Is SARS-CoV-2 viral load a predictor of mortality in COVID-19 acute respiratory distress syndrome patients?

Abstract

Objective

Viral load varies during infection and is higher during the initial stages of disease. Given the importance of the intensive care unit (ICU) in the late stages of COVID-19 infection, analyzing cycle threshold values to detect viral load upon ICU admission can be a clinically valuable tool for identifying patients with the highest mortality risk.

Methods

This was a retrospectively designed study. Patients older than 18 years who tested positive for SARS-CoV-2 PCR and had a PaO₂/FiO₂ ratio <200 were included in the study. The patient population was divided into two groups: survivors and non-survivors.

Results

Two hundred patients were included in the study. In non-survivors, age, relevant ICU admission scores, and procalcitonin levels were significantly higher whereas PaO₂/FiO₂ ratios and cycle threshold levels were significantly lower than in survivors.

Conclusion

Viral load at ICU admission has significant prognostic value. In combination with age, comorbidities, and severity scores, viral load may assist clinicians in identifying individuals who need more intensive monitoring. Increased awareness may improve outcomes by allowing the more effective monitoring and treatment of patients. More prospective studies are needed to determine how a high viral load worsens disease and how to avoid irreversible results.

Keywords

Inflammation mortality viral load cycle threshold intensive care unit coronavirus disease

Introduction

In some viral diseases, viral load monitoring can provide information about the life expectancy of a patient.¹ The standard-of-care test for detecting SARS-CoV-2 includes semi-quantitative viral load data such as cycle threshold (Ct) values. Lower Ct values are associated with more severe disease.^2–5 Although the SARS-CoV-2 viral load is broadly researched, the majority of studies contain non-intensive care unit (ICU) patients.^2,3,5–7 However, viral load varies during the course of infection and is higher during the initial stages of the disease.^2,3,6 In contrast, the importance of viral load in the late stages of infection or in persistent positives and the clearance of SARS-CoV-2 from respiratory samples remain unclear. Given the importance of the ICU in the late stages of COVID-19 infection, analyzing Ct values to detect viral load upon ICU admission can be a clinically valuable tool for identifying patients at the highest risk of mortality.⁸ For this reason, in this study, we evaluated the relationship between Ct values and mortality in patients with COVID-19 in the ICU. We hypothesized that SARS-CoV-2 viral load at ICU admission predicts ICU mortality.

Methods

Study Population and Setting

We conducted a retrospective, double-center, observational cohort study among COVID-19 patients admitted to the ICUs of two hospitals between April 2020 and June 2021. The reporting of this study conforms to STROBE guidelines.⁹ The study was approved by the Ministry of Health, the special COVID-19 Ethics Committee of Istanbul University, and the Ethics Committee of Acibadem University (approval number: 2021-09/36, date: 26.05.2021). The requirement for patient informed consent was waived given the retrospective study design. Patients older than 18 years with positive SARS-CoV-2 polymerase chain reaction (PCR) and PaO₂/FiO₂ ratio less than 200 were randomly included in the study whereas patients whose samples were negative for SARS-CoV-2 at ICU admission, whose samples were positive but analyzed at a different hospital, or whose PaO₂/FiO₂ ratio was greater than 200 were excluded from the study.

Data Collection

Data were collected from electronic and manual medical records. Patients' demographics (age, sex, and body mass index); ICU admission scores such as the Acute Physiology and Chronic Health Evaluation System (APACHE) II, the Sequential Organ Failure Assessment (SOFA), and the Charlson Comorbidity Index (CCI) score; PaO₂/FiO₂ ratios; leucocyte counts; lymphocyte counts; neutrophil-lymphocyte count ratios; Ct values; C-reactive protein; procalcitonin; ferritin; d-dimer; and lactate dehydrogenase levels were recorded upon ICU admission. Outcomes such as length of ICU stay and mortality rate were further recorded. All patient data were de-identified.

The Ct value represents the number of replication cycles required for sufficient gene amplification to produce a fluorescent signal that crosses a predefined threshold. RNA isolation from swab samples was performed manually with a viral nucleic acid isolation kit (vNAT®; Bioeksen, Istanbul, Turkey). Reverse transcriptase for real-time Q-PCR was performed on a Rotor-Gene Q (QIAGEN, Hilden, Germany) device using the Biospeedy® kit (Bioeksen, Istanbul, Turkey) and targeting the ORF1ab and N gene regions of SARS-CoV-2 RNA. The human ribonuclease P gene was used as an internal control. The positivity of human ribonuclease P indicates that the test is working correctly. A positive result for COVID-19 is obtained by detecting the amplification curves of the RdRp gene region.

In this study, we divided Ct values into two groups: low (<20 cycles) and high (>20 cycles). The patient population was divided into two groups: 1) survivors and 2) non-survivors.

Statistical analyses

SPSS Version 23.0 (IBM Corp., Armonk, NY, USA) was used to conduct statistical analyses. According to the distribution of the values, data were presented as means, medians, and interquartile ranges. The Kolmogorov–Smirnov test was employed to determine whether the distribution was normal. We employed student t, chi-square, and Mann–Whitney U tests to analyze both groups. Receiver operating characteristic curve analysis was used to find the cut-off and area under the curve values of all relevant variables in the non-survivor group. In a multivariate logistic regression model predicting the likelihood of mortality, we added all significantly different parameters in the non-survivor group. A p-value of less than 0.05 was used to determine statistical significance.

The estimated power of this study was 0.99 based on the group sizes (n = 87 and n = 113), the mean difference of the Ct values of the groups (3.8) and α of 0.05.

Results

Two hundred patients were included in the study (Figure 1). The mortality rate and Ct level for all patients (n = 113) were 56.5% and 23.7 ± 5.9, respectively (Table 1). In non-survivors, age (p < 0.001), APACHE II score (p = 0.002), SOFA score (p = 0.020), CCI score (p < 0.001), and procalcitonin levels were significantly higher than in survivors whereas PaO₂/FiO₂ ratios (p < 0.001) and Ct levels (p < 0.001) were significantly lower than in survivors (Table 2). Table 3 lists the cut-off values for mortality. In the multivariate logistic regression model, the likelihood of mortality increased 4.2-fold (2.1–8.2; p < 0.001), 3.7-fold (1.7–8.0; p = 0001), 2.4-fold (1.3–4.7; p = 0.008) and 2.0-fold (1.1–3.9; p = 0.033) with Ct ≤ 24.7, CCI score ≥ 4, PaO₂/FiO₂ ratio < 107, and procalcitonin ≥ 0.28, respectively (Table 4).

Figure 1.

Study flowchart.ICU, intensive care unit; PCR, polymerase chain reaction.

Table 1.

Demographic data and outcomes for all patients.

Patients, n	200
Demographic data
Age, years	63 (54–71)
Male, n (%)	123 (61.5)
BMI, kg/m²	26.2 (25.1–28.4)
APACHE II score	18 (14–23)
SOFA score	7 (5–8)
CCI score	4 (2–6)
At ICU admission
PaO₂/FiO₂ ratio	106 (88–136)
Ct, cycles	23.7 ± 5.9
Leukocyte count, x10³	9.0 (6.8–13.5)
Lymphocyte count, ×10³	0.60 (0.39–0.83)
NLCR	14.8 (10.3–20.8)
C-reactive protein, mg/dL	100 (56–159
Procalcitonin, ng/mL	0.29 (0.13– 0.91)
Ferritin, ng/mL	1065 (603 –1896)
D-dimer, mg/L	1.7 (0.9–3.6)
Lactate dehydrogenase, U/L	423 (319–583)
Outcomes
Duration of MV, days	8 (3–15)
Length of ICU stay, days	12 (4 –20)
Mortality, n (%)	113 (56.5)

APACHE, acute physiologic and chronic health evaluation; BMI, body mass index; CCI, Charlson comorbidity index; Ct, threshold; ICU, intensive care unit; MV, mechanical ventilation; NLCR, neutrophil-lymphocyte count ratio; SOFA, sequential organ failure assessment.

Table 2.

Comparison between survivors and non-survivors.

	Survivors (n = 87)	Non-survivors (n = 113)	p
Demographic data
Age, years	58 (52–67)	66 (56 –73)	< 0.001
Male, n (%)	57 (65.5)	66 (58.4)	0.306
BMI, kg/m²	26.2 (25.1 –28.1)	26.3 (24.8 –28.7)	0.741
APACHE II score	16 (13–20)	20 (14 –27)	0.002
SOFA score	6 (5–8)	7 (6–8)	0.020
CCI score	3 (1–5)	5 (3–7)	<0.001
At ICU admission
PaO₂/FiO₂ ratio	123 (97–156)	97 (86–125)	<0.001
Ct, cycle	25.9 ± 5.2	22.1 ± 5.9	<0.001
Leukocyte count, ×10³	9.2 (6.9–13.5)	8.7 (6.8–13.5)	0.890
Lymphocyte count, ×10³	0.69 (0.39–0.84)	0.60 (0.37–0.83)	0.181
NLCR	15.9 (10.6–19.4)	14.5 (10.0–22.8)	0.511
C-reactive protein, mg/dL	85 (57–140)	115 (51–180)	0.067
Procalcitonin, ng/mL	0.20 (0.09–0.40)	0.40 (0.19–1.73)	<0.001
Ferritin, ng/mL	1005 (674–1417)	1150 (558–2066)	0.454
D-dimer, mg/L	1.5 (0.8–3.6)	1.8 (1.0–3.6)	0.416
Lactate dehydrogenase, U/L	415 (315–553)	442 (325–624)	0.200
Outcomes
Duration of MV, days	8 (4–14)	7 (2–17)	0.536
Length of ICU stay, days	13 (6–20)	10 (4–20)	0.347

APACHE, acute physiologic and chronic health evaluation; BMI, body mass index; CCI, Charlson comorbidity index; Ct, cycle threshold; ICU, intensive care unit; MV, mechanical ventilation; NLCR, neutrophil-lymphocyte count ratio; SOFA, sequential organ failure assessment.

Table 3.

Cut-off and area under the curve values for mortality.

Variables	AUC (95% CI)	P
CCI score ≥ 4	0.74 (0.67–0.81)	<0.001
Procalcitonin ≥ 0.28	0.70 (0.62–0.77)	<0.001
PaO₂/FiO₂ ratio < 107	0.69 (0.61–0.76)	<0.001
Ct ≤ 24.7	0.68 (0.61–0.76)	<0.001
Age > 63	0.63 (0.55–0.71)	0.002
APACHE II ≥ 17	0.63 (0.55–0.70)	0.002
SOFA score ≥ 7	0.60 (0.52–0.67)	0.021

APACHE, acute physiologic and chronic health evaluation; AUC, area under the curve; CCI, Charlson comorbidity index; Ct, cycle threshold; SOFA, sequential organ failure assessment.

Table 4.

Logistic regression model for the likelihood of mortality.

	OR (95% CI)	P
Ct ≤ 24.7	4.2 (2.1–8.2)	<0.001
CCI score ≥ 4	3.7 (1.7–8.0)	0.001
PaO₂/FiO₂ ratio < 107	2.4 (1.3–4.7)	0.008
Procalcitonin ≥ 0.28	2.0 (1.1–3.9)	0.033
APACHE II score ≥ 17	1.6 (0.8–3.2)	0.219
SOFA score ≥ 7	1.2 (0.6 –2.5)	0.574
Age > 63	1.0 (0.5–2.2)	0.971

APACHE, acute physiologic and chronic health evaluation; CCI, Charlson comorbidity index; Ct, cycle threshold; SOFA, sequential organ failure assessment.

