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Meta-Analysis
. 2020 Nov 19;11(11):CD013787.
doi: 10.1002/14651858.CD013787.

Routine laboratory testing to determine if a patient has COVID-19

Inge Stegeman  1   2   3 , Eleanor A Ochodo  4   5 , Fatuma Guleid  6 , Gea A Holtman  7 , Bada Yang  2 , Clare Davenport  8   9 , Jonathan J Deeks  8   9 , Jacqueline Dinnes  8   9 , Sabine Dittrich  10 , Devy Emperador  10 , Lotty Hooft  11 , René Spijker  11   12 , Yemisi Takwoingi  8   9 , Ann Van den Bruel  13 , Junfeng Wang  14 , Miranda Langendam  2 , Jan Y Verbakel  13 , Mariska Mg Leeflang  2 ; Cochrane COVID-19 Diagnostic Test Accuracy Group  9
Affiliations
Meta-Analysis

Routine laboratory testing to determine if a patient has COVID-19

Inge Stegeman et al. Cochrane Database Syst Rev. .

Abstract

Background: Specific diagnostic tests to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and resulting COVID-19 disease are not always available and take time to obtain results. Routine laboratory markers such as white blood cell count, measures of anticoagulation, C-reactive protein (CRP) and procalcitonin, are used to assess the clinical status of a patient. These laboratory tests may be useful for the triage of people with potential COVID-19 to prioritize them for different levels of treatment, especially in situations where time and resources are limited.

Objectives: To assess the diagnostic accuracy of routine laboratory testing as a triage test to determine if a person has COVID-19.

Search methods: On 4 May 2020 we undertook electronic searches in the Cochrane COVID-19 Study Register and the COVID-19 Living Evidence Database from the University of Bern, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions.

Selection criteria: We included both case-control designs and consecutive series of patients that assessed the diagnostic accuracy of routine laboratory testing as a triage test to determine if a person has COVID-19. The reference standard could be reverse transcriptase polymerase chain reaction (RT-PCR) alone; RT-PCR plus clinical expertise or and imaging; repeated RT-PCR several days apart or from different samples; WHO and other case definitions; and any other reference standard used by the study authors.

Data collection and analysis: Two review authors independently extracted data from each included study. They also assessed the methodological quality of the studies, using QUADAS-2. We used the 'NLMIXED' procedure in SAS 9.4 for the hierarchical summary receiver operating characteristic (HSROC) meta-analyses of tests for which we included four or more studies. To facilitate interpretation of results, for each meta-analysis we estimated summary sensitivity at the points on the SROC curve that corresponded to the median and interquartile range boundaries of specificities in the included studies.

