Classical electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH) are well studied in older populations and patients with hypertension. Their utility in young pre-participation cohorts is unclear.

We aimed to develop machine learning models for detection of echocardiogram-diagnosed LVH from ECG, and compare these models with classical criteria.

Between November 2009 and December 2014, pre-participation screening ECG and subsequent echocardiographic data was collected from 17 310 males aged 16 to 23, who reported for medical screening prior to military conscription. A final diagnosis of LVH was made during echocardiography, defined by a left ventricular mass index >115 g/m2. The continuous and threshold forms of classical ECG criteria (Sokolow-Lyon, Romhilt-Estes, Modified Cornell, Cornell Product, and Cornell) were compared against machine learning models (Logistic Regression, GLMNet, Random Forests, Gradient Boosting Machines) using receiver-operating characteristics curve analysis. We also compared the important variables identified by machine learning models with the input variables of classical criteria.

Prevalence of echocardiographic LVH in this population was 0.82% (143/17310). Classical ECG criteria had poor performance in predicting LVH. Machine learning methods achieved superior performance Logistic Regression (area under the curve [AUC], 0.811; 95% confidence interval [CI], 0.738-0.884), GLMNet (AUC, 0.873; 95% CI, 0.817-0.929), Random Forest (AUC, 0.824; 95% CI, 0.749-0.898), Gradient Boosting Machines (AUC, 0.800; 95% CI, 0.738-0.862).

Machine learning methods are superior to classical ECG criteria in diagnosing echocardiographic LVH in the context of pre-participation screening.

Machine learning methods are superior to classical ECG criteria in diagnosing echocardiographic LVH in the context of pre-participation screening.

Investigate the rate of hearing loss progression and incidence of cochlear implant candidacy in children with enlarged vestibular aqueduct (EVA).

EVA is the most common congenital malformation of the inner ear, is responsible for a large percentage of children with hearing loss, and is associated with hearing loss progression. Rates and degree of progression of hearing loss to cochlear implantation candidacy have not been well described.

Review of children with EVA who presented to a single academic medical center. Audiometric data were reviewed to determine subjects who met criteria for cochlear implantation (≥75 dB pure-tone average) at presentation. For those not meeting criteria, serial audiometric data were reviewed for progression to candidacy.

A total of 257 ears met inclusion criteria. One hundred ninety-two (74.7%) met cochlear implant candidacy criteria by age 12, yet only 117 ears (60.9%) actually received implants before turning 13. One hundred fifty-three ears (59.5%) met implant candidacy criteria at presentation. Tanespimycin Nearly 50% of those not initially meeting implantation criteria had a ≥15 dB shift in pure-tone average by age 12, with 37.5% of this subgroup meeting implant candidacy criteria before their teen years at an average age of 7.10 years.

The majority of children with EVA reach cochlear implant candidacy before reaching adulthood, yet implantation rates for candidate ears remain at 60% and delay in implantation persist. Parents of children with EVA should be counseled on the likelihood of progression and closely monitored for cochlear implant candidacy.

The majority of children with EVA reach cochlear implant candidacy before reaching adulthood, yet implantation rates for candidate ears remain at 60% and delay in implantation persist. Parents of children with EVA should be counseled on the likelihood of progression and closely monitored for cochlear implant candidacy.

The default mapping procedure for electric-acoustic stimulation (EAS) devices uses the cochlear implant recipient’s unaided detection thresholds in the implanted ear to derive the acoustic settings and assign the lowest frequency filter of electric stimulation. Individual differences for speech recognition with EAS may be due to discrepancies between the electric frequency filters of individual electrode contacts and the cochlear place of stimulation, known as a frequency-to-place mismatch. Frequency-to-place mismatch of greater than 1/2 octave has been demonstrated in up to 60% of EAS users. Aligning the electric frequency filters via a place-based mapping procedure using postoperative imaging may improve speech recognition with EAS.

Masked sentence recognition was evaluated for normal-hearing subjects (n = 17) listening with vocoder simulations of EAS, using a place-based map and a default map. Simulation parameters were based on audiometric and imaging data from a representative 24-mm electrode array recipient and EAS user. The place-based map aligned electric frequency filters with the cochlear place frequency, which introduced a gap between the simulated acoustic and electric output. The default map settings were derived from the clinical programming software and provided the full speech frequency range.

Masked sentence recognition was significantly better for simulated EAS with the place-based map as compared with the default map.

The simulated EAS place-based map supported better performance than the simulated EAS default map. This indicates that individualizing maps may improve performance in EAS users by helping them achieve better asymptotic performance earlier and mitigate the need for acclimatization.

The simulated EAS place-based map supported better performance than the simulated EAS default map. This indicates that individualizing maps may improve performance in EAS users by helping them achieve better asymptotic performance earlier and mitigate the need for acclimatization.

To 1) describe changes in the electrical stapedial reflex threshold (eSRT), within and across patients over time and 2) to identify the clinical relationship between eSRT and an individual’s upper limit of loudness.

Retrospective chart review and analysis using a multilevel modeling approach to describe changes in eSRT over time.

Secondary care center.

Two-hundred five cochlear implant recipients treated at the cochlear implant center during a 3-year time period.

Cochlear implantation, eSRT testing, and, electrical upper limits of loudness.

The eSRT over multiple appointments and the cochlear implant recipients’ final upper limits of loudness.

Analysis of the eSRT testing indicated stability over time; no global trend was seen in trajectory across the population, b = -0.010, p = 0.899. The relationship between eSRT and user upper limits of loudness revealed a mean decrease of 19.47, units for manufacturer 1, 30.53 units for manufacturer 2, and 0.7 units for manufacturer 3.

Electrical stapedial reflex thresholds remain consistent for individual subjects over time with implant experience being the only variable correlated with eSRT stability (increase in 5% of one standard deviation with each year of experience).