Diagnosing conductive dysfunction in infants using wideband acoustic immittance: development and validation of predictive models

Abstract

Background. Infants with early onset of otitis media have greater risk of recurrent and chronic infections that can affect language and development. Diagnostic tools able to quickly and accurately identify middle ear pathology early in infancy could help to facilitate timely intervention for these children.Wideband acoustic immittance (WAI) is an emerging technology for assessing middle ear function with significant advantages over established clinical tests such as tympanometry. WAI does not require pressurization of the ear canal, and is a high-resolution test, measuring middle ear function over a wider frequency range than is possible with tympanometry. Preliminary studies in infants have shown promising results, but further research assessing the diagnostic performance of WAI is needed. Also, the large amount of data generated by WAI can make results difficult to interpret. Research into suitable quantitative techniques to analyse results is still in its infancy. Prediction models are an attractive method for analysis of multivariate data as they can provide individualized probabilities that an infant has middle ear dysfunction. A clinically useful prediction model must be able to accurately discriminate between normal ears and those with middle ear dysfunction, and be well calibrated (i.e., give accurate predictions). However, the number of variables generated by WAI can cause issues with overfitting when developing multivariate models. An overfitted model will accurately describe the data it was developed on, but is likely to perform poorly when applied to new samples. Some form of data reduction or penalization is therefore necessary when modelling WAI. Another issue with developing WAI models is that there are substantial maturational effects on WAI through infancy that need to be controlled for. This can be achieved by either developing models for specific age groups (e.g., a model specifically for neonates), or by controlling for the effect of age by including interactions between age and WAI variables in a model, or by only using developmentally stable regions as predictor variables.Objective. The aim of this work was to investigate the diagnostic performance of WAI in infants by developing predictive models. Data reduction strategies such as selecting predictors based on prior research and principal component analysis were used to increase the likelihood that models would generalize to new samples. The effect of age was initially accounted for by developing age-specific models for neonates, 6-month infants, and 12-month infants (Chapters 2, 3 and 4, respectively). Longitudinal developmental effects on WAI through infancy were then investigated in Chapter 5, and this knowledge was used to develop a model controlling for the effect of age through infancy (6 to 18 months) (Chapter 6, Study 1). The neonate model was assessed for generalizability by applying the model to results from a new sample of infants (Chapter 6, Study 2)ii Methods. Tympanometry, distortion product otoacoustic emissions (DPOAEs) and ...

Swept-Tone Stimulus-Frequency Otoacoustic Emissions in Human Newborns

“…We used pressure reflectance values recorded with each probe fit to calculate ear-canal energy reflectance, which is the square of the pressure reflection coefficient (Keefe, Bulen, Arehart, & Burns, 1993). Energy reflectance can provide an indicator of probe orientation (i.e., wedged against or near the ear canal), pressure changes in the ear canal, or the presence of ear canal/middle-ear debris and fluid (Aithal, Aithal, Kei, & Manuel, 2019; Hunter, Keefe, Feeney, & Fitzpatrick, 2017; Myers et al., 2019b; Sanford & Feeney, 2008) and has been reliably associated with poor OAE outcomes (Keefe & Simmons, 2003; Myers et al., 2019a). Figure 3 shows the mean magnitude of the energy reflectance for the newborn group with present SFOAEs (solid black) and those with absent SFOAEs (dashed).…”