Symptoms that predict positive COVID-19 testing and hospitalization: an analysis of 9,000 patients

PurposeTo develop a reliable tool that predicts which patients are most likely to be COVID-19 positive and which ones have an increased risk of hospitalization. MethodsFrom February 2020 to April 2021, trained nurses recorded age, gender, and symptoms in an outpatient COVID-19 testing center. All positive patients were followed up by phone for 14 days or until symptom-free. We calculated the symptoms odds ratio for positive results and hospitalization and proposed a "random forest" machine-learning model to predict positive testing. ResultsA total of 8,998 patients over 16 years old underwent COVID-19 RT-PCR, with 1,914 (21.3%) positives. Fifty patients needed hospitalization (2.6% of positives), and three died (0.15%). Most common symptoms were: cough, headache, sore throat, coryza, fever, myalgia (57%, 51%, 44%, 36%, 35%, 27%, respectively). Cough, fever, and myalgia predicted positive COVID-19 test, while others behaved as protective factors. The best predictors of positivity were fever plus anosmia/ageusia (OR=6.31), and cough plus anosmia/ageusia (OR=5.82), both p<0.0001. Our random forest model had an ROC-AUC of 0.72 (specificity=0.70, sensitivity=0.61, PPV=0.38, NPV=0.86). Having steady fever during the first days of infection and persistent dyspnea increased the risk of hospitalization (OR=6.66, p<0.0001 and OR=3.13, p=0.003, respectively), while anosmia-ageusia (OR=0.36, p=0.009) and coryza (OR=0.31, p=0.014) were protective. ConclusionPresent study and algorithm may help identify patients at higher risk of having SARS-COV-2 (online calculator http://wdchealth.covid-map.com/shiny/calculator/), and also disease severity and hospitalization based on symptoms presence, pattern, and duration, which can help physicians and health care providers.

Time-analysis of COVID-19 dispersion among health care workers and the general population

IntroductionHeath care workers with direct (HCW-D) or indirect (HCW-A) patient contact represent 4.2% to 17.8% of COVID-19 cases. We evaluate the temporal COVID-19 infection behavior among HCW-D, HCW-A, and non-HCW. MethodsFrom February 2020 to April 2021, trained nurses recorded age, gender, occupation, and symptoms in a COVID-19 testing outpatient health center. We allocated data into weekly time fractals and calculated the proportion of COVID-19 positive among HCW vs. non-HCW and incorporated an ARFIMA model (traditionally used in weather forecast) to predict future cases of COVID-19. ResultsAmong 8,998 COVID-19 RT-PCR tests, 3,462 (42%) patients were HCW-D, and 933 (11%) were HCW-A. Overall, 1,914 (21.3%) returned positive, representing 27%, 25% and 19% of HCW-D, HCW-A and non-HCW, respectively. HCW-D or HCW-A were significantly more likely to test positive for COVID-19 than non-HCW (OR=1.5, p<0.0001). The percentage of positive to negative test results remained steady over time. In the positive cases, the percentage of HCW to non-HCW declined significantly over time (Mann-Kendal trend test: tau=-0.58, p<0.0001). Our ARFIMA model showed a long-memory infection pattern in the occurrence of new COVID-19 cases lasting for months. Average error was 1.9 cases per week comparing predicted to actual values three months later (May-July 2021). ConclusionHCW have a sustained 50% higher risk of COVID-19 positivity in the pandemic. Time-series analysis showed a long-memory infection pattern with virus spread mainly among HCWs before the general population. The tool http://wdchealth.covid-map.com/shiny/covid-map/ will be updated according to population previous infection and vaccination impact.

