, 51 patients diagnosed with SSNHL and 30 healthy controls were collected from the same department during the same period as the validation set. The inclusion criteria for SSNHL were as follows: SSNHL within 72 hours and a decrease of at least 30 dB HL in three consecutive frequencies on the pure-tone audiogram. Patients were excluded from the study if they met any of the following exclusion criteria: (1) incomplete clinical data, (2) history of genetic hearing loss, (3) history of head trauma or ear surgery, (4) history of other confirmed autoimmune diseases, (5) history of excessive noise exposure, (6) history of ototoxic drug use, or (7) presence of post-cochlear lesions, such as vestibular schwannoma and stroke.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp;\u003cstrong\u003eData Collection\u003c/strong\u003e\u003cbr\u003eData collected for included patients included gender, age, education level, marital status, occupation, living situation, fasting blood glucose, triglycerides, history of hypertension, rhinitis, pharyngitis, ear fullness, dizziness, headache, auditory hypersensitivity (assessed using the visual analog scale, VAS), self-rating anxiety scale (SAS) scores\u003csup\u003e14\u003c/sup\u003e, self-rating depression scale (SDS) scores\u003csup\u003e15\u003c/sup\u003e, tinnitus handicap inventory (THI) scores\u003csup\u003e16\u003c/sup\u003e, type of persistent tinnitus, and sleep quality.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp;\u003cstrong\u003eStatistical Methods\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;For continuous variables, mean \u0026plusmn; standard deviation or median and interquartile range were used to describe data, depending on whether they followed a normal distribution. For categorical variables, frequencies and percentages were used. The t-test was used for the comparison of continuous variables between groups when the data followed a normal distribution and had homogeneity of variance. The Kruskal-Wallis rank sum test was used for continuous variables that did not follow a normal distribution or had heterogeneity of variance. The chi-square test or Fisher\u0026rsquo;s exact test was used for categorical variables. Univariate logistic regression analysis was performed to assess the association between the occurrence of SSNHL and clinical parameters. Variables with a P-value \u0026lt;0.1 were selected for inclusion in the multivariate logistic regression analysis to further screen the variables. A P-value \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Development and Validation of the Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe variables with a P-value \u0026lt;0.05 from the multivariate logistic regression analysis were included in the risk prediction model to create the nomogram. Internal and external validation were performed using the bootstrap method with 1,000 resamples. The Hosmer-Lemeshow goodness-of-fit test was used to assess the calibration of the nomogram model, and calibration curves were plotted to compare the predicted probabilities with the observed probabilities. The calibration and discrimination of the nomogram model were evaluated using receiver operating characteristic (ROC) curves. Decision curve analysis (DCA) curves and clinical impact curves (CIC) were used to evaluate the clinical utility of the model. Statistical analysis was performed using R software (version 4.3.2, The R Foundation, https://www.r-project.org/).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e1. \u003cstrong\u003eComparison of General Characteristics between the Modeling and Validation Sets\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;A total of 270 patients were included in this study, with 189 patients (70%) in the modeling group and 81 patients (30%) in the validation group. There were no statistically significant differences in gender, age, education level, marital status, occupation, living situation, history of hypertension, fasting blood glucose, triglycerides, rhinitis, pharyngitis, ear fullness, dizziness, headache, auditory hypersensitivity (VAS scores), anxiety (SAS scores), depression (SDS scores), tinnitus (THI scores), type of persistent tinnitus, and sleep quality between the two groups (P \u0026gt; 0.05). The detailed information were presented in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1: Comparison of General Characteristics between the Modeling and Validation Sets\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"725\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 42.4828%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003eDevelopment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003eValidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003eGender (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e167 (61.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e117 (61.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e50 (61.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e103 (38.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e72 (38.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e31 (38.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 42.4828%;\"\u003e\n \u003cp\u003eAge (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e54.00 [39.00, 63.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e53.00 [38.00, 62.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e55.00 [41.00, 65.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003eEducation level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003ejunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e126 (46.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e93 (49.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e33 (40.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003ejunior college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e19 (7.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e11 (5.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e8 (9.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003esenior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e67 (24.