Routine biochemical and hematological tests have been reported to be useful in the stratification and prognostication of pediatric and adult patients with diagnosed coronavirus disease (COVID-19), correlating with poor outcomes such as the need for mechanical ventilation or intensive care, progression to multisystem organ failure, and/or death. While these tests are already well established in most clinical laboratories, there is still debate regarding their clinical value in the management of COVID-19, particularly in pediatrics, as well as the value of composite clinical risk scores in COVID-19 prognostication. This document by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Task Force on COVID-19 provides interim guidance on: (A) clinical indications for testing, (B) recommendations for test selection and interpretation, (C) considerations in test interpretation, and (D) current limitations of biochemical/hematological monitoring of COVID-19 patients. These evidence-based recommendations will provide practical guidance to clinical laboratories worldwide, underscoring the contribution of biochemical and hematological testing to our collective pandemic response.
Along with the essential role for diagnosing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and for assessing the presence and extent of an immune response against the virus, laboratory medicine makes an important contribution towards risk stratification and monitoring of infected patients. Whilst “routine” hematology and biochemistry tests are not specific enough to diagnose SARS-CoV-2 infection, they play a role in several aspects of the coronavirus disease 2019 (COVID-19) care pathway, including patient management and prognosis.
This document by the IFCC Task Force on COVID-19 provides interim guidance on: (A) clinical indications for testing, (B) recommendations for test selection and interpretation, (C) considerations in test interpretation, and (D) current limitations of biochemical/hematological monitoring of COVID-19 patients.
Abnormal hematology and biochemistry test results in infected patients may help in:
Diagnosing infection-related tissue and organ injury;
Identifying infected patients at lower risk of severe disease;
Recognizing patients who are likely to have poor prognosis (e.g., need for mechanical ventilation or intensive care, progression to multisystem organ failure, death);
Monitoring disease course.
Many review articles and meta-analyses have been published on the clinical utility of some conventional laboratory tests in SARS-CoV-2 infection. These are summarized and discussed below.
SARS-CoV-2 has been shown to have a direct cytopathic injury on lymphocytes, with a number of morphological changes seen on the peripheral blood smear of infected patients . Lymphopenia has become a hallmark of SARS-CoV-2 infection and is present to a variable extent in almost all symptomatic patients. There is also evidence that the magnitude of lymphocyte count reduction associates with disease severity .
An elevated neutrophil count has been found to herald poor prognosis in COVID-19 infection , . Taken in combination with low lymphocyte count, an elevated neutrophil-to-lymphocyte ratio (NLR) can be used as a marker of adverse outcomes .
A marked coagulopathy is a key feature of SARS-CoV-2 infection. Coagulopathy most commonly manifests as a pro-thrombotic state with increased incidence of both venous and arterial thrombosis , . The mechanisms underlying this complication are not fully understood, but are likely to involve a complex interplay between inflammatory and pro-thrombotic factors, with endotheliitis and the formation of intravascular neutrophil extracellular traps playing an important role , , . In addition, a subset of patients with severe disease develop disseminated intravascular coagulation (DIC), with activation of the fibrinolytic pathway and consumption of platelets and clotting factors , .
An elevated D-dimer in infected patients has consistently been associated with unfavourable disease progression , , . In addition, COVID-19-associated coagulopathy may present with prolongation of prothrombin time (PT) and activated partial thromboplastin time (APTT) , and with an increased fibrinogen concentration, as a result of the strong pro-inflammatory state . Conversely, patients who develop DIC may have a low fibrinogen concentration and thrombocytopenia .
Thrombocytopenia is another aspect that characterizes unfavourable disease progression . The low platelet count is attributable to many convergent mechanisms, encompassing enhanced platelet consumption, lung injury with associated megakaryocyte damage, drug-induced and immune thrombocytopenia, enhanced platelet clearance, and reduced thrombopoietin production and bone marrow depression .
|Complete blood count||↓Lymphocytes
|D-dimer||Increased||To identify those at risk of adverse outcome.|
|Platelets||Decreased||Associated with poor clinical outcomes.|
|PT/APTT||Increased||To identify and monitor coagulopathy|
PT, prothrombin time; APTT, activated partial thromboplastin time; DIC, disseminated intravascular coagulation.
