Dear Editor,
The kinetics of the SARS-CoV-2 viral load in respiratory airways and other tissues is of great interest to understand the pathogenesis, course, and the management of COVID-19 patients. Therefore, we read with much interest the systematic literature review recently published in the Journal of Infection by Walsh et al.1, concluding that viral load in upper respiratory samples peaks around the time of symptoms onset or a few days thereafter, and becomes undetectable about two weeks after symptom onset; moreover, there is evidence of prolonged virus detection in stool samples, with unclear clinical significance. Information regarding the use of other samples to improve patients’ management is lacking or inconsistent.1, 2, 3 Thus, the risk factors for bloodstream infection and the clinical meaning of SARS-CoV-2 RNAemia detection has not yet been completely elucidated.
In this regard, we conducted a prospective multicentre cohort study of consecutive COVID-19 adult patients aimed to identify the factors associated with the detection of SARS-CoV-2 RNAemia at hospital admission and if its presence is associated with an unfavourable outcome, defined as intensive care unit (ICU) admission and/or death. Information regarding the study design and the methodology used is provided in the Supplementary Materials file.
Seventy-two patients were included, with a median age of 61 years old. Forty-one (56.9%) were male and 41 (56.9%) had a Charlson comorbidity index ≥3 (Table 1 ). After their evaluation in the emergency room, sixty-three (87.5%) patients were admitted to the hospital, and nine (12.5%) were managed in an outpatient´ setting. SARS-CoV-2 RNAemia was detected in eleven (15.3%) patients, 10 of them admitted to the hospital (Table 1).
Table 1.
Demographics and baseline characteristics of patients with and without SARS-CoV-2 RNAemia.
| Variables, N (%) | N = 72 patients | ORa | P-valueb | |
|---|---|---|---|---|
| With viremia (N = 11) | Without viremia (N = 61) | |||
| Demographics | ||||
| Age (median [IQR]) | 66 (57–77) | 61 (52–75) | [..] | 0.531 |
| Male sex | 6 (54.5%) | 35 (57.4%) | 0.891 (0.245–3.241) | 1.000 |
| Underlying conditions | ||||
| Any underlying chronic disease | 8 (72.7%) | 40 (65.6%) | 1.400 (0.336–5.839) | 0.908 |
| Chronic kidney disease | 2 (18.2%) | 7 (11.5%) | 1.714 (0.306–9.599) | 0.901 |
| Chronic liver disease | 3 (27.3%) | 0 (0.0%) | 0.116 (0.060–0.222) | 0.001 |
| Connective tissue disease | 2 (18.2%) | 4 (6.4%) | 3.167 (0.504–19.883) | 0.489 |
| Solid organ transplantation | 4 (36.4%) | 1 (1.6%) | 34.284 (3.346–351.308) | 0.001 |
| Charlson index ≥ 3 | 8 (72.7%) | 33 (54.1%) | 2.236 (0.547–9.354) | 0.413 |
| Previous Treatment | ||||
| Previous statins | 1 (9.1%) | 12 (19.7%) | 0.408 (0.048–3.507) | 0.679 |
| Previous ACEI | 1 (9.1%) | 12 (19.7%) | 0.408 (0.048–3.507) | 0.647 |
| Clinical symptoms at diagnosis | ||||
| Arthro-myalgias | 5 (45.5%) | 7 (11.5%) | 6.429 (1.547–26.709) | 0.019 |
| Weakness | 4 (36.4%) | 20 (32.8%) | 1.171 (0.307–4.473) | 1.000 |
| Cough | 7 (63.6%) | 38 (62.3%) | 1.059 (0.279–4.018) | 1.000 |
| Dyspnoea | 7 (63.6%) | 24 (42.9%) | 2.233 (0.612–8.890) | 0.206 |
| Coryza | 0 (0%) | 3 (4.9%) | 0.841 (0.758–0.932) | 1.000 |
| Odynophagia | 1 (9.1%) | 7 (11.5%) | 0.771 (0.085–6.971 | 1.000 |
