Score of Hearing Handicap Inventory for the Elderly (HHIE) Compared to Whisper Test on Presbycusis.
Purnami NyiloEndang Fittrih MulyaningsihTitiek Hidayati AhadiahBudi UtomoAndrew SmithPublished in: Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India (2020)
Presbycusis is a sensorineural type of hearing loss caused by a degenerative process of the hearing organ. Examination was done to detect hearing loss, with Audiometry as the diagnostic gold standard and screening with whisper test and using Hearing Handicap Inventory for the Elderly-Screening (HHIE-S) questionnaire. This study was aimed to compare the sensitivity and specificity between Hearing Handicap Inventory for the Elderly Screening questionnaire score and Whisper test in hearing loss of presbycusis patients in Dr. Soetomo Hospital. Subjects were elderly patients in outpatient clinic of Geriatry and Audiology of Dr. Soetomo General Hospital in Surabaya. Data samples were collected by consecutive sampling. All collected samples were analyzed statistically by Pearson correlation test to identify the correlation between variables. Results: Statistic analysis with Pearson correlation test obtained p -value = 0.001 and correlation coefficient (r) = 0.691 for HHIE-S questionnaire and p = 0.001 and (r) = 0.298 for Whisper test. The sensitivity of the Whisper test was 72.73% while the HHIE-S questionnaire was 61.82%. Both tests had the same specificity of 80%. Conclusions: The Whisper test is more sensitive than HHIE-S questionnaires in detecting hearing loss in presbycusis patients in outpatient clinic of Geriatry and Audiology of Dr. Soetomo General Hospital in Surabaya.
Keyphrases
- hearing loss
- psychometric properties
- end stage renal disease
- healthcare
- newly diagnosed
- ejection fraction
- chronic kidney disease
- primary care
- cross sectional
- patient reported
- prognostic factors
- magnetic resonance imaging
- emergency department
- magnetic resonance
- patient reported outcomes
- machine learning
- acute care
- artificial intelligence