Discussion

Our results showed that viral load at ICU admission can predict the mortality of patients with acute respiratory distress syndrome (ARDS) due to COVID-19. In the most severe type of ARDS, the virus can damage the pulmonary endothelium through direct inhalation or by inducing an inflammatory response.¹⁰ This damage is more prominent in SARS-CoV-2 because, to enter host cells, the virus uses angiotensin-converting enzyme 2 (ACE2) receptors,¹¹ which are mainly expressed in alveolar epithelial cells—indicating that the lungs are the primary target of SARS-CoV-2.¹² This may explain why lower respiratory tract specimens had a higher viral load but a slower resolution of viral shedding in comparison with upper respiratory specimens.⁶ The endothelium changes to allow immune cells to migrate to the infection site¹³ and the body attempts to eliminate microbial invaders; these processes play a major role in the acute inflammatory response.¹⁴ Inter‐individual variability during this process may contribute to disease severity.¹⁵ However, all of these changes raise the question of whether increased viral load leads to a greater inflammatory response. Many studies have shown a correlation between viral load and COVID-19 severity^2,5 and higher viral load is associated with more severe disease.⁴ Viral load remains higher for the first 12 days from the onset of symptoms,⁵ but whether the load remains elevated in patients with severe disease remains unclear. Moreover, whether the timing of the COVID-19-induced ‘cytokine storm’—which is approximately 7 to 10 days after the onset of symptoms¹⁶—overlaps with this period could not be addressed in our article. A single pathway cannot be blamed for COVID-19 ARDS pathophysiology, which is complex and involves multiple interacting mechanisms.¹⁵ However, the question of whether a greater virus load causes a stronger immune response remains unanswered. In addition to its direct effect, SARS-CoV-2 dysregulates the immune response, features a ‘cytokine storm’, downregulates ACE2 receptors, and causes immunothrombosis—all of which interplay in disease progression.¹⁷ These complicated pathways may explain why COVID-19 individuals have more severe diffuse alveolar damage, more cellular fibromyxoid exudates in the alveoli, and small airways.¹⁸ However, because all of these mechanisms are virus-induced, the idea that more virus causes more dysregulation cannot be ignored and requires further investigation.

The high rate of mortality (56.5%) observed in the study was surprising. The wide range in the incidence of ARDS among COVID-19 patients—between 40% and 96%^19,20—results from differences in the organization of health systems, ICU bed availability, and length of follow-up.²¹ Importantly, the patients included in our study were more severe and had a PaO₂/FiO₂ ratio of 97 (86–125; p < 0.001). A low PaO₂/FiO₂ ratio at ICU admission is an independent factor associated with mortality.²² We also demonstrated a significant difference (p < 0.001; Table 2) between the Ct values of the survivor (n = 87) and non-survivor (n = 113) groups that were admitted to the ICU based on SARS-CoV-PCR test results. A positive reaction in a real-time PCR assay is detected by the accumulation of a fluorescent signal. The Ct value represents the number of replication cycles required for sufficient gene amplification to produce a fluorescent signal that crosses a predefined threshold.²³ This is a semi-quantitative measure of viral genetic material that is inversely proportional to the amount of target nucleic acid in the sample. Therefore, a preliminary estimation of viral load is possible, although it is not a complete quantitative measurement with the Ct value. A similar result was reported by Huang et al., who showed that lower Ct values were correlated with an increased risk of death.²⁴ In another study, viral load was not associated with mortality, but older age, C-reactive protein positivity, and increased chest computed tomography severity were significant risk factors;²⁵ however, the patients included in this study were not pure ARDS patients.

In addition to viral load, the association between mortality and COVID-19 ARDS is driven by age (p < 0.001) and comorbidities (CCI score < 0.001); this finding was expected because these variables have already been shown to be important risk factors for severe COVID-19.²⁶ Similar findings have been observed in MERS-CoV infection, in which disease severity increases with age and severe disease is uncommon among pediatric patients.²⁷ Older patients and patients with comorbidities may have decreased physiologic reserves and are thus less likely to tolerate injury caused by COVID-19. The immunosenescence observed in these patients is associated with a decreased response to pathogens.²⁸ In addition, older people have higher alveolar levels of ACE2 receptors,²⁹ which are thought to facilitate virus entry into host cells.¹¹ Zheng et al. observed a correlation between age and the duration of the virus. The reasons for higher viral loads observed in severe individuals compared with less severe patients are not well understood and warrant further investigation.

