Cluster analysis of insomnia symptoms during COVID-19 pandemic: a general population web-based survey in Iran.

OBJECTIVE: To investigate the prevalence of insomnia and its different phenotypes as well as their association with fear of COVID-19 in the general population. MATERIAL AND METHODS: This was a cross-sectional study conducted using an online survey (e-poll). All available participants who completed the online survey form were included in the current study. All individuals with a history of sleep problems were excluded. A questionnaire package consisted of insomnia severity index (ISI), and FCV-19 for corona fear was administered for all participants. Insomnia was defined as ISI≥8. Insomnia phenotypes were considered as: (a) DIS: difficulty initiating sleep; (b) DMS: difficulty maintaining sleep; (c) EMA: early morning awakening; and (d) combined insomnia. RESULTS: A total of 1,223 participants [827 (67.6%) female, mean age=39.82±10.75 years old], enrolled in the current survey. Based on ISI, 675 (55.2% [95%CI=52.40-57.98]) were categorized into the insomnia group. Insomnia was more prevalent in females (p=0.006), participants with 50 years old or higher (p=0.04), or high fear of COVID-19 (p<0.0001). Totally, 67.4%, 66.4%, and 55% of all participants had DIS, DMS, and EMA, respectively, in the current outbreak. Besides, 79% had impaired daily functioning, 51.6% had impaired quality of life, and 62% were worried about their sleep problem. Notably that a considerable percentage of individuals with normal ISI scores had at least one insomnia phenotype or impaired daily functioning and quality of life. Further analyses revealed a significant increasing trend in all four insomnia phenotypes prevalence with an increase in fear of COVID-19 (all p-values<0.0001). CONCLUSION: Individuals with higher age, female gender, or higher fear of COVID-19 are at higher risk of all types of insomnia as well as impaired daytime performance or quality of life.

Factors Predicting Outcome in Intensive Care Unit-Admitted COVID-19 Patients: Using Clinical, Laboratory, and Radiologic Characteristics.

PURPOSE: To investigate the factors contributing to mortality in coronavirus disease 2019 (COVID-19) patients admitted in the intensive care unit (ICU) and design a model to predict the mortality rate. METHOD: We retrospectively evaluated the medical records and CT images of the ICU-admitted COVID-19 patients who had an on-admission chest CT scan. We analyzed the patients' demographic, clinical, laboratory, and radiologic findings and compared them between survivors and nonsurvivors. RESULTS: Among the 121 enrolled patients (mean age, 62.2 ± 14.0 years; male, 82 (67.8%)), 41 (33.9%) survived, and the rest succumbed to death. The most frequent radiologic findings were ground-glass opacity (GGO) (71.9%) with peripheral (38.8%) and bilateral (98.3%) involvement, with lower lobes (94.2%) predominancy. The most common additional findings were cardiomegaly (63.6%), parenchymal band (47.9%), and crazy-paving pattern (44.4%). Univariable analysis of radiologic findings showed that cardiomegaly (p : 0.04), pleural effusion (p : 0.02), and pericardial effusion (p : 0.03) were significantly more prevalent in nonsurvivors. However, the extension of pulmonary involvement was not significantly different between the two subgroups (11.4 ± 4.1 in survivors vs. 11.9 ± 5.1 in nonsurvivors, p : 0.59). Among nonradiologic factors, advanced age (p : 0.002), lower O2 saturation (p : 0.01), diastolic blood pressure (p : 0.02), and hypertension (p : 0.03) were more commonly found in nonsurvivors. There was no significant difference between survivors and nonsurvivors in terms of laboratory findings. Three following factors remained significant in the backward logistic regression model: O2 saturation (OR: 0.91 (95% CI: 0.84-0.97), p : 0.006), pericardial effusion (6.56 (0.17-59.3), p : 0.09), and hypertension (4.11 (1.39-12.2), p : 0.01). This model had 78.7% sensitivity, 61.1% specificity, 90.0% positive predictive value, and 75.5% accuracy in predicting in-ICU mortality. CONCLUSION: A combination of underlying diseases, vital signs, and radiologic factors might have prognostic value for mortality rate prediction in ICU-admitted COVID-19 patients.

Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system.

OBJECTIVE: Proposing a scoring tool to predict COVID-19 patients' outcomes based on initially assessed clinical and CT features. METHODS: All patients, who were referred to a tertiary-university hospital respiratory triage (March 27-April 26, 2020), were highly clinically suggestive for COVID-19 and had undergone a chest CT scan were included. Those with positive rRT-PCR or highly clinically suspicious patients with typical chest CT scan pulmonary manifestations were considered confirmed COVID-19 for additional analyses. Patients, based on outcome, were categorized into outpatient, ordinary-ward admitted, intensive care unit (ICU) admitted, and deceased; their demographic, clinical, and chest CT scan parameters were compared. The pulmonary chest CT scan features were scaled with a novel semi-quantitative scoring system to assess pulmonary involvement (PI). RESULTS: Chest CT scans of 739 patients (mean age = 49.2 ± 17.2 years old, 56.7% male) were reviewed; 491 (66.4%), 176 (23.8%), and 72 (9.7%) cases were managed outpatient, in an ordinary ward, and ICU, respectively. A total of 439 (59.6%) patients were confirmed COVID-19 cases; their most prevalent chest CT scan features were ground-glass opacity (GGO) (93.3%), pleural-based peripheral distribution (60.3%), and multi-lobar (79.7%), bilateral (76.6%), and lower lobes (RLL and/or LLL) (89.1%) involvement. Patients with lower SpO2, advanced age, RR, total PI score or PI density score, and diffuse distribution or involvement of multi-lobar, bilateral, or lower lobes were more likely to be ICU admitted/expired. After adjusting for confounders, predictive models found cutoffs of age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 (15) for ICU admission (death). A combination of all three factors showed 89.1% and 95% specificity and 81.9% and 91.4% accuracy for ICU admission and death outcomes, respectively. Solely evaluated high PI score had high sensitivity, specificity, and NPV in predicting the outcome as well. CONCLUSION: We strongly recommend patients with age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 or even only high PI score to be considered as high-risk patients for further managements and care plans. KEY POINTS: • Chest CT scan is a valuable tool in prioritizing the patients in hospital triage. • A more accurate and novel 35-scale semi-quantitative scoring system was designed to predict the COVID-19 patients' outcome. • Patients with age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 or even only high PI score should be considered high-risk patients.