Literature DB >> 33119425

Association of SARS-CoV-2 Genomic Load with Outcomes in Patients with COVID-19.

Ioannis M Zacharioudakis1, Prithiv J Prasad1, Fainareti N Zervou1, Atreyee Basu1, Kenneth Inglima1, Scott A Weisenberg1, Maria E Aguero-Rosenfeld1.   

Abstract

Entities:  

Year:  2021        PMID: 33119425      PMCID: PMC8086542          DOI: 10.1513/AnnalsATS.202008-931RL

Source DB:  PubMed          Journal:  Ann Am Thorac Soc        ISSN: 2325-6621


× No keyword cloud information.
Since its recognition, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has caused 31.2 million cases of coronavirus disease (COVID-19) worldwide (1). The surge in infections has overwhelmed the healthcare systems, and identifying patients who have a high risk for poor outcomes is critically important (2). Host factors have been associated with mortality (3–7), whereas the data on the association of viral factors with COVID-19 outcomes remain conflicting (8–11). We aimed to study the association of SARS-CoV-2 genomic load in nasopharyngeal samples with clinical outcomes. We used the cycle threshold (Ct) value, the number of amplification cycles needed to yield a positive fluorescent signal in a real-time reverse transcription–polymerase chain reaction (RT-PCR), as a surrogate for viral load.

Methods

We conducted a retrospective cohort study at the New York University Langone Medical Center, a tertiary academic medical center in New York City. We evaluated all patients who presented to the emergency department between March 31, 2020, and April 10, 2020, with clinical and radiographic findings of viral pneumonia and positive screening for SARS-CoV-2 who required hospitalization. We excluded patients who were tested more than 24 hours into the admission, as our goal was to study the association of the genomic load at the time of admission to the hospital with patient outcomes. The qualitative Cepheid Xpert Xpress SARS-CoV-2 assay was used for in-house diagnosis of COVID-19 (12). This assay detects two nucleic acid targets, namely, N2 and E, and reports the Ct values. The Ct values provide a semiquantitative measure of genomic load, with an inverse relationship between the genomic load and Ct value (13). The N2 target is specific for SARS-CoV-2, whereas the E nucleic acid can also be found in SARS-CoV-1. A positive assay result implies that either N2 and E or N2 target alone were detected, whereas detection of the E nucleic acid alone is considered a presumptive positive result (the latter were excluded from this study). The primary study outcome was the association of the genomic load in patients admitted to the hospital with COVID-19–related pneumonia with disease outcomes. We used a composite outcome of death or discharge to hospice care and use of mechanical ventilation or extracorporeal membrane oxygenation. All patients were followed up until April 30, 2020. The Charlson Comorbidity Index (CCI) and the pneumonia severity index (PSI) were calculated. Patients were divided into five classes based on the PSI, with a higher class at the time of admission being associated with worse outcomes (14). The duration of symptoms before presentation was also extrapolated. We categorized Ct values into the following three SARS-CoV-2 genomic load groups based on tertiles: low (≥34.2), intermediate (27.7–34.2), and high (≤27.7). We compared the patients in the three genomic load groups on the basis of demographic characteristics, body mass index (BMI), smoking history, CCI, comorbidities, immunosuppressive diseases, duration of symptoms, and the PSI using the χ2 test. A multivariate logistic regression analysis was performed to examine the association of the SARS-CoV-2 genomic load with the primary composite outcome adjusted for patient demographics, BMI, smoking history, comorbidities, transplant status, PSI, and duration of symptoms. The marginal method was used to estimate the probability of the composite outcome among patients with low, intermediate, and high genomic loads when all the other variables were fixed at their means (15). All calculations were performed using the Stata version 14.2 software package (Stata Corporation). A P value of less than 0.05 was considered statistically significant. This study was approved with a waiver of informed consent by the New York University Institutional Review Board.

