Electrically evoked auditory cortical responses elicited from individually fitted stimulation parameters in cochlear implant users.

OBJECTIVE: To investigate electrically evoked auditory cortical responses (eACR) elicited from the stimulation of intracochlear electrodes based on individually fitted stimulation parameters in cochlear implant (CI) users. DESIGN: An eACR setup based on individual fitting parameters is proposed. A 50-ms alternating biphasic pulse train was used to stimulate apical, medial, and basal electrodes and to evoke auditory cortical potentials (N1-P2 complex). STUDY SAMPLE: The eACR setup proposed was validated with 14 adult CI users. RESULTS: Individual and grand-average eACR waveforms were obtained. The eACR amplitudes were lower in the basal than in the apical and medial regions. Earlier N1 latencies were found in CI users with lower maximum comfortable loudness levels and shorter phase duration in response to apical stimulation, while medial and basal stimulation resulted in earlier N1 latencies and larger N1-P2 amplitudes in users with longer CI experience. CONCLUSIONS: eACR could be elicited by direct intracochlear stimulation using individual fitting parameters with a success rate of 71%. The highest cortical peak-to-peak amplitudes were obtained in response to apical stimulation. Unlike the P2, the N1 component appeared to be a consistent cortical potential to determine eACR and gain knowledge of the auditory processing beyond the cochlea in CI users. HighlightseACR can be elicited through direct stimulation of intracochlear electrodes.Stimulation of apical and medial regions yielded the highest N1-P2 amplitudes.CI users with lower maximum comfortable loudness levels had shorter N1 latencies during apical stimulation.The present dataset of mainly well-performing CI users suggests better cortical processing, that is, higher amplitudes and shorter latencies of N1.The N1 potential appears a more consistent and reliable potential than the P2 to determine eACR responses in CI users.

Analysis of Prevalence and Predictive Factors of Long-Lasting Olfactory and Gustatory Dysfunction in COVID-19 Patients.

BACKGROUND: Although smell and taste disorders are highly prevalent symptoms of COVID-19 infection, the predictive factors leading to long-lasting chemosensory dysfunction are still poorly understood. METHODS: 102 out of 421 (24.2%) mildly symptomatic COVID-19 patients completed a second questionnaire about the evolution of their symptoms one year after the infection using visual analog scales (VAS). A subgroup of 69 patients also underwent psychophysical evaluation of olfactory function through UPSIT. RESULTS: The prevalence of chemosensory dysfunction decreased from 82.4% to 45.1% after 12 months, with 46.1% of patients reporting a complete recovery. Patients older than 40 years (OR = 0.20; 95% CI: [0.07, 0.56]) and with a duration of loss of smell longer than four weeks saw a lower odds ratio for recovery (OR = 0.27; 95% CI: [0.10, 0.76]). In addition, 28 patients (35.9%) reported suffering from parosmia, which was associated with moderate to severe taste dysfunction at the baseline (OR = 7.80; 95% CI: [1.70, 35.8]). Among the 69 subjects who underwent the UPSIT, 57 (82.6%) presented some degree of smell dysfunction, showing a moderate correlation with self-reported VAS (r = -0.36, p = 0.0027). CONCLUSION: A clinically relevant number of subjects reported persistent chemosensory dysfunction and parosmia one year after COVID-19 infection, with a moderate correlation with psychophysical olfactory tests.

Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach.

The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction.