BACKGROUND: Neutrophils present as major inflammatory cells in refractory chronic rhinosinusitis with nasal polyps (CRSwNP), regardless of the endotype. However, their role in the pathophysiology of CRSwNP remains poorly understood. We investigated factors predicting the surgical outcomes of CRSwNP patients with focus on neutrophilic localization. METHODS: We employed machine-learning methods such as the decision tree and random forest models to predict the surgical outcomes of CRSwNP. Immunofluorescence analysis was conducted to detect human neutrophil elastase (HNE), Bcl-2, and Ki-67 in NP tissues. We counted the immunofluorescence-positive cells and divided them into three groups based on the infiltrated area, namely, epithelial, subepithelial, and perivascular groups. RESULTS: On machine learning, the decision tree algorithm demonstrated that the number of subepithelial HNE-positive cells, Lund-Mackay (LM) scores, and endotype (eosinophilic or non-eosinophilic) were the most important predictors of surgical outcomes in CRSwNP patients. Additionally, the random forest algorithm showed that, after ranking the mean decrease in the Gini index or the accuracy of each factor, the top three ranking factors associated with surgical outcomes were the LM score, age, and number of subepithelial HNE-positive cells. In terms of cellular proliferation, immunofluorescence analysis revealed that Ki-67/HNE-double positive and Bcl-2/HNE-double positive cells were significantly increased in the subepithelial area in refractory CRSwNP. CONCLUSION: Our machine-learning approach and immunofluorescence analysis demonstrated that subepithelial neutrophils in NP tissues had a high expression of Ki-67 and could serve as a cellular biomarker for predicting surgical outcomes in CRSwNP patients.

Increasing recognition of anatomical obstruction has resulted in a large variety of sleep surgeries to improve anatomic collapse of obstructive sleep apnea (OSA) and the prediction of whether sleep surgery will have successful outcome is very important. The aim of this study is to assess a machine learning-based clinical model that predict the success rate of sleep surgery in OSA subjects. The predicted success rate from machine learning and the predicted subjective surgical outcome from the physician were compared with the actual success rate in 163 male dominated-OSA subjects. Predicted success rate of sleep surgery from machine learning models based on sleep parameters and endoscopic findings of upper airway demonstrated higher accuracy than subjective predicted value of sleep surgeon. The gradient boosting model showed the best performance to predict the surgical success that is evaluated by pre- and post-operative polysomnography or home sleep apnea testing among the logistic regression and three machine learning models, and the accuracy of gradient boosting model (0.708) was significantly higher than logistic regression model (0.542). Our data demonstrate that the data mining-driven prediction such as gradient boosting exhibited higher accuracy for prediction of surgical outcome and we can provide accurate information on surgical outcomes before surgery to OSA subjects using machine learning models.

Importance: Consumer-level sleep analysis technologies have the potential to revolutionize the screening for obstructive sleep apnea (OSA). However, assessment of OSA prediction models based on in-home recording data is usually performed concurrently with level 1 in-laboratory polysomnography (PSG). Establishing the predictability of OSA using sound data recorded from smartphones based on level 2 PSG at home is important.Objective: To validate the performance of a prediction model for OSA using breathing sound recorded from smartphones in conjunction with level 2 PSG at home.Design, Setting, and Participants: This diagnostic study followed a prospective design, involving participants who underwent unattended level 2 home PSG. Breathing sounds were recorded during sleep using 2 smartphones, one with an iOS operating system and the other with an Android operating system, simultaneously with home PSG in participants’ own home environment. Participants were 19 years and older, slept alone, and had either been diagnosed with OSA or had no previous diagnosis. The study was performed between February 2022 and February 2023.Main Outcomes and Measures: Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds.Results: Of the 101 participants included during the study duration, the mean (SD) age was 48.3 (14.9) years, and 51 (50.5%) were female. For the iOS smartphone, the sensitivity values at apnea-hypopnea index (AHI) levels of 5, 15, and 30 per hour were 92.6%, 90.9%, and 93.3%, respectively, with specificities of 84.3%, 94.4%, and 94.4%, respectively. Similarly, for the Android smartphone, the sensitivity values at AHI levels of 5, 15, and 30 per hour were 92.2%, 90.0%, and 92.9%, respectively, with specificities of 84.0%, 94.4%, and 94.3%, respectively. The accuracy for the iOS smartphone was 88.6%, 93.3%, and 94.3%, respectively, and for the Android smartphone was 88.1%, 93.1%, and 94.1% at AHI levels of 5, 15, and 30 per hour, respectively.Conclusions and Relevance: This diagnostic study demonstrated the feasibility of predicting OSA with a reasonable level of accuracy using breathing sounds obtained by smartphones during sleep at home.

