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Behav Sci (Basel) ; 12(7)2022 Jul 15.
Article in English | MEDLINE | ID: covidwho-1938699


Purpose: To identify clinical phenotypes and biomarkers for best mortality prediction considering age, symptoms and comorbidities in COVID-19 patients with chronic neurological diseases in intensive care units (ICUs). Subjects and Methods: Data included 1252 COVID-19 patients admitted to ICUs in Cuba between January and August 2021. A k-means algorithm based on unsupervised learning was used to identify clinical patterns related to symptoms, comorbidities and age. The Stable Sparse Classifiers procedure (SSC) was employed for predicting mortality. The classification performance was assessed using the area under the receiver operating curve (AUC). Results: Six phenotypes using a modified v-fold cross validation for the k-means algorithm were identified: phenotype class 1, mean age 72.3 years (ys)-hypertension and coronary artery disease, alongside typical COVID-19 symptoms; class 2, mean age 63 ys-asthma, cough and fever; class 3, mean age 74.5 ys-hypertension, diabetes and cough; class 4, mean age 67.8 ys-hypertension and no symptoms; class 5, mean age 53 ys-cough and no comorbidities; class 6, mean age 60 ys-without symptoms or comorbidities. The chronic neurological disease (CND) percentage was distributed in the six phenotypes, predominantly in phenotypes of classes 3 (24.72%) and 4 (35,39%); χ² (5) 11.0129 p = 0.051134. The cerebrovascular disease was concentrated in classes 3 and 4; χ² (5) = 36.63, p = 0.000001. The mortality rate totaled 325 (25.79%), of which 56 (17.23%) had chronic neurological diseases. The highest in-hospital mortality rates were found in phenotypes 1 (37.22%) and 3 (33.98%). The SSC revealed that a neurological symptom (ageusia), together with two neurological diseases (cerebrovascular disease and Parkinson's disease), and in addition to ICU days, age and specific symptoms (fever, cough, dyspnea and chilliness) as well as particular comorbidities (hypertension, diabetes and asthma) indicated the best prediction performance (AUC = 0.67). Conclusions: The identification of clinical phenotypes and mortality biomarkers using practical variables and robust statistical methodologies make several noteworthy contributions to basic and experimental investigations for distinguishing the COVID-19 clinical spectrum and predicting mortality.

Neuroimage ; 256: 119190, 2022 08 01.
Article in English | MEDLINE | ID: covidwho-1829283


This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

Brain Diseases , COVID-19 , Brain/diagnostic imaging , Brain Mapping , Electroencephalography/methods , Humans
Rev Med Inst Mex Seguro Soc ; 58(Supl 2): S215-223, 2020 09 21.
Article in Spanish | MEDLINE | ID: covidwho-1485704


Since the World Health Organization (WHO) announced the COVID-19 pandemic, SARS-CoV-2 infections have had a profound impact on public health. In this scenario an increasing number of women will be affected; equally, fetuses and newborns could be particularly vulnerable to the harmful effects of congenital or perinatally-acquired infections. In this study it is reviewed the available evidence on the potential intrauterine vertical SARS-CoV-2 transmission, after an exhaustive review of publications indexed until April 2020 in the United States' National Library of Medicine (PubMed/Medline). Starting from the analogies made with TORCH infections (Toxoplasma gondii, rubella virus, cytomegalovirus, and herpes virus), and other coronaviruses, it is provided a pensive look about the potential impact of SARS-CoV-2 on the central nervous system (CNS). Lessons learned from the effects on CNS of other epidemics from TORCH viruses, as Zika virus in Brazil, and the analogy with the findings in animal models, pose the risk of congenital and perinatally-acquired infections, which are related to SARS-CoV-2. The effects of SARS-CoV-2 infection in the first trimester of pregnancy are unknown, and there are still many questions about its potential impact on CNS.

Desde que la Organización Mundial de la Salud (OMS) declaró la pandemia de COVID-19, las infecciones por SARS-CoV-2 han tenido un profundo impacto en la salud pública. En este escenario se afectará a un número creciente de mujeres embarazadas; asimismo, los fetos y los recién nacidos podrían ser especialmente vulnerables a las consecuencias dañinas de la infección adquirida de manera congénita o perinatal. En este trabajo se revisan las evidencias disponibles sobre la potencial transmisión vertical intrauterina de la infección por SARS-CoV-2, tras una revisión exhaustiva de las publicaciones indexadas hasta abril de 2020 en la Biblioteca Nacional de Medicina de los Estados Unidos (PubMed/Medline). Partiendo de las analogías con infecciones TORCH (Toxoplasma gondii, virus de la rubéola, citomegalovirus y virus del herpes) y otros coronavirus, se ofrece una mirada reflexiva sobre los efectos potenciales en el sistema nervioso central (SNC). Las lecciones aprendidas sobre los efectos en el SNC de otras epidemias por virus TORCH, como la del virus Zika en Brasil, y la analogía con los hallazgos en modelos animales, plantean el riesgo de infecciones congénitas y adquiridas perinatalmente, las cuales están relacionadas con el SARS-CoV-2. Se desconocen hoy las consecuencias de la infección por el SARS-CoV-2 en el primer trimestre del embarazo, y persisten muchas interrogantes sobre su impacto potencial en el SNC.