Multi-objective optimization identifies a specific and interpretable COVID-19 host response signature.

The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.

Digital PCR applications for the diagnosis and management of infection in critical care medicine.

Infection (either community acquired or nosocomial) is a major cause of morbidity and mortality in critical care medicine. Sepsis is present in up to 30% of all ICU patients. A large fraction of sepsis cases is driven by severe community acquired pneumonia (sCAP), which incidence has dramatically increased during COVID-19 pandemics. A frequent complication of ICU patients is ventilator associated pneumonia (VAP), which affects 10-25% of all ventilated patients, and bloodstream infections (BSIs), affecting about 10% of patients. Management of these severe infections poses several challenges, including early diagnosis, severity stratification, prognosis assessment or treatment guidance. Digital PCR (dPCR) is a next-generation PCR method that offers a number of technical advantages to face these challenges: it is less affected than real time PCR by the presence of PCR inhibitors leading to higher sensitivity. In addition, dPCR offers high reproducibility, and provides absolute quantification without the need for a standard curve. In this article we reviewed the existing evidence on the applications of dPCR to the management of infection in critical care medicine. We included thirty-two articles involving critically ill patients. Twenty-three articles focused on the amplification of microbial genes: (1) four articles approached bacterial identification in blood or plasma; (2) one article used dPCR for fungal identification in blood; (3) another article focused on bacterial and fungal identification in other clinical samples; (4) three articles used dPCR for viral identification; (5) twelve articles quantified microbial burden by dPCR to assess severity, prognosis and treatment guidance; (6) two articles used dPCR to determine microbial ecology in ICU patients. The remaining nine articles used dPCR to profile host responses to infection, two of them for severity stratification in sepsis, four focused to improve diagnosis of this disease, one for detecting sCAP, one for detecting VAP, and finally one aimed to predict progression of COVID-19. This review evidences the potential of dPCR as a useful tool that could contribute to improve the detection and clinical management of infection in critical care medicine.

Presenting symptoms of COVID-19 and clinical outcomes in hospitalised older adults.

BACKGROUND: In July 2020, a COVID-19 outbreak was recognised in the geriatric wards at a subacute campus of the Royal Melbourne Hospital affecting patients and staff. Patients were also admitted to this site after diagnosis in residential care. AIMS: To describe the early symptoms and the outcomes of COVID-19 in older adults. METHODS: Patients diagnosed with COVID-19 at the facility in July or August 2020 were identified and their medical records were examined to identify symptoms present before and after their diagnosis and to determine their outcomes. RESULTS: Overall, 106 patients were identified as having COVID-19, with median age of 84.3 years (range 41-104 years); 64 were diagnosed as hospital inpatients after a median length of stay of 49 days, 31 were transferred from residential aged care facilities with a known diagnosis and 11 were diagnosed after discharge. There were 95 patients included in an analysis of symptom type and timing onset. Overall, 61 (64.2%) were asymptomatic at the time of diagnosis of COVID-19, having been diagnosed through screening initiated on site. Of these, 88.6% developed symptoms of COVID-19 within 14 days. The most common initial symptom type was respiratory, but there was wide variation in presentation, including fever, gastrointestinal and neurological symptoms, many initially not recognised as being due to COVID-19. Of 104 patients, 32 died within 30 days of diagnosis. CONCLUSIONS: COVID-19 diagnosis is challenging due to the variance in symptoms. In the context of an outbreak, asymptomatic screening can identify affected patients early in the disease course.

[Case Series of 103 Children with SARS-CoV-2 Infection in Portugal]. / Série de Casos de 103 Crianças com Infeção por SARS-CoV-2 em Portugal.

