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PURPOSE: Cochlear implantation is a standard approach to hearing rehabilitation and encompasses three main stages: appropriate patient selection, a challenging surgical procedure, which should be as atraumatic as possible and preserve cochlear structures, and lifelong postoperative follow-up. Computed tomography (CT) is performed to assess postoperative implant position. The Siemens Advanced Radar Target Identification System (ARTIS) Pheno provides fluoroscopic imaging during surgery and has so far been mainly used by cardiologists, neurosurgeons and trauma surgeons. METHODS: Six patients with difficult anatomy or a challenging medical history were selected for a surgical procedure, during which we planned to use the ARTIS Pheno to accurately position and assess implant position under fluoroscopy during and immediately after surgery. In all six cases, the ARTIS Pheno was used directly in the surgical setting. The procedures were performed in cooperation with the neuroradiology department in an interdisciplinary manner. RESULTS: In all six patients, fluoroscopy was used to visualise the procedure at different stages of surgery. In five patients, the procedure was successfully completed. This approach allowed us to finally assess implant position and confirm the correct and complete insertion of the electrode while the patient was still under anaesthesia. CONCLUSION: These cases showed positive surgical outcomes. Although the procedure is more complex than a standard approach, patients can be managed in a safe, effective and appropriate manner. The assessment of implant position in real time during surgery leads to greater patient and surgeon satisfaction. The approach presented here ensures a high quality of cochlear implant surgery even in difficult surgical situations and meets the requirements of modern surgery.
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BACKGROUND: Results of neurotological function diagnostics in the context of interdisciplinary vertigo assessment are usually formulated as free-text reports (FTR). These are often subject to high variability, which may lead to loss of information. The aim of the present study was to evaluate the completeness of structured reports (SR) and referrer satisfaction in the neurotological assessment of vertigo. MATERIALS AND METHODS: Neurotological function diagnostics performed as referrals (nâ¯= 88) were evaluated retrospectively. On the basis of the available raw data, SRs corresponding to FTRs from clinical routine were created by means of a specific SR template for neurotological function diagnostics. FTRs and SRs were evaluated for completeness and referring physician satisfaction (nâ¯= 8) using a visual analog scale (VAS) questionnaire. RESULTS: Compared to FTRs, SRs showed significantly increased overall completeness (73.7% vs. 51.7%, pâ¯< 0.001), especially in terms of patient history (92.5% vs. 66.7%, pâ¯< 0.001), description of previous findings (87.5% vs. 38%, pâ¯< 0.001), and neurotological (33.5% vs. 26.7%, pâ¯< 0.001) and audiometric function diagnostics (58% vs. 32.3%, pâ¯< 0.001). In addition, SR showed significantly increased referring physician satisfaction (VAS 8.8 vs. 4.9, pâ¯< 0.001). CONCLUSION: Neurotological SRs enable a significantly increased report completeness with higher referrer satisfaction in the context of interdisciplinary assessment of vertigo. Furthermore, SRs are particularly suitable for scientific data analysis, especially in the context of big data analyses.
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STUDY OBJECTIVES: Obstructive sleep apnea (OSA) is a very prevalent disease and its diagnosis is based on polysomnography (PSG). We investigated whether snoring-sound-, very low frequency electrocardiogram (ECG-VLF)- and thoraco-abdominal effort- PSG signal entropy values could be used as surrogate markers for detection of OSA and OSA severity classification. METHODS: The raw data of the snoring-, ECG- and abdominal and thoracic excursion signal recordings of two consecutive full-night PSGs of 86 consecutive patients (22 female, 53.74 ± 12.4 years) were analyzed retrospectively. Four epochs (30 s each, manually scored according to the American Academy of Sleep Medicine standard) of each sleep stage (N1, N2, N3, REM, awake) were used as the ground truth. Sampling entropy (SampEn) of all the above signals was calculated and group comparisons between the OSA severity groups were performed. In total, (86x4x5 = )1720 epochs/group/night were included in the training set as an input for a support vector machine (SVM) algorithm to classify the OSA severity classes. Analyses were performed for first- and second-night PSG recordings separately. RESULTS: Twenty-seven patients had mild (RDI = ≥ 5/h but <15/h), 21 patients moderate (RDI ≥15/h but <30/h) and 23 patients severe OSA (RDI ≥30/h). Fifteen patients had an RDI <5/h and were therefore considered non-OSA. Using SE on the above three PSG signal data and using a SVM pipeline, it was possible to distinguish between the four OSA severity classes. The best metric was snoring signal-SE. The area-under-the-curve (AUC) calculations showed reproducible significant results for both nights of PSG. The second night data were even more significant, with non-OSA (R) vs. light OSA (L) 0.61, R vs. moderate (M) 0.68, R vs. heavy OSA (H) 0.84, L vs. M 0.63, M vs. H 0.65 and L vs. H 0.82. The results were not confounded by age or gender. CONCLUSIONS: SampEn of either snoring-, very low ECG-frequencies- or thoraco-abdominal effort signals alone may be used as a surrogate marker to diagnose OSA and even predict OSA severity. More specifically, in this exploratory study snoring signal SampEn showed the greatest predictive accuracy for OSA among the three signals. Second night data showed even more accurate results for all three parameters than first-night recordings. Therefore, technologies using only parts of the PSG signal, e.g. sound-recording devices, may be used for OSA screening and OSA severity group classification.
