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. 2021 Jul 13;16(7):e0254640. doi: 10.1371/journal.pone.0254640

On-admission SARS-CoV-2 RNAemia as a single potent predictive marker of critical condition development and mortality in COVID-19

Shoji Miki 1, Hiroaki Sasaki 2,*, Hiroshi Horiuchi 2, Nobuyuki Miyata 2, Yukihiro Yoshimura 2, Kazuhito Miyazaki 3, Takayuki Matsumura 4, Yoshimasa Takahashi 4, Tadaki Suzuki 5, Tetsuro Matano 1,6,7, Ai Kawana-Tachikawa 1,6,7, Natsuo Tachikawa 2
Editor: Aleksandar R Zivkovic8
PMCID: PMC8277033  PMID: 34255796

Abstract

Background

This study aimed to clarify how SARS-CoV-2 RNAemia is related to COVID-19 critical condition development and mortality in comparison with other predictive markers and scoring systems.

Methods

This is a retrospective cohort study conducted at Yokohama Municipal Citizen’s Hospital and National Institute of Infectious Diseases. We recruited adult patients with COVID-19 admitted between March 2020 and January 2021. We compared RNAemia with clinical status on admission including scoring systems such as the 4C Mortality, CURB-65, and A-DROP, as well as the Ct value of the nasopharyngeal PCR, in predicting COVID-19 mortality and critical condition development.

Results

Of the 92 recruited patients (median age, 58; interquartile range, 45–71 years), 14 (14.9%) had RNAemia. These patients had an older age (median, 68 years vs. 55.5 years; p = 0.011), higher values of lactated dehydrogenase (median, 381 U/L vs. 256.5 U/L, p < 0.001), C-reactive protein (median, 10.9 mg/dL vs. 3.8 mg/dL; p < 0.001), D-dimer (median, 2.07 μg/mL vs. 1.28 μg/mL; p = 0.015), lower values of lymphocyte (median, 802/μL vs. 1007/μL, p = 0.025) and Ct of the nasopharyngeal PCR assay (median, 20.59 vs. 25.54; p = 0.021) than those without RNAemia. Univariate analysis showed RNAemia was associated with mortality (odds ratio [OR], 18.75; 95% confidence interval [CI], 3.92–89.76; area under the receiver operating characteristic curve [AUC], 0.7851; p = 0.002) and critical condition (OR, 72.00; 95% CI, 12.98–399.29; AUC, 0.8198; p < 0.001). Plus, multivariate analysis also revealed the association of RNAemia with critical condition (adjusted OR, 125.71; 95% CI, 11.47–1377.32; p < 0.001).

Conclusion

On-admission SARS-CoV-2 RNAemia is a potent predictive marker of COVID-19 critical condition and mortality. The adjusted OR for critical condition was as high as 125.71.

Introduction

COVID-19 pandemic is an ongoing global concern. Its clinical features range from an asymptomatic condition to critical diseases such as respiratory failure, shock, and multiple organ failure [1]. Predicting severity before deterioration is crucial for the timely treatment and appropriate management of patients, considering the limited medical resources during this overwhelming pandemic. Several risk factors, including lactated dehydrogenase (LDH), C-reactive protein (CRP), ferritin, and lymphocytopenia, have been proposed [2–4]. In addition, many risk scoring systems, such as the CURB-65 [5, 6], A-DROP [7, 8], and 4C Mortality [9], appear to be useful when predicting a fatal or critical condition but require reevaluation or validation because of the high risk of bias in the studies [10]. Furthermore, a clinically reliable prediction model remains urgently needed. In critical COVID-19 condition, systemic inflammation and multiple organ failure occur. In the analogy of various respiratory viral diseases, viral nucleic acids present in the blood is associated with a worse outcome; for instance, RNAemia of influenza A (H1N1) pdm09 or MERS-CoV infection is reportedly related to disease severity [11, 12]. In fact, RNAemia might play a role in the development of systemic inflammation in COVID-19 [13–16], but the clinical impact remains unclear. Hence, in this study, we analyzed the relationship between SARS-CoV-2 RNAemia and COVID-19 mortality and critical condition development. We also assessed whether RNAemia at the initial evaluation of hospital visit could be used as a predictor of a fatal or critical condition and to compare its utility with that of the preexisting prognostic indicators.

Material and methods

Study design and population

This retrospective cohort study was conducted at Yokohama Municipal Citizen’s Hospital in Kanagawa, Japan, and National Institute of Infectious Diseases in Tokyo, Japan, and was approved by the ethics committee of these institutions. We recruited adult patients with COVID-19 aged ≥18 years who were admitted at the abovementioned hospital between March 2020 and January 2021. COVID-19 was verified by the positivity of the SARS-CoV-2 PCR assay of nasopharyngeal swab samples. Pregnant women were excluded. All eligible patients provided written informed consent for study participation.