The development of a sustained and progressively systemic pro-inflammatory condition (the so-called “cytokine storm”) has been demonstrated in patients with adverse progression of COVID-19, at higher risk of requiring intensive care and suffering fatal outcomes , . Therefore, the measurement of some inflammatory biomarkers is very important for early and accurate identification of COVID-19 patients at higher risk of unfavourable progression.
C-reactive protein (CRP) is a commonly measured non-specific biomarker of inflammation. Increased CRP concentration has consistently been shown to be associated with poor outcome in SARS-CoV-2 infection , , .
Erythrocyte sedimentation rate (ESR) is an inflammatory marker which may be considered as an alternative to CRP in resource-limited environments, with a similar relationship seen between adverse outcomes in SARS-CoV-2 infection and high biomarker values .
Ferritin is a positive acute phase protein, which is easily measured and may be a marker of adverse outcomes in individuals infected with SARS-CoV-2 .
Procalcitonin may be beneficial in identifying individuals with bacterial co-infections, who may require specific antibiotic therapy and who have a worse prognosis .
Many additional inflammatory biomarkers have been studied and associated with poor outcome in SARS-CoV-2 infection. Examples include interleukin-6 (IL-6), interferon gamma-induced protein 10, monocyte chemotactic protein-3 and presepsin , , . However, given that such biomarkers cannot be easily assayed in all laboratories and that evidence is unclear as to whether they add any clinical value beyond that already obtained through measurement of more standard inflammatory markers, we would not currently recommend their routine measurement in the absence of further research on clinical utility.
In line with the evidence that COVID-19 may progress to a systemic disease, cardiac involvement may frequently develop in patients with SARS-CoV-2 infection as a result of direct cytopathic injury, cytokine-mediated damage, ischemia or even exacerbation of preexisting cardiac diseases , . Several studies have shown that cardiac troponins are higher in patients with more severe illness, compared to those with milder disease , , . The American College of Cardiology (ACC) points out that increased cardiac troponins and NT-proBNP do not necessarily suggest acute coronary syndrome or heart failure and need to be interpreted in the right clinical context and with the key presenting features of the patient in mind . An elevated cardiac troponin in COVID-19 is likely to reflect an acute myocardial injury induced by either the virus or host immune response, rather than myocardial infarction due to rupture of an atherosclerotic plaque . It is now commonly acknowledged that there is strong association between elevated troponin and adverse outcomes in COVID-19 patients . Similar relationships have been seen for other cardiac biomarkers, including creatine kinase-MB, myoglobin and natriuretic peptides , although these analytes do not necessarily add further clinical value to that already provided by cardiac troponins.
COVID-19 can be associated with liver injury during disease progression and treatment, in patients with or without pre-existing liver disease. Elevated values of aspartate aminotransferase (AST), alanine aminotransferase (ALT) and bilirubin, and low albumin and prealbumin concentrations have all been associated with poor outcome , ,  In addition, some drugs used in the treatment of COVID-19 are associated with the development of elevated liver biomarkers , , . For this reason, at a minimum, it is recommended to monitor ALT, bilirubin and albumin during treatment of patients with hepatotoxic medications, and in those with pre-existing liver disease.
Kidney injury is a relatively frequent complication in patients with COVID-19, especially in those with severe illness. Elevations of both serum creatinine and urea (blood urea nitrogen, BUN) have been associated with unfavorable clinical outcome , .
Lactate dehydrogenase (LDH) is a non-specific marker of tissue damage. Probably because it is found in many different tissues, LDH emerges as one of the most consistently elevated markers in patients infected with COVID-19 at higher risk of developing adverse outcome , [, 521].
Worsening SARS-CoV-2 infection is associated with hypoxemia and metabolic acidosis , which may progress to acute respiratory distress syndrome (ARDS). As such, there is a role for the measurement of arterial blood gas parameters especially pH, pO2, pCO2,HCO3− and lactate , in patients with progressive disease. Monitoring of arterial blood gases is considered routine for the management of any critically unwell patient, regardless of the underlying condition. Results from a blood gas analyzer may also have the benefit of providing a rapid assessment of electrolyte status, which is often abnormal in patients with severe disease .
|Arterial blood gas||Variable||To identify and monitor hypoxemia and metabolic acidosis associated with severe infection.|
|CRP||Increased||Associated with worse clinical outcome.|
|Ferritin||Increased||Associated with worse clinical outcome.|
|ESR||Increased||Alternative to CRP/ferritin in resource-limited settings.|
|Procalcitonin||Increased||Associated with secondary bacterial infection.|
|Cardiac troponins||Increased||Associated with COVID-19- induced cardiac disease and poor prognosis.|
|ALT, bilirubin||Increased||To be monitored in patients treated with drugs known to affect liver function (e.g., lopinavir/ritonavir).|
|Albumin||Decreased||Reflects an acute inflammatory state and/or synthetic liver dysfunction.|
|Creatinine, urea (BUN)||Increased||Associated with poor prognosis.|
|LDH||Increased||Associated with worse clinical outcomes.|
|Interleukin-6, interferon gamma-induced protein 10, monocyte chemotactic protein-3, presepsin||Increased||For research use only.
Associated with poor clinical outcomes (if validated assay clinically available).
CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; ALT, alanine aminotransferase; BUN, blood urea nitrogen; LDH, lactate dehydrogenase.
In contrast to adults, SARS-CoV-2 infection in children tends to be comparatively mild. This observation is reflected by the fact that, when present, laboratory abnormalities in infected children are likely to be relatively less severe. Specifically, children are less likely to have abnormal white blood cell parameters, but mild elevations of inflammatory biomarkers (CRP, procalcitonin, IL-6) and D-dimer have been identified in some cases , . There is a paucity of data regarding the correlation between biomarker concentration and disease severity in children, although it has been suggested that a CRP test result above the cut-off may be associated with radiological evidence of pneumonia .
A small proportion of pediatric cases develop a separate entity termed Multisystem Inflammatory Syndrome in Children (MIS-C). MIS-C is characterized by a hyper-inflammatory state progressing to severe end organ damage and multiple organ failure, which is hence not so different from the same detrimental inflammatory reaction observed in some adults. Elevated inflammatory biomarkers are considered to be part of the diagnostic criteria for MIS-C . Laboratory abnormalities in children with MIS-C more closely represent those seen in adults, with lymphopenia, and elevations of inflammatory biomarkers, D-dimer, cardiac troponin and natriuretic peptides being commonly reported findings , , .
Given the association between elevations in certain biomarkers and disease severity, laboratory results have been included in a number of clinical risk algorithms, developed for either diagnosing SARS-CoV-2 infection or identifying patients at higher risk of unfavourable disease progression , , 
The Corona-Score is a clinical risk score intended to predict the probability of SARS-CoV-2 infection in symptomatic patients presenting to emergency departments . The score encompasses eight parameters, five of which are laboratory test results (absolute neutrophil count, absolute lymphocyte count, CRP, ferritin, LDH). A Corona-score <4 had 96% sensitivity for excluding SARS-CoV-2 infection using RT-PCR as a reference standard for diagnosis. This model was derived using laboratory data from 375 patients across three hospitals presenting to emergency departments with respiratory symptoms, or suspected COVID-19 infection, and was validated in an independent multi-centre cohort of 592 Dutch patients . A separate study of the Corona-Score in the USA yielded a lower sensitivity (82 vs. 96%) and area under the receiver operating characteristic curve (AUC) (0.74 vs. 0.91) than in the original Dutch cohort . Another study carried out in a metropolitan hospital in New York City at the peak of the SARS-CoV-2 outbreak used a machine learning model based on patient demographics combined with 27 routine laboratory tests to predict SARS-CoV-2 infection status. Diagnostic accuracy of the model was compared to viral RNA testing by RT-PCR as a reference test and the AUC was 0.854 (95% CI: 0.829–0.878) in the training set and 0.838 in an independent validation set .
Clinical risk scores have also been developed for identifying patients more likely to develop severe disease. One such example (COVID-GRAM) utilises three laboratory parameters (NLR, LDH, direct bilirubin) and seven clinical or radiological parameters to calculate a probability of developing severe disease .
The main limitation of these clinical risk scores (and in fact most tests for detecting SARS-CoV-2 infection) is that they perform differently depending upon the geographical location and local disease prevalence, or other differences in the selection of cases and reference standards used in test evaluations. A systematic review of clinical scores for SARS-CoV-2 infection reported a high risk of bias in the methodology used in such studies . In addition, some of the assays used in clinical risk scores are not well-standardised across manufacturers. Therefore we recommend caution when applying a cut-off developed in one particular setting on one analyzer platform to that from a different setting and from a different manufacturer. Laboratories should carefully design their local verification of tests or testing algorithms, including diagnostic or clinical risk scores used in managing this pandemic.
