Prevalence of Dysexecutive Symptoms in High School Students during the COVID-19 Pandemic.

This is an observational cross-sectional study designed to ascertain the prevalence and severity of dysexecutive symptoms in high school students during the COVID-19 pandemic. The validated Spanish version of the Dysexecutive Questionnaire (DEX) was used. A total of 2396 participants aged 14-22 years were included. Our sample yielded a mean DEX scale score of 28.14 ± 17.42. By the DEX classification, 889 (37.1%) students achieved optimal scores, 384 (16%) reported mild dysexecutive symptoms, 316 (13.2%) reported moderate dysexecutive symptoms, and 807 (33.7%) reported strong dysexecutive symptoms. We found a significant difference between those with and those without employed mothers, with the former scoring higher (p = 0.004), the same as those with both parents employed (p = 0.004). Adolescents face emotional susceptibility and changes in their family, social, and educational environment related to isolation, resulting in altered emotional responses and social interaction.

Sex-Dependent Performance of the Neutrophil-to-Lymphocyte, Monocyte-to-Lymphocyte, Platelet-to-Lymphocyte and Mean Platelet Volume-to-Platelet Ratios in Discriminating COVID-19 Severity.

Background: The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and mean platelet volume-to-platelet ratio (MPR) are combined hematology tests that predict COVID-19 severity, although with different cut-off values. Because sex significantly impacts immune responses and the course of COVID-19, the ratios could be biased by sex. Purpose: This study aims to evaluate sex-dependent differences in the contribution of NLR, PLR, MLR, and MPR to COVID-19 severity and mortality upon hospital admission using a sample of pneumonia patients with SARS-CoV-2 infection. Methods: This single-center observational cross-sectional study included 3,280 confirmed COVID-19 cases (CDC 2019-Novel Coronavirus real-time RT-PCR Diagnostic) from Quito (Ecuador). The receiver operating characteristic (ROC) curve analysis was conducted to identify optimal cut-offs of the above parameters when discriminating severe COVID-19 pneumonia and mortality risks after segregation by sex. Severe COVID-19 pneumonia was defined as having PaO2 < 60 mmHg and SpO2 < 94%, whereas non-severe COVID-19 pneumonia was defined as having PaO2 ≥ 60 mmHg and SpO2 ≥ 94%. Results: The mortality rate of COVID-19 among men was double that in women. Severe COVID-19 pneumonia and non-surviving patients had a higher level of NLR, MLR, PLR, and MPR. The medians of NLR, MLR, and MPR in men were significantly higher, but PLR was not different between men and women. In men, these ratios had lower cut-offs than in women (NLR: 2.42 vs. 3.31, MLR: 0.24 vs. 0.35, and PLR: 83.9 vs. 151.9). The sensitivity of NLR, MLR, and PLR to predict pneumonia severity was better in men (69-77%), whereas their specificity was enhanced in women compared to men (70-76% vs. 23-48%). Conclusion: These ratios may represent widely available biomarkers in COVID-19 since they were significant predictors for disease severity and mortality although with different performances in men and women.

Biomarkers of severe COVID-19 pneumonia on admission using data-mining powered by common laboratory blood tests-datasets.

In the epidemiological COVID-19 research, artificial intelligence is a unique approach to make predictions about disease severity to manage COVID-19 patients. A limitation of artificial intelligence is, however, the high risk of bias. We investigated the skill of data mining and machine learning, two advanced forms of artificial intelligence, to predict severe COVID-19 pneumonia based on routine laboratory tests. A sample of 4009 COVID-19 patients was divided into Severe (PaO2< 60 mmHg, 489 cases) and Non-Severe (PaO2 ≥ 60 mmHg, 3520 cases) groups according to blood hypoxemia on admission and their laboratory datasets analyzed by the R software and WEKA workbench. After curation, data were processed for the selection of the most influential features including hemogram, pCO2, blood acid-base balance, prothrombin time, inflammation biomarkers, and glucose. The best fit of variables was successfully confirmed by either the Multilayer Perceptron, a feedforward neural network algorithm that performed machine recognition of severe COVID-19 with 96.5% precision, or by the C4.5 software, a supervised learning algorithm based on an objective-predefined variable (severity) that generated a decision tree with 89.4% precision. Finally, a complex bivariate Pearson's correlation matrix combined with advanced hierarchical clustering (dendrograms) were conducted for knowledge discovery. The hidden structure of the datasets revealed shift patterns related to the development of COVID-19-induced pneumonia that involved the lymphocyte-to-C-reactive protein and leukocyte-to-C-protein ratios, neutrophil %, pH and pCO2. The data mining approaches to the hematological fluctuations associated with severe COVID-19 pneumonia could not only anticipate adverse clinical outcomes, but also reveal putative therapeutic targets.

Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients.