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e45 (23.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e22 (27.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003euniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e58 (21.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e40 (21.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e18 (22.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003eMarital status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003edivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e2 (0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e2 (1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003emarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e235 (87.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e160 (84.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e75 (92.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003eunmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e30 (11.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e24 (12.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e6 (7.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003ewidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e3 (1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e3 (1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003eOccupation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003eboth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e26 (9.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e20 (10.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e6 (7.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003ebrainwork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e63 (23.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e43 (22.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e20 (24.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003ehandiwork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e62 (22.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e43 (22.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e19 (23.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003eneither\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e119 (44.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e83 (43.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e36 (44.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003eResidence (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003elive alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e56 (20.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e40 (21.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e16 (19.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003elive together\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e214 (79.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e149 (78.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e65 (80.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e185 (68.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e132 (69.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e53 (65.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e85 (31.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e57 (30.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e28 (34.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 42.4828%;\"\u003e\n \u003cp\u003eTriglyceride (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e1.26 [1.08, 1.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e1.28 [1.09, 1.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e1.24 [1.07, 1.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 42.4828%;\"\u003e\n \u003cp\u003eFasting blood glucose (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e5.40 [4.90, 5.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e5.40 [4.90, 6.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e5.40 [4.80, 5.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 42.4828%;\"\u003e\n \u003cp\u003eRhinitis (VAS score) (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 42.4828%;\"\u003e\n \u003cp\u003ePharyngitis (VAS score) (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 42.4828%;\"\u003e\n \u003cp\u003eEar stuffy (VAS score) (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e0.00 [0.00, 2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e0.00 [0.00, 2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e0.00 [0.00, 2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 42.4828%;\"\u003e\n \u003cp\u003eVertigo (VAS score) (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 42.4828%;\"\u003e\n \u003cp\u003eHeadache (VAS score) (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 42.4828%;\"\u003e\n \u003cp\u003eAuditory sensitivity (VAS score) (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 42.4828%;\"\u003e\n \u003cp\u003eTinnitus THI score (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e7.00 [0.00, 30.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e8.00 [0.00, 30.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e6.00 [0.00, 28.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003ePersistent type of tinnitus (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003econtinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e111 (41.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e81 (42.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e30 (37.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e127 (47.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e87 (46.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e40 (49.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003eIntermittent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e32 (11.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e21 (11.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e11 (13.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 42.4828%;\"\u003e\n \u003cp\u003eAnxiety SAS score (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e50.00 [12.00, 56.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e51.00 [12.00, 56.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e50.00 [12.00, 56.