Background: Obstructive Sleep Apnea (OSA) syndrome is a respiratory sleep disorder characterized by partial or complete episodes of upper airway collapse with reduction or complete cessation of airflow. Although the connection remains debated, several mechanisms such as intermittent hypoxemia, sleep deprivation, hypercapnia disruption of the hypothalamic–pituitary–adrenal axis have been associated with poor neurocognitive performance. Different treatments have been proposed to treat OSAS patients as continuous positive airway pressure (CPAP), mandibular advancement devices (MAD), surgery; however, the effect on neurocognitive functions is still debated. This article presents the effect of OSAS treatments on neurocognitive performance by reviewing the literature. Methods: We performed a comprehensive review of the English language over the past 20 years using the following keywords: neurocognitive performance and sleep apnea, neurocognitive improvement and CPAP, OSAS, and cognitive dysfunction. We included in the analysis papers that correlated OSA treatment with neurocognitive performance improvement. All validated tests used to measure different neurocognitive performance improvements were considered. Results: Seventy papers reported neurocognitive Performance improvement in OSA patients after CPAP therapy. Eighty percent of studies found improved executive functions such as verbal fluency or working memory, with partial neural recovery at long-term follow-up. One article compared the effect of MAD, CPAP treatment on cognitive disorders, reporting better improvement of CPAP and MAD than placebo in cognitive function. Conclusions: CPAP treatment seems to improve cognitive defects associated with OSA. Limited studies have evaluated the effects of the other therapies on cognitive function.

(1) Introduction: Laryngeal cancer is one of the most common types of cancer affecting the upper aerodigestive tract. Despite ensuring good oncological outcome in many locoregionally advanced cases, total laryngectomy is associated with relevant physical and psychological sequelae. Treatment through tracheo-esophageal speech, if promising, can lead to very variable outcomes. Not all laryngectomee patients with vocal prosthesis benefit from the same level of rehabilitation mainly due to the development of prosthetic or fistula related problems. The relating sequelae in some cases are even more decisive in the patient quality of life, having a higher impact than communicational or verbal skills. (2) Material and Methods: A retrospective study was conducted on 63 patients initially enrolled with a history of total laryngectomy and voice rehabilitation, treated at the University Hospital of Catania from 1 January 2010 to 31 December 2018. Quality of life (QoL) evaluation through validated self-administrated questionnaires was performed. (3) Results: The Voice-Related Quality of Life questionnaire revealed significantly better outcomes in both socio-emotional and functional domains of the tracheoesophageal patient group compared to the esophageal group (p = 0.01; p = 0.01, respectively), whereas in the Voice Handicap Index assessment, statistically significant scores were not achieved (p = 0.33). (4) Discussion: The significant differences reported through the V-RQOL and Voice Handicap Index scales in the presence of fistula related problems and device lifetime reduction when compared to the oesophageal speech group have demonstrated, as supported by the literature, a crucial role in the rehabilitative prognosis. (5) Conclusions: The criteria of low resistance to airflow, optimal tracheoesophageal retention, prolonged device life, simple patient maintenance, and comfortable outpatient surgery are the reference standard for obtaining good QoL results, especially over time. Furthermore, the correct phenotyping of the patient based on the main outcomes achieved at clinical follow-up guarantees the primary objective of the identification of a better quality of life.

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Objectives: To evaluate the role of clinical scores assessing the risk of disease severity in patients with clinical suspicion of obstructive sleep apnea syndrome (OSA). The hypothesis was tested by applying artificial intelligence (AI) to demonstrate its effectiveness in distinguishing between mild–moderate OSA and severe OSA risk. Methods: A support vector machine model (SVM) was developed from the samples included in the analysis (N = 498), and they were split into 75% for training (N = 373) with the remaining for testing (N = 125). Two diagnostic thresholds were selected for OSA severity: mild to moderate (apnea–hypopnea index (AHI) ≥ 5 events/h and AHI < 30 events/h) and severe (AHI ≥ 30 events/h). The algorithms were trained and tested to predict OSA patient severity. Results: The sensitivity and specificity for the SVM model were 0.93 and 0.80 with an accuracy of 0.86; instead, the logistic regression full mode reported a value of 0.74 and 0.63, respectively, with an accuracy of 0.68. After backward stepwise elimination for features selection, the reduced logistic regression model demonstrated a sensitivity and specificity of 0.79 and 0.56, respectively, and an accuracy of 0.67. Conclusion: Artificial intelligence could be applied to patients with symptoms related to OSA to identify individuals with a severe OSA risk with clinical-based algorithms in the OSA framework.

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