INTRODUCTION: The North Lisbon University Hospital Center was activated for referral of SARS-CoV-2 infected patients on the 11th March 2020. The aim of this study is to describe the experience at the Department of Pediatrics in the approach and the clinical outcomes of infected children. MATERIAL AND METHODS: A descriptive observational study was performed. Children and adolescents (0 to 18 years) with SARS-CoV-2 infection, diagnosed in the emergency room or admitted to the Department of Pediatrics between March 11th and June 18th, were included. Hospital records and Trace COVID-19 platform were reviewed and patient caregivers were interviewed to assess follow up. RESULTS: Among 103 diagnosed children, 83% had a known previous contact with an infected patient, 43% presented fever and 42% presented respiratory symptoms. Ten percent had risk factors and 21% were aged under one year old. Ten percent were hospitalised, one needing intensive care, with paediatric inflammatory multisystem syndrome. Blood tests were performed in 9% and chest radiograph in 7%. No children required ventilation, antiviral therapy or underwent thoracic computed tomography scan. Eight percent of children returned to the emergency room and one child was hospitalised. The clinical outcome is known in 101 patients and is favourable in all. DISCUSSION: Most children had an epidemiological link and little clinical repercussion, even during the first year of life. The expected mild severity in children justified the use of established clinical criteria and recommendations for similar conditions, regarding tests and hospitalizations. No antiviral treatments were given due to lack of evidence of its benefits. CONCLUSION: This strategy contributed to a low consumption of hospital resources and proved safe in this series.

Introdução: O Centro Hospitalar Universitário Lisboa Norte foi ativado para referência de doentes com infeção SARS-CoV-2 em 11 de março de 2020. O objetivo deste estudo é descrever a experiência do Departamento de Pediatria na abordagem e evolução clínica de crianças infetadas.Material e Métodos: Realizámos um estudo observacional descritivo. Incluímos as crianças e adolescentes (0 aos 18 anos) com infeção por SARS-CoV-2 diagnosticados na urgência e internamento do nosso departamento entre 11 de março e 18 de junho de 2020. Consultámos registos internos e a plataforma Trace COVID-19 e contactámos os cuidadores para avaliação de seguimento.Resultados: De 103 crianças diagnosticadas, 83% tiveram contacto prévio identificado com doente infetado, 43% doentes apresentaram febre e 42% sintomas respiratórios. Em 10% havia fatores de risco; 21% tinham idade inferior a um ano. Foram internadas 10% das crianças, uma em cuidados intensivos com síndrome inflamatória multissistémica pediátrica. Foi efetuada avaliação laboratorial em 9%, radiografia torácica em 7%. Nenhum recebeu suporte ventilatório, terapêutica antiviral ou realizou tomografia computorizada torácica. Foram reobservadas em serviço de urgência 8% das crianças, sendo internada uma. A evolução foi conhecida em 101 casos sendo favorável em todos.Discussão: A maioria dos doentes tinha link epidemiológico familiar e pouca repercussão clínica, mesmo no primeiro ano de vida. A menor gravidade esperada na criança motivou a adoção de critérios habituais noutros quadros clínicos semelhantes para a realização de exames complementares de diagnóstico e internamento hospitalar. Não foi administrada terapêutica antiviral em nenhum doente por se considerar haver pouca evidência de benefício.Conclusão: Esta estratégia traduziu-se num baixo consumo de recursos hospitalares e revelou-se segura nesta série.

Using Artificial Intelligence in Infection Prevention.

Purpose of Review: Artificial intelligence (AI) offers huge potential in infection prevention and control (IPC). We explore its potential IPC benefits in epidemiology, laboratory infection diagnosis, and hand hygiene. Recent Findings: AI has the potential to detect transmission events during outbreaks or predict high-risk patients, enabling development of tailored IPC interventions. AI offers opportunities to enhance diagnostics with objective pattern recognition, standardize the diagnosis of infections with IPC implications, and facilitate the dissemination of IPC expertise. AI hand hygiene applications can deliver behavior change, though it requires further evaluation in different clinical settings. However, staff can become dependent on automatic reminders, and performance returns to baseline if feedback is removed. Summary: Advantages for IPC include speed, consistency, and capability of handling infinitely large datasets. However, many challenges remain; improving the availability of high-quality representative datasets and consideration of biases within preexisting databases are important challenges for future developments. AI in itself will not improve IPC; this requires culture and behavior change. Most studies to date assess performance retrospectively so there is a need for prospective evaluation in the real-life, often chaotic, clinical setting. Close collaboration with IPC experts to interpret outputs and ensure clinical relevance is essential.