A consideration of publication-derived immune-related associations in Coronavirus and related lung damaging diseases

Geifman, Nophar; Whetton, Anthony D.

doi:10.1186/s12967-020-02472-z

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…Inconsistent results were reported for chronic obstructive pulmonary disease and asthma as risk factors. 6,24,27 Two studies analyzing the correlation between ABO blood group and clinical outcome suggested that blood group A individuals as well as those with rhesus antigen positivity were more susceptible to SARS-CoV-2 infection compared with blood groups O or AB. 28,29 However, blood groups are not among the risk factors for either disease severity or complications.…”

Section: Clinical Characteristics and Risk Factors Associated With Sa...mentioning

confidence: 99%

Predicting Disease Severity and Outcome in COVID-19 Patients: A Review of Multiple Biomarkers

Tjendra

Mana

Espejo

et al. 2020

Archives of Pathology &Amp; Laboratory Medicine

202

126

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Context: An abundance of clinical reports focused on specific laboratory parameters have been reported on COVID-19, but a systematic analysis synthesizing these findings has not been performed. Objective: To review and summarize the current available literature on the predictive role of various biomarkers in COVID-19 patients. Data Sources: A literature search was performed using databases including PubMed, medRxiv, and bioRxiv. A total of 72 papers were reviewed including 54 peer reviewed papers and 18 non-peer reviewed preprints. Conclusions: While non-specific, acute phase reactants including CRP, ferritin, SAA, and procalcitonin were reported as sensitive markers of acute COVID-19 disease. Significantly elevated WBC count, marked lymphopenia, decreased CD3, CD4 or CD8 T-lymphocyte counts, high neutrophil count, thrombocytopenia, and markedly elevated inflammatory biomarkers were associated with severe disease and the risk of developing sepsis with rapid progression. Trends observed by serial laboratory measurements during hospitalization including progressive decrease of lymphocyte count, thrombocytopenia, elevated CRP, procalcitonin, increased liver enzymes, decreased renal function, and coagulation derangements were more common in critically ill patient groups and associated with a high incidence of clinical complications. Elevated IL-6 level and markedly increased SAA were most often reported in severely and critically ill patients. Indicators of systemic inflammation such as neutrophil-lymphocyte ratio (NLR), systemic immune-inflammation (SII) index or COVID-19 Severity Score may be utilized to predict disease severity, outcome, and mortality. Interpretation of the data reported in the studies reviewed here is limited due to study design (mostly retrospective), limited sample size, and a lack of defined clinical criteria.

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Section: Clinical Characteristics and Risk Factors Associated With Sa...mentioning

confidence: 99%

Predicting Disease Severity and Outcome in COVID-19 Patients: A Review of Multiple Biomarkers

Tjendra

Mana

Espejo

et al. 2020

Archives of Pathology &Amp; Laboratory Medicine

202

126

View full text Add to dashboard Cite

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“…We have previously described an approach ( 7 , 8 ) that can mine PubMed records for associations and linkages between different, formally defined, concepts and fields using Medical Subject Heading (MeSH) terms. This quantitative approach is a rapid tool that can be used iteratively to search for trends.…”

Section: Methodsmentioning

confidence: 99%

Identifying developments over a decade in the digital health and telemedicine landscape in the UK using quantitative text mining

Geifman

Armes

Whetton

2023

Front. Digit. Health

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The use of technologies that provide objective, digital data to clinicians, carers, and service users to improve care and outcomes comes under the unifying term Digital Health. This field, which includes the use of high-tech health devices, telemedicine and health analytics has, in recent years, seen significant growth in the United Kingdom and worldwide. It is clearly acknowledged by multiple stakeholders that digital health innovations are necessary for the future of improved and more economic healthcare service delivery. Here we consider digital health-related research and applications by using an informatics tool to objectively survey the field. We have used a quantitative text-mining technique, applied to published works in the field of digital health, to capture and analyse key approaches taken and the diseases areas where these have been applied. Key areas of research and application are shown to be cardiovascular, stroke, and hypertension; although the range seen is wide. We consider advances in digital health and telemedicine in light of the COVID-19 pandemic.

