ABSTRACT
COVID-19 disease is spread worldwide and diagnostic techniques have been studied in order to contain the pandemic. Immunochromatographic (IC) assays are feasible and a low-cost alternative especially in low and middle-income countries, which lack structure to perform certain diagnostic techniques. Here we evaluate the sensitivity and specificity of eleven different IC tests in 145 serum samples from confirmed cases of COVID-19 using RT-PCR and 100 negative serum samples from blood donors collected in February 2019. We also evaluated the cross-reactivity with dengue using 20 serum samples from patients with confirmed diagnosis for dengue collected in early 2019 through four different tests. We found high sensitivity (92%), specificity (100%) and an almost perfect agreement (Kappa 0.92) of IC assay, especially when we evaluated IgG and IgM combined after 10 days from the onset of symptoms with RT-PCR. However, we detected cross-reactivity between dengue and COVID-19 mainly with IgM antibodies (5 to 20% of cross-reaction) and demonstrated the need for better studies about diagnostic techniques for these diseases.
Subject(s)
COVID-19 , Dengue , Antibodies, Viral , COVID-19/diagnosis , Dengue/diagnosis , Humans , Immunoassay/methods , Immunoglobulin G , Immunoglobulin M , SARS-CoV-2 , Sensitivity and SpecificityABSTRACT
OBJECTIVE: To identify more severe COVID-19 presentations. METHODS: Consecutive intensive care unit-admitted patients were subjected to a stepwise clustering method. RESULTS: Data from 147 patients who were on average 56 ± 16 years old with a Simplified Acute Physiological Score 3 of 72 ± 18, of which 103 (70%) needed mechanical ventilation and 46 (31%) died in the intensive care unit, were analyzed. From the clustering algorithm, two well-defined groups were found based on maximal heart rate [Cluster A: 104 (95%CI 99 - 109) beats per minute versus Cluster B: 159 (95%CI 155 - 163) beats per minute], maximal respiratory rate [Cluster A: 33 (95%CI 31 - 35) breaths per minute versus Cluster B: 50 (95%CI 47 - 53) breaths per minute], and maximal body temperature [Cluster A: 37.4 (95%CI 37.1 - 37.7)°C versus Cluster B: 39.3 (95%CI 39.1 - 39.5)°C] during the intensive care unit stay, as well as the oxygen partial pressure in the blood over the oxygen inspiratory fraction at intensive care unit admission [Cluster A: 116 (95%CI 99 - 133) mmHg versus Cluster B: 78 (95%CI 63 - 93) mmHg]. Subphenotypes were distinct in inflammation profiles, organ dysfunction, organ support, intensive care unit length of stay, and intensive care unit mortality (with a ratio of 4.2 between the groups). CONCLUSION: Our findings, based on common clinical data, revealed two distinct subphenotypes with different disease courses. These results could help health professionals allocate resources and select patients for testing novel therapies.
OBJETIVO: Identificar apresentações mais graves de COVID-19. MÉTODOS: Pacientes consecutivamente admitidos à unidade de terapia intensiva foram submetidos à análise de clusters por meio de método de explorações sequenciais. RESULTADOS: Analisamos os dados de 147 pacientes, com média de idade de 56 ± 16 anos e Simplified Acute Physiological Score 3 de 72 ± 18, dos quais 103 (70%) demandaram ventilação mecânica e 46 (31%) morreram na unidade de terapia intensiva. A partir do algoritmo de análise de clusters, identificaram-se dois grupos bem definidos, com base na frequência cardíaca máxima [Grupo A: 104 (IC95% 99 - 109) batimentos por minuto versus Grupo B: 159 (IC95% 155 - 163) batimentos por minuto], frequência respiratória máxima [Grupo A: 33 (IC95% 31 - 35) respirações por minuto versus Grupo B: 50 (IC95% 47 - 53) respirações por minuto] e na temperatura corpórea máxima [Grupo A: 37,4 (IC95% 37,1 - 37,7)ºC versus Grupo B: 39,3 (IC95% 39,1 - 39,5)ºC] durante o tempo de permanência na unidade de terapia intensiva, assim como a proporção entre a pressão parcial de oxigênio no sangue e a fração inspirada de oxigênio quando da admissão à unidade de terapia intensiva [Grupo A: 116 (IC95% 99 - 133) mmHg versus Grupo B: 78 (IC95% 63 - 93) mmHg]. Os subfenótipos foram distintos em termos de perfis inflamatórios, disfunções orgânicas, terapias de suporte, tempo de permanência na unidade de terapia intensiva e mortalidade na unidade de terapia intensiva (com proporção de 4,2 entre os grupos). CONCLUSÃO: Nossos achados, baseados em dados clínicos universalmente disponíveis, revelaram dois subfenótipos distintos, com diferentes evoluções de doença. Estes resultados podem ajudar os profissionais de saúde na alocação de recursos e seleção de pacientes para teste de novas terapias.
Subject(s)
COVID-19/physiopathology , Critical Care/methods , Critical Illness/therapy , Intensive Care Units , Respiration, Artificial/statistics & numerical data , Adult , Aged , Algorithms , COVID-19/mortality , COVID-19/therapy , Cluster Analysis , Cohort Studies , Female , Humans , Length of Stay , Male , Middle Aged , Patient Selection , Phenotype , Respiratory Rate/physiology , Retrospective Studies , Severity of Illness IndexABSTRACT
BACKGROUND: Approximately 5% of COVID-19 patients develop respiratory failure and need ventilatory support, yet little is known about the impact of mechanical ventilation strategy in COVID-19. Our objective was to describe baseline characteristics, ventilatory parameters, and outcomes of critically ill patients in the largest referral center for COVID-19 in Sao Paulo, Brazil, during the first surge of the pandemic. METHODS: This cohort included COVID-19 patients admitted to the intensive care units (ICUs) of an academic hospital with 94 ICU beds, a number expanded to 300 during the pandemic as part of a state preparedness plan. Data included demographics, advanced life support therapies, and ventilator parameters. The main outcome was 28-day survival. We used a multivariate Cox model to test the association between protective ventilation and survival, adjusting for PF ratio, pH, compliance, and PEEP. RESULTS: We included 1503 patients from March 30 to June 30, 2020. The mean age was 60 ± 15 years, and 59% were male. During 28-day follow-up, 1180 (79%) patients needed invasive ventilation and 666 (44%) died. For the 984 patients who were receiving mechanical ventilation in the first 24 h of ICU stay, mean tidal volume was 6.5 ± 1.3 mL/kg of ideal body weight, plateau pressure was 24 ± 5 cmH2O, respiratory system compliance was 31.9 (24.4-40.9) mL/cmH2O, and 82% of patients were ventilated with protective ventilation. Noninvasive ventilation was used in 21% of patients, and prone, in 36%. Compliance was associated with survival and did not show a bimodal pattern that would support the presence of two phenotypes. In the multivariable model, protective ventilation (aHR 0.73 [95%CI 0.57-0.94]), adjusted for PF ratio, compliance, PEEP, and arterial pH, was independently associated with survival. CONCLUSIONS: During the peak of the epidemic in Sao Paulo, critically ill patients with COVID-19 often required mechanical ventilation and mortality was high. Our findings revealed an association between mechanical ventilation strategy and mortality, highlighting the importance of protective ventilation for patients with COVID-19.