No single biochemical or hematological test can confer adequate information regarding the likely diagnosis or outcome of SARS-CoV-2 infection. None of the tests described in this section are specific for SARS-CoV-2 infection or its disease progression. Rather, the results of a group of relevant tests should be reviewed in the context of the patient’s clinical presentation. Only in this way can these biomarkers provide useful information in the clinical management of COVID-19.
It is important to emphasize that some differences between laboratory results in patients with severe and non-severe disease can be modest. For example, in one meta-analysis, the weighted mean difference in ALT results between non-severe and severe patients was only 8 U/L . This finding was nonetheless statistically significant and has been replicated in other studies. Based on these observations we recommend that test results are closely scrutinised in view of the overall clinical presentation, so that the significance of small deviations from the reference interval or patient’s baseline are not misinterpreted. Nevertheless, whilst most changes could be modest in absolute terms, they could become more meaningful during longitudinal monitoring, given that the intra-individual variability of clinical laboratory parameters is lower than the inter-individual variation.
As COVID-19 is a new condition, there are ongoing challenges regarding the translation of literature findings to current clinical practice. When writing these interim guidelines, we have attempted to include information replicated in multiple studies to add to the veracity of our recommendations. Nonetheless, most studies referenced suffer from limitations, including small sample sizes, restriction to a single site (or country), study population selection bias, considerable heterogeneity across the studies included in published meta-analyses and use of different endpoints of disease severity.
In addition, much of the published research does not include information on the analytical methods used for testing. For example, the wide variety of approaches used to measure and report D-dimer presents challenges when considering the expected changes in D-dimer concentration in COVID-19 patients .
Research funding: None declared.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
1. Singh, A, Sood, N, Narang, V, Goyal, A. Morphology of COVID-19-affected cells in peripheral blood film. BMJ Case Rep 2020;13: e236117. https://doi.org/10.1136/bcr-2020-236117. Search in Google Scholar
2. Huang, I, Pranata, R. Lymphopenia in severe coronavirus disease-2019 (COVID-19): systematic review and meta-analysis. J. Intensive Care 2020; 8: 36. https://doi.org/10.1186/s40560-020-00453-4. Search in Google Scholar
3. Henry, BM, De Oliveira, MHS, Benoit, S, Plebani, M, Lippi, G. Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chem Lab Med 2020; 58: 1021–8. https://doi.org/10.1515/cclm-2020-0369. Search in Google Scholar
4. Zhang, J, Dong, X, Cao, Y, Yuan, Y, Yang, Y, Yan, Y, et al. Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Eur J Allergy Clin Immunol 2020; 75: 1730–41. https://doi.org/10.1111/all.14238. Search in Google Scholar
5. Bao, J, Li, C, Zhang, K, Kang, H, Chen, W, Gu, B. Comparative analysis of laboratory indexes of severe and non-severe patients infected with COVID-19. Clin Chim Acta 2020; 509: 180–94. https://doi.org/10.1016/j.cca.2020.06.009. Search in Google Scholar
6. Lagunas-Rangel, FA. Neutrophil-to-lymphocyte ratio and lymphocyte-to-C-reactive protein ratio in patients with severe coronavirus disease 2019 (COVID-19): a meta-analysis. J Med Virol Apr 3 2020. https://doi.org/10.1002/jmv.25819 [Epub ahead of print]. Search in Google Scholar
8. Lippi, G, Sanchis-Gomar, F, Henry, BM. COVID-19: unravelling the clinical progression of nature’s virtually perfect biological weapon. Ann Transl Med 2020; 8: 693. https://doi.org/10.21037/atm-20-3989. Search in Google Scholar
