Frequency specificity of contralateral, ipsilateral and bilateral medial olivocochlear acoustic reflexes in humans

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references.A variety of evidence indicates that the brain controls the gain of mechanical amplification in the cochlea in a frequency specific manner through the medial olivocochlear (MOC) efferent pathway, but the degree of MOC frequency specificity in humans is poorly understood. This thesis investigates the tuning properties of the human MOC acoustic reflex at different cochlear frequency regions and with different MOC-elicitor lateralities and frequency contents. Effects produced by the MOC reflex were quantified by the magnitude of the induced changes in stimulus frequency otoacoustic emissions (deltaSFOAEs) at probe frequencies of 0.5, 1 and 4 kHz. With MOC activity elicited by a mid-level (60 dB SPL) tone or half-octave-band of noise, significant MOC-induced deltaSFOAEs were seen over a wide range of elicitor frequencies, e.g. for elicitor frequencies at least 11/2 octaves away from each probe frequency. deltaSFOAE-versus-elicitor-frequency patterns were sometimes skewed so that elicitors at frequencies above (0.5 kHz probe) or below (1 kHz probe) the probe frequency were most effective. In contrast to the wide frequency range of MOC effects from mid-level elicitors, for 1 kHz probes MOC-effect tuning curves (TCs) were narrow with Ql0s of -2, sharper than the MOC-fiber TCs with best frequencies near I kHz in cats and guinea pigs. When MOC effects were looked at as the MOC-inhibited SFOAE relative to the original SFOAE, the SFOAE magnitude decreases and phase changes appeared to be separate functions of elicitor frequency: SFOAE magnitude inhibition was largest for on-frequency elicitors (elicitor frequencies near the probe frequency) while MOC-induced SFOAE phase leads were largest for off-frequency elicitors.(cont) One hypothesis to account for this is that on-frequency elicitors predominantly inhibit the traveling wave from the probe-tone, whereas off-frequency elicitors shift it along the frequency axis by selectively inhibiting apical or basal parts of the traveling-wave. These results are consistent with an anti-masking role of MOC efferents and suggest that MOC efferents do more than just provide feedback to a narrow frequency region around the elicitor frequency.by Watjana Lilaonitkul.Ph.D

Clinical deployment environments: Five pillars of translational machine learning for health

Machine Learning for Health (ML4H) has demonstrated efficacy in computer imaging and other self-contained digital workflows, but has failed to substantially impact routine clinical care. This is no longer because of poor adoption of Electronic Health Records Systems (EHRS), but because ML4H needs an infrastructure for development, deployment and evaluation within the healthcare institution. In this paper, we propose a design pattern called a Clinical Deployment Environment (CDE). We sketch the five pillars of the CDE: (1) real world development supported by live data where ML4H teams can iteratively build and test at the bedside (2) an ML-Ops platform that brings the rigour and standards of continuous deployment to ML4H (3) design and supervision by those with expertise in AI safety (4) the methods of implementation science that enable the algorithmic insights to influence the behaviour of clinicians and patients and (5) continuous evaluation that uses randomisation to avoid bias but in an agile manner. The CDE is intended to answer the same requirements that bio-medicine articulated in establishing the translational medicine domain. It envisions a transition from "real-world" data to "real-world" development

Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes

The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model-SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes (General Haemodynamic, Renal and Immunological) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease

Phenotyping of ABCA4 Retinopathy by Machine Learning Analysis of Full-Field Electroretinography

PURPOSE: Biallelic pathogenic variants in ABCA4 are the commonest cause of monogenic retinal disease. The full-field electroretinogram (ERG) quantifies severity of retinal dysfunction. We explored application of machine learning in ERG interpretation and in genotype–phenotype correlations. METHODS: International standard ERGs in 597 cases of ABCA4 retinopathy were classified into three functional phenotypes by human experts: macular dysfunction alone (group 1), or with additional generalized cone dysfunction (group 2), or both cone and rod dysfunction (group 3). Algorithms were developed for automatic selection and measurement of ERG components and for classification of ERG phenotype. Elastic-net regression was used to quantify severity of specific ABCA4 variants based on effect on retinal function. RESULTS: Of the cohort, 57.6%, 7.4%, and 35.0% fell into groups 1, 2, and 3 respectively. Compared with human experts, automated classification showed overall accuracy of 91.8% (SE, 0.169), and 96.7%, 39.3%, and 93.8% for groups 1, 2, and 3. When groups 2 and 3 were combined, the average holdout group accuracy was 93.6% (SE, 0.142). A regression model yielded phenotypic severity scores for the 47 commonest ABCA4 variants. CONCLUSIONS: This study quantifies prevalence of phenotypic groups based on retinal function in a uniquely large single-center cohort of patients with electrophysiologically characterized ABCA4 retinopathy and shows applicability of machine learning. Novel regression-based analyses of ABCA4 variant severity could identify individuals predisposed to severe disease. Translational Relevance: Machine learning can yield meaningful classifications of ERG data, and data-driven scoring of genetic variants can identify patients likely to benefit most from future therapies

Frequency specificity of the ipsilateral, contralateral and binaural medical efferent reflexes in humans

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (leaves 41-45).by Watjana Lilaonitkul.S.M