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“…Prior studies on the biomarkers associated with SARS-CoV-2 immune response and morbidity include interferon-gamma (IFN-γ), interferon-beta (IFN-β), and interleukin-8 (IL-8) [12]. Other previous research on immune parameters associated with SARS-CoV-2 severity and prognosis have involved interleukin-1 beta (IL-1β) and interleukin-6 (IL-6).…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Explanation of the Pathogen-Immune Relationship of SARS-CoV-2 (COVID-19), and a Model to Predict Immunity and Therapeutic Opportunity: A Comparative Effectiveness Research Study

Luellen¹

2020

JMIRx Med

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Background Approximately 80% of those infected with COVID-19 are immune. They are asymptomatic unknown carriers who can still infect those with whom they come into contact. Understanding what makes them immune could inform public health policies as to who needs to be protected and why, and possibly lead to a novel treatment for those who cannot, or will not, be vaccinated once a vaccine is available. Objective The primary objectives of this study were to learn if machine learning could identify patterns in the pathogen-host immune relationship that differentiate or predict COVID-19 symptom immunity and, if so, which ones and at what levels. The secondary objective was to learn if machine learning could take such differentiators to build a model that could predict COVID-19 immunity with clinical accuracy. The tertiary purpose was to learn about the relevance of other immune factors. Methods This was a comparative effectiveness research study on 53 common immunological factors using machine learning on clinical data from 74 similarly grouped Chinese COVID-19–positive patients, 37 of whom were symptomatic and 37 asymptomatic. The setting was a single-center primary care hospital in the Wanzhou District of China. Immunological factors were measured in patients who were diagnosed as SARS-CoV-2 positive by reverse transcriptase-polymerase chain reaction (RT-PCR) in the 14 days before observations were recorded. The median age of the 37 asymptomatic patients was 41 years (range 8-75 years); 22 were female, 15 were male. For comparison, 37 RT-PCR test–positive patients were selected and matched to the asymptomatic group by age, comorbidities, and sex. Machine learning models were trained and compared to understand the pathogen-immune relationship and predict who was immune to COVID-19 and why, using the statistical programming language R. Results When stem cell growth factor-beta (SCGF-β) was included in the machine learning analysis, a decision tree and extreme gradient boosting algorithms classified and predicted COVID-19 symptom immunity with 100% accuracy. When SCGF-β was excluded, a random-forest algorithm classified and predicted asymptomatic and symptomatic cases of COVID-19 with 94.8% AUROC (area under the receiver operating characteristic) curve accuracy (95% CI 90.17%-100%). In total, 34 common immune factors have statistically significant associations with COVID-19 symptoms (all c<.05), and 19 immune factors appear to have no statistically significant association. Conclusions The primary outcome was that asymptomatic patients with COVID-19 could be identified by three distinct immunological factors and levels: SCGF-β (>127,637), interleukin-16 (IL-16) (>45), and macrophage colony-stimulating factor (M-CSF) (>57). The secondary study outcome was the suggestion that stem-cell therapy with SCGF-β may be a novel treatment for COVID-19. Individuals with an SCGF-β level >127,637, or an IL-16 level >45 and an M-CSF level >57, appear to be predictively immune to COVID-19 100% and 94.8% (AUROC) of the time, respectively. Testing levels of these three immunological factors may be a valuable tool at the point of care for managing and preventing outbreaks. Further, stem-cell therapy via SCGF-β and M-CSF appear to be promising novel therapeutics for patients with COVID-19.

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A consideration of publication-derived immune-related associations in Coronavirus and related lung damaging diseases

Cited by 6 publications

References 28 publications

Predicting Disease Severity and Outcome in COVID-19 Patients: A Review of Multiple Biomarkers

Predicting Disease Severity and Outcome in COVID-19 Patients: A Review of Multiple Biomarkers

Identifying developments over a decade in the digital health and telemedicine landscape in the UK using quantitative text mining

A Machine Learning Explanation of the Pathogen-Immune Relationship of SARS-CoV-2 (COVID-19), and a Model to Predict Immunity and Therapeutic Opportunity: A Comparative Effectiveness Research Study

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