Results

Of the 457 patients who presented to our emergency department with positive for SARS-CoV-2 between March 31, 2020, and April 10, 2020, 314 met the inclusion criteria and were included in the final analysis. Among the included patients, the median age was 64 years (interquartile range [IQR], 54–72 yr), 205 (65.3%) were male, 140 (44.6%) were white, and the median BMI was 28.3 (IQR, 25.1–32.3). In terms of comorbidities, the median CCI was 3 (IQR, 1–5), and 117 patients (37.3%) were obese (i.e., BMI ≥30 kg/m2). In addition, 50 patients (15.9%) had at least one pulmonary comorbidity, 72 (23.5%) were active or former smokers, 21 (6.7%) were transplant recipients (20 solid organ transplantations and one hematopoietic stem cell transplantation) and four had human immunodeficiency virus (three of them virologically suppressed). The median duration of symptoms before presentation was 7 days (IQR, 5–10 d). Nine patients were classified in class I based on their PSI (2.9%), 78 were classified in class II (24.8%), 84 were classified in class III (26.8%), 102 were classified in class IV (32.5%) and 41 were classified in class V (13%). Of the 314 included patients, 107 (34.1%) were categorized into the low, 103 (32.8.%) into the intermediate, and 104 (33.1%) into the high SARS-CoV-2 genomic load category (Table 1). Patients with high genomic loads had higher CCI scores (P = 0.006), were more likely to be transplant recipients (P < 0.001), and had a significantly shorter duration of symptoms (P = 0.004). The PSI was significantly higher in patients with high genomic loads (P = 0.03). Transplant history (odds ratio [OR], 5.37; 95% confidence interval [CI], 1.15–25.0) and duration of symptoms (OR, 0.93; 95% CI, 0.88–0.97) remained significantly associated with high genomic load in multivariate analysis.
Table 1.

Association of SARS-CoV-2 genomic load with patient characteristics

Patient characteristicsLow Genomic Load (n = 107)Intermediate Genomic Load (n = 103)High Genomic Load (n = 104)
Age, yr, n (%)   
 18–4414 (13.1)10 (9.7)13 (9.6)
 45–6452 (48.6)35 (34.0)38 (36.5)
 ≥6541 (38.3)58 (56.3)53 (50.9)
Race, n (%)   
 White44 (41.1)54 (52.4)42 (40.4)
 Black11 (10.3)12 (11.7)18 (17.3)
 Hispanic11 (10.3)10 (9.7)14 (13.5)
 Other/Unknown41 (38.3)25 (35.2)30 (28.8)
Sex, M, n (%)68 (63.6)65 (63.1)72 (69.2)
Obesity (BMI ≥ 30), n (%)44 (41.1)39 (37.9)34 (32.7)
Smoking (current/former), n (%)21 (20.2)30 (29.1)21 (20.2)
Any pulmonary comorbidity, n (%)15 (14.0)23 (22.3)12 (11.5)
Transplant,n (%)2 (1.9)4 (3.9)15 (14.4)
CCI,*n (%)   
 Low58 (54.2)41 (39.8)35 (33.7)
 Medium33 (30.8)28 (27.2)36 (34.6)
 High16 (15.0)34 (33.0)33 (31.7)
Symptoms for ≤7 d,n (%)47 (43.9)63 (61.2)68 (65.4)

Definition of abbreviations: BMI = body mass index; CCI = Charlson Comorbidity Index; SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2.

Low CCI 1–2, Medium CCI 3–4, High CCI ≥5.

Statistically significant.

Association of SARS-CoV-2 genomic load with patient characteristics Definition of abbreviations: BMI = body mass index; CCI = Charlson Comorbidity Index; SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2. Low CCI 1–2, Medium CCI 3–4, High CCI ≥5. Statistically significant. The follow-up period was a median of 25 days (IQR, 21–28 d). At the end of follow-up, the composite outcome was reached by 74 patients (23.6%). Median time to primary outcome was 3.5 days (IQR, 1–6 d). On the day of censoring, 309 patients (98.4%) had either reached the primary outcome or were discharged. Compared with patients with low genomic load, patients with high genomic load had a significantly higher unadjusted risk to die (P < 0.001) and reach the composite outcome of death, intubation, or extracorporeal membrane oxygenation (P = 0.004) (Table 2).
Table 2.

Association of SARS-CoV-2 genomic load with patient outcomes

Patient characteristicsLow (n = 107)Intermediate (n = 103)High (n = 104)
Fever, n (%)78 (72.9)79 (76.7)77 (74.0)
CRP, n (%)   
 ≤8035 (32.7)35 (34.0)37 (35.6)
 80–16038 (35.5)34 (33.0)36 (34.6)
 ≥16034 (31.8)34 (33.0)31 (29.8)
Pulmonary severity index,*n (%)   
 I–II41 (38.3)25 (24.3)21 (20.2)
 III28 (26.2)29 (28.2)27 (26.0)
 IV–V38 (35.5)49 (47.5)56 (53.8)
Death,*n (%)8 (7.5)9 (8.7)21 (20.2)
Composite outcome,*n (%)17 (15.9)21 (20.4)36 (34.6)

Definition of abbreviations: CRP = C-reactive protein; SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2.

