key: cord-0748680-go1pn0k7 authors: Ram-Mohan, Nikhil; Kim, David; Zudock, Elizabeth J; Hashemi, Marjan M; Tjandra, Kristel C; Rogers, Angela J; Blish, Catherine A; Nadeau, Kari C; Newberry, Jennifer A; Quinn, James V; O’Hara, Ruth; Ashley, Euan; Nguyen, Hien; Jiang, Lingxia; Hung, Paul; Blomkalns, Andra L; Yang, Samuel title: SARS-CoV-2 RNAemia predicts clinical deterioration and extrapulmonary complications from COVID-19 date: 2021-05-05 journal: Clin Infect Dis DOI: 10.1093/cid/ciab394 sha: 2c7d99430e68bf8d6f2a8e59b082255eb1e38976 doc_id: 748680 cord_uid: go1pn0k7 BACKGROUND: The determinants of COVID-19 disease severity and extrapulmonary complications (EPCs) are poorly understood. We characterized relationships between SARS-CoV-2 RNAemia and disease severity, clinical deterioration, and specific EPCs. METHODS: We used quantitative (qPCR) and digital (dPCR) PCR to quantify SARS-CoV-2 RNA from plasma in 191 patients presenting to the Emergency Department (ED) with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterized the role of RNAemia in predicting clinical severity and EPCs using elastic net regression. RESULTS: 23.0% (44/191) of SARS-CoV-2 positive patients had viral RNA detected in plasma by dPCR, compared to 1.4% (2/147) by qPCR. Most patients with serial measurements had undetectable RNAemia within 10 days of symptom onset, reached maximum clinical severity within 16 days, and symptom resolution within 33 days. Initially RNAaemic patients were more likely to manifest severe disease (OR 6.72 [95% CI, 2.45 – 19.79]), worsening of disease severity (OR 2.43 [95% CI, 1.07 – 5.38]), and EPCs (OR 2.81 [95% CI, 1.26 – 6.36]). RNA load correlated with maximum severity (r = 0.47 [95% CI, 0.20 – 0.67]). CONCLUSIONS: dPCR is more sensitive than qPCR for the detection of SARS-CoV-2 RNAemia, which is a robust predictor of eventual COVID-19 severity and oxygen requirements, as well as EPCs. Since many COVID-19 therapies are initiated on the basis of oxygen requirements, RNAemia on presentation might serve to direct early initiation of appropriate therapies for the patients most likely to deteriorate. As of April 2021, SARS-CoV-2 has caused over 136 million infections and 2.9 million deaths [1] . Wide variation in clinical trajectories of COVID-19 poses challenges for clinicians seeking to identify patients at risk for deterioration. While COVID-19 often manifests as a viral pneumonia, extrapulmonary complications (EPCs) can produce more severe and recalcitrant disease [2, 3] . SARS-CoV-2, typically isolated from nasopharyngeal (NP) samples, has been detected in lower titers in whole blood [4] [5] [6] , serum [6] [7] [8] [9] [10] , plasma [11] [12] [13] [14] , and stool [6, 10, 15] . Histopathological surveys have identified the virus in myocardial [16, 17] , renal [16, 18] , gastrointestinal [19] , and neurological tissues [16] . Is hematogenous spread of the virus or viral components associated with EPCs? The clinical and pathophysiological significance of SARS-CoV-2 RNA in blood remains poorly understood. SARS-CoV-2 RNAemia, detected with quantitative reverse transcriptase real-time PCR (qPCR), has been correlated with severity of COVID-19 [12, 14, [20] [21] [22] [23] [24] . However, rates of RNAemia detected by qPCR have ranged from 0% -41% [6] [7] [8] [9] [10] [11] [12] [20] [21] [22] 24, 25] . qPCR lacks the precision to measure low viral loads [26] , and its sensitivity to protocol and threshold decisions hinders comparison across studies. Digital PCR (dPCR) has superior sensitivity, precision, and reproducibility, allowing absolute quantification of RNA without standard curves. Given its tolerance to inhibitors, dPCR facilitates detecting dilute targets in blood [27] . dPCR studies have accordingly reported higher rates of RNAemia (42.4% and 74.1%), and have linked RNAemia to disease severity [13, 14] and dysregulated host response [14] . M a n u s c r i p t 5 In this prospective, longitudinal, observational study of COVID-19 patients presenting to the Emergency Department (ED), we characterized relationships between SARS-CoV-2 RNAemia and clinical severity, future deterioration, and specific EPCs. We used array-based dPCR to maximize reliability and replicability, and to