Why use tympanometry in general practice: A review

Eustachian tube dysfunction in an intensive care unit: A prospective observational study

Background

Eustachain tube (ET) dysfunction can aggravate the morbidity in ICU patients, but is often ignored. In this prospective observational study we followed a cohort of patients (without pre-existing ET dysfunction) requiring in-patient management, hospitalized either to ICU or to non ICU wards, for any development of ET dysfunction during hospitalization.

Methods

Patients requiring hospitalization to ICU or non ICU wards from Dec 2018 to Jun 2019 were included. Those with pre-existing ET dysfunction, disease of ear/nose or sinuses, head neck tumours and history of radiotherapy or glucocorticoid therapy were excluded. All patients were evaluated by serial tympanometry. Type A curve was considered normal while type B, C1 and C2 were considered as ET dysfunction.

Results

There were 385 patients, 258 (67.01%) males and 127 (32.99%) females in the ICU group; while non ICU group comprised 129 patients, 86 (66.67%) males and 43 (33.33%) females. ET dysfunction developed in 107 (27.79%) patients in ICU group, but only in 3 (2.3%) in non ICU group (Relative risk 11.95, 95% CI 3.86 to 36.99, P < 0.0001). Within ICU, ET dysfunction showed significant positive association with endotracheal intubation, Ramsay sedation score and number of days on tracheostomy; but not with age, male gender, number of days in ICU, mechanical ventilation or route of feeding.

Conclusion

Our study demonstrates high incidence of ET dysfunction in patients admitted to ICU.

Detecting middle ear fluid using smartphones

The presence of middle ear fluid is a key diagnostic marker for two of the most common pediatric ear diseases: acute otitis media and otitis media with effusion. We present an accessible solution that uses speakers and microphones within existing smartphones to detect middle ear fluid by assessing eardrum mobility. We conducted a clinical study on 98 patient ears at a pediatric surgical center. Using leave-one-out cross-validation to estimate performance on unseen data, we obtained an area under the curve (AUC) of 0.898 for the smartphone-based machine learning algorithm. In comparison, commercial acoustic reflectometry, which requires custom hardware, achieved an AUC of 0.776. Furthermore, we achieved 85% sensitivity and 82% specificity, comparable to published performance measures for tympanometry and pneumatic otoscopy. Similar results were obtained when testing across multiple smartphone platforms. Parents of pediatric patients (n = 25 ears) demonstrated similar performance to trained clinicians when using the smartphone-based system. These results demonstrate the potential for a smartphone to be a low-barrier and effective screening tool for detecting the presence of middle ear fluid.