The NOSE scale is a questionnaire assessing QOL related with nasal obstruction. The aim of this study was to culturally adapt the NOSE scale into Italian (I-NOSE). Prospective instrument validation study. Cross-cultural adaptation and validation were accomplished. Cronbach α was used to test internal consistency in 116 patients complaining nasal obstruction and 232 asymptomatic subjects. Pearson and ICC tests were used for test-retest reliability analysis. Normative data were gathered from the 232 asymptomatic subjects. Mann-Whitney test was used to compare the I-NOSE scores in patients and asymptomatic subjects and in 40 patients before and after septoplasty. I-NOSE scores obtained in 60 patients were correlated with rhinomanometric results and with the score of a visual analog scale (VAS) measuring the subjective sensation of nasal obstruction. Good internal consistency and good test-retest reliability were found. I-NOSE mean score of the normal cohort was 12.1 ± 13.2. Asymptomatic subjects scored lower than patients with nasal obstruction (p = 0.001). Positive correlations between I-NOSE scores and VAS and rhinomanometric results were found. The mean I-NOSE score improved from 64.4 ± 23.6 to 22.1 ± 13.5 after septoplasty (p < 0.001). The I-NOSE scale is a reliable, valid, self-administered, symptom-specific questionnaire; its application is recommended.

Although previous studies demonstrated that patients with obstructive sleep apnea syndrome (OSAS) may present subclinical manifestations of dysphagia, in not one were different textures and volumes systematically studied. The aim of this study was to analyze the signs and symptoms of oropharyngeal dysphagia using fiberoptic endoscopic evaluation of swallowing (FEES) with boluses of different textures and volumes in a large cohort of patients with OSAS. A total of 72 OSAS patients without symptoms of dysphagia were enrolled. The cohort was divided in two groups: 30 patients with moderate OSAS and 42 patients with severe OSAS. Each patient underwent a FEES examination using 5, 10 and 20 ml of liquids and semisolids, and solids. Spillage, penetration, aspiration, retention, and piecemeal deglutition were considered. The penetration-aspiration scale (PAS), pooling score (PS), and dysphagia outcome and severity scale (DOSS) were used for quantitative analysis. Each patient completed the SWAL-QOL questionnaire. Forty-six patients (64 %) presented spillage, 20 (28 %) piecemeal deglutition, 26 (36 %) penetration, and 30 (44 %) retention. No differences were found in the PAS, PS, and DOSS scores between patients with moderate and severe OSAS. Patients with severe OSAS scored higher General Burden and Food selection subscales of the SWAL-QOL. Depending on the DOSS score, the cohort of patients was divided into those with and those without signs of dysphagia. Patients with signs of dysphagia scored lower in the General Burden and Symptoms subscales of the SWAL-QOL. OSAS patients show signs of swallowing impairment in about half of the population; clinicians involved in the management of these patients should include questions on swallowing when taking the medical history.