| Diarrhoea | 4 (36.6%) | 12 (19.7%) | 2.333 (0.586–9.286) | 0.406 |
| Headache | 3 (27.3%) | 12 (19.7%) | 1.531 (0.352–6.656 | 0.867 |
| Anosmia | 1 (9.1%) | 11 (18%) | 0.455 (0.053–3.929) | 0.770 |
| Dysgeusia | 1 (9.1%) | 9 (14.8%) | 0.578 (0.066–5.081) | 0.979 |
| Vital signs, exploration, and severity scores at diagnosis | ||||
| Temperature ( °C, median [IQR]) |
36.4 (36–37.8) | 36.6 (36.1–37.6) | [..] | 0.982 |
| SBP < 90 mmHg | 0 (0%) | 2 (3.3%) | 0.843 (0.762–0.933) | 1.000 |
| DBP < 60 mmHg | 2 (18.2%) | 1 (1.6%) | 13.333 (1.094–162.532) | 0.088 |
| SatO2< 95% at diagnosis | 6 (54.5%) | 15 (24.6%) | 3.680 (0.981–13.806) | 0.099 |
| HR ≥ 100 bpm (N = 64) | 6 (66.7%) | 15 (27.3%) | 5.333 (1.181–24.085) | 0.051 |
| RR ≥ 20 bpm(N = 60) | 1 (9.1%) | 0 (0%) | 0.169 (0.096–0.289) | 0.409 |
| qSOFA ≥ 2 | 1 (9.1%) | 11 (18%) | 0.455 (0.053–3.929) | 0.770 |
| Chest x-ray findings | ||||
| Pneumonia | 9 (81.8%) | 47 (77%) | 1.340 (0.259–6.940) | 1.000 |
| Bilateral infiltrates | 8 (88.9%) | 32 (78.0%) | 2.250 (0.248–20.438) | 0.665 |
| CURB-65 ≥ 2 | 5 (55.5%) | 15 (31.9%) | 2.556 (0.681–9.587) | 0.291 |
| Laboratory results | ||||
| Leucocytes (x103/µL, median [IQR]) |
5.22 (3.47–7.06) | 7.00 (5.24–9.20) | [..] | 0.030 |
| Leucocytes > 11,000 /μL | 1 (9.1%) | 8 (13.1%) | 0.663 (0.074–5.896) | 1.000 |
| Neutrophils (x103/µL, median [IQR]) |
3.49 (2.96–5.90) | 4.79 (3.30–6.88) | [..] | 0.348 |
| Neutrophils > 7500 /μL | 1 (9.1%) | 11 (18.0%) | 0.455 (0.053–3.929) | 0.677 |
| Lymphocytes (103/µL median [IQR]) |
0.58 (0.39–1.24) | 1.36 (0.92–1.80) |
[..] | 0.002 |
| Lymphocytes < 1000 /µL | 7 (63.6%) | 18 (29.5%) | 4.181 (1.088–16.063) | 0.065 |
| Platelets (x103/µL, median [IQR]) |
158 (129–201) | 248 (175–325) |
[..] | 0.002 |
| Platelets < 130,000 /μL | 3 (27.3%) | 4 (6.6%) | 5.344 (1.006–28.383) | 0.067 |
| Haemoglobin (g/L, median [IQR]) |
13 (11.2–15.1) | 13.8 (12.10–14.8) |
[..] | 0.191 |
| AST (IU/L, median [IQR]) (N = 63) |
37 (26–68) | 26 (20–41) |
[..] | 0.074 |
| AST > 30 IU/L | 8 (72.7%) | 19 (36.5%) | 4.632 (1.095–19.587) | 0.063 |
| ALT (IU/L, median [IQR]) (N = 70) | 33 (17–40) | 23 (17–44) |
[..] | 0.374 |
| ALT > 40 IU/L | 2 (18.2%) | 16 (27.1%) | 0.597 (0.116–3.067) | 0.805 |
| Bilirubin (mg/dL, mean ± SD) (N = 61) |
0.59 (0.36–0.68) | 0.46 (0.35–0.81) |
[..] | 0.911 |
| Sodium < 135 mEq/L (N = 71) | 2 (18.2%) | 4 (6.7%) | 3.111 (0.495–19.541) | 0.501 |
| Potassium > 5 mEq/L (N = 70) | 2 (18.2%) | 1 (1.7%) | 12.889 (1.057–157.184) | 0.095 |
| Creatinine > 1.3 mg/dL (N = 62) | 4 (44.4%) | 6 (10.7%) | 6.667 (1.395–31.849) |
0.035 |
| C-reactive protein (mg/L, median [IQR]) (N = 71) |
97.9 (33.9–205.0) | 44.9 (17.1–98.5) | [..] | 0.187 |
| C-reactive protein > 100 mg/L (N = 71) | 5 (45.5%) | 14 (23.3%) | 2.738 (0.725–10.343) | 0.249 |
| Ferritin (ng/L, median [IQR]) (N = 63) |
625.6 (366.5–1009.2) | 442 (191.4–817.3) | [..] | 0.275 |
| Ferritin > 1000 ng/mL (N = 63) | 2 (20%) | 10 (18.9%) | 1.075 (0.197–5.858) | 1.000 |
| D-dimers (ng/L, median [IQR]) (N = 70) |
1430 (770–2620) | 620 (380–1140) | [..] | 0.043 |
| D-dimers > 600 ng/mL (N = 70) | 10 (90.9%) | 30 (58.8%) | 9.667 (1.163–80.337) | 0.033 |
| LDH (UI/L, median [IQR]) (N = 65) |
450 (312–660) | 251.5 (213.0–320.5) | [..] | 0.001 |
