Nutrition delivery across hospitalisation in critically ill patients with COVID-19: An observational study of the Australian experience.

BACKGROUND: Data on nutrition delivery over the whole hospital admission in critically ill patients with COVID-19 are scarce, particularly in the Australian setting. OBJECTIVES: The objective of this study was to describe nutrition delivery in critically ill patients admitted to Australian intensive care units (ICUs) with coronavirus disease 2019 (COVID-19), with a focus on post-ICU nutrition practices. METHODS: A multicentre observational study conducted at nine sites included adult patients with a positive COVID-19 diagnosis admitted to the ICU for >24 h and discharged to an acute ward over a 12-month recruitment period from 1 March 2020. Data were extracted on baseline characteristics and clinical outcomes. Nutrition practice data from the ICU and weekly in the post-ICU ward (up to week four) included route of feeding, presence of nutrition-impacting symptoms, and nutrition support received. RESULTS: A total of 103 patients were included (71% male, age: 58 ± 14 years, body mass index: 30±7 kg/m2), of whom 41.7% (n = 43) received mechanical ventilation within 14 days of ICU admission. While oral nutrition was received by more patients at any time point in the ICU (n = 93, 91.2% of patients) than enteral nutrition (EN) (n = 43, 42.2%) or parenteral nutrition (PN) (n = 2, 2.0%), EN was delivered for a greater duration of time (69.6% feeding days) than oral and PN (29.7% and 0.7%, respectively). More patients received oral intake than the other modes in the post-ICU ward (n = 95, 95.0%), and 40.0% (n = 38/95) of patients were receiving oral nutrition supplements. In the week after ICU discharge, 51.0% of patients (n = 51) had at least one nutrition-impacting symptom, most commonly a reduced appetite (n = 25; 24.5%) or dysphagia (n = 16; 15.7%). CONCLUSION: Critically ill patients during the COVID-19 pandemic in Australia were more likely to receive oral nutrition than artificial nutrition support at any time point both in the ICU and in the post-ICU ward, whereas EN was provided for a greater duration when it was prescribed. Nutrition-impacting symptoms were common.

Nutrition care processes across hospitalisation in critically ill patients with COVID-19 in Australia: A multicentre prospective observational study.

BACKGROUND: The COVID-19 pandemic highlighted major challenges with usual nutrition care processes, leading to reports of malnutrition and nutrition-related issues in these patients. OBJECTIVE: The objective of this study was to describe nutrition-related service delivery practices across hospitalisation in critically ill patients with COVID-19 admitted to Australian intensive care units (ICUs) in the initial pandemic phase. METHODS: This was a multicentre (nine site) observational study in Australia, linked with a national registry of critically ill patients with COVID-19. Adult patients with COVID-19 who were discharged to an acute ward following ICU admission were included over a 12-month period. Data are presented as n (%), median (interquartile range [IQR]), and odds ratio (OR [95% confidence interval {CI}]). RESULTS: A total of 103 patients were included. Oral nutrition was the most common mode of nutrition (93 [93%]). In the ICU, there were 53 (52%) patients seen by a dietitian (median 4 [2-8] occasions) and malnutrition screening occurred in 51 (50%) patients most commonly with the malnutrition screening tool (50 [98%]). The odds of receiving a higher malnutrition screening tool score increased by 36% for every screening in the ICU (1st to 4th, OR: 1.39 [95% CI: 1.05-1.77] p = 0.018) (indicating increasing risk of malnutrition). On the ward, 51 (50.5%) patients were seen by a dietitian (median time to consult: 44 [22.5-75] hours post ICU discharge). The odds of dietetic consult increased by 39% every week while on the ward (OR: 1.39 [1.03-1.89], p = 0.034). Patients who received mechanical ventilation (MV) were more likely to receive dietetic input than those who never received MV. CONCLUSIONS: During the initial phases of the COVID-19 pandemic in Australia, approximately half of the patients included were seen by a dietitian. An increased number of malnutrition screens were associated with a higher risk score in the ICU and likelihood of dietetic consult increased if patients received MV and as length of ward stay increased.

Gene expression profiling of SARS-CoV-2 infections reveal distinct primary lung cell and systemic immune infection responses that identify pathways relevant in COVID-19 disease.

