Utility of Routine Laboratory Biomarkers to Detect COVID-19: A Systematic Review and Meta-Analysis

Logistic regression analysis of the value of biomarkers, clinical symptoms, and imaging examinations in COVID-19 for SARS-CoV-2 nucleic acid detection

The detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) nucleic acid detection provides a direct basis for diagnosing Coronavirus Disease 2019. However, nucleic acid test false-negative results are common in practice and may lead to missed diagnosis. Certain biomarkers, clinical symptoms, and imaging examinations are related to SARS-CoV-2 nucleic acid detection and potential predictors. We examined nucleic acid test results, biomarkers, clinical symptoms, and imaging examination data for 116 confirmed cases and asymptomatic infections in Zhuhai, China. Patients were divided into nucleic acid-positive and -false-negative groups. Predictive values of biomarkers, symptoms, and imaging for the nucleic acid-positive rate were calculated by Least Absolute Shrinkage and Selection Operators regression analysis and binary logistic regression analysis, and areas under the curve of these indicators were calculated. Hemoglobin (OR = 1.018, 95% CI: 1.006-1.030; P = .004) was higher in the respiratory tract-positive group than the nucleic acid-negative group, but platelets (OR = 0.996, 95% CI: 0.993-0.999; P = .021) and eosinophils (OR = 0.013, 95% CI: 0.001-0.253; P = .004) were lower; areas under the curve were 0.563, 0.614, and 0.642, respectively. Some biomarkers can predict SARS-CoV-2 viral nucleic acid detection rates in Coronavirus Disease 2019 and are potential auxiliary diagnostic tests.

The Relationship Between Laboratory Findings and Mortality in COVID-19 Patients Requiring Intensive Care

INTRODUCTION

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shows a wide clinical manifestation from asymptomatic infection to life-threatening respiratory failure. This study aimed to determine the relationship between the survival and demographic data, comorbidity status, and laboratory parameters of coronavirus disease 2019 (COVID-19) patients requiring intensive care.

MATERIAL AND METHODS

We retrospectively analyzed 236 patients requiring intensive care whose diagnosis was confirmed by the SARS-CoV-2 reverse transcription-polymerase chain reaction (RT-PCR) test. The patients were divided into two groups in terms of survival. Demographic data; procalcitonin and C-reactive protein (CRP) levels; leukocyte, lymphocyte, and neutrophil counts in hemogram and neutrophil-to-lymphocyte ratio (NLR) levels; and lower respiratory and blood cultures were examined, and the relationships between these parameters and survival were evaluated with hypothesis testing.

RESULTS

 In the study, 156 (66.1%) males and 80 (33.9%) females, a total of 236 patients, were included. Sixty-seven (28.3%) surviving patients were determined as Group 1, and 169 (71.7%) deceased patients were determined as Group 2. A statistically significant difference was found between the groups in terms of mean age (p<0.001) and gender distribution (p=0.011). In laboratory parameters, a significant difference was observed between the groups in lymphocyte count (p=0.001), NLR (p<0.001), and procalcitonin levels (p<0.001). Although leukocyte (p=0.075), neutrophil (p=0.031), and CRP (p=0.112) levels were higher in Group 2, there was no statistical difference. Mortality was found to be higher in patients with comorbidity (p=0.012) or co-infection (p=0.002).

CONCLUSION

High levels of neutrophil count, NLR, and procalcitonin; low lymphocyte count; the presence of comorbidity; and secondary bacterial infection were found to be associated with mortality in COVID-19 patients in the intensive care unit.

COVID19 biomarkers: What did we learn from systematic reviews?

The coronavirus disease 2019 (COVID19) pandemic continues to represent a substantial public health concern. It can rapidly progress to severe disease, with poor prognosis and a high mortality risk. An early diagnosis and specific prognostic tools can help healthcare providers to start interventions promptly, understand the likely prognosis and to identify and treat timely individuals likely to develop severe disease with enhanced mortality risk. Here we focused on an impressive set of systematic reviews and meta-analyses that were performed since the start of the COVID19 pandemic and summarized their results related to the levels of hematologic, inflammatory, immunologic biomarkers as well as markers of cardiac, respiratory, hepatic, gastrointestinal and renal systems and their association with the disease progression, severity and mortality. The evidence outlines the significance of specific biomarkers, including inflammatory and immunological parameters (C-reactive protein, procalcitonin, interleukin-6), hematological (lymphocytes count, neutrophil-to-lymphocyte ratio, D-dimer, ferritin, red blood cell distribution width), cardiac (troponin, CK-MB, myoglobin), liver (AST, ALT, total bilirubin, albumin) and lung injury (Krebs von den Lungen-6) that can be used as prognostic biomarkers to aid the identification of high-risk patients and the prediction of serious outcomes, including mortality, in COVID19. Thus, these parameters should be used as essential tools for an early risk stratification and adequate intervention in improving disease outcomes in COVID19 patients.

White Blood Cells, COVID-19, and Mendelian Randomization

Many observational studies have shown an association between the severity of COVID-19 and the different white blood cell counts, most frequently neutrophils, lymphocytes, and eosinophils. The studies aimed to predict the prognosis, and therefore, a causal relationship was unnecessary. However, if we begin to look at these biomarkers as potential therapeutic targets, then causality is essential. Observational studies cannot prove a causal relationship, and randomized trials are not always feasible. In this case, Mendelian randomization studies, considered more valid than observational studies, could add to the arguments for causality. Two Mendelian randomization studies tested for a causal relationship between the number of different white cell populations and COVID-19 severity, but their results are different; therefore, the problem of causality is not settled in this case.