The World Health Organization (WHO) declared coronavirus disease-2019 (COVID-19) a global pandemic on 11 March 2020. In Ecuador, the first case of COVID-19 was recorded on 29 February 2020. Despite efforts to control its spread, SARS-CoV-2 overran the Ecuadorian public health system, which became one of the most affected in Latin America on 24 April 2020. The Hospital General del Sur de Quito (HGSQ) had to transition from a general to a specific COVID-19 health center in a short period of time to fulfill the health demand from patients with respiratory afflictions. Here, we summarized the implementations applied in the HGSQ to become a COVID-19 exclusive hospital, including the rearrangement of hospital rooms and a triage strategy based on a severity score calculated through an artificial intelligence (AI)-assisted chest computed tomography (CT). Moreover, we present clinical, epidemiological, and laboratory data from 75 laboratory tested COVID-19 patients, which represent the first outbreak of Quito city. The majority of patients were male with a median age of 50 years. We found differences in laboratory parameters between intensive care unit (ICU) and non-ICU cases considering C-reactive protein, lactate dehydrogenase, and lymphocytes. Sensitivity and specificity of the AI-assisted chest CT were 21.4% and 66.7%, respectively, when considering a score >70%; regardless, this system became a cornerstone of hospital triage due to the lack of RT-PCR testing and timely results. If health workers act as vectors of SARS-CoV-2 at their domiciles, they can seed outbreaks that might put 1,879,047 people at risk of infection within 15 km around the hospital. Despite our limited sample size, the information presented can be used as a local example that might aid future responses in low and middle-income countries facing respiratory transmitted epidemics.

Cycle Threshold Values in the Context of Multiple RT-PCR Testing for SARS-CoV-2.

PURPOSE: Discharge or follow up of confirmed coronavirus disease 2019 (COVID-19) cases depend on accurate interpretation of RT-PCR. Currently, positive/negative interpretations are based on amplification instead of quantification of cycle threshold (Ct) values, which could be used as proxies of patient infectiousness. Here, we measured Ct values in hospitalized confirmed COVID-19 patients at different times and its implications in diagnosis and follow up. PATIENTS AND METHODS: Observational study between March 17th-May 12th, 2020 using multiple RT-PCR testing. A cohort of 118 Hispanic hospitalized patients with confirmed COVID-19 diagnosis in a reference hospital in Quito, Ecuador. Multiple RT-PCR tests were performed using deep nasal swab samples and the assessment of SARS-CoV-2 genes N, RdRP, and E. RESULTS: Patients' median age was of 49 years (range: 24-91) with a male majority (62.7%). We found increasing levels of Ct values in time, with a mean Ct value of 29.13 (n = 61, standard deviation (sd) = 5.55) for the first test and 34.38 (n = 60, sd = 4), 35.52 (n = 20, sd = 2.85), and 36.12 (n = 6, sd = 3.28), for the second, third, and fourth tests, respectively. Time to RT-PCR lack of amplification for all tests was of 34 days while time to RT-PCR Ct values >33 was of 30 days. CONCLUSION: Cycle thresholds can potentially be used to improve diagnosis, management and control. We found that turnover time for negativity can be large for hospitalized patients and that 11% cases persisted with infectious Ct values for more time than the current isolation recommendations.

[Prevalence of SARS-CoV-2 coronavirus infection in patients and professional staff at a medium or long-stay hospital in Spain]. / Prevalencia de infección por coronavirus SARS-CoV-2 en pacientes y profesionales de un hospital de media y larga estancia en España.

BACKGROUND AND GOALS: The aim of the study is to know the prevalence of SARS-CoV-2 infection in patients and professional staff of a medium or long-stay hospital during the peak period of the pandemic in Spain, spring 2020. MATERIAL AND METHODS: At the end of February 2020, we developed at the hospital a strategy to diagnose the SARS-CoV-2 infection consisting of complementing the realization of PCR tests at real time with a quick technique of lateral flow immunochromatography to detect IgG and IgM antibodies against the virus. We also developed a protocol to realize those diagnostic tests and considered an infection (current or past) a positive result in any of the above tests. We included 524 participants in the study (230 patients and 294 hospital staff), and divided them into hospital patients and Hemodialysis outpatients. Furthermore, we divided the hospital staff into healthcare and non-healthcare staff. The documented period was from March, 20th to April, 21st, 2020. RESULTS: 26 out of 230 patients tested positive in any of the diagnostic techniques (PCR, antibodies IgG, IgM) with a 11.30% prevalence. According to patients groups, we got a 14.38% prevalence in hospital patients vs. 5.95% in outpatients, with a significantly higher risk in admitted patients after adjustment for age and gender (OR=3,309, 95%CI: 1,154-9,495). 24 out of 294 hospital staff tested positive in any of the diagnostic techniques, with a 8.16% prevalence. According to the groups, we got a 8.91% prevalence in healthcare staff vs. 4.26% in non-healthcare staff. Thus, we do not see any statistically significant differences between hospital staff and patients as far as prevalence is concerned (P=0,391), (OR=2,200, 95%CI: 0,500-9,689). CONCLUSIONS: The result of the study was a quite low prevalence rate of SARS-CoV-2 infection, in both patients and hospital staff, being the hospital patients' prevalence rate higher than the outpatients', and the healthcare staff higher than the non-healthcare's. Combining PCR tests (gold standard) with antibodies tests proved useful as a diagnostic strategy.