Kim JW, et al. J Korean Med Sci. 2021 Jan;36(4):e40. https://doi.org/10.3346/jkms.2021.36.e40

Background: Understanding the characteristics of residual hearing at low frequencies and its natural course in relation to molecular genetic etiology may be important in developing rehabilitation strategies. Thus, we aimed to explore the characteristics and natural course of residual hearing at low frequencies associated with the two most frequent deafness genes: GJB2 and SLC26A4. Methods: Initially, 53 GJB2 and 65 SLC26A4 subjects were enrolled, respectively. Only those whose audiograms exhibited hearing thresholds ≤70 dB at 250 and 500 Hz, and who had at least 1-year follow-up period between the first and last audiograms, were included. Collectively, the clinical characteristics of 14 ears from eight subjects with GJB2 variants, and 31 ears from 22 subjects with SLC26A4 variants fulfilled the strict criteria. In this study, a dropout rate refers to an incidence of dropping out of the cohort by cochlear implant surgery due to severe hearing deterioration. Results: Among the ears with complete serial audiogram data set, significant residual hearing at low frequencies at the time of inclusion was observed in 18.8% of those with GJB2 variants (15 out of 80 ears) and 42.6% of those with SLC26A4 variants (46 out of 108 ears), revealing a difference between two deafness genes. Subsequently, ears with SLC26A4 variants (11 of 46 ears, 23.9%) turned out to have a higher dropout rate for cochlear implantation due to hearing deterioration within the first year than those with GJB2 variants (1 of 15, 6.7%), albeit with no statistical significance. Throughout the follow-up period (mean: 37.2 ± 6.8, range: 12 to 80 months), deterioration of residual hearing at low frequencies at 250 Hz (dB HL/y) and 500 Hz (dB HL/y) of those with GJB2 variants exhibited 3.1 ± 1.3 (range: 0 to 15) and 5.2 ± 1.6 (range: 0 to 20), respectively, suggesting the deterioration of residual hearing in GJB2 variants was rather slow and gradual. Specifically, GJB2 p.Leu79Cysfs*3 show less remarkable residual hearing at low frequencies, but then a relatively stable nature. In contrast, SLC26A4 variants demonstrated a significantly higher dropout rate due to severe hearing deterioration requiring cochlear implantation compared with the GJB2 variants. This trend was observed not only in the first-year follow-up period but also in the follow-up periods thereafter. The p.His723Arg;c.919-2A>G genotype of SLC26A4, in particular, was associated with a high propensity for sudden hearing deterioration, as indicated by the dropout rate, which was as high as 46.2% for cochlear implantation due to hearing deterioration during the first year follow-up period. Furthermore, the dropout rate for cochlear implantation was observed in 7.1% of those with GJB2 variants (one out of 14 ears) and 30.3% of those with SLC26A4 variants (10 out of 33 ears) throughout the entire follow-up period. Conclusions: Our results suggest that there is a difference with respect to the progressive nature of residual hearing at low frequencies between the two most common genes responsible for hearing loss, which may provide clinical implications of having individualized rehabilitation and timely intervention.