simplify potential clinical adoption of RNAemia testing. Beginning in April 2020, we collected blood and NP swabs from patients enrolled in the Stanford University COVID-19 Biobank. Eligible patients were 18 years or older, with a positive RT-PCR SARS-CoV-2 NP swab on initial ED presentation. We repeated blood draws on one or more of days three, seven and 30 in hospitalized patients, and asked discharged patients to return for repeat blood draws. We collected blood in ethylenediaminetetraacetic acid-chelated vacutainers (Becton, Dickinson, and Co.), and stored plasma at -80°C after 1200g centrifugation for ten minutes at 25°C. We collected NP swabs in 1mL of RNA Shield Stabilizing Solution (Zymo Research) and stored them at -80°C. We processed samples under biosafety level 2+ precautions (Stanford University APB- A c c e p t e d M a n u s c r i p t 6 SARS-CoV-2 RNA extraction We extracted RNA from research NP swabs and plasma using the QIAamp Viral RNA Mini Kit and QIACube Connect (QIAGEN). We extracted from 140µL of sample solution, and eluted RNA in 50µL of elution buffer after manual lysing for 10 minutes. Multiplexed qPCR and dPCR reactions included extracted RNA, |Q| Triplex Assay (Combinati), and 4x RT-dPCR MM (Combinati). The |Q| Triplex Assay (Combinati) included primers and probes targeting N1 and N2 regions of the nucleoprotein gene, and the human ribonuclease P gene (RP). We divided the reactions as follows: 10µL for qPCR using the QuantStudio 5 (Applied Biosystems by Thermo Fisher Scientific) and 9µL for dPCR using the array-based |Q| (Combinati) (Supplementary Materials). We considered qPCR specimens positive if cycle threshold (Ct) for RP, N1, and N2 were <40. For positive samples, we used the lesser of N1/N2 Ct for quantitative analysis. dPCR samples were positive if N1 and N2 concentrations were ≥0.23 copies/µL, with the greater of the two used for quantitative analysis. We set negative qPCR results to 40, and negative dPCR results to zero. We repeated dPCR for inconclusive (N1 or N2 <0.23 copies/µL) and low results (N1 and N2 <1 copy/µL), and used the results from the repeated tests. We calculated pairwise Pearson's correlations between measures of qPCR Ct and log-transformed RNA concentrations from dPCR. A c c e p t e d M a n u s c r i p t We calculated maximum severity scores for initially RNAemic and non-RNAemic patients, and compared mean scores with a two-sample t-test. Among RNAemic patients, we calculated correlations between log-transformed RNA concentration and maximum severity (WHO 1-8). We calculated proportions of RNAemic and non-RNAemic patients who manifested mild (WHO 1-2), moderate (WHO 3-4), and severe (WHO 5-8) disease, who were hospitalized, who manifested EPCs, and who worsened after presentation (maximum WHO score > score at enrollment), using chisquared tests with continuity corrections. We calculated the odds ratio for deterioration, by RNAemia on enrollment (for consistency with logistic regression results), and calculated exact 95% confidence intervals [28] . We compared median length-of-hospitalization in days, and median A c c e p t e d M a n u s c r i p t 9 degree of clinical worsening (difference between initial and maximum WHO score), for RNAemic and non-RNAemic patients, using the Wilcoxon rank-sum test with continuity correction. Predictive models for severe disease, EPCs, and RNAemia We developed a predictive model for severe (WHO 5-8) disease, using as potential predictors all data available upon ED presentation: demographic features, comorbidities, binary indicators of abnormal vital signs, pneumonia on chest imaging, patient-reported symptoms, and abnormal lab values. Because therapies such as remdesivir and dexamethasone were generally initiated based on oxygen requirements (the main component of the severity score), we excluded specific treatments from our models. To prevent overfitting due to the large number of potential predictors, we selected variables via elastic net regularization (glmnet 4.0 in R), using logistic models and 10-fold cross-validation, selecting the regularization parameter λ minimizing mean cross-validated error. We used the selected variables in a logistic model, and estimated odds ratios and 95% CIs for