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 42.4828%;\"\u003e\n \u003cp\u003eDepressed SDS score (median [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e33.00 [11.00, 44.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e32.00 [11.00, 43.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e34.00 [13.00, 46.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003eSleep disorder (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003emildly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e42 (15.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e32 (16.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e10 (12.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003emoderately\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e11 (4.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e10 (5.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e1 (1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e216 (80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e146 (77.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e70 (86.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4138%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.069%;\"\u003e\n \u003cp\u003esevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8276%;\"\u003e\n \u003cp\u003e1 (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2414%;\"\u003e\n \u003cp\u003e1 (0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6897%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.75862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Data presented as n (%) or mean \u0026plusmn; standard deviation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFactor Selection for Risk Prediction Model in the Modeling Set\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate and\u0026nbsp;multivariate logistic regression analyses were conducted to analyze the data of the modeling group patients (Table 2). The results of the univariate logistic regression analysis indicated that gender, education level, marital status, living situation, triglycerides, pharyngitis (VAS score), dizziness (VAS score), tinnitus (THI score), and type of persistent tinnitus had P-values \u0026lt; 0.1. These variables were included in the multivariate logistic regression analysis. Ultimately, gender, education level, marital status, living situation, pharyngitis (VAS score), and type of persistent tinnitus were selected as the variables for the predictive model construction and are presented in the form of a nomogram (Figure 1).\u003c/p\u003e\n\u003cp\u003eTable 2: Univariate and Multivariate Logistic Regression Analysis for the Occurrence of Sudden Sensorineural Hearing Loss\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 40.0651%;\"\u003e\n \u003cp\u003eUnivariate regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 35.9935%;\"\u003e\n \u003cp\u003emultivariate regression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.13-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e0.014-0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.99-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eEducation level\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.62-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e0.217-0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.19-0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e0.003-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eoccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.82-1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eresidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.12-0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e0.008-0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003ehypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.32-1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003etriglyceride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.27-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e0.082-1.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eFasting blood glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.92-1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eRhinitis (VAS score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.9-1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003ePharyngitis (VAS score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.67-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e0.254-0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eEar stuffy (VAS score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e12475762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0-Inf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eVertigo (VAS score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e1.08-3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e2.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e0.934-9.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eheadache (VAS score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.75-1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eAuditory sensitivity (VAS score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e3338131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0-Inf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eTinnitus THI score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e1.1-1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e0.955-1.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003ePersistent type of tinnitus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e16.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e7.16-40.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e24.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e5.089-194.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eAnxiety (SAS score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e237.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0-Inf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eDepressed (SDS score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e86.