Validation of the French version of HHIE-S (Hearing Handicap Inventory for the Elderly - Screening) questionnaire in French over-60 year-olds

Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S

OBJECTIVES

The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults.

METHODS

We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model-which had the greatest AUC of 0.83 (95% CI 0.81-0.85)-was then only used on the holdout testing data.

RESULTS

On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79-0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques-specifically, the LGBM classifier-with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects.

CONCLUSIONS

Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely.

Italian validation of the Hearing Handicap Inventory for Elderly - Screening version (HHIE-S-It)

Objective

Validate the Italian version of the Hearing Handicap Inventory for Elderly - Screening version (HHIE-S-It).

Methods

After translation, psychometric properties and attributes were analysed by administering the HHIE-S-It to 167 elderly outpatients together with the Psychological General Well-Being Index (PGWBI).

Results

The Cronbach's α coefficient was 0.908 for the total score, and 0.832 and 0.816 for its two subscales. Significant test-retest reliability was observed (p < 0.001). Moderate to high correlations were found between HHIE-S-It and pure tone average in the better ear (p < 0.001). The ANOVA test confirmed the significant difference in HHIE-S-It scores across groups according to the degree of hearing loss (p < 0.001). Only very low and low significant correlations were observed between HHIE-S-It and PGWBI. The criterion HHIE-S-It > 11 was observed as the best cut-off with highest sensitivity (86.4%), specificity (72.4%), positive predictive value (52.8%), negative predictive value (93.7%) and likelihood ratios (3.12 and 0.19).

Conclusions

Since the HHIE-S-It presented acceptable psychometric properties, its adoption is justified for both clinical and research purposes. Acceptable diagnostic attributes allow its use as a screening tool for age-related hearing loss.