prediction of severe disease. We calculated mean cross-validated area under the receiver-operating characteristic curve (AUROC) of the resulting model. We predicted development of EPCs in analogous fashion, excluding symptoms and laboratory markers potentially constitutive of EPCs. We again selected variables via cross-validated elastic-net regularization, estimated odds ratios for the most robust predictors, and characterized overall predictive accuracy by AUROC. A c c e p t e d M a n u s c r i p t 10 We predicted the presence of RNAemia in analogous fashion, using as potential predictors demographic features, comorbidities, and symptoms, but excluding radiographic and laboratory findings, to approximate the predictability of RNAemia upon initial presentation. We enrolled 191 COVID-19 positive ED patients and sampled their plasma on the day of enrollment (day zero). Some patients had additional NP or plasma samples collected at one or more of days: three, seven, 30. 49.2% (94/191) of participants were women. Median age was 47 years (IQR 34-61). Patients had a median of one comorbidity (IQR 0-3), and four (IQR 2-6) symptoms. Patient characteristics at enrollment are summarized in Table 2 . SARS-CoV-2 RNA prevalence by sample type, method, and day of collection dPCR was more sensitive than qPCR, detecting RNAemia in 23 We observed a modest negative correlation (r = -0.30) between qPCR Ct values and dPCR RNA concentrations from the same plasma specimens ( Figure S1 ). Notably, NP and plasma dPCR values for 48 paired specimens were weakly correlated (r = 0.16). Plasma RNA by dPCR on day zero was moderately correlated with concentrations on day 3/7 (r = 0.42). (Figure 4) , with statistically significant differences in rates of hepatobiliary, hematologic, and immunologic complications (p < 0.05, chi-squared tests for equality of proportions with continuity correction). Table S1 shows specific comorbidities, EPCs, SARS-CoV-2 RNA concentrations, and severity scores for all patients RNAemic at enrollment. In an elastic-net regularized, cross-validated logistic model, significant predictors for the development of one or more EPCs were: chronic kidney disease, obesity, and RNAemia (OR 2.81 [95% CI, 1.26 -6.36]). The overall predictive performance of the model was fair, with mean crossvalidated AUROC of 0.73 (Table 4 ). We predicted RNAemia on presentation based on patient demographics, comorbidities, symptoms, and ED vital signs. In an analogous model (Table S2) The pathogenesis of COVID-19, its temporal dynamics, and the determinants of disease severity and EPCs are incompletely understood. We explored the performance and clinical utility of dPCR in quantifying SARS-CoV-2 RNA in the nasopharynx and plasma, and characterized the relationships between RNAemia and disease severity, clinical deterioration, and EPCs. Array-based dPCR was much more sensitive than qPCR for the detection of SARS-CoV-2 in plasma, where mean concentration of viral RNA was three orders of magnitude less than in the nasopharynx. RNAemia manifests early in the course of illness, while clinical manifestations peak later and are more prolonged. RNAemia at presentation predicts severe disease, ongoing clinical deterioration, and specific EPCs. We found dPCR to be markedly more sensitive than qPCR, even with more stringent detection criteria (N1 and N2 ≥0.23 copies/µL) than other studies (e.g., N1 or N2 ≥0.1 copies/µL) [14] . dPCR was also more consistent in multiplex detection of both targets (N1 and N2), likely because dPCR's partition format reduces preferential amplifications observed in bulk PCR [29] . Moreover, a microwell array dPCR platform enhances partition consistency, improves sensitivity by minimizing dead volume, and has a qPCR-like workflow well-suited to clinical adoption. Early in an outbreak, or upon emergence of novel variants, viral RNA standard curves are not widely available, making dPCR a natural choice for detecting novel pathogens. RNAemia on presentation was a robust predictor of both severe disease and EPCs, after accounting for demographics, comorbidities, symptoms, vital signs, and a host of laboratory markers. Moreover, RNAemic patients were more likely than non-RNAemic patients to worsen after