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0-Inf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9414%;\"\u003e\n \u003cp\u003eSleep disorder\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4007%;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.798%;\"\u003e\n \u003cp\u003e0.7-1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8664%;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4235%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9609%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.60912%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eInf=infimum\u003c/p\u003e\n\u003cp\u003eNote: Variables with P-values less than 0.1 in the univariate logistic regression analysis were included in the multivariate logistic regression analysis. Triglycerides, dizziness (VAS score), and tinnitus (THI score) were excluded from the multivariate analysis.\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eConstruction of a column line chart prediction model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the results of multivariate logistic regression, a nomogram model combining six factors is constructed (Figure 1). The sum of scores for each factor corresponds to the predicted probability of occurrence of SSNHL, which represents the risk value. A higher total score on the nomogram represented a higher risk of SSNHL occurrence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003ePerformance evaluation of the nomogram prediction model\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The chi-square values for the goodness-of-fit test (Hosmer-Lemeshow test) for the modeling set and validation set were 1.2512 and 0.1744, respectively, with corresponding p-values of 0.5349 and 0.6763. The C-index for the model was 0.963 for the modeling set and 0.980 for the validation set (Figure 2), indicating excellent predictive accuracy of the column line chart model. The area under the ROC curve (AUC) was 0.970 (95% CI: 0.951-0.990) for the modeling set and 0.992 (95% CI: 0.981-1) for the validation set (Figure 3), indicating excellent discrimination ability of the nomogram model. The decision curve analysis (DCA) curve (Figure 4) and clinical impact curve (CIC) (Figure 5) both demonstrate the wide clinical utility of this prediction model.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSSNHL is a common sensory disorder that can significantly impact the quality of life of patients\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The diagnosis of SSNHL is primarily based on audiograms, but relying solely on subjective audiograms may lead to inaccurate diagnosis and potential misjudgment. In this study, we established a reliable nomogram model for diagnosing SSNHL using univariate and multivariate logistic regression, and the results showed excellent predictive performance of the model.\u003c/p\u003e \u003cp\u003eThe nomogram model that we developed includes six indicators, including gender, education level, marital status, living situation, pharyngitis (VAS score), and type of tinnitus duration. All of these indicators can be obtained without the requirement for examination or testing (most of them are sociological indicators), making them highly practical in clinical application and easier to implement compared to previous research findings. Among them, the unmarried status has a score of 100 in the nomogram model, indicating a significant influence on the results. We know that living alone is not equivalent to being unmarried, but to some extent, the two are strongly related. Research has shown that living alone can affect dietary habits, and a lack of companionship can lead to a monotonous diet and nutrient deficiencies\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. If poor dietary habits persist for a long time, it may lead to the occurrence of vascular diseases and even immune deficiency, thereby promoting the occurrence of sudden deafness\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Additionally, individuals who live alone often experience higher psychological stress, anxiety, and depression as well as higher sensitivity to the environment than the general population\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Therefore, it is not difficult to understand why the risk of sudden deafness is higher in unmarried and single individuals.\u003c/p\u003e \u003cp\u003eNakamura et al. found that alcohol intake is an independent risk factor for sudden deafness, especially in individuals with severe hearing loss who often have higher alcohol intake\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Based on this, we speculate that men may be more prone to sudden deafness compared to women, possibly due to higher alcohol intake. Individuals with lower education levels often engage in more outdoor work. Tsai et al., in an 11-year follow-up study, found that individuals with long-term exposure to polluted air are more likely to develop sudden deafness\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The influence of outdoor noise pollution cannot be ruled out. Besides, our nomogram also includes an indicator for the absence of pharyngitis. Although sudden deafness and pharyngitis are both common otolaryngologic diseases, no studies have reported their correlation, and further exploration is needed.\u003c/p\u003e \u003cp\u003eAt the same time, we also recognize the limitations of this study. For example, this study is a single-center retrospective study, and the results may only be applicable to specific populations in certain regions. It is unclear whether the nomogram can be applied in other centers. The indicators included in the study still do not cover all sociological behaviors, such as smoking, alcohol intake, staying up late, dietary habits, etc., leaving potential for further research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors contributed to study conceptualization, data collection and analysis, and manuscript writing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported by the science and technology talent support plan of Shaanxi Provincial People\u0026apos;s Hospital(SPPHPR2022050).