Reflex Control of the Human Inner Ear: A Half-Octave Offset in Medial Efferent Feedback That Is Consistent With an Efferent Role in the Control of Masking

The high sensitivity and frequency selectivity of the mammalian cochlea is due to amplification produced by outer hair cells (OHCs) and controlled by medial olivocochlear (MOC) efferents. Data from animals led to the view that MOC fibers provide frequency-specific inhibitory feedback; however, these studies did not measure intact MOC reflexes. To test whether MOC inhibition is primarily at the frequency that elicits the MOC activity, acoustically elicited MOC effects were quantified in humans by the change in otoacoustic emissions produced by 60-dB SPL tone and half-octave-band noise elicitors at different frequencies relative to a 40-dB SPL, 1-kHz probe tone. On average, all elicitors produced MOC effects that were skewed (elicitor frequencies -1 octave below the probe produced larger effects than those -1 octave above). The largest MOC effects were from elicitors below the probe frequency for contra- and bilateral elicitors but were from elicitors centered at the probe frequency for ipsilateral elicitors. Typically, ipsilateral elicitors produced larger effects than contralateral elicitors and bilateral elicitors produced effects near the ipsi+contra sum. Elicitors at levels down to 30-dB SPL produced similar patterns. Tuning curves (TCs) interpolated from these data were V-shaped with Q10s ∼2. These are sharper than MOC-fiber TCs found near 1 kHz in cats and guinea pigs. Because cochlear amplification is skewed (more below the best frequency of a cochlear region), these data are consistent with an anti-masking role of MOC efferents that reduces masking by reducing the cochlear amplification seen at 1 kHz

Reduced oxygen saturation entropy is associated with poor prognosis in critically ill patients with sepsis

Abstract Recent studies have found that oxygen saturation (SpO2) variability analysis has potential for noninvasive assessment of the functional connectivity of cardiorespiratory control systems during hypoxia. Patients with sepsis have suboptimal tissue oxygenation and impaired organ system connectivity. Our objective with this report was to highlight the potential use for SpO2 variability analysis in predicting intensive care survival in patients with sepsis. MIMIC‐III clinical data of 164 adults meeting Sepsis‐3 criteria and with 30 min of SpO2 and respiratory rate data were analyzed. The complexity of SpO2 signals was measured through various entropy calculations such as sample entropy and multiscale entropy analysis. The sequential organ failure assessment (SOFA) score was calculated to assess the severity of sepsis and multiorgan failure. While the standard deviation of SpO2 signals was comparable in the non‐survivor and survivor groups, non‐survivors had significantly lower SpO2 entropy than those who survived their ICU stay (0.107 ± 0.084 vs. 0.070 ± 0.083, p < 0.05). According to Cox regression analysis, higher SpO2 entropy was a predictor of survival in patients with sepsis. Multivariate analysis also showed that the prognostic value of SpO2 entropy was independent of other covariates such as age, SOFA score, mean SpO2, and ventilation status. When SpO2 entropy was combined with mean SpO2, the composite had a significantly higher performance in prediction of survival. Analysis of SpO2 entropy can predict patient outcome, and when combined with SpO2 mean, can provide improved prognostic information. The prognostic power is on par with the SOFA score. This analysis can easily be incorporated into current ICU practice and has potential for noninvasive assessment of critically ill patients

Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes

Abstract The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model—SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes (General Haemodynamic, Renal and Immunological) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease

Human Medial Olivocochlear Reflex: Effects as Functions of Contralateral, Ipsilateral, and Bilateral Elicitor Bandwidths

Animal studies have led to the view that the acoustic medial olivocochlear (MOC) efferent reflex provides sharply tuned frequency-specific feedback that inhibits cochlear amplification. To determine if MOC activation is indeed narrow band, we measured the MOC effects in humans elicited by 60-dB sound pressure level (SPL) contralateral, ipsilateral, and bilateral noise bands as a function of noise bandwidth from 1/2 to 6.7 octaves. MOC effects were quantified by the change in stimulus frequency otoacoustic emissions from 40 dB SPL probe tones near 0.5, 1, and 4 kHz. In a second experiment, the noise bands were centered 2 octaves below probe frequencies near 1 and 4 kHz. In all cases, the MOC effects increased as elicitor bandwidth increased, with the effect saturating at about 4 octaves. Generally, the MOC effects increased as the probe frequency decreased, opposite expectations based on MOC innervation density in the cochlea. Bilateral-elicitor effects were always the largest. The ratio of ipsilateral/contralateral effects depended on elicitor bandwidth; the ratio was large for narrow-band probe-centered elicitors and approximately unity for wide-band elicitors. In another experiment, the MOC effects from increasing elicitor bandwidths were calculated from measurements of the MOC effects from adjacent half-octave noise bands. The predicted bandwidth function agreed well with the measured bandwidth function for contralateral elicitors, but overestimated it for ipsilateral and bilateral elicitors. Overall, the results indicate that (1) the MOC reflexes integrate excitation from almost the entire cochlear length, (2) as elicitor bandwidth is increased, the excitation from newly stimulated cochlear regions more than overcomes the reduced excitation at frequencies in the center of the elicitor band, and (3) contralateral, ipsilateral, and bilateral elicitors show MOC reflex spatial summation over most of the cochlea, but ipsilateral spatial summation is less additive than contralateral