| LDH > 300 UI/L(N = 65) | 9 (81.8%) | 17 (31.5%) | 9.794 (1.907–50.302) | 0.006 |
| SARS-CoV-2 in nasopharynx Log10 copies/mL, median (IQR) |
7.3 (6.6–8.8) | 6.6 (5.1–7.9) | [..] | 0.262 |
| Hospital admission | 10 (90.9%) | 53 (86.9%) | 1.509 (0.170–13.432) | 1.000 |
| Treatments | ||||
| Antiviral treatment | 9 (81.8%) | 55 (90.2%) | 1.244 (0.339–4.563) | 0.772 |
| LPV/r | 0 (0%) | 5 (8.2%) | 0.836 (0.755–0.929) | 0.734 |
| Hydroxychloroquine | 1 (9.1%) | 21 (34.4%) | 0.190 (0.023–1.591) | 0.186 |
| LPV/r + hydroxychloroquine | 6 (54.5%) | 24 (39.3%) | 1.850 (0.508–6.742) | 0.542 |
| LPV/r + hydroxychloroquine + IFN-β | 2 (18.2%) | 2 (3.3%) | 6.551 (0.818–52.56) | 0.204 |
| Remdesivir | 0 (0%) | 7 (11.5%) | 0.831 (0.744–0.921) | 0.529 |
| Tocilizumab | 3 (27.3%) | 4 (6.6%) | 5.344 (1.006–28.383) | 0.114 |
| Initial antibacterial treatment | 5 (45.5%) | 25 (41%) | 1.200 (0.330–4.367) | 1.000 |
ACEI: angiotensin-converting enzyme inhibitors; SBP: systolic blood pressure; DBP: diastolic blood pressure; HR: heart rate; RR: respiratory rate. AST: aspartate aminotransferase; ALT: alanine aminotransferase; LDH: lactate dehydrogenase; LPV/r: lopinavir/ritonavir; IFN-β: beta interferon. aRisk estimation from Chi-squared test, Student´s t-test and U-value from the Mann-Whitney´s test. 95% confidence intervals, according to indication, appear in parentheses. bTwo-tailed test.
Arthro-myalgias were the only symptom more frequently observed in COVID-19 patients with SARS-CoV-2 RNAemia compared to those without RNAemia. SARS-CoV-2 RNAemia was detected more frequently in patients with chronic liver disease (27.3% vs. 0.0%, P = 0.001) and in solid organ transplant (SOT) recipients (36.4% vs. 1.6%, P = 0.001). Fifty-six (77.8%) patients had pneumonia, 49 (87.5%) of them were admitted to the hospital; 20 (35.7%) of the pneumonia cases presented a CURB-65 score ≥2, with no differences between the groups with and without RNAemia (Table 1). Other laboratory analytical and chest X-rays data, and therapy, in patients with and without SARS-CoV-2 RNAemia are detailed in Table 1.
The median viral load in plasma for the 11 patients with SARS-CoV-2 RNAemia was 2.88 Log10 copies/mL (IQR, 2.43–4.07) and the median viral load in NP swabs of the 72 patients was 6.98 Log10 copies/mL (IQR, 5.15–8.20). There was no significant difference in the viral load in NP swabs between patients with (7.29 Log10 copies/mL [IQR, 6.56–8.78]) and without RNAemia (6.64 Log10 copies/mL [5.14–7.86], P = 0.262) (Supplementary Figure 1), and we didn't find a correlation between the viral load in NP and blood samples for the eleven patients with RNAemia (Supplementary Figure 2). Additionally, we found a unique case (1.4%) of co-infection with metapneumovirus and parainfluenza virus 3, both detected in blood of a patient without RNAemia.
As for their clinical outcomes, patients with SARS-CoV-2 RNAemia required more frequently ICU admission (45.50% vs. 8.2%, P = 0.005), showed more frequently acute respiratory distress syndrome (ARDS) (54.5% vs. 9.8%, P = 0.01) and required in more cases invasive mechanical ventilation (36.4% vs. 6.6%, P = 0.018). Mortality (36.4% vs. 4.9%, P = 0.007) and unfavourable outcome (63.6% vs. 13.1%, P = 0.001), were also more frequent in patients with SARS-CoV-2 RNAemia (Table 2 ).
Table 2.
