In December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the origin of coronavirus disease 2019 (COVID-19), emerged in Wuhan, China (Zhu et al., 2020). Although many COVID-19 patients remain asymptomatic, there exists a subset of patients who present with severe illness. Early treatment with dexamethasone appears to improve outcomes in these patients. However, it is not always initially clear which patients would benefit from this therapy (The RECOVERY Collaborative Group, 2020). Moreover, COVID-19 infection can be accompanied by a severe inflammatory response characterized by the release of pro-inflammatory cytokines, an event known as cytokine storm (CS) (Tang et al., 2020; Ragab et al., 2020). Thus far, this COVID-19-associated CS has predominantly been characterized by the presence of IL-1β, IL-2, IL-17, IL-8, TNF, CCL2, and most notably IL-6 (Tang et al., 2020; Merad and Martin, 2020; McGonagle et al., 2020; Wan et al., 2020; Otsuka and Seino, 2020). Severe cases of CS can be life threatening, and early diagnosis as well as treatment of this condition can lead to improved outcome. We hypothesize that cytokine profiles combined with clinical information can predict disease severity, potentially giving clinicians an additional tool when evaluating patients for preemptive intervention.

Analysis was performed for 36 PCR-confirmed COVID-19 (+) and 36 (−) human plasma samples (Figure 1—source data 1). The COVID-19 Severity Score (CSS) was developed to categorize patients based on their status upon presentation to the emergency department. CSS is graded as follows: 0 = COVID (−), no symptoms, healthy control (n = 24); 1 = COVID (−), symptoms (n = 12); 2 = COVID (+), discharged from emergency room (n = 15); 3 = COVID (+), admitted, but who did not require supplemental oxygen (n = 7); 4 = COVID (+), admitted and required any amount of supplemental oxygen or positive pressure ventilation (n = 8); and 5 = COVID (+), admitted to ICU/step-down (n = 6) (Figure 1). CSS was used as the outcome variable for a mutual information minimum-redundancy maximum-relevance algorithm (Kratzer and Furrer, 2018; Figure 1), with the goal of selecting a subset of variables most predictive of CSS. The algorithm confirmed the predictive value of clinical variables such as age and chest x-ray abnormality and also ranked the information gain provided by each of 15 cytokines tested. Several cytokines were able to add unique predictive value to the mutual information model in addition to what was provided by clinical factors such as age or patient comorbidities. This algorithm also deprioritized factors when their predictive value was redundant with the most predictive variables. Macrophage colony-stimulating factor (M-CSF) was ranked second after age as it was the factor that added the most predictive power to the algorithm with minimal redundancy with age. It ranked ahead of abnormalities on chest x-ray because while both were relevant in predicting COVID severity, part of the predictiveness of chest x-ray abnormality was also explained by age differences (Figure 2). The top four cytokines combined with age were predictive of the most severe CSS (4–5) and had a receiver operating characteristic (Figure 2), with an area under the curve of 0.86. Multiple cytokines, including M-CSF (p<0.01), interferon-inducible protein 10 (IP-10) (p<0.01), interleukin 18 (IL-18) (p<0.01), and interleukin-1 receptor antagonist (IL-1RA) (p<0.01), were more relevant in predicting CSS than more frequently characterized cytokines in the context of COVID-19 such as IL-6 (p<0.01). These cytokines showed a statistically significant difference in their profiles when segregated by CSS (Figure 3), yet the mutual information algorithm prioritized them differently than would be expected based on univariate analyses. This indicates that the mutual information algorithm is prioritizing cytokines whose predictive value for COVID-19 severity cannot be fully explained by other clinical variables such as age or medical comorbidities.

Figure 1

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Figure 1—source data 1

Mutual algorithm criteria table.

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Figure 2

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Figure 2—source data 1

Patient information.

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Figure 3

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Figure 3—source data 1

Raw data.

Source Data Table 1. Mutual algorithm criteria table. *Documented obesity or overweight for height < 99th percentile. Source Data Table 2. Patient information.