Main results: We included 21 studies in this review, including 14,126 COVID-19 patients and 56,585 non-COVID-19 patients in total. Studies evaluated a total of 67 different laboratory tests. Although we were interested in the diagnotic accuracy of routine tests for COVID-19, the included studies used detection of SARS-CoV-2 infection through RT-PCR as reference standard. There was considerable heterogeneity between tests, threshold values and the settings in which they were applied. For some tests a positive result was defined as a decrease compared to normal vaues, for other tests a positive result was defined as an increase, and for some tests both increase and decrease may have indicated test positivity. None of the studies had either low risk of bias on all domains or low concerns for applicability for all domains. Only three of the tests evaluated had a summary sensitivity and specificity over 50%. These were: increase in interleukin-6, increase in C-reactive protein and lymphocyte count decrease. Blood count Eleven studies evaluated a decrease in white blood cell count, with a median specificity of 93% and a summary sensitivity of 25% (95% CI 8.0% to 27%; very low-certainty evidence). The 15 studies that evaluated an increase in white blood cell count had a lower median specificity and a lower corresponding sensitivity. Four studies evaluated a decrease in neutrophil count. Their median specificity was 93%, corresponding to a summary sensitivity of 10% (95% CI 1.0% to 56%; low-certainty evidence). The 11 studies that evaluated an increase in neutrophil count had a lower median specificity and a lower corresponding sensitivity. The summary sensitivity of an increase in neutrophil percentage (4 studies) was 59% (95% CI 1.0% to 100%) at median specificity (38%; very low-certainty evidence). The summary sensitivity of an increase in monocyte count (4 studies) was 13% (95% CI 6.0% to 26%) at median specificity (73%; very low-certainty evidence). The summary sensitivity of a decrease in lymphocyte count (13 studies) was 64% (95% CI 28% to 89%) at median specificity (53%; low-certainty evidence). Four studies that evaluated a decrease in lymphocyte percentage showed a lower median specificity and lower corresponding sensitivity. The summary sensitivity of a decrease in platelets (4 studies) was 19% (95% CI 10% to 32%) at median specificity (88%; low-certainty evidence). Liver function tests The summary sensitivity of an increase in alanine aminotransferase (9 studies) was 12% (95% CI 3% to 34%) at median specificity (92%; low-certainty evidence). The summary sensitivity of an increase in aspartate aminotransferase (7 studies) was 29% (95% CI 17% to 45%) at median specificity (81%) (low-certainty evidence). The summary sensitivity of a decrease in albumin (4 studies) was 21% (95% CI 3% to 67%) at median specificity (66%; low-certainty evidence). The summary sensitivity of an increase in total bilirubin (4 studies) was 12% (95% CI 3.0% to 34%) at median specificity (92%; very low-certainty evidence). Markers of inflammation The summary sensitivity of an increase in CRP (14 studies) was 66% (95% CI 55% to 75%) at median specificity (44%; very low-certainty evidence). The summary sensitivity of an increase in procalcitonin (6 studies) was 3% (95% CI 1% to 19%) at median specificity (86%; very low-certainty evidence). The summary sensitivity of an increase in IL-6 (four studies) was 73% (95% CI 36% to 93%) at median specificity (58%) (very low-certainty evidence). Other biomarkers The summary sensitivity of an increase in creatine kinase (5 studies) was 11% (95% CI 6% to 19%) at median specificity (94%) (low-certainty evidence). The summary sensitivity of an increase in serum creatinine (four studies) was 7% (95% CI 1% to 37%) at median specificity (91%; low-certainty evidence). The summary sensitivity of an increase in lactate dehydrogenase (4 studies) was 25% (95% CI 15% to 38%) at median specificity (72%; very low-certainty evidence).

Authors' conclusions: Although these tests give an indication about the general health status of patients and some tests may be specific indicators for inflammatory processes, none of the tests we investigated are useful for accurately ruling in or ruling out COVID-19 on their own. Studies were done in specific hospitalized populations, and future studies should consider non-hospital settings to evaluate how these tests would perform in people with milder symptoms.

PubMed Disclaimer

Conflict of interest statement

Inge Stegeman: has provided freelance consultancy for approved professional organizations and learned societies (physiotherapists, optometrists, opticians), and has no known conflicts of interest in relation to this review.

Eleanor A Ochodo: none known

Fatuma Guleid: none known.

Gea A. Holtman: none known.

Bada Yang: none known.

Jane Cunningham: none known.

Clare Davenport: none known.

Jonathan J Deeks: none known.

Jacqueline Dinnes: none known.

Sabine Dittrich: is employed by FIND. FIND has several clinical research projects to evaluate multiple new diagnostic tests against published Target Product Profiles that have been defined through consensus processes. These studies are for diagnostic products developed by private sector companies who provide access to know‐how, equipment/reagents, and contribute through unrestricted donations as per FIND policy and external SAC review.

Devy Emperador: is employed by FIND. FIND has several clinical research projects to evaluate multiple new diagnostic tests against published Target Product Profiles that have been defined through consensus processes. These studies are for diagnostic products developed by private sector companies who provide access to know‐how, equipment/reagents, and contribute through unrestricted donations as per FIND policy and external SAC review.

Lotty Hooft: none known.

René Spijker: the Dutch Cochrane Centre (DCC) has received grants for performing commissioned systematic reviews. In no situation, the commissioner had any influence on the results of the work.

Yemisi Takwoingi: none known.

Ann Van den Bruel: none known.

Junfeng Wang: has received consultancy fee from Biomind, an Artificial Intelligence (AI) company providing machine intelligence solutions in medical imaging. The consultancy service was about design of clinical studies, not related to this review. The company had no influence on the results of the work.

Miranda Langendam: none known.

Jan Verbakel: none known.

Mariska MG Leeflang: none known.