Detection of SARS-CoV-2 RNA in plasma and nasopharyngeal swab

Viral RNA was extracted from 140 μL of plasma and eluted with 30 μL of H2O using Viral RNA Extraction Kit (QIAGEN, Hilden, Germany). We utilized 5 μL of the extracted RNA for the real-time RT-PCR of SARS-CoV-2, with the use of the N2 primer/probe set as previously described [17], in duplicate with QuantiTect Probe RT-PCR Kit (QIAGEN) and QuantStudio 5 real-time PCR system (ThermoFisher, Waltham, MA, USA). We repeated the real-time RT-PCR independently; thus, each sample was tested in four wells. If one of the wells demonstrated slope elevation during 45 cycles, the plasma sample was deemed SARS-CoV-2 RNA positive. The lower limit of the standard curve was 62.5 copies/reaction, and the Ct value was almost 40. The SARS-CoV-2 RNA of the nasopharyngeal swab was detected by amplifying E gene with SARS-CoV-2 RT qPCR kit and Roche Light Cycler 480 (Roche, Basel, Switzerland) [18]. If the slope rose during 40 cycles, the sample was considered to be SARS-CoV-2 RNA positive.

Definitions

The plasma samples were collected after confirming the informed consent to this study without delay from admission. Patients with a positive SARS-CoV-2 PCR assay in the plasma sample were categorized as the RNAemia group, whereas those with a negative assay were classified as the negative group. The negative group also included patients who were SARS-CoV-2 PCR negative in the first plasma but turned positive in the follow-up plasma.

The disease severity was defined according to the National Institutes of Health’s Coronavirus Disease 2019 (COVID-19) Treatment Guidelines as follows [19]. Mild illness referred to the presence of signs and symptoms of COVID-19, excluding shortness of breath, dyspnea, or abnormal chest imaging. Having a lower respiratory involvement during clinical assessment or imaging and an oxygen saturation (SpO2) of ≥94% on room air at sea level indicated moderate illness. For severe illness, the manifestation included SpO2 < 94% on room air at sea level, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2/FiO2) < 300 mm Hg, respiratory rate > 30 breaths/min, or lung infiltrates > 50%. Lastly, critical illness was defined as the occurrence of respiratory failure, septic shock, and/or multiple organ dysfunction.

Data collection and statistical analysis

Using the electronic medical records system, we obtained patient data such as the demographic characteristics, the days since the onset, on-admission condition, and clinical data, such as the laboratory data including Ct value of the PCR assay for SARS-CoV-2 E protein with the nasopharyngeal swab sample, disease severity during hospital stay, and in-hospital all-cause mortality. We also calculated the 4C Mortality, CURB-65, and A-DROP scores according to the on-admission condition. We then assessed the association of RNAemia with the on-admission condition and the outcome. All statistical data were analyzed using SAS Institute JMP version 15.2.1. Patients with missing data were excluded in each analysis. The Ct value was considered to be 40 in the statistical analysis, if PCR assay result obtained from the nasopharyngeal sample on admission was negative: those patients were diagnosed as COVID-19 before the admission. The association of RNAemia with demographic characteristics, the days of sampling since the onset, and on-admission clinical data including scoring systems was evaluated using Mann-Whitney U test and two-tailed Fisher’s exact test. We initially employed a univariate logistic regression model to evaluate the effect of each demographic characteristic and on-admission status on both critical condition and mortality. We then employed the multivariate logistic regression analysis for critical condition. Predictive variables were chosen by the forward stepwise method among significant variables in univariate logistic regression models, excluding 4C Mortality, CURB-65, and A-DROP scores because of strong correlation with other individual clinical indicators. Then, to assess the impact of remdesivir use on the outcome of RNAemia group, we analyzed with two-tailed Fisher’s exact test. In those analyses, p < 0.05 indicated statistical significance.

Results

During study period, we forced to cease inclusion because of the shortage of human and facility resources between November 2020 and January 2021. As a result, of the 391 patients admitted, only 92 were eligible for the analysis (median age, 57.5 years; interquartile range, 45–71 years) (Fig 1). All the clinical raw data of the patients are presented in the S1 Table. This study population consisted of 59 males and 33 females, with 14 (15.2%) in the RNAemia group and 78 (84.8%) in the negative group (Table 1).

Fig 1. Enrollment and result of plasma PCR.

Fig 1

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The diagram represents the number of patients admitted to Yokohama Municipal Citizen’s Hospital, patients enrolled into the study, and the result of plasma PCR assay.

Table 1. Association between plasma PCR status and other clinical data.