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We found that a small number of clinical variables when combined with cytokine expression are predictive of presenting COVID-19 disease severity. Cytokines singled out for relevance by the mutual information algorithm shared a connection to macrophage activation syndrome (MAS), raising questions about the mechanism by which SARS-CoV-2 creates severe illness in a subset of patients. First, we examined the significant contribution of IP-10 to CSS. IP-10 is secreted by monocytes, fibroblasts, and endothelial cells in response to interferon gamma (IFN-γ), which is secreted by T cells (mainly, Th1), macrophages, mucosal epithelial cells, and natural killer (NK) cells (Liu et al., 2011). This release of IFN-γ induces several cell types to produce IP-10, which consequently recruits more Th1 cells, contributing to a positive feedback loop. IP-10 is also chemoattractant to CXCR3-postitive cells such as macrophages, dendritic cells, NK cells, and T cells. It has been proposed that macrophages recruited by IP-10, in the presence of persistent IFN-γ production, can lead to MAS (Merad and Martin, 2020; McGonagle et al., 2020; Otsuka and Seino, 2020). MAS is characterized as a state of systemic hyperinflammation often accompanied by CS, which, without intervention, can lead to severe tissue damage and, in extreme cases, death (Otsuka and Seino, 2020).

Moreover, the cytokine most relevant in predicting CSS was M-CSF, which is secreted by eukaryotic cells in response to viral infection and stimulates hematopoietic stem cells to differentiate into macrophages. Currently, there are three separate immune stages that describe the progression of COVID-19. The first stage is characterized by a potent induction of interferons that marks the early activation of the immune system that is important in the viral response, and the second stage is characterized by a delayed interferon response (Merad and Martin, 2020). These stages may prime the body for a third stage comprised of detrimental hyperinflammation characterized by CS and MAS (Merad and Martin, 2020). This excessive macrophage activation could explain the increase in IL1-RA that we observed, a cytokine abundantly produced by macrophages.

Steroids have shown a survival benefit for COVID-19, likely by suppressing such detrimental hyperinflammation (The RECOVERY Collaborative Group, 2020). Our analysis identified a pattern of cytokine alterations on presentation associated with COVID-19 severity. The ability to identify a cytokine pattern less redundant with known clinical factors such as age and chest x-ray could help better identify patients in need of immunomodulatory treatment without the confounders of current models where the measured cytokines correlate as much with age as with severity (Pierce et al., 2020). Further studies should be conducted to clarify the mechanistic role that these cytokines and macrophages play in the various stages of COVID-19 and correlate them with other hematologic parameters that were not collected in this database. The results of these future studies could identify more targeted immunomodulatory strategies beyond steroid administration such as treatment with MEK inhibitors (Zhou et al., 2020), as well as the ideal timing of these interventions to maximize therapeutic efficacy. Future studies could also address the size limitations of this study, which was not powered to explore race- or ethnicity-related differences in COVID-19 severity. Finally, we present the application of this mutual information algorithm as a way to evaluate the dataset as a whole and elucidate the most important cytokines in predicting the presenting severity of COVID-19. COVID-19 severity is influenced by many clinical factors, such as age, and this algorithm is able to identify cytokines that contribute information not present in the tested clinical variables. Identifying the most important variables for severe presentation of COVID-19 within a more complete cytokine profile may help determine global immune mechanisms of disease severity.

Source data and source code files have been provided.

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Acceptance summary:

It's now known that SARS-CoV-2 infection can cause numerous changes in cytokines, and it has been hypothesized that in critically ill patients these changes are correlated with the severity of disease. This paper is of interest to the overall COVID-19 community in that presents an analysis of cytokines that identifies Macrophage Activation Syndrome as a correlate with disease severity.

Decision letter after peer review:

Thank you for submitting your article "Cytokine ranking via mutual information algorithm correlates cytokine profiles with disease severity in COVID-19" for consideration by eLife. Your article has been reviewed by two peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Mone Zaidi as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.

Summary:

The premise behind this manuscript is important and timely both for scientists focused on better understanding the variable immune response against the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) in different individuals and for physicians intent on improving the outcomes for their SARS-CoV-2 infected patients. The paper correlates the COVID-19 Severity Score (CSS) with a cytokine array along with demographic factors and a chest x-ray to see which components are predictive of severe COVID-19. The analysis is well presented and the experiments are straight forward in nature. Overall, the data are collected, controlled, and analyzed properly, and support the conclusions of the manuscript within the current context. If replicated in follow-on studies of independent patients, the findings may ultimately help to inform the development of improved diagnostics and therapeutics.

Essential revisions:

Overall the comments from the reviewers were positive, and suggest that the manuscript can be further strengthened by the revisions that are mostly explanatory in nature as indicated below.