Clinical outcomes of patients with and without SARS-CoV-2 RNAemia.
| Variables N (%) | N = 72 patients | ORa | P-valueb | |
|---|---|---|---|---|
| With viremia (N = 11) | Without viremia (N = 61) | |||
| ARDS | 6 (54.5%) | 6 (9.8%) | 11.0 (2.563–47.112) | 0.001 |
| IMV | 4 (36.4%) | 4 (6.6%) | 8.143 (1.656–40.041) | 0.018 |
| Multiple organ failure | 1 (9.1%) | 0 (0%) | 0.141 (0.079–0.250) | 0.331 |
| ICU admission | 5 (45.5%) | 5 (8.2%) | 9.33 (2.086–41.765) | 0.005 |
| Length of stayDays, median (IQR) | 5 (0–19) | 6 (2.5–11) | [..] | 0.440 |
| Mortality | 4 (36.4%) | 3 (4.9%) | 11.048 (2.039–59.868) | 0.007 |
| Unfavourable outcome (ICU admission and/or death) | 7 (63.6) | 8 (13.1) | 11.59 (2.76–48.73) | 0.001 |
ARDS: Acute Respiratory Distress Syndrome; IMV: invasive mechanical ventilation; ICU: Intensive Care Unit. aRisk estimation from Chi-squared test, Student´s t-test and U-value from the Mann-Whitney´s test. 95% confidence intervals, according to indication, appear in parentheses. bTwo-tailed test.
Results from other studies show discordant rates of SARS-CoV-2 detection in serum, ranging from 10.4% to 74.1%,2 , 4, 5, 6, 7 while other authors do not find any patient8 or report only 1% of RNAemia.2 Veyer et al. also found higher frequency of SARS-CoV-2 RNAemia in more severely ill patients, however they were included at the time of respiratory deterioration and those with pre-existing unstable chronic disorders were excluded.6 Most patients presented with chronic underlying diseases (66.7%), a percentage that shows high variability, from the 23.7% reported by Guan et al.9 to higher percentages (79%) depending on the number and type of the comorbidities considered in each case.5
Our results confirm those from Prebensen et al. who did not find an association between the viral load in NP samples and the presence of SARS-CoV-2 RNAemia nor correlation with the viral load in blood.7 In the present study, the worst clinical evolution and outcome in patients with RNAemia and the lack of correlation between the viral load in NP samples and blood, besides the absence of difference in the NP viral load between patients with and without SARS-CoV-2 RNAemia, support that it is a better indicator of the clinical evolution of COVID-19 patients than NP viral load.
SARS-CoV-2 RNAemia has been shown to be associated with high levels of IL-6 in critically ill COVID-19 patients, and both factors were related to mortality.4 According to our experience, the levels of d-dimers, which are also used as markers of inflammation, were also higher in patients with SARS-CoV-2 RNAemia. The frequency of patients with elevated levels of AST and LDH, and those with decreased counts of lymphocytes and platelets were in agreement with previous reports,2 , 9 , 10 although in our cohort these findings were associated with the presence of SARS-CoV-2 RNAemia.
Regarding the clinical meaning of the SARS-CoV-2 RNAemia, our results agree with those reported by other authors, suggesting an association with underlying diseases and a worst clinical evolution, although without the limitations of including only patients more severely ill, or excluding those with underlying chronic diseases or receiving therapies that may influence the outcome.5, 6, 7 Our results show that COVD-19 patients with SARS-CoV-2 RNAemia are more likely to develop ARDS than those without RNAemia and show increased needs of ICU admission, in agreement with Prebensen et al.,7 and invasive mechanical ventilation.
In conclusion, the results of the present study show that the presence of the SARS-CoV-2 RNAemia, at the first evaluation in the emergency room, occurs more frequently in patients with severe underlying chronic diseases, such as chronic liver disease and solid organ transplantation, is not predicted by the viral load in the upper respiratory airways, and it is associated with unfavourable outcome.
Declaration of Competing Interest
None of the study authors have conflicts of interest to declare.
Acknowledgments
Acknowledgments
Supported by Plan Nacional de I + D + i 2013–2016 and Instituto de Salud Carlos III, Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Economía, Industria y Competitividad, Spanish Network for Research in Infectious Diseases (REIPI, RD16/0016/0001, RD16/0016/0005, RD16/0016/0007, RD16/0016/0009, RD16/0016/0010, R D16/0016/0013) cofinanced by European Development Regional Fund “A way to achieve Europe”, Operative program Intelligent Growth 2014–2020. JSC and EC received grants from the Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación, Proyectos de Investigación sobre el SARS-CoV-2 y la enfermedad COVID-19 (COV20/00580; COV20/00370). J.S.C. is a researcher belonging to the program “Nicolás Monardes” (C-0059–2018), Servicio Andaluz de Salud, Junta de Andalucía, Spain.
Ethics approval
The study protocol was approved by the Ethics Committee of Virgen Macarena and Virgen del Rocío University Hospitals (C.I. 0771-N-20) and complied the Declaration of Helsinki.
Consent for publication
All authors have approved the manuscript and its publication.
Footnotes
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jinf.2020.11.024.
Appendix. Supplementary materials
References
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