Biomarkers and ICU admissions among COVID‑19 patients

Existing research identified a number of biomarkers that are associated with disease severity, and mortality among hospitalized COVID‑19 patients and may help to predict need for ICU care. Early detection of identified biomarkers may improve patient management and help identify high-risk patients. Associations were found between COVID‑19 disease severity and early biomarkers of inflammation and organ dysfunction including lymphopenia, thrombocytopenia, and elevated levels of D-dimer, C-reactive protein (CRP), Procalcitonin (PCT), Lactate Dehydrogenase (LDH) as well as high levels of cardiac troponin I and aspartate aminotransferase (AST). Furthermore, there is a strong association between increases in biomarkers including CRP, D-dimer, and decreased platelet count, and increased mortality. There are also prediction models that may be useful in identifying COVID‑19 patients with high risk of death within two months. One model suggests that age, respiratory failure, white cell count, lymphocytes, platelets, D-dimer, and lactate dehydrogenase are key determinants of death among hospitalized COVID‑19 patients. Comorbidities including diabetes, hypertension, cardiovascular disease, chronic obstructive pulmonary disease, and chronic kidney disease are associated with an increase in severe COVID‑19 symptoms and mortality. Patients with hypertension, cerebrovascular accident, and heart disease may be at increased risk of needing ICU care, requiring intubation, and death.  

Biomarkers associated with severe COVID disease and mortality identified to date, are general markers of inflammation and organ dysfunction, and not specific to COVID-19. COVID-specific biomarkers for severe disease and mortality have not yet been identified. Our search did not find higher-level evidence (i.e., evidence from systematic reviews, meta-analyses, or rapid reviews) focusing on biomarkers associated with ICU admission. Further research to identify biomarkers predictive of need for ICU level care is needed. 

There are various research projects funded across Canada investigating different approaches to predicting COVID‑19 severity (including ICU admission) such as genetic biomarkers, immunologic markers, remote symptom monitoring, and mathematical modelling. These include:

• “Identification of Biomarkers that Predict Severity of Infection in COVID‑19 Patients” (Melissa Kathryn Andrew, Dalhousie University)
• “Population-estimable frailty using “big data” to predict Covid-19 infection and illness severity, Institute of Clinical Evaluative Sciences” (Douglas Lee, Institute of Clinical Evaluation on Sciences, University of Toronto)
• “AI-empowered Real-time COVID‑19 Symptom Monitoring and Prediction among Senior Residents” (Rahimi Samira, McGill University)

• “An Optimized COVID‑19 Diagnostic Test Incorporating Host Responses for Predicting Disease Course and Healthcare Needs” (Jeremy Hirota, McMaster University) 
• “CovidFree@Home: Development and validation of a multivariable prediction model of deterioration in patients diagnosed with COVID‑19 who are managing at home” (Nisha Andany, Sunnybrook Research Institute)
• “Genomic biomarkers to predict outcome and treatment response in hospitalized COVID‑19 patients” (Matthew Cheng, McGill University)  
• “COVID-19: Comprehensive biomarker analysis for prediction of clinical course and patient treatment outcomes (COVID-BEACONS)” (Paul Y Kim, McMaster University)
• “Development of a Predictive Serologic Test for Cytopathogenic Autoantibodies in COVID‑19 Patients” (Robert K. Rottapel, University Health Network)

There are also on-going reviews initiated internationally (Brazil, Germany, Belgium, Malaysia, China, India, Spain, and Italy) which address possible genetic, clinical, diagnostic, and sociodemographic predictors of COVID‑19 outcomes and prognosis. One systematic review by Malaysian researcher Yean Yean Chan titled, Impact of mutational profile of SARS-CoV-2 on transmissibility and disease severity: A systematic review and meta-analysis, is looking to answer questions around whether there are associations between viral load levels and transmissibility and severity of illness.