Statistically significant.

Composite outcome = death or discharge to hospice or intubation or extracorporeal membrane oxygenation.

Association of SARS-CoV-2 genomic load with patient outcomes Definition of abbreviations: CRP = C-reactive protein; SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2. Statistically significant. Composite outcome = death or discharge to hospice or intubation or extracorporeal membrane oxygenation. In the multivariate model, controlling for patient age, sex, BMI, CCI, smoking and transplant history, duration of symptoms, and PSI, high genomic load remained an independent risk factor for the composite outcome (OR, 1.59; P = 0.02). In addition, the duration of symptoms (OR, 0.93; P = 0.05) and PSI at the time of admission (OR, 3.7; P < 0.01) were also significantly associated with the composite outcome in multivariate analysis. The margins analysis indicated that the average probability of the composite outcome would be 24% (95% CI, 0.14–0.33) if everyone had a high genomic load compared with 11% (95% CI, 0.05–0.18) if everyone had a low genomic load (Figures 1A and 1B). Among patients with a high PSI, the expected probability of the composite outcome was 49% (95% CI, 0.36–0.62) for those with a high genomic load as opposed to 31% (95% CI, 0.17–0.44) for those with a low genomic load (Figure 1C).
Figure 1.

(A) Prediction of outcomes based on genomic load category. (B) Prediction of outcomes based on cycles threshold. (C) Prediction of outcomes based on genomic load and pneumonia severity index. CI = confidence interval.

(A) Prediction of outcomes based on genomic load category. (B) Prediction of outcomes based on cycles threshold. (C) Prediction of outcomes based on genomic load and pneumonia severity index. CI = confidence interval.

Discussion

We found that patients with a short duration of symptoms and high comorbidity index, as well as transplant recipients, were more likely to have a high SARS-CoV-2 genomic load at the time of hospital admission. The patients with high genomic load had a more severe clinical presentation and two times higher odds of dying or being intubated, independent of age, comorbidities, and severity of illness on presentation. Among patients with a severe clinical presentation at the time of hospital admission, patients with high genomic load were almost twice as likely to die or get intubated. The contribution of viral factors in disease severity is less understood, with conflicting evidence in the literature (9, 11, 16, 17). In our study, we examined the utility of genomic load from the upper respiratory tract in making inferences for the disease outcomes. Although still unclear, it is plausible that lower respiratory samples may be more closely associated with clinical outcomes than nasopharyngeal samples (18). However, the difficulty in obtaining such samples makes it unlikely that this will be of significant value in daily clinical practice. Current evidence suggests that there is active replication of SARS-CoV-2 in the upper respiratory tissues during the first 5 days after the onset of symptoms (19), a finding that correlates with our observation of higher genomic load in patients presenting within 7 days of symptom onset. Limitations of this study should be acknowledged and arise primarily from its retrospective design. However, both the primary outcome and the genomic load are objective measures that would not be influenced by incomplete reporting. Second, this study relies on Ct values obtained through a single assay, and the generalizability of the outcomes across different RT-PCR methods should be examined. Next, variation in the technique of obtaining the nasopharyngeal swab or collection of the specimen at different phases of the respiratory cycle could potentially cause fluctuation in the genomic load detected by the assay. In summary, we showed that SARS-CoV-2 genomic load is an independent predictor of adverse outcomes in patients admitted to the hospital with COVID-19–related pneumonia and that above and beyond age, comorbidities, and severity of illness on presentation, genomic load may be used to risk-stratify patients in an era in which appropriate triaging is of utmost importance.
  18 in total

1.  A prediction rule to identify low-risk patients with community-acquired pneumonia.

Authors:  M J Fine; T E Auble; D M Yealy; B H Hanusa; L A Weissfeld; D E Singer; C M Coley; T J Marrie; W N Kapoor
Journal:  N Engl J Med       Date:  1997-01-23       Impact factor: 91.245

2.  Detectable Serum Severe Acute Respiratory Syndrome Coronavirus 2 Viral Load (RNAemia) Is Closely Correlated With Drastically Elevated Interleukin 6 Level in Critically Ill Patients With Coronavirus Disease 2019.