Background Italy has experienced severe consequences (ie, hospitalizations and deaths) during the COVID-19 pandemic. Online decision support systems (DSS) and self-triage applications have been used in several settings to supplement health authority recommendations to prevent and manage COVID-19. A digital Italian health tech startup, Paginemediche, developed a noncommercial, online DSS with a chat user interface to assist individuals in Italy manage their potential exposure to COVID-19 and interpret their symptoms since early in the pandemic. Objective This study aimed to compare the trend in online DSS sessions with that of COVID-19 cases reported by the national health surveillance system in Italy, from February 2020 to March 2021. Methods We compared the number of sessions by users with a COVID-19–positive contact and users with COVID-19–compatible symptoms with the number of cases reported by the national surveillance system. To calculate the distance between the time series, we used the dynamic time warping algorithm. We applied Symbolic Aggregate approXimation (SAX) encoding to the time series in 1-week periods. We calculated the Hamming distance between the SAX strings. We shifted time series of online DSS sessions 1 week ahead. We measured the improvement in Hamming distance to verify the hypothesis that online DSS sessions anticipate the trends in cases reported to the official surveillance system. Results We analyzed 75,557 sessions in the online DSS; 65,207 were sessions by symptomatic users, while 19,062 were by contacts of individuals with COVID-19. The highest number of online DSS sessions was recorded early in the pandemic. Second and third peaks were observed in October 2020 and March 2021, respectively, preceding the surge in notified COVID-19 cases by approximately 1 week. The distance between sessions by users with COVID-19 contacts and reported cases calculated by dynamic time warping was 61.23; the distance between sessions by symptomatic users was 93.72. The time series of users with a COVID-19 contact was more consistent with the trend in confirmed cases. With the 1-week shift, the Hamming distance between the time series of sessions by users with a COVID-19 contact and reported cases improved from 0.49 to 0.46. We repeated the analysis, restricting the time window to between July 2020 and December 2020. The corresponding Hamming distance was 0.16 before and improved to 0.08 after the time shift. Conclusions Temporal trends in the number of online COVID-19 DSS sessions may precede the trend in reported COVID-19 cases through traditional surveillance. The trends in sessions by users with a contact with COVID-19 may better predict reported cases of COVID-19 than sessions by symptomatic users. Data from online DSS may represent a useful supplement to traditional surveillance and support the identification of early warning signals in the COVID-19 pandemic.

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BACKGROUND Italy has experienced very severe consequences in terms of hospitalizations and deaths during the COVID-19 pandemic. Online decision support systems and self-triage applications have been used in several settings to supplement recommendations from health authorities to prevent and manage COVID-19. A digital Italian health tech startup developed a non-commercial online decision support system to assist individuals in managing their potential exposure to COVID-19 and interpret their symptoms, with a chat user interface, available since the early phases of the pandemic in Italy. OBJECTIVE To compare the trend of sessions in this online support decision system with that of COVID-19 cases reported by the national health surveillance system in Italy, from February 2020 to March 2021. METHODS We analyzed the number of sessions by users with a COVID-19 positive contact and by users with symptoms compatible with COVID-19, with the number of cases reported by the National surveillance system. To calculate the distance between the time series, we used the Dynamic Time Warping algorithm. We also applied Symbolic Aggregate approXimation (SAX) encoding to the time series in one-week periods and we calculated the Hamming distance between the SAX strings. We shifted time series of sessions from the online decision support system one week ahead and we measured the improvement in Hamming distance to verify the hypothesis that sessions in the online decision support systems anticipate the trends in cases reported to the official surveillance system. RESULTS We analyzed a total of 75,557 sessions in the online decision support system. Among them, 65,207 were sessions by users with symptoms, while 19,062 were by contacts with individuals with COVID-19. The highest number of sessions in the online decision support system was recorded in the early phases of the pandemic. A second peak was observed in October 2020 and a third peak was observed in March 2021, in parallel with the surge of reported cases. Peaks in sessions of the online decision support system preceded the surge of COVID-19 notified cases by approximately one week. The distance between sessions by users with COVID 19 contacts and reported cases calculated by dynamic time warping was 61.23 while the distance with sessions by users with symptoms was 93.72. As the time series of users with a COVID 19 contact was more consistent with the trend of confirmed cases, we applied Symbolic Aggregate approXimation encoding and we measured the Hamming distance between these two time series. After applying a one-week shift, the Hamming distance between the time series of sessions by users with a COVID-19 contact and reported cases improved from 0.49 to 0.46. We repeated the analysis restricting the time window to the time period between July and December 2020. The corresponding Hamming distance was 0.16 before shifting the time series, and improved to 0.08 after the time shift. CONCLUSIONS Temporal trends in the number of sessions of an online COVID-19 online decision support system may precede the trend of reported COVID-19 cases obtained through traditional surveillance. The trends of sessions by users with a contact with COVID-19 cases may better predict reported cases of COVID-19 than sessions by users with symptoms. Data from online decision support systems may represent a useful information source to supplement traditional surveillance and to support the identification of early warning signals in the COVID-19 pandemic.