9. Bikdeli, B, Madhavan, MV, Jimenez, D, Chuich, T, Dreyfus, I, Driggin, E, et al. COVID-19 and thrombotic or thromboembolic disease: implications for prevention, antithrombotic therapy, and follow-up: JACC state-of-the-art review. J Am Coll Cardiol 2020; 75: 2950–73. https://doi.org/10.1016/j.jacc.2020.04.031. Search in Google Scholar
10. Bikdeli, B, Madhavan, MV, Gupta, A, Jimenez, D, Burton, JR, Der Nigoghossian, C, et al. Pharmacological agents targeting thromboinflammation in COVID-19: review and implications for future research. Thromb Haemost 2020; 120: 1004–24. https://doi.org/10.1055/s-0040-1713152. Search in Google Scholar
11. Leppkes, M, Knopf, J, Naschberger, E, Lindemann, A, Singh, J, Herrmann, I, et al. Vascular occlusion by neutrophil extracellular traps in COVID-19. EBio Med 2020; 58: 10295. https://doi.org/10.1016/j.ebiom.2020.102925. Search in Google Scholar
12. Tang, N, Li, D, Wang, X, Sun, Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost 2020; 18: 1233–4. https://doi.org/10.1111/jth.14768. Search in Google Scholar
13. Gando, S, Fujishima, S, Saitoh, D, Shiraishi, A, Yamakawa, K, Kushimoto, S, et al. The significance of disseminated intravascular coagulation on multiple organ dysfunction during the early stage of acute respiratory distress syndrome. Thromb Res 2020; 191: 15–21. https://doi.org/10.1016/j.thromres.2020.03.023. Search in Google Scholar
14. Lippi, G, Favaloro, EJ. D-dimer is associated with severity of coronavirus disease 2019: a pooled analysis. Thromb Haemost 2020; 120: 876–7. https://doi.org/10.1055/s-0040-1709650. Search in Google Scholar
15. Al-Samkari, H, Karp Leaf, RS, Dzik, WH, Carlson, JC, Fogerty, AE, Waheed, A, et al. COVID and coagulation: bleeding and thrombotic manifestations of SARS-CoV2 infection. Blood 2020; 136: 489–500. https://doi.org/10.1182/blood.2020006520. Search in Google Scholar
16. Lippi, G, Plebani, M, Henry, BM. Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: a meta-analysis. Clin Chim Acta 2020; 506: 145–8. https://doi.org/10.1016/j.cca.2020.03.022. Search in Google Scholar
17. Zhang, Y, Zeng, X, Jiao, Y, Li, Z, Liu, Q, Yang, M, et al. Mechanisms involved in the development of thrombocytopenia in patients with COVID-19. Thromb Res 2020; 193: 110–5. https://doi.org/10.1016/j.thromres.2020.06.008. Search in Google Scholar
18. Mehta, P, McAuley, DF, Brown, M, Sanchez, E, Tattersall, RS, Manson, JJ. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet 2020; 395: 1033. https://doi.org/10.1016/s0140-6736(20)30628-0. Search in Google Scholar
20. Yamada, T, Wakabayashi, M, Yamaji, T, Chopra, N, Mikami, T, Miyashita, H, et al. Value of leukocytosis and elevated C-reactive protein in predicting severe coronavirus 2019 (COVID-19): a systematic review and meta-analysis. Clin Chim Acta 2020; 509: 235–43. https://doi.org/10.1016/j.cca.2020.06.008. Search in Google Scholar
21. Poggiali, E, Zaino, D, Immovilli, P, Rovero, L, Losi, G, Dacrema, A, et al. Lactate dehydrogenase and C-reactive protein as predictors of respiratory failure in CoVID-19 patients. Clin Chim Acta 2020; 509: 135–8. https://doi.org/10.1016/j.cca.2020.06.012. Search in Google Scholar
22. Lapić, I, Rogić, D, Plebani, M, Plebani, M. Erythrocyte sedimentation rate is associated with severe coronavirus disease 2019 (COVID-19): a pooled analysis. Clin Chem Lab Med 2020; 58: 1146–48. https://doi.org/10.1515/cclm-2020-0620. Search in Google Scholar
23. Zeng, F, Huang, Y, Guo, Y, Yin, M, Chen, X, Xiao, L, et al. Association of inflammatory markers with the severity of COVID-19: a meta-analysis. Int J Infect Dis 2020; 96: 467–74. https://doi.org/10.1016/j.ijid.2020.05.055. Search in Google Scholar