Authors:  Xiaohua Chen; Binghong Zhao; Yueming Qu; Yurou Chen; Jie Xiong; Yong Feng; Dong Men; Qianchuan Huang; Ying Liu; Bo Yang; Jinya Ding; Feng Li
Journal:  Clin Infect Dis       Date:  2020-11-05       Impact factor: 9.079

3.  SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients.

Authors:  Lirong Zou; Feng Ruan; Mingxing Huang; Lijun Liang; Huitao Huang; Zhongsi Hong; Jianxiang Yu; Min Kang; Yingchao Song; Jinyu Xia; Qianfang Guo; Tie Song; Jianfeng He; Hui-Ling Yen; Malik Peiris; Jie Wu
Journal:  N Engl J Med       Date:  2020-02-19       Impact factor: 91.245

4.  SARS-CoV-2 Viral Load in Clinical Samples from Critically Ill Patients.

Authors:  Yongbo Huang; Sibei Chen; Zifeng Yang; Wenda Guan; Dongdong Liu; Zhimin Lin; Yu Zhang; Zhiheng Xu; Xiaoqing Liu; Yimin Li
Journal:  Am J Respir Crit Care Med       Date:  2020-06-01       Impact factor: 21.405

5.  Nasopharyngeal viral load predicts hypoxemia and disease outcome in admitted COVID-19 patients.

Authors:  Amir Shlomai; Haim Ben-Zvi; Ahinoam Glusman Bendersky; Noa Shafran; Elad Goldberg; Ella H Sklan
Journal:  Crit Care       Date:  2020-09-01       Impact factor: 9.097

6.  Kidney disease is associated with in-hospital death of patients with COVID-19.

Authors:  Yichun Cheng; Ran Luo; Kun Wang; Meng Zhang; Zhixiang Wang; Lei Dong; Junhua Li; Ying Yao; Shuwang Ge; Gang Xu
Journal:  Kidney Int       Date:  2020-03-20       Impact factor: 10.612

7.  Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China.

Authors:  J Zhang; X Wang; X Jia; J Li; K Hu; G Chen; J Wei; Z Gong; C Zhou; H Yu; M Yu; H Lei; F Cheng; B Zhang; Y Xu; G Wang; W Dong
Journal:  Clin Microbiol Infect       Date:  2020-04-15       Impact factor: 8.067

8.  Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study.

Authors:  Tao Chen; Di Wu; Huilong Chen; Weiming Yan; Danlei Yang; Guang Chen; Ke Ma; Dong Xu; Haijing Yu; Hongwu Wang; Tao Wang; Wei Guo; Jia Chen; Chen Ding; Xiaoping Zhang; Jiaquan Huang; Meifang Han; Shusheng Li; Xiaoping Luo; Jianping Zhao; Qin Ning
Journal:  BMJ       Date:  2020-03-26

9.  Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study.

Authors:  Fei Zhou; Ting Yu; Ronghui Du; Guohui Fan; Ying Liu; Zhibo Liu; Jie Xiang; Yeming Wang; Bin Song; Xiaoying Gu; Lulu Guan; Yuan Wei; Hui Li; Xudong Wu; Jiuyang Xu; Shengjin Tu; Yi Zhang; Hua Chen; Bin Cao
Journal:  Lancet       Date:  2020-03-11       Impact factor: 79.321

10.  SARS-CoV-2 viral load predicts COVID-19 mortality.

Authors:  Elisabet Pujadas; Fayzan Chaudhry; Russell McBride; Felix Richter; Shan Zhao; Ania Wajnberg; Girish Nadkarni; Benjamin S Glicksberg; Jane Houldsworth; Carlos Cordon-Cardo
Journal:  Lancet Respir Med       Date:  2020-08-06       Impact factor: 30.700

View more
  18 in total

1.  Phylogenomics and population genomics of SARS-CoV-2 in Mexico during the pre-vaccination stage reveals variants of interest B.1.1.28.4 and B.1.1.222 or B.1.1.519 and the nucleocapsid mutation S194L associated with symptoms.

Authors:  Francisco Barona-Gómez; Luis Delaye; Erik Díaz-Valenzuela; Fabien Plisson; Arely Cruz-Pérez; Mauricio Díaz-Sánchez; Christian A García-Sepúlveda; Alejandro Sanchez-Flores; Rafael Pérez-Abreu; Francisco J Valencia-Valdespino; Natali Vega-Magaña; José Francisco Muñoz-Valle; Octavio Patricio García-González; Sofía Bernal-Silva; Andreu Comas-García; Angélica Cibrián-Jaramillo
Journal:  Microb Genom       Date:  2021-11

2.  Temporal trends in clinical characteristics and in-hospital mortality among patients with COVID-19 in Japan for waves 1, 2, and 3: A retrospective cohort study.