  Plasma PCR
RNAemia (n = 14) Negative (n = 78) p value
Days between symptoms onset and sampling 8 [6.5–8.5] 5 [2.25–8] 0.052
Demographic characteristics
Sex (number of patients)
    Male 9 50 1.000
    Female 5 28
Age (years; Median [IQR]) 68 [60–81] 55.5 [44–70] 0.011
Laboratory data (Median [IQR])
LDH (U/L) 381 [304.5–502] 256.5 [200.25–327.5] <0.001
CRP (mg/dL) 10.9 [8.1–16.4] 3.8 [0.9–8.1] <0.001
D-dimer (μg/mL) 2.07 [1.48–2.48] 1.28 [0.97–1.96] 0.015
Ferritin (ng/mL) 991.3 [216.3–2040.2] 390.6 [147.0–881.0] 0.109
Absolute lymphocyte count (/μL) 802 [521–1021] 1007 [770–1389] 0.025
Ct value of the nasopharyngeal PCR 20.59 [17.81–21.62] 25.54 [20.94–32.2] 0.021
Risk scoring system (Median [IQR])
4C Mortality Score 11 [10–13] 5 [3–9] <0.001
CURB-65 2 [1–3] 0 [0–1] 0.001
A-DROP 2 [1–3] 0 [0–1] <0.001
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The p value was evaluated using two-tailed Fisher’s exact test and Mann-Whitney U test. Abbreviations: IQR, interquartile range; LDH, lactate dehydrogenase; CRP, C-reactive protein.

There was no significant difference in the days of plasma sampling from the onset of symptoms between two groups. Sex was also not significantly different between two groups. However, the RNAemia group was older than the negative group (median, 68 vs. 55.5 years, p = 0.011). Additionally, compared to the negative group, the RNAemia group had higher LDH (p < 0.001), CRP (p < 0.001), and D-dimer (p = 0.015) values but had a lower absolute lymphocyte count (p = 0.025). The Ct value of the nasopharyngeal PCR was also lower in the RNAemia group than in the negative group (p = 0.021). Ferritin was not significantly different between two groups (p = 0.109). Furthermore, the 4C Mortality (p < 0.001), CURB-65 (p = 0.001), and A-DROP (p < 0.001) scores were higher in the RNAemia group than in the negative group.

Regarding the outcome, the RNAemia group had 12 critical cases including 6 deaths, 1 severe case, 1 moderate case, and no mild cases (Fig 2), whereas the negative group had 6 critical cases including 3 deaths, 24 severe cases, 34 moderate cases, and 14 mild cases. 14 mild cases included two patients whose primary reasons of admission were other than COVID-19: One was drug eruption during chemotherapy for breast cancer and the other was stroke. In one critical but not fatal case in the negative group, plasma SARS-CoV-2 PCR turned positive 7 days after the admission. According to the outcome, RNAemia group accounted for 66.7% of fatal cases (6 out of 9), 66.7% of critical cases (12 out of 18), 4.0% of severe cases (1 out of 25), and 2.9% of moderate cases (1 out of 35). All the mild cases were found in the negative group.

Fig 2. Outcome of the patients in each group.

Fig 2

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In the RNAemia group, 85.7% (twelve out of 14) were critical cases, including fatal cases, which accouted for 42.9% (six out of 14). In contrast, in the negative group, critical cases accouted for only 7.7% (six out of 78) and 3.8% (three out of 78) of the patients who died.

By the univariate analysis, RNAemia group was associated with a higher mortality (odds ratio [OR], 18.75; 95% CI, 3.92–89.76; area under the receiver operating characteristic curve [AUC], 0.7851; p < 0.001) and critical condition development (OR, 72.00; 95% CI, 12.98–399.29; AUC, 0.8198; p < 0.001) (Table 2). Additionally, mortality was significantly associated with female sex (OR, 4.1481; 95% CI, 0.9633–17.8618; AUC, 0.6707; p = 0.048), older age (OR, 1.1835; 95% CI, 1.0695–1.3097; AUC, 0.9418; p < 0.001), high D-dimer level (OR, 1.5525; 95% CI, 1.0707–2.2509; AUC, 0.8889; p < 0.001), and low Ct value of the nasopharyngeal PCR (OR, 0.6769; 95% CI, 0.5027–0.9114; AUC, 0.8656; p < 0.001). Meanwhile, critical condition was significantly associated with older age (OR, 1.0853; 95% CI, 1.0410–1.1315; AUC, 0.8247; p < 0.001), higher LDH level (OR, 1.0091; 95% CI, 1.0039–1.0143; AUC, 0.7508; p < 0.001), higher CRP level (OR, 1.1162; 95% CI, 1.0292–1.2107; AUC, 0.7455; p = 0.007), higher D-dimer level (OR, 1.3244; 95% CI, 0.9882–1.7748; AUC, 0.7734; p = 0.004), and lower Ct value of the nasopharyngeal PCR (OR, 0.6698; 95% CI, 0.5080–0.8833, AUC, 0.8849; p < 0.001). In all the critical cases, the Ct value of the nasopharyngeal PCR was <22. While all the patients with a Ct value of <22 were critical in the viraemia group, only three out of 16 patients (18.8%) in the negative group were critical (Fig 3). Regarding the scoring systems, a higher rate was significantly associated with a higher mortality, with OR values of 1.7441 (95% CI, 1.2694–2.3964; AUC, 0.9532; p < 0.001), 4.5482 (95% CI, 2.0303–10.1889; AUC, 0.9244; p < 0.001), and 4.4331 (95% CI, 2.0729–9.4803; AUC, 0.9351; p < 0.001) for the 4C Mortality, CURB-65, and A-DROP scores, respectively. A higher score rate was also significantly associated with critical condition development, with OR values of 1.5507 (95% CI, 1.2601–1.9069; AUC, 0.8983; p < 0.001), 3.6584 (95% CI, 2.0649–6.4816; AUC, 0.8465; p < 0.001), and 3.7778 (95% CI, 2.1474–6.6462; AUC, 0.8904; p < 0.001) for the 4C Mortality, CURB-65, and A-DROP scores, respectively. By forward stepwise regression analysis, RNAemia, age, and D-dimer was chosen as an dependent variable for multivariate regression analysis for critical condition, which showed that RNAemia was most significant variable (adjusted OR, 125.71; 95% CI, 11.47–1377.32; p < 0.001) (Table 3).

Table 2. Univariate logistic regression analysis of each factor associated with mortality and critical condition.

On admission status Mortality Critical condition
OR AUC p value OR AUC p value
(95% CI) (95% CI)
Plasma PCR 18.75 0.7851 0.002 72.0000 0.8198 <0.001
(positive to negative) (3.9168–89.7584) (12.9830–399.2905)
Sex (female to male) 4.1481 0.6707 0.048 1.5680 0.5533 0.403
(0.9633–17.8618) (0.5503–4.4677)
Age (years) 1.1835 0.9412 <0.001 1.0853 0.8247 <0.001
(1.0695–1.3097) (1.0410–1.1315)
LDH (U/L) 1.0049 0.6633 0.098 1.0091 0.7508 <0.001
(0.9992–1.0107) (1.0039–1.0143)
CRP (mg/dL) 1.0697 0.6968 0.196 1.1162 0.7455 0.007
(0.9686–1.1814) (1.0292–1.2107)
D-dimer (μg/mL) 1.5524 0.8889 <0.001 1.3244 0.7734 0.004
(1.0707–2.2509) (0.9882–1.7748)
Ferritin (ng/mL) 1.0002 0.5890 0.664 1.0006 0.6343 0.036
(0.9994–1.0009) (1.0000–1.0012)
Absolute lymphocyte count (/μL) 0.9987 0.6827 0.108 0.9990 0.6201 0.094
(0.9970–1.0004) (0.9978–1.0002)
Ct value of the
nasopharyngeal PCR		 0.6767 0.8656 <0.001 0.6698 0.8849 <0.001
(0.5029–0.9106) (0.5080–0.8833)
4C Mortality Score 1.7441 0.9532 <0.001 1.5507 0.8983 <0.001
(1.2694–2.3964) (1.2610–1.9069)
CURB-65 4.5482 0.9244 <0.001 3.6584 0.8465 <0.001
(2.0303–10.1889) (2.0649–6.4816)
A-DROP 4.4331 0.9351 <0.001 3.7778 0.8904 <0.001
(2.0729–9.4803) (2.1474–6.6462)
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Abbreviations: OR, odds ratio; CI, confidence interval; AUC, area under the receiver operating characteristic curve; LDH, lactate dehydrogenase; CRP, C-reactive protein.

Fig 3. Distribution of Ct value of the nasopharyngeal PCR in each group.

Fig 3

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Each red rhombus represents critical cases including fatal cases, whereas each blue circle represents mild to severe cases. If nasopharyngeal PCR was negative, Ct value is represented as 40 in this figure. The median of Ct value of the nasopharyngeal PCR was significantly lower in RNAemia group than that in the negative group. While all the patients whose Ct value of nasopharyngeal PCR was <22 (represented as horizontal blue line) were critical in the RNAemia group, only three out of 16 those patients (18.8%) in negative group were critical.

Table 3. Mulivariate logistic regression analysis for the critical condition.

On admission status adjusted OR (95% CI) p value
Plasma PCR (positive to negative) 125.71 (11.47–1377.32) <0.001
Age (years) 1.1092 (1.0256–1.1997) 0.001
D-dimer (μg/mL) 1.1137 (0.7855–1.5791) 0.492
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Of the 14 patients in RNAemia group, two (50.0%) out of four patients who were treated with remdesivir died, while four (40.0%) out of ten who were treated without remdesivir died, but the mortality was not affected with the remdesivir use (p = 1.000).

Variables were chosen by the forward stepwise method among significant variables in univariate logistic regression models, excluding 4C Mortality, CURB-65, and A-DROP scores. Abbreviation: OR, odds ratio; CI, confidence interval.

Discussion

This study demonstrated that SARS-CoV-2 RNAemia is a potent predictive marker of mortality and critical condition development in COVID-19. Although multiple clinical markers are significant by univariate logistic regression analysis, only three–RNAemia, age, and D-dimer–were left with the stepwise regression analysis and RNAemia was most significant. The adjusted OR for critical condition was as high as 125.71.

COVID-19 symptoms range from an asymptomatic condition to severe pneumonia with respiratory failure, shock, and multiple organ failure [1]. As mentioned, predicting severity during the initial hospital visit is vital to promptly decide on the therapeutic intervention for each individual patient to prevent deterioration as well as on the appropriate medical resource management during this pandemic. To predict mortality risk, researchers have been using several scoring systems, such as the CURB-65, A-DROP, and 4C Mortality [6, 8, 9]. These scoring systems were useful in our study, consistent with previous studies. SARS-CoV-2 RNAemia, although single laboratory entity, was comparable with these scoring systems in predicting critical condition development in terms of high AUCs and ORs. Our study analyzed the Ct value of the SARS-CoV-2 PCR from the nasopharyngeal swab sample because it is the gold-standard virological assay in diagnosing COVID-19. High viral load in the upper respiratory tract might cause RNAemia. Indeed, unlike previous study [16], the low Ct value of the nasopharyngeal PCR was associated with RNAemia as well as fatal/critical condition. However, only few of our patients with relatively low nasopharyngeal Ct values developed RNAemia and critical condition; thus, nasopharyngeal PCR could not replace plasma PCR in precisely recognizing RNAemia and predicting subsequent critical condition. In this study, we assessed RNAemia as an on-admission indicator of later disease development, and only RNAemia at the first plasma PCR was considered. In one critical case, however, plasma viral PCR turned positive after the initial plasma PCR assay, suggesting the importance of follow-up of plasma PCR during hospital stay. This issue remained to be dealt with, however, since we did not follow plasma PCR in all cases.

SARS-CoV-2 RNAemia and its association with the severity of COVID-19 has been reported in China, Spain, the United States, and Norway [13–16]. Consistent with previous reports, our study revealed that RNAemia is associated with a worse outcome among Japanese patients, possibly reflecting that this inclination is universal among various ethnicities. This report is the first to clarify the usefulness of RNAemia as a prognostic marker by directly comparing it with other clinical markers including the Ct value of the nasopharyngeal PCR and the established scoring systems among the same patient group.

Our study has limitations. One major limitation is the small sample size, resulting in extremely wide confidence intervals of the odds ratios reported in the analysis, so that our quantitative result should be dealt with cautiously. The inadequate number of cases also restricted subgroup analysis. Our cases included various demographic and clinical characteristics, without considering important classifications such as specific age groups and immunocompromised patients. Pregnant women were also excluded in our study. In addition, most of the included ethnicities were Japanese, so other ethnic groups were not considered; nevertheless, our results are consistent with previous reports focusing on other ethnicities, as previously described. Therefore, a large-scale multicenter, multinational study is needed to deal with these limitations.

Another limitation is the selection bias. We analyzed inpatients only; hence, many asymptomatic and mild cases were not evaluated. Furthermore, only 92 out of 391 inpatients were included in this study because of the cease of inclusion between November 2020 and January 2021, which marked the largest surge of COVID-19 cases in our community, due to the limited human and facility resources. Thus, certain selection bias could not be eliminated, although we had no intentions to include any specific condition.

Our study also lacks the adequate consideration of the treatment provided. RECOVERY trial is regarded as a breakthrough because it revealed the benefit of suppressing the inflammatory and immune reactions in severe and critical COVID-19 cases [20]. The cytotoxicity of SARS-CoV-2 is not so prominent compared with that of SARS-CoV-1 [21]; this finding explains the major cause of having a large population of asymptomatic patients, making the infection control of COVID-19 even more challenging worldwide. The pathophysiology of critical COVID-19 elicits an inappropriate response of the inflammatory and immune systems to the viral infection rather than the viral toxicity. Thus, immediate detection of the start of an inappropriate reaction is crucial. Based on the correlation of RNAemia and severity we herein revealed, we could pose a hypothesis that the invasion of a viral portion and/or the virus itself into the bloodstream could trigger such inappropriate inflammatory and immune reactions. If so, compared with other clinical indicators adopted in several scoring systems, RNAemia is the direct predictive marker of critical condition development. Antiviral treatments such as remdesivir and convalescent plasma have failed to demonstrate clear and major benefits in patients with COVID-19 overall but have appeared to be beneficial under certain conditions. For example, in ACTT-1 [22], which is a multinational, randomized, placebo-controlled trial, remdesivir reduced the time to recovery only in patients who were on low oxygen flow at baseline. Among critical patients who were on mechanical ventilation or extracorporeal membrane oxygenation at baseline, the time to recovery was similar to that of those with remdesivir therapy and placebo. Moreover, convalescent plasma showed no significant differences in clinical status or overall mortality between patients with COVID-19 overall [23]; however, when high-titer convalescent plasma against SARS-CoV-2 was early administered to mildly ill infected older adults, COVID-19 progression mitigated [24]. Given that such treatments are intended to reduce the virus itself rather than the host response, RNAemia might be the indicator necessary to make clinical decisions for initiating antiviral treatment. We failed to prove the superiority of remdesivir, probably because of too small number of cases with RNAemia. Further studies are warranted to verify our proposition.

Conclusion

On-admission SARS-CoV-2 RNAemia is a potent predictive marker of mortality and critical condition development in hospitalized COVID-19 cases. The adjusted OR for critical condition was as high as 125.71.

Supporting information

S1 Table. All clinical data of the patients who were included in the present study.

Plasma PCR of patient No. 18 in the negative group turned positive on day seven. Abbreviation: No, number; CCI, Charlson Comorbidity Index; RR, respiratory rate; SBP, systolic blood pressure; DBP diastolic blood pressure; GCS, Glasgow Coma Scale; LDH, lactate dehydrogenase; BUN, blood urea nitrogen; CRP, C-reactive protein; ND, no data.

(XLSX)

Click here for additional data file. (19.8KB, xlsx)

Acknowledgments

We thank Sachie Daigen for technical assistance.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This work was supported by the Japan Agency for Medical Research and Development (AMED) under Grant Numbers JP19fk0108104j0801.

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Decision Letter 0

Aleksandar R Zivkovic

14 May 2021

PONE-D-21-12753

On-admission SARS-CoV-2 viraemia as a single potent predictive marker of critical condition development and mortality in COVID-19

PLOS ONE

Dear Dr. Sasaki,

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PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer #1: The authors present an observational cohort study of patients admitted to a hospital in Japan with COVID-19, relating biological and clinical parameters at admission to disease severity/mortality. The authors suggest that SARS-CoV-2 RT-PCR in plasma is a useful marker to predict a severe clinical course and in-hospital mortality, and potentially identify patients for antiviral therapy.

I have several comments:

1. As the authors state, the study is quite small, including 92 out of 391 COVID-19 inpatients during the inclusion period of 8 months. The authors do acknowledge the potential for selection bias – more information about why some patients were included and not others would be useful to evaluate this risk further. The manuscript cites human resources, while the flow chart cites a lack of consent. This should be clarified. With regards to systematic bias, do the authors have any data about patients not included in the study?

2. 14 out of 92 included patients had mild disease, and thus should normally not require hospitalisation. Was COVID-19 the cause of admission in all patients, or were some patients admitted for other diagnoses?

3. The authors give all data as median (25th/75th percentile), as for non-normal data, but the group comparisons are t-tests, which are generally used for normally-distributed data. Would Mann-Whitney tests be more suitable/robust?

4. There is no doubt that plasma SARS-CoV-2 RNA is strongly associated with severe disease and mortality in the presented data. However, evaluating the relative strengths of different predictors by directly comparing the odds ratios for mortality/critical illness doesn’t make sense – the OR for a binary classifier such as viremia will be much higher than for a continuous variable such as age or 4C Mortality score. Comparing the area under the ROC curve could be a better way of comparing the predictors with each other.

5. COVID-19 is a dynamic disease with viral and inflammatory phases. It would be useful to know the time from symptom onset to sampling.

6. The authors state that the first plasma sample after admission was analysed. Were these samples from the emergency department? First 24h of admission?

7. A number of patients have missing data for upper respiratory RNA measurements. Were they diagnosed with COVID-19 in another sample/before hospitalisation?

8. In the discussion the authors state that theirs is the first study to compare plasma RNA with upper respiratory CT value as a prognostic marker in SARS-CoV-2. This is inaccurate, Prebensen et al. published a study in Clinical Infectious Diseases in 2020, finding a strong prognostic value of plasma RNA but not nasopharyngeal RNA.

9. On lines 252-3 the authors seem to state that viral RNA in the circulation is the cause of inappropriate inflammation in COVID-19. Correlation is not causation, and I would suggest that the authors rephrase this, as the statement is not adequately supported by their data.