To identify key gene expression pathways altered with infection of the novel coronavirus SARS-CoV-2, we performed the largest comparative genomic and transcriptomic analysis to date. We compared the novel pandemic coronavirus SARS-CoV-2 with SARS-CoV and MERS-CoV, as well as influenza A strains H1N1, H3N2 and H5N1. Phylogenetic analysis confirms that SARS-CoV-2 is closely related to SARS-CoV at the level of the viral genome. RNAseq analyses demonstrate that human lung epithelial cell responses to SARS-CoV-2 infection are distinct. Extensive Gene Expression Omnibus literature screening and drug predictive analyses show that SARS-CoV-2 infection response pathways are closely related to those of SARS-CoV and respiratory syncytial virus infections. We validated SARS-CoV-2 infection response genes as disease-associated using Kaplan-Meier survival estimates in lung disease patient data. We also analysed COVID-19 patient peripheral blood samples, which identified signalling pathway concordance between the primary lung cell and blood cell infection responses.

Pathogenetic profiling of COVID-19 and SARS-like viruses.

The novel coronavirus (2019-nCoV) has recently emerged, causing COVID-19 outbreaks and significant societal/global disruption. Importantly, COVID-19 infection resembles SARS-like complications. However, the lack of knowledge about the underlying genetic mechanisms of COVID-19 warrants the development of prospective control measures. In this study, we employed whole-genome alignment and digital DNA-DNA hybridization analyses to assess genomic linkage between 2019-nCoV and other coronaviruses. To understand the pathogenetic behavior of 2019-nCoV, we compared gene expression datasets of viral infections closest to 2019-nCoV with four COVID-19 clinical presentations followed by functional enrichment of shared dysregulated genes. Potential chemical antagonists were also identified using protein-chemical interaction analysis. Based on phylogram analysis, the 2019-nCoV was found genetically closest to SARS-CoVs. In addition, we identified 562 upregulated and 738 downregulated genes (adj. P ≤ 0.05) with SARS-CoV infection. Among the dysregulated genes, SARS-CoV shared ≤19 upregulated and ≤22 downregulated genes with each of different COVID-19 complications. Notably, upregulation of BCL6 and PFKFB3 genes was common to SARS-CoV, pneumonia and severe acute respiratory syndrome, while they shared CRIP2, NSG1 and TNFRSF21 genes in downregulation. Besides, 14 genes were common to different SARS-CoV comorbidities that might influence COVID-19 disease. We also observed similarities in pathways that can lead to COVID-19 and SARS-CoV diseases. Finally, protein-chemical interactions suggest cyclosporine, resveratrol and quercetin as promising drug candidates against COVID-19 as well as other SARS-like viral infections. The pathogenetic analyses, along with identified biomarkers, signaling pathways and chemical antagonists, could prove useful for novel drug development in the fight against the current global 2019-nCoV pandemic.

TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets.

COVID-19, caused by SARS-CoV2 infection, varies greatly in its severity but presents with serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals. Uncertainty remains over key aspects of the virus infectiousness (particularly the newly emerging variants) and the disease has had severe economic impacts globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially influence public opinions and in some cases can exacerbate the widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topic extracting model named TClustVID that analyzes COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed on these datasets which enabled the exploration of the performance of traditional classification and TClustVID. Our analysis found that TClustVID showed higher performance compared to traditional methodologies that are determined by clustering criteria. Finally, we extracted significant topics from the clusters, split them into positive, neutral and negative sentiments, and identified the most frequent topics using the proposed model. This approach is able to rapidly identify commonly prevailing aspects of public opinions and attitudes related to COVID-19 and infection prevention strategies spreading among different populations.

A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients.

The recent outbreak of the respiratory ailment COVID-19 caused by novel coronavirus SARS-Cov2 is a severe and urgent global concern. In the absence of effective treatments, the main containment strategy is to reduce the contagion by the isolation of infected individuals; however, isolation of unaffected individuals is highly undesirable. To help make rapid decisions on treatment and isolation needs, it would be useful to determine which features presented by suspected infection cases are the best predictors of a positive diagnosis. This can be done by analyzing patient characteristics, case trajectory, comorbidities, symptoms, diagnosis, and outcomes. We developed a model that employed supervised machine learning algorithms to identify the presentation features predicting COVID-19 disease diagnoses with high accuracy. Features examined included details of the individuals concerned, e.g., age, gender, observation of fever, history of travel, and clinical details such as the severity of cough and incidence of lung infection. We implemented and applied several machine learning algorithms to our collected data and found that the XGBoost algorithm performed with the highest accuracy (>85%) to predict and select features that correctly indicate COVID-19 status for all age groups. Statistical analyses revealed that the most frequent and significant predictive symptoms are fever (41.1%), cough (30.3%), lung infection (13.1%) and runny nose (8.43%). While 54.4% of people examined did not develop any symptoms that could be used for diagnosis, our work indicates that for the remainder, our predictive model could significantly improve the prediction of COVID-19 status, including at early stages of infection.