Authors:  Hideki Endo; Kyunghee Lee; Tetsu Ohnuma; Senri Watanabe; Kiyohide Fushimi
Journal:  J Infect Chemother       Date:  2022-06-29       Impact factor: 2.065

3.  Demographic characteristics, acute care resource use and mortality by age and sex in patients with COVID-19 in Ontario, Canada: a descriptive analysis.

Authors:  Stephen Mac; Kali Barrett; Yasin A Khan; David M J Naimark; Laura Rosella; Raphael Ximenes; Beate Sander
Journal:  CMAJ Open       Date:  2021-03-22

4.  Severity of SARS-CoV-2 infection as a function of the interferon landscape across the respiratory tract of COVID-19 patients.

Authors:  Benedetta Sposito; Achille Broggi; Laura Pandolfi; Stefania Crotta; Roberto Ferrarese; Sofia Sisti; Nicola Clementi; Alessandro Ambrosi; Enju Liu; Vanessa Frangipane; Laura Saracino; Laura Marongiu; Fabio A Facchini; Andrea Bottazzi; Tommaso Fossali; Riccardo Colombo; Massimo Clementi; Elena Tagliabue; Antonio E Pontiroli; Federica Meloni; Andreas Wack; Nicasio Mancini; Ivan Zanoni
Journal:  bioRxiv       Date:  2021-03-30

5.  SARS-CoV-2 Viral Persistence Based on Cycle Threshold Value and Liver Injury in Patients With COVID-19.

Authors:  Grace Lai-Hung Wong; Terry Cheuk-Fung Yip; Vincent Wai-Sun Wong; Yee-Kit Tse; David Shu-Cheong Hui; Shui-Shan Lee; Eng-Kiong Yeoh; Henry Lik-Yuen Chan; Grace Chung-Yan Lui
Journal:  Open Forum Infect Dis       Date:  2021-04-23       Impact factor: 3.835

6.  Viral load and disease severity in COVID-19.

Authors:  Rahul Dnyaneshwar Pawar; Lakshman Balaji; Shivani Mehta; Andrew Cole; Xiaowen Liu; Natia Peradze; Anne Victoria Grossestreuer; Mahmoud Salah Issa; Parth Patel; James Edward Kirby; Christopher Francis Rowley; Katherine Margaret Berg; Ari Moskowitz; Michael William Donnino
Journal:  Intern Emerg Med       Date:  2021-06-16       Impact factor: 5.472

7.  Association of SARS-CoV-2 genomic load trends with clinical status in COVID-19: A retrospective analysis from an academic hospital center in New York City.

Authors:  Ioannis M Zacharioudakis; Fainareti N Zervou; Prithiv J Prasad; Yongzhao Shao; Atreyee Basu; Kenneth Inglima; Scott A Weisenberg; Maria E Aguero-Rosenfeld
Journal:  PLoS One       Date:  2020-11-17       Impact factor: 3.240

8.  A blood RNA transcriptome signature for COVID-19.

Authors:  Philip Kam Weng Kwan; Gail B Cross; Claire M Naftalin; Bintou A Ahidjo; Chee Keng Mok; Felic Fanusi; Intan Permata Sari; Siok Ching Chia; Shoban Krishna Kumar; Rawan Alagha; Sai Meng Tham; Sophia Archuleta; October M Sessions; Martin L Hibberd; Nicholas I Paton
Journal:  BMC Med Genomics       Date:  2021-06-11       Impact factor: 3.063

9.  Dye-Loaded Polymersome-Based Lateral Flow Assay: Rational Design of a COVID-19 Testing Platform by Repurposing SARS-CoV-2 Antibody Cocktail and Antigens Obtained from Positive Human Samples.

Authors:  Faezeh Ghorbanizamani; Kerem Tok; Hichem Moulahoum; Duygu Harmanci; Simge Balaban Hanoglu; Ceren Durmus; Figen Zihnioglu; Serap Evran; Candan Cicek; Ruchan Sertoz; Bilgin Arda; Tuncay Goksel; Kutsal Turhan; Suna Timur
Journal:  ACS Sens       Date:  2021-07-16       Impact factor: 7.711

Review 10.  The association of smoking status with SARS-CoV-2 infection, hospitalization and mortality from COVID-19: a living rapid evidence review with Bayesian meta-analyses (version 7).

Authors:  David Simons; Lion Shahab; Jamie Brown; Olga Perski
Journal:  Addiction       Date:  2020-11-17       Impact factor: 7.256

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.