10. Finally, there are a number of typographical errors in the manuscript/tables, and I would recommend a thorough proof-reading before resubmission.

Reviewer #2: This is a useful study that supports other studies of a similar kind. For example, https://www.medrxiv.org/content/10.1101/2021.02.24.21252357v1

I have the following specific comments about the study:

1. The sample size is modest and incidence of viremia small. This is confirmed by the extremely wide confidence intervals of the odds ratios reported in the univariate logistic regression. This is a major limitation and makes this study primarily a hypothesis generating one rather than confirmatory.

2. The authors do not create a multivariate regression model (? cox proportional hazards or alternates). Careful selection of variables to create this model would be crucial to identify the additive value of measuring SARS CoV-2 plasma PCR in clinical practice.

3. What proportion of patients were initially negative by plasma PCR and later turned positive?

4. The lack of inclusion of therapeutic modalities is a major limitation as acknowledged by the authors? Is therapeutic data not available? It would be particularly interesting to see the impact of remdesivir on outcomes in the patients with viremia.

**********

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Reviewer #1: Yes: Christian Prebensen

Reviewer #2: No

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PLoS One. 2021 Jul 13;16(7):e0254640. doi: 10.1371/journal.pone.0254640.r002

Author response to Decision Letter 0

29 Jun 2021

Response to reviewers

Thank you for the thoughtful and constructive feedback you provided regarding our manuscript, PONE-D-21-12753, “On-admission SARS-CoV-2 viraemia as a single potent predictive marker of critical condition development and mortality in COVID-19.”

We agree with your suggestions to revision of our manuscript and we have amended this to addresses the points from the reviewers, as well as based on our contemplation in order to better conform with the formatting and content rules of PLOS ONE.

With these changes to our final manuscript, we hereby resubmit our manuscript for a secondary evaluation. Thank you once again for your consideration of our paper. We would be glad to respond to any further questions and comments that you may have.

Change to the authorship

We added Takayuki Matsumura as an author, because of his contribution to our research by involving laboratory procedures.

Change to terminology

We changed the term “viraemia” to “RNAemia” in accordance with previous studies we cited in the manuscript. That’s because we have believed that the term “RNAemia” would be more precise, given that we did not isolate virus itself from blood i.e., viraemia but just showed the existence of RNA in blood.

Change to the reference list

We added reference No. 16, as mentioned below in the Response to Reviewer #1.

Responses to Reviewer #1

Thank you for providing for constructive insights.

Our responses to each comment are as follows.

1. As the authors state, the study is quite small, including 92 out of 391 COVID-19 inpatients during the inclusion period of 8 months. The authors do acknowledge the potential for selection bias – more information about why some patients were included and not others would be useful to evaluate this risk further. The manuscript cites human resources, while the flow chart cites a lack of consent. This should be clarified. With regards to systematic bias, do the authors have any data about patients not included in the study?

Response & Changes: Most of the excluded inpatients were hospitalized between November 2020 and January 2021, which marked the largest surge of COVID-19 patients in our community, that’s because we ceased inclusion to this study because of human and facility (mainly inadequate space of specimen storage) resources during the period. We clarified this pause of inclusion in the Figure 1 and manuscript lines 136–137, 271–273.

2. 14 out of 92 included patients had mild disease, and thus should normally not require hospitalisation. Was COVID-19 the cause of admission in all patients, or were some patients admitted for other diagnoses?

Response & Changes: The reason of admission of mild patients were miscellaneous. 2 out of 14 mild patients was admitted due to other diagnosis (drug eruption and stroke). We clarify this in the manuscript in lines 163–165. 6 mild cases were hospitalized because of relatively intense symptoms e.g., shortness of breath, general malaise, or decreased appetite, although categorized “mild” based on the NIH criteria. The others were admitted according to the government instructions at the given time for the purpose of quarantine.

3. The authors give all data as median (25th/75th percentile), as for non-normal data, but the group comparisons are t-tests, which are generally used for normally-distributed data. Would Mann-Whitney tests be more suitable/robust?

Response & Changes: We accepted the suggestion, reanalyzed, and revised the manuscript.

4. There is no doubt that plasma SARS-CoV-2 RNA is strongly associated with severe disease and mortality in the presented data. However, evaluating the relative strengths of different predictors by directly comparing the odds ratios for mortality/critical illness doesn’t make sense – the OR for a binary classifier such as viremia will be much higher than for a continuous variable such as age or 4C Mortality score. Comparing the area under the ROC curve could be a better way of comparing the predictors with each other.

Response & Changes: We added Area under the ROC curve as well as multivariate logistic regression analysis, to verify the superiority of plasma PCR to other predictors for the prediction of critical condition. However, AUC of the scoring systems such as 4C Mortality Score were higher than that of plasma PCR, so we changed the expression of superiority to comparable although it is single marker in lines 239–240.

