Systemic steroid administration combined with intratympanic steroid injection in the treatment of a unilateral sudden hearing loss prognosis prediction model: A retrospective observational study

Yuan, Hao; Liu, Chengcheng; Ma, Peng-Wei; Chen, Jiawei; Wang, Weilong; Gao, Wei; Lu, Pei-Heng; Ding, Xuerui; Lun, Yu-Qiang; Lu, Lianjun

doi:10.3389/fneur.2022.976393

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…In the end, 7 article met the inclusion criteria and were included in this systematic review. 13,[24][25][26][27][28][29] Reasons for exclusion are shown in the flow diagram in Figure 1.…”

Section: Literature Searchmentioning

confidence: 99%

Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review

Aghakhani,

Yousefi,

Yekaninejad

2023

Ann Otol Rhinol Laryngol

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Background: Machine Learning models have been applied in various healthcare fields, including Audiology, to predict disease outcomes. The prognosis of sudden sensorineural hearing loss is difficult to predict due to the variable course of the disease. Hence, researchers have attempted to utilize ML models to predict the outcome of patients with sudden sensorineural hearing loss. The objectives of this study were to review the performance of these machine learning models and assess their applicability in real-world settings. Methods: A systematic search was conducted in PubMed, Web of Science and Scopus. Only studies that built machine learning prediction models were included, and studies that used algorithms such as logistic regression only for the purpose of adjusting for confounding variables were excluded. The risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Results: After screening, a total of 7 papers were eligible for synthesis. In total, these studies built 48 ML models. The most common utilized algorithms were Logistic Regression, Support Vector Machine (SVM) and boosting. The area under the curve of the receiver operating characteristic curve ranged between 0.59 and 0.915. All of the included studies had a high risk of bias; hence there are concerns regarding their applicability. Conclusion: Although these models showed great performance and promising results, future studies are still needed before these models can be applied in a real-world setting. Future studies should employ multiple cohorts, different feature selection methods, and external validation to further validate the models’ applicability.

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“…In the end, 7 article met the inclusion criteria and were included in this systematic review. 13,[24][25][26][27][28][29] Reasons for exclusion are shown in the flow diagram in Figure 1.…”

Section: Literature Searchmentioning

confidence: 99%

Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review

Aghakhani,

Yousefi,

Yekaninejad

2023

Ann Otol Rhinol Laryngol

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A Prognostic Model to Predict Hearing Recovery in Patients With Idiopathic Sudden Onset Sensorineural Hearing Loss

Mandavia,

Joshi,

Hannink

et al. 2024

JAMA Otolaryngol Head Neck Surg

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ImportanceThe prognosis of idiopathic sudden onset sensorineural hearing loss (iSSNHL) is uncertain, which creates challenges in clinical decision-making for ear, nose, and throat (ENT) physicians and adds to the burden of the condition experienced by patients.ObjectiveTo develop and internally validate a prognostic model for hearing recovery among patients with iSSNHL to support ENT surgeons in making informed and individualized treatment decisions.Design, Settings, and ParticipantsThis prognostic study and model used cohort data from the Sudden Onset Sensorineural Hearing Loss study, which included 812 patients (age ≥16 years) diagnosed with iSSNHL at 76 National Health Service ENT departments in the UK from December 2019 to May 2022. Nine variables previously reported as independent prognostic factors for complete recovery of patients with iSSNHL were selected for inclusion. The final model was internally validated using bootstrapping with 500 repetitions, then coefficients were adjusted for the degree of optimism in the model. The model intercept was reassessed after adjustment of model coefficients. Impact of individual predictors was evaluated by estimating odds ratios with corresponding 95% CIs. Model performance was re-evaluated after internal validation and expressed by discrimination, calibration, and clinical utility. Data analyses were performed from March 2022 to April 2024.InterventionRoutine treatment (per National Health Service standards), including oral steroids and intratympanic steroid injections.Main Outcome and MeasuresComplete hearing recovery defined as a return to within 10 dB of the patient’s before iSSNHL hearing levels at all frequencies in the affected ear at 6 to 16 weeks after iSSNHL symptom onset.ResultsThe study sample included 498 patients (mean [SD] age, 58.7 [16.0] years; 215 [46.9%] females and 243 [53.1%] males) who met the criteria for inclusion in the model. Of those, 210 (46%) were classified as having experienced complete hearing recovery. Five variables were found to be independent predictors for complete hearing recovery: steroid treatment within 7 days from symptom onset (OR, 5.23 vs no treatment ), lower severity of hearing loss at presentation (OR, 0.19 if loss is mild), absence of vertigo (OR, 0.56 vs no vertigo), younger patient age (OR, 0.64 per year), and a history of cardiovascular disease (OR, 1.84 vs no cardiovascular disease). The model showed good performance after internal validation with a c-index of 0.77 (95% CI, 0.7-0.81). Predictions for complete recovery aligned well with observed complete recovery rates, and greater clinical utility than treat all or treat none strategies was shown.Conclusion and RelevanceThis prognostic model evaluated in this study may be able to assist ENT surgeons in making informed treatment decisions for individual patients with iSSNHL. It is available online at no cost.

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The construction and validation of prognostic prediction model for sudden sensorineural hearing loss in middle-aged and elderly people

Zhang,

Yu,

Wang

et al. 2024

Auris Nasus Larynx

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Systemic steroid administration combined with intratympanic steroid injection in the treatment of a unilateral sudden hearing loss prognosis prediction model: A retrospective observational study

Cited by 4 publications

References 25 publications

Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review

Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review

A Prognostic Model to Predict Hearing Recovery in Patients With Idiopathic Sudden Onset Sensorineural Hearing Loss

The construction and validation of prognostic prediction model for sudden sensorineural hearing loss in middle-aged and elderly people

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