presentation, and A c c e p t e d M a n u s c r i p t 15 worsened by a greater degree. Previous studies have associated RNAemia with disease severity and mortality [12, 14, 23, 24] , but reported associations with EPCs are more varied [2, 30, 31] . We included a more comprehensive scope of potential confounders than previous studies [14] . We also use crossvalidation not only for model selection, but to assess the relative predictability of clinical severity (good, AUROC 0.82), EPCs (fair, AUROC 0.73), and RNAemia itself (poor, AUROC 0.66). The poor predictability of RNAemia from patient features at presentation, and the weak correlations between NP and plasma RNA concentrations, suggest that RNAemia is not simply a consequence of sufficient viral load at the typical site of inoculation (which may also be subject to greater variation in sample quality), but may instead signal unique pathophysiologic and prognostic features [10, 32, 33] . The etiology of SARS-CoV-2 RNAemia, and the mechanisms through which it affects disease severity and EPCs, require further investigation. RNAemia might arise from spillage from the respiratory tract, or from active viral replication in vascular endothelial [34] or perivascular cells [35] . Whether RNAemia represents genomic fragments, immuno-complexed or otherwise neutralized virus, or replication-competent intact virus cannot be determined from our data, and an attempt to culture SARS-CoV-2 from serum with low RNA levels was not successful [36] . SARS-CoV-1, however, has been found to replicate in circulating lymphocytes, monocytes, macrophages, and dendritic cells [37] [38] [39] . The RNAemia kinetics we observed follow a typical viral kinetic pattern, with high peak viral load early in the infection, followed by rapid decay (likely reflecting the innate immune response), before a slower clearance by acquired immunity [40] . Reduction of RNAemia has been correlated with appearance of antibodies [41] , and worsening RNAemia with critical illness and death [42] . RNAemia prior to symptom onset has been anecdotally reported; more data is needed to better assess pre-symptomatic dynamics [36] . Since our findings suggest that RNAemia on presentation reflects the likelihood of subsequent disease progression, early testing for RNAemia could guide the initiation and monitoring of antiviral therapies [40] . A c c e p t e d M a n u s c r i p t 16 We found a stronger association between RNAemia and EPCs (defined conservatively based on new diagnoses at discharge, rather than surrogate biomarkers) than previous reports [31] . Extrapulmonary injury could result from direct viral toxicity, endothelial cell damage and thromboinflammation, dysregulation of the immune response, or dysregulation of the reninangiotensin-aldosterone system [2] . RNA load is associated with increased chemokines, IL-6, CRP, ferritin, coagulation activation, and tissue damage [14] . Transaminitis, frequently observed in RNAemic patients, might result from direct hepatocellular injury by SARS-CoV-2, from cytokine storm and hypoxia-associated metabolic derangement, or from antiviral drug-induced liver injury. The trend we observed toward higher incidence of acute kidney injury in RNAemic patients is consistent with prior evidence for renal tropism [16] . We found that SARS-CoV-2 RNAemia at initial ED presentation is a robust predictor of patients' eventual clinical severity and EPCs. Despite the limited generalizability of a single center study, the substantial predictive value of RNAemia in multiple aspects of the disease course suggests a role for plasma dPCR in triage and disposition. Since we use a measure of severity based primarily on oxygen requirements, and since many COVID-19 therapies are initiated on the basis of such requirements, RNAemia on presentation might serve to direct early initiation of appropriate therapies to the patients most likely to deteriorate. M a n u s c r i p t 26 To prevent over-fitting, predictors were selected via elastic net regression of severe disease on these features with 10-fold cross-validation, selecting the regularization parameter λ minimizing mean cross- WHO Coronavirus (COVID-19) Dashboard. 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