5. COVID-19 is a dynamic disease with viral and inflammatory phases. It would be useful to know the time from symptom onset to sampling.

Response & Changes: We included the interval between onset and sampling, revealing there were no significant difference between two groups. We incorporated those findings into our manuscript in lines 152–153.

6. The authors state that the first plasma sample after admission was analysed. Were these samples from the emergency department? First 24h of admission?

Response & Changes: To be accurate, plasma samples used in this study were obtained after confirming the informed consent to this study. We revised the manuscript to clarify this in lines 98–99. As for most of the patients, the sample was obtained within two days.

7. A number of patients have missing data for upper respiratory RNA measurements. Were they diagnosed with COVID-19 in another sample/before hospitalisation?

Response & Changes: Those patients were also diagnosed with the positive PCR status from nasopharyngeal swab sample, but it was with qualitative results and Ct values are not available.

8. In the discussion the authors state that theirs is the first study to compare plasma RNA with upper respiratory CT value as a prognostic marker in SARS-CoV-2. This is inaccurate, Prebensen et al. published a study in Clinical Infectious Diseases in 2020, finding a strong prognostic value of plasma RNA but not nasopharyngeal RNA.

Response & Changes: Thank you for pointing out, we added this article as reference #16 and incorporated the comparison with previous study with regards to Ct value of the upper respiratory sample in line 244.

9. On lines 252-3 the authors seem to state that viral RNA in the circulation is the cause of inappropriate inflammation in COVID-19. Correlation is not causation, and I would suggest that the authors rephrase this, as the statement is not adequately supported by their data.

Response & Changes: We accepted the suggestion and clarified that the causation is nothing but a hypothesis in lines 238–284.

10. Finally, there are a number of typographical errors in the manuscript/tables, and I would recommend a thorough proof-reading before resubmission.

Response & Changes: Thank you for your recommendation.

Responses to Reviewer #2

Thank you for providing for constructive insights. As you pointed out, our study was consistent with several reports, and we cited several published articles in the manuscript.

1. The sample size is modest and incidence of viremia small. This is confirmed by the extremely wide confidence intervals of the odds ratios reported in the univariate logistic regression. This is a major limitation and makes this study primarily a hypothesis generating one rather than confirmatory.

Response & Changes: We accepted and incorporated this into the manuscript in line 260–262.

2. The authors do not create a multivariate regression model (? cox proportional hazards or alternates). Careful selection of variables to create this model would be crucial to identify the additive value of measuring SARS CoV-2 plasma PCR in clinical practice.

Response & Changes: We created the multivariate regression model adopting variables by the forward stepwise method among significant variables in univariate logistic regression models and showed statistically significant impact of plasma PCR and age on the outcome.

3. What proportion of patients were initially negative by plasma PCR and later turned positive?

Response & Changes: In one critical case, we recognized follow-up plasma PCR turned positive, but not all the patients’ plasma RNAemia were followed. We mentioned this in line 252.

4. The lack of inclusion of therapeutic modalities is a major limitation as acknowledged by the authors? Is therapeutic data not available? It would be particularly interesting to see the impact of remdesivir on outcomes in the patients with viremia.

Response & Changes: We evaluated the effect of remdesivir on mortality in RNAemia group with Fisher’s exact test, failed to reveal significant difference, apparently due to extremely small number of patients. We incorporated this into our manuscript.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (23.3KB, docx)

Decision Letter 1

Aleksandar R Zivkovic

1 Jul 2021

On-admission SARS-CoV-2 RNAemia as a single potent predictive marker of critical condition development and mortality in COVID-19

PONE-D-21-12753R1

Dear Dr. Sasaki,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Aleksandar R. Zivkovic

Academic Editor

PLOS ONE

Acceptance letter

Aleksandar R Zivkovic

5 Jul 2021

PONE-D-21-12753R1

On-admission SARS-CoV-2 RNAemia as a single potent predictive marker of critical condition development and mortality in COVID-19

Dear Dr. Sasaki:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Aleksandar R. Zivkovic

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. All clinical data of the patients who were included in the present study.

    Plasma PCR of patient No. 18 in the negative group turned positive on day seven. Abbreviation: No, number; CCI, Charlson Comorbidity Index; RR, respiratory rate; SBP, systolic blood pressure; DBP diastolic blood pressure; GCS, Glasgow Coma Scale; LDH, lactate dehydrogenase; BUN, blood urea nitrogen; CRP, C-reactive protein; ND, no data.

    (XLSX)

    Click here for additional data file. (19.8KB, xlsx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (23.3KB, docx)

    Data Availability Statement

    All relevant data are within the paper and its Supporting Information files.

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