24. Henry, BM, Benoit, SW, de Oliveira, MHS, Hsieh, WC, Benoit, J, Ballout, RA, et al. Laboratory abnormalities in children with mild and severe coronavirus disease 2019 (COVID-19): a pooled analysis and review. Clin Biochem 2020; 81: 1–8. https://doi.org/10.1016/j.clinbiochem.2020.05.012. Search in Google Scholar
25. Zhu, J, Pang, J, Ji, P, Zhong, Z, Li, H, Li, B, et al. Elevated interleukin-6 is associated with severity of COVID-19: a meta-analysis. J Med Virol May 29 2020 https://doi.org/10.1002/jmv.26085 [Epub ahead of print]. Search in Google Scholar
26. Yang, Y, Shen, C, Li, J, Yuan, J, Wei, J, Huang, F, et al. Plasma IP-10 and MCP-3 levels are highly associated with disease severity and predict the progression of COVID-19. J Allergy Clin 2020; 146: 119–27. https://doi.org/10.1016/j.jaci.2020.04.027. Search in Google Scholar
27. Zaninotto, M, Mion, MM, Cosma, C, Rinaldi, D, Plebani, M. Presepsin in risk stratification of SARS-CoV-2 patients. Clin Chim Acta 2020; 507: 161–3. https://doi.org/10.1016/j.cca.2020.04.020. Search in Google Scholar
30. Lippi, G, Lavie, CJ, Sanchis-Gomar, F. Cardiac troponin I in patients with coronavirus disease 2019 (COVID-19): evidence from a meta-analysis. Prog Cardiovasc Dis 2020; 63: 390–1. https://doi.org/10.1016/j.pcad.2020.03.001. Search in Google Scholar
31. Zhou, F, Yu, T, Du, R, Fan, G, Liu, Y, Liu, Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020; 395: 1054–62. https://doi.org/10.1016/s0140-6736(20)30566-3. Search in Google Scholar
32. Lala, A, Johnson, KW, Januzzi, JL, Russak, AJ, Paranjpe, I, Richter, F, et al. Prevalence and impact of myocardial injury in patients hospitalized with COVID-19 infection. J Am Coll Cardiol 2020; 76: 533–46. https://doi.org/10.1016/j.jacc.2020.06.007. Search in Google Scholar
33. American College of Cardiology. Heart Failure Biomarkers in COVID-19. Available from: https://www.acc.org/latest-in-cardiology/articles/2020/07/27/09/25/heart-failure-biomarkers-in-covid-19 [Accessed Aug 2020]. Search in Google Scholar
34. Chapman, AR, Bularga, A, Mills, NL. High-Sensitivity cardiac troponin can be an ally in the fight against COVID-19. Circulation 2020; 141: 1733–5. https://doi.org/10.1161/circulationaha.120.047008. Search in Google Scholar
35. Guo, T, Fan, Y, Chen, M, Wu, X, Zhang, L, He, T, et al. Cardiovascular implications of fatal outcomes of patients with coronavirus disease 2019 (COVID-19). JAMA Cardiol 2020; 5: 1–8. https://doi.org/10.1001/jamacardio.2020.1017. Search in Google Scholar
36. Mattiuzzi, C, Lippi, G. Serum prealbumin values predict the severity of coronavirus disease 2019 (COVID‐19). J Med Virol May 29 2020 https://doi.org/10.1002/jmv.26085 [Epub ahead of print]. Search in Google Scholar
37. Parohan, M, Yaghoubi, S, Seraji, A. Liver injury is associated with severe coronavirus disease 2019 (COVID‐19) infection: a systematic review and meta‐analysis of retrospective studies. Hepatol Res 2020; 50: 924–35. https://doi.org/10.1111/hepr.13510. Search in Google Scholar
38. Wang, Y, Shi, L, Wang, Y, Yang, H. An updated meta-analysis of AST and ALT levels and the mortality of COVID-19 patients. Am J Emerg Med 2020;S0735–6757:30426–5. https://doi.org/10.1016/ajem.2020.05.063. Search in Google Scholar
40. Bertolini, A, van de Peppel, IP, Bodewes, FAJA, Moshage, H, Fantin, A, Farinati, F, et al. Abnormal liver function tests in COVID‐19 patients: relevance and potential pathogenesis. Hepatology Jul 23 2020. https://doi.org/10.1002/hep.31480 [Epub ahead of print]. Search in Google Scholar
41. Wong, GLH, Wong, VWS, Thompson, A, Jia, J, Hou, J, Lesmana, CRA, et al. Management of patients with liver derangement during the COVID-19 pandemic: an Asia-Pacific position statement. Lancet Gastroenterol Hepatol 2020; 5: 776–87. https://doi.org/10.1016/s2468-1253(20)30190-4. Search in Google Scholar