Another expected result was the positive association of both the APACHE II (p = 0.002) and SOFA (p = 0.020) scores with mortality. Scoring systems based on physiologic abnormalities have been successful in measuring the risk of death among critically ill patients.³⁰ Furthermore, the predictive role of the CCI score in mortality is known.³¹

Although we showed that viral load is predictive of ICU mortality, live virus was not isolated from sample cultures obtained 8 days from the onset of symptoms.³² However, this result can be attributed to insufficient viruses reproducing in the viral culture. Nevertheless, the persistence of viral RNA may not be associated with disease severity but may indicate that the immune response is unable to promote virus RNA clearance. The lack of information regarding the persistence of viral RNA and infectivity, disease severity, and immune response supports the current guidance to confirm viral clearance before patients are transferred out of dedicated COVID-19 wards or before ending isolation for patients with mild illness.³³

This study had several limitations that must be acknowledged. First, because this was a retrospective and double-center study, it did not account for potential differences in the quality of care across ICUs. Second, viral dynamics during infection could not be observed because the study only included a single sample of PCR testing. Finally, information on pre-ICU management was lacking.

Conclusion

In combination with age, comorbidities, and severity scores, viral load may assist clinicians in identifying individuals who require more intensive monitoring. Increased awareness may improve outcomes by ensuring the more effective monitoring and treatment of patients. More prospective studies are required to determine how high viral load worsens disease and how to avoid irreversible results.

Research Data

Research Data for Is SARS-CoV-2 viral load a predictor of mortality in COVID-19 acute respiratory distress syndrome patients?

Research Data for Is SARS-CoV-2 viral load a predictor of mortality in COVID-19 acute respiratory distress syndrome patients? by Lerzan Dogan, Aytaj Allahverdiyeva, Mustafa Önel, Sevim Meşe, Esra Saka Ersin, İlkay Anaklı, Zeynep Tuğçe Sarıkaya, Rehile Zengin, Bulent Gucyetmez, Neval Yurtturan Uyar, Perihan Ergin Özcan, Ayse Sesin Kocagöz, Hayriye Kırkoyun Uysal, İbrahim Ozkan Akinci and Ali Ağaçfidan in Journal of International Medical Research

Footnotes

Author contributions

Medical Practice : Lerzan Dogan, Aytaj Allahverdiyeva; Mustafa Onel; Esra Saka Ersin; Ilkay Anakli; Tugce Sarikaya; Rehile Zengin; Neval Yurtturan Uyar;

Concept: Lerzan Dogan, Mustafa Onel, Perihan Ergin Ozcan; Sesin Kocagoz; Hayriye Kirkoyun; Ali Agacfidan;

Design: Lerzan Dogan, Mustafa Onel, Sevim Mese, Sesin Kocagoz; Hayriye Kirkoyun;

Data Collection and Processing: Lerzan Dogan, Aytaj Allahverdiyeva; Esra Saka Ersin; Ilkay Anakli, Tugce Sarikaya; Rehile Zengin; Bulent Gucyetmez;

Analysis and Interpretation: Lerzan Dogan, Mustafa Onel, Sevim Mese, Bulent Gucyetmez; Perihan Ergin Ozcan; Sesin Kocagoz; Hayriye Kirkoyun, Ibrahim Ozkan Akinci; Ali Agacfidan;

Literature Search: Lerzan Dogan, Sevim Mese, Bulent Gucyetmez; Neval Yurtturan Uyar;

Writing: Lerzan Dogan; Sesin Kocagoz; Ibrahim Ozkan Akinci; Ali Agacfidan;

Data availability statement

The data supporting this study's findings are available from the corresponding author upon reasonable request.

Declaration of conflict of interests

The authors declare that there is no conflict of interest.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

ORCID iD

Lerzan Dogan

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