Figures

1
1
Study flow diagram. Studies were retrieved in a combined search process for all DTA reviews about tests for COVID‐19 and then divided over the different review teams. Due to this process, some preprints only came to light after the data‐extraction phase
2
2
Risk of bias and applicability concerns summary: review authors' judgements about each domain for each included study
3
3
Summary ROC plot of tests. 1: white blood cell count (WBC) increase; 2: WBC decrease
4
4
Summary ROC plot of tests: neutrophil count increase, and neutrophil count decrease
5
5
Summary ROC plot of monocyte count increase
6
6
Summary ROC plot of lymphocyte count decrease
7
7
Summary ROC plot of 22 platelets, decreased
8
8
Summary ROC plot of tests: alanine aminotransferase (ALT) increase, aspartate aminotransferase( AST) increase.
9
9
Summary ROC plot of tests: 30 total bilirubin (TBIL) increase, 36 albumin (ALB) decrease
10
10
Summary ROC plot of tests: CRP increase and procalcitonin (PCT) increase
11
11
Summary ROC plot of 53 interleukin‐6 (IL‐6) increase. Height and width of the symbols represent the number of cases and non‐cases in the studies
12
12
Summary ROC plot of tests: 24 Serum creatinine increased, 25 Creatine kinase ‐ increase, 55 lactate dehydrogenase (LDH) increase
13
13
Summary ROC plot of tests: 12 lymphocyte count decrease, 32 CRP increase, 47 interleukin‐6 (IL‐6) increase
14
14
Summary ROC plot of tests: 12 lymphocyte count decrease, 32 CRP increase
1
1. Test
WBC increase
2
2. Test
WBC decrease
3
3. Test
Leukocyturia
4
4. Test
Monocyte count increase
5
5. Test
Monocyte count decrease
6
6. Test
Monocyte percentage increase
7
7. Test
Neutrophil count increase
8
8. Test
Neutrophil count decrease
9
9. Test
Neutrophil percentage increase
10
10. Test
Neutrophil Percentage decrease
11
11. Test
Lymphocyte count increase
12
12. Test
Lymphocyte count decrease
13
13. Test
Lymphocyte percentage increase
14
14. Test
Lymphocyte percentage decrease
15
15. Test
Eosinophil count increase
16
16. Test
Eosinophil count decrease
17
17. Test
Eosinophil percentage increase
18
18. Test
Basophil count increase
19
19. Test
Basophil percentage increase
20
20. Test
Red Blood Cell volume distribution increase
21
21. Test
RBC decrease
22
22. Test
Platelets decreased
23
23. Test
Haemoglobin (HGB) Decreased
24
24. Test
Serum creatinine increased
25
25. Test
Creatine Kinase ‐ increase
26
26. Test
Creatine Kinase MB ‐ increase
27
27. Test
Urea increase
28
28. Test
ALT increase
29
29. Test
AST increase
30
30. Test
Total bilirubin (TBIL) increase
31
31. Test
Erythrocyte Sedimentation Rate (ESR) increase
32
32. Test
CRP increase
33
33. Test
a‐HBDH increased
34
34. Test
HCT increased
35
35. Test
HCT decreased
36
36. Test
Albumin (ALB) decreased
37
37. Test
Globulin (GLB) increase
38
38. Test
Globulin (GLB) decrease
39
39. Test
Procalcitonin (PCT) increase
40
40. Test
eGFR
41
41. Test
Proteinuria
42
42. Test
Prothrombin time (PT) increase
43
43. Test
GGT increased
44
44. Test
D‐dimer increase
45
45. Test
IL‐2
46
46. Test
IL‐4
47
47. Test
Interleukin‐6 (IL‐6) increase
48
48. Test
IL‐8
49
49. Test
IL‐10
50
50. Test
TNF alpha
51
51. Test
ALP increased
52
52. Test
pro‐BNP
53
53. Test
Hematuria
54
54. Test
INR increase
55
55. Test
LDH increase
56
56. Test
Mean corpuscular volume increase
57
57. Test
Mean corpuscular volume decrease
58
58. Test
Erythrocyte mean corpuscular hemoglobin increase
59
59. Test
Erythrocyte mean corpuscular hemoglobin decrease
60
60. Test
Erythrocytemean corpuscular hemoglobin concentrate increase
61
61. Test
Erythrocytemean corpuscular hemoglobin concentrate decrease
62
62. Test
Mean Platelet Volume
63
63. Test
Direct bilirubin
64
64. Test
unconjugated bilirubin
65
65. Test
Total protein
66
66. Test
Total bile acid
67
67. Test
Troponin I
See this image and copyright information in PMC

References

References to studies included in this review

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