42. Mojoli, F, Mongodi, S, Orlando, A, Arisi, E, Pozzi, M, Civardi, L, et al. Our recommendations for acute management of COVID-19. Crit Care 2020; 24: 207. https://doi.org/10.1186/s13054-020-02930-6. Search in Google Scholar
43. Tobin, MJ. Basing respiratory management of COVID-19 on physiological principles. Am J Respir Crit Care Med 2020; 201: 1319–20. https://doi.org/10.1164/rccm.202004-1076ed. Search in Google Scholar
44. Lippi, G, South, AM, Henry, BM. Electrolyte imbalances in patients with severe coronavirus disease 2019 (COVID-19). Ann Clin Biochem 2020; 57: 262–5. https://doi.org/10.1177/0004563220922255. Search in Google Scholar
45. Hoang, A, Chorath, K, Moreira, A, Evans, M, Burmeister-Morton, F, Burmeister, F, et al. COVID-19 in 7780 pediatric patients: a systematic review. EClinicalMedicine 2020; 24: 100433. https://doi.org/10.1016/j.eclinm.2020.100433. Search in Google Scholar
46. Henry, BM, Lippi, G, Plebani, M. Laboratory abnormalities in children with novel coronavirus disease 2019. Clin Chem Lab Med 2020; 58: 1135–38. https://doi.org/10.1515/cclm-2020-0272. Search in Google Scholar
47. Centers for disease control multisystem inflammatory syndrome in children (MIS-C) associated with coronavirus disease 2019 (COVID-19). Preparedness E. Emergency preparedness and response multisystem inflammatory syndrome in children (MIS-C) associated with coronavirus disease 2019. Available from: https://www.cdc.gov/mis-c/hcp/ [Accessed Aug 2020]. Search in Google Scholar
48. Feldstein, LR, Rose, EB, Horwitz, SM, Collins, JP, Newhams, MM, Son, MBF, et al. Multisystem inflammatory syndrome in U.S. Children and adolescents. N Engl J Med 2020; 383: 334–46. https://doi.org/10.1056/nejmoa2021680. Search in Google Scholar
49. Dufort, EM, Koumans, EH, Chow, EJ, Rosenthal, EM, Muse, A, Rowlands, J, et al. Multisystem inflammatory syndrome in children in New York state. N Engl J Med 2020; 383: 347–58. https://doi.org/10.1056/nejmoa2021756. Search in Google Scholar
50. Lippi, G, Henry, BM, Hoehn, J, Benoit, S, Benoit, J. Validation of the Corona-Score for rapid identification of SARS-CoV-2 infections in patients seeking emergency department care in the United States. Clin Chem Lab Med 2020; 58: e311–e313. https://doi.org/10.1515/cclm-2020-112. Search in Google Scholar
51. Yang, HS, Hou, Y, Vasovic, L V, Steel, P, Chadburn, A, Racine-Brzostek, SE, et al. Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning. Clin Chem Aug 21 2020. https://doi.org/10.1093/clinchem/hvaa200 [Epub ahead of print]. Search in Google Scholar
52. Kurstjens, S, van der Horst, A, Herpers, R, Geerits, MWL, Kluiters-de Hingh, YCM, Göttgens, E-L, et al. Rapid identification of SARS-CoV-2-infected patients at the emergency department using routine testing. Clin Chem Lab Med Jun 29 2020. https://doi.org/10.1515/cclm-2020-059310.1101/2020.04.20.20067512 [Epub ahead of print]. Search in Google Scholar
53. Liang, W, Liang, H, Ou, L, Chen, B, Chen, A, Li, C, et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern Med 2020; 180: 1–9. https://doi.org/10.1001/jamainternmed.2020.2033. Search in Google Scholar
54. Wynants, L, Van Calster, B, Bonten, MMJ, Collins, GS, Debray, TPA, De Vos, M, et al. Prediction models for diagnosis and prognosis of covid-19 infection: Systematic review and critical appraisal. BMJ Apr 7 2020. https://doi.org/10.1136/bmj.m1328 [Epub ahead of print]. Search in Google Scholar
55. Favaloro, EJ, Thachil, J. Reporting of D-dimer data in COVID-19: some confusion and potential for misinformation. Clin Chem Lab Med 2020; 58: 1191–99. https://doi.org/10.1515/cclm-2020-0573. Search in Google Scholar
56. Wu, Q, Xing, Y, Shi, L, Li, W, Gao, Y, Pan, S, et al. Coinfection and other clinical characteristics of COVID-19 in children. Pediatrics. Am Acad Pediatr Jul 2020 https://doi.org/10.1542/peds.2020-0961 [Epub ahead of print]. Search in Google Scholar
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