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are not publicly available due to ethical considerations, but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants have reviewed and signed a consent form to indicate their agreement to take part in the research. The study received approval from the Institutional Review Board (IRB) at the corresponding author\u0026rsquo;s hospital (SPPH-LLBG-17-3-2). All methods and studies were carried out in accordance with relevant guidelines and regulations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants have reviewed and signed a consent form to indicate their agreement to allow for the use of their shared insights for research and publication. No personal information or identifiable photos are used in this publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChandrasekhar SS, Tsai Do BS, Schwartz SR, et al. Clinical Practice Guideline: Sudden Hearing Loss (Update). Otolaryngol Head Neck Surg. 2019;161(1suppl):S1\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0194599819859885\u003c/span\u003e\u003cspan address=\"10.1177/0194599819859885\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiss D, B\u0026ouml;cker AJ, Koopmann M, Savvas E, Borowski M, Rudack C. Predictors of hearing recovery in patients with severe sudden sensorineural hearing loss. J Otolaryngol Head Neck Surg. 2017;46(1):27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40463-017-0207-1\u003c/span\u003e\u003cspan address=\"10.1186/s40463-017-0207-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerchant SN, Adams JC, Nadol JB. Pathology and pathophysiology of idiopathic sudden sensorineural hearing loss. Otol Neurotol. 2005;26(2):151\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00129492-200503000-00004\u003c/span\u003e\u003cspan address=\"10.1097/00129492-200503000-00004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLindsay JR, Davey PR, Ward PH. Inner ear pathology in deafness due to mumps. Ann Otol Rhinol Laryngol. 1960;69:918\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/000348946006900318\u003c/span\u003e\u003cspan address=\"10.1177/000348946006900318\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCabe BF. Autoimmune sensorineural hearing loss. Ann Otol Rhinol Laryngol. 1979;88(5 Pt 1):585\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/000348947908800501\u003c/span\u003e\u003cspan address=\"10.1177/000348947908800501\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg B, Crowson MG, Lin V. Management of sudden sensorineural hearing loss among primary care physicians in Canada: a survey study. J Otolaryngol Head Neck Surg. 2021;50(1):22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40463-021-00498-x\u003c/span\u003e\u003cspan address=\"10.1186/s40463-021-00498-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26(8):1364\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.2007.12.9791\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2007.12.9791\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Yu C, Wang X, et al. The construction and validation of prognostic prediction model for sudden sensorineural hearing loss in middle-aged and elderly people. Auris Nasus Larynx. 2023;51(2):276\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.anl.2023.10.001\u003c/span\u003e\u003cspan address=\"10.1016/j.anl.2023.10.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang S, Li P, Fan F, et al. Nomogram for predicting the prognosis of sudden sensorineural hearing loss patients based on clinical characteristics: a retrospective cohort study. Ann Transl Med. 2023;11(2):104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/atm-22-5647\u003c/span\u003e\u003cspan address=\"10.21037/atm-22-5647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou W, Yuan H, Yang Y, Liu S, Huang J, Zhang H. Nomogram for predicting the prognostic role in idiopathic sudden sensorineural hearing loss. Am J Otolaryngol. 2023;44(2):103736. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.amjoto.2022.103736\u003c/span\u003e\u003cspan address=\"10.1016/j.amjoto.2022.103736\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang GJ, Luo MS, Lu BQ, Li SH. Noninvasive prognostic factors and web predictive tools for idiopathic sudden sensorineural hearing loss. Am J Otolaryngol. 2023;44(6):103965. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.amjoto.2023.103965\u003c/span\u003e\u003cspan address=\"10.1016/j.amjoto.2023.103965\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu H, Wan W, Jiang H, Xiong Y. Prognosis of Idiopathic Sudden Sensorineural Hearing Loss: The Nomogram Perspective. Ann Otol Rhinol Laryngol. 2023;132(1):5\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/00034894221075114\u003c/span\u003e\u003cspan address=\"10.1177/00034894221075114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng C, Yang Y, Huang S, et al. Development and validation for multifactor prediction model of sudden sensorineural hearing loss. Front Neurol. 2023;14:1134564. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fneur.2023.1134564\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2023.1134564\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZung WW. A rating instrument for anxiety disorders. Psychosomatics. 1971;12(6):371\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0033-3182(71)71479-0\u003c/span\u003e\u003cspan address=\"10.1016/S0033-3182(71)71479-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZung WW, A SELF-RATING DEPRESSION SCALE. Arch Gen Psychiatry. 1965;12:63\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/archpsyc.1965.01720310065008\u003c/span\u003e\u003cspan address=\"10.1001/archpsyc.1965.01720310065008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman CW, Jacobson GP, Spitzer JB. Development of the Tinnitus Handicap Inventory. Arch Otolaryngol Head Neck Surg. 1996;122(2):143\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/archotol.1996.01890140029007\u003c/span\u003e\u003cspan address=\"10.1001/archotol.1996.01890140029007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoo M, Hwang JH. Risk of sudden sensorineural hearing loss in patients with common preexisting sensorineural hearing impairment: a population-based study in Taiwan. PLoS ONE. 2015;10(3):e0121190. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0121190\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0121190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandri E, P\u0026eacute;rez-Bermejo M, Cabo A, Cerd\u0026aacute;-Olmedo G. Living Alone: Associations with Diet and Health in the Spanish Young Adult Population. Nutrients. 2023;15(11):2516. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu15112516\u003c/span\u003e\u003cspan address=\"10.3390/nu15112516\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelgado-Lista J, Alcala-Diaz JF, Torres-Pe\u0026ntilde;a JD, et al. Long-term secondary prevention of cardiovascular disease with a Mediterranean diet and a low-fat diet (CORDIOPREV): a randomised controlled trial. Lancet. 2022;399(10338):1876\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(22)00122-2\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(22)00122-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakamura M, Whitlock G, Aoki N, et al. Japanese and Western diet and risk of idiopathic sudden deafness: a case-control study using pooled controls. Int J Epidemiol. 2001;30(3):608\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/30.3.608\u003c/span\u003e\u003cspan address=\"10.1093/ije/30.3.608\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilmoth JM, Chen PC. Immigrant status, living arrangements, and depressive symptoms among middle-aged and older adults. J Gerontol B Psychol Sci Soc Sci. 2003;58(5):S305\u0026ndash;313. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/geronb/58.5.s305\u003c/span\u003e\u003cspan address=\"10.1093/geronb/58.5.s305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakamura M, Aoki N, Nakashima T, et al. Smoking, alcohol, sleep and risk of idiopathic sudden deafness: a case-control study using pooled controls. J Epidemiol. 2001;11(2):81\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2188/jea.11.81\u003c/span\u003e\u003cspan address=\"10.2188/jea.11.81\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai SCS, Hsu YC, Lai JN, et al. Long-term exposure to air pollution and the risk of developing sudden sensorineural hearing loss. J Transl Med. 2021;19(1):424. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12967-021-03095-8\u003c/span\u003e\u003cspan address=\"10.1186/s12967-021-03095-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sudden sensorineural hearing loss, nomogram, risk prediction model","lastPublishedDoi":"10.21203/rs.3.rs-6312681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6312681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To establish and validate a namogram for predicting the risk of sudden sensorineural hearing loss (SSNHL).\u003cbr\u003e\n \u003cstrong\u003eMethods:\u003c/strong\u003e A retrospective analysis was conducted on 119 patients diagnosed with SSNHL in the Department of Otolaryngology-Head and Neck Surgery at Shaanxi Provincial People’s Hospital between October 1, 2022, and November 1, 2023 (observation group), as well as 70 healthy controls (control group), to create a modeling set. Additionally, 51 patients diagnosed with SSNHL and 30 healthy controls were collected from the same department during the same period as a validation set. Patient information, including gender, age, education level, marital status, occupation, living situation, history of hypertension, fasting blood glucose, triglycerides, rhinitis, pharyngitis, ear fullness, dizziness, headache, VAS scores for auditory hypersensitivity, SAS scores for anxiety, SDS scores for depression, THI scores for tinnitus, tinnitus duration, and sleep quality, were collected. Univariate and multivariate logistic regression analyses were performed to compare the clinical parameters of the modeling and validation sets, and a nomogram for predicting the risk of SSNHL was constructed and evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThere were no statistically significant differences in general characteristics between the modeling and validation sets (P\u0026gt;0.05). Using univariate and multivariate logistic regression, six variables were selected for inclusion in the final predictive model: gender, education level, marital status, living situation, pharyngitis (VAS score), and tinnitus duration. A nomogram was constructed based on these variables. The H-L goodness-of-fit tests yielded P values of 0.5349 and 0.6763 for the modeling and validation sets, respectively. The C-index values were 0.963 and 0.980, indicating excellent predictive accuracy. The AUC values of the ROC curves were 0.970 (95% CI: 0.951-0.990) and 0.992 (95% CI: 0.981-1) for the modeling and validation sets, respectively, demonstrating excellent discriminative ability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Male gender, lower education level, unmarried status, living alone, absence of pharyngitis, and presence of persistent tinnitus were identified as independent risk factors for SSNHL in the western part of China. The nomogram based on these risk factors can effectively assess and quantify the risk of SSNHL.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Risk Prediction Model for Sudden Sensorineural Hearing Loss Based on a Nomogram","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 01:28:03","doi":"10.21203/rs.3.rs-6312681/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-05-05T17:25:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-09T12:08:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-09T01:04:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-09T01:03:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2025-03-26T13:06:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96122cd3-bd03-41bd-a540-e796067f4f2f","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-13T01:28:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 01:28:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6312681","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6312681","identity":"rs-6312681","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}