Skin imprints to provide noninvasive metabolic profiling of COVID-19 patients

As the current COVID-19 pandemic progresses, more symptoms and signals related to how the disease manifests in the human body arise in the literature. Skin lesions and coagulopathies may be confounding factors on routine care and patient management. We analyzed the metabolic and lipidic profile of the skin from COVID-19 patients using imprints in silica plates as a non-invasive alternative, in order to better understand the biochemical disturbances caused by SARS-CoV-2 in the skin. One hundred and one patients (64 COVID-19 positive patients and 37 control patients) were enrolled in this cross-sectional study from April 2020 to June 2020 during the first wave of COVID-19 in Sao Paulo, Brazil. Fourteen biomarkers were identified related to COVID-19 infection (7 increased and 7 decreased in COVID-19 patients). Remarkably, oleamide has shown promising performance, providing 79.0% of sensitivity on a receiver operating characteristic curve model. Species related to coagulation and immune system maintenance such as phosphatidylserines were decreased in COVID-19 patients; on the other hand, cytokine storm and immunomodulation may be affected by molecules increased in the COVID-19 group, particularly primary fatty acid amides and N-acylethanolamines, which are part of the endocannabinoid system. Our results show that skin imprints may be a useful, noninvasive strategy for COVID-19 screening, by electing a pool of biomarkers with diagnostic potential.

Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine-Learning

COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with instrumental analysis using mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study with 728 patients (369 confirmed COVID-19 and 359 controls) was enrolled from three Brazilian epicentres (Sao Paulo capital, Sao Paulo countryside and Manaus) in the months of April, May, June and July 2020. We were able to elect and identify 21 molecules that are related to the diseases pathophysiology and 26 features to patients health-related outcomes. With specificity >97% and sensitivity >83% from blinded data, this screening approach is understood as a tool with great potential for real-world application.

The prevalence of overweight and obesity in adolescents in Bahia, Brazil / La prevalencia de sobrepeso y obesidad en adolescentes de Bahía, Brasil

Aim: A cross-sectional study was conducted with 1,477 middle school students enrolled in the public school network in Salvador, Bahia, Brazil to assess the prevalence of overweight and obesity. Methods: The sample was determined using a two-stage cluster sampling technique for selecting schools and classes. A posteriori error was estimated. The students were classified as overweight or obese according to the World Health Organization's 2007 classifications. They were also classified according to age, stage of sexual maturity, socioeconomic class and the presence of abdominal obesity. Results: Overall, 9.3% of the students were overweight and 6.4% were obese; therefore, 15.7% of the students were considered to have excess weight (obesity + overweight), at a 95% confidence interval. Abdominal obesity was found in 12.9% of all students and in 13% of those of normal weight. An association was found between excess weight and age < 14 years (p = 0.030) and abdominal obesity (p = 0.001). Conclusions: Intervention programs should be implemented to prevent and treat obesity in childhood and adolescence. In addition, professionals working with individuals in this age group should be sensitized to this problem. The need to standardize the anthropometric indicators used in the different studies is also emphasized (AU)

Objetivo: Un estudio transversal se llevó a cabo con 1477 estudiantes de primaria matriculados en las escuelas públicas de Salvador, Bahía, Brasil, para evaluar la prevalencia del sobrepeso y la obesidad. Métodos: La muestra se determinó por la técnica de muestreo por conglomerados en dos etapas para la selección de escuelas y clases. Más tarde, posteriori error se calculó. Los estudiantes fueron clasificados como sobrepeso u obesos de acuerdo con la clasificación de la Organización Mundial de la Salud, 2007. Ellos también fueron clasificados de acuerdo a la edad, la etapa de maduración sexual, y la presencia de obesidad abdominal. Resultados: En general, el 9,3% de los estudiantes tenían sobrepeso y el 6,4% eran obesos, por lo tanto, el 15,7% de los alumnos se considera que tienen exceso de peso (sobrepeso + obesidad), con un intervalo de confianza del 95%. De la población total estudiada el 12,9% presentaron obesidad abdominal, esta condición se observa en el 13% de su peso normal. Se encontró asociación entre el sobrepeso y la edad < 14 años (p = 0,030) y obesidad abdominal (p = 0,001). Conclusiones: Los programas de intervención deben ser implementados para prevenir y tratar la obesidad en la infancia y la adolescencia. Además, los profesionales que trabajan con las personas en este grupo de edad deben ser sensibles a este problema. La necesidad de estandarizar los indicadores antropométricos utilizados en los diferentes estudios se enfatiza también (AU)