1) Please include data on common lab parameters if available. For example, if there is data on CRP or fibrinogen, that would be interesting to add. Likewise, since the absolute monocyte count and the relative percentage of monocytes in the total white blood cell count are standard clinical assays obtained routinely in all SARS-CoV-2 infected patients presenting to hospital EDs for evaluating the possibility of progression in the less severely affected subset of subjects as well as in the subset of more severely affected subjects at presentation, can you discuss what you found when these two immunologically-relevant cellular parameters were included in the analysis you performed. If you did not analyze them, can you explain why not? Either way, a sentence or two should be included at this point in the Discussion section that these routinely measured cellular hematologic parameters would be a logical biomarker to explore in light of your main findings and conclusions, which include M-CSF as one of the four cytokines you identified that increase predictiveness.

2) Please state when the patient-derived blood samples were obtained. This is critical information and should be described here. Based on your use of "presenting" in the title and other places in the text, we are quite sure that the patient samples were collected on their arrival in the Emergency Dept of the Rhode Island Hospital, which would also have to be the case if the samples from all of the subjects (other than the normal subjects whose samples were purchased) were supposed to have been drawn at the same clinically-relevant point-in-time, i.e. as some of SARS-CoV-2 infected patients were asymptomatic and not admitted to the hospital, etc. Please also mention whether all of the patients were unrelated or if any were related, as this would then require accounting for the non-independence of the data generated from them, although this is mainly important for genetic association studies.

3) Please describe the centrifugation parameters employed including the g-force and length of time of the spin and mention what was the exact sample that was obtained. Was it, for example, citrated plasma samples used for coagulation studies? If so, why were the tubes needing to be spun again as whole blood samples centrifuged to remove aliquots of citrated plasma are spun at high speed two times to ensure platelet poor plasma which would also ensure cellular debris would have been removed.

4) The text of the manuscript, both in the Materials and methods section and elsewhere, creates the impression you performed a multiplex assay to measure the levels of the 15 cytokines listed in each study subject's plasma sample. But here, in the Materials and methods section, you mention (for the first time) that it is each subject's "production of" cytokines in the culture supernatant" which was used for the multiplex measurement. Please provide details as to: (i) what blood cell type or types (e.g., PBMCs) were used; (ii) how you isolated the blood cell(s); (iii) how you cultured them, i.e. in which medium and under what atmosphere they were cultured, and for how long, etc.; and (iv) how the conditioned medium was collected, processed and assayed.

5) Please explain briefly if and how in these statistical analyses you accounted for the issue of multiple testing (i.e., for the multiple cytokine measures).

6) It is important to include information on the well-known race- and ethnicity-based disparities in case fatality rates (CFRs) in African Americans and Mexican Americans. Please also include it as one of your "clinical variables" in your analysis. If you did incorporate it in your analysis, please add text to discuss it. If you did not, please discuss why not?

7) Figure 3 is cited in the text of the manuscript proper before Figure 2 is cited. Switch the numbering please.

8) In the legend for Figure 3B the labels for the y-axis ("years") and x-axis ("M-CSF concentration in pg/mL") are reversed to what is in the actual figure. Please correct this discrepancy.

9) It would be helpful to also show in Figure 3C the ROC curve(s) for predicting CSS 4-5 using age alone, and possibly, age together with only M-CSF levels.

10) Please correct the stylistic points and/or typos which we have highlighted yellow and/or struck through in the marked up word version of your manuscript.

Essential revisions:

Overall the comments from the reviewers were positive, and suggest that the manuscript can be further strengthened by the minor revisions that are mostly explanatory in nature as indicated below.

1) Please include data on common lab parameters if available. For example, if there is data on CRP or fibrinogen, that would be interesting to add. Likewise, since the absolute monocyte count and the relative percentage of monocytes in the total white blood cell count are standard clinical assays obtained routinely in all SARS-CoV-2 infected patients presenting to hospital EDs for evaluating the possibility of progression in the less severely affected subset of subjects as well as in the subset of more severely affected subjects at presentation, can you discuss what you found when these two immunologically-relevant cellular parameters were included in the analysis you performed. If you did not analyze them, can you explain why not? Either way, a sentence or two should be included at this point in the Discussion section that these routinely measured cellular hematologic parameters would be a logical biomarker to explore in light of your main findings and conclusions, which include M-CSF as one of the four cytokines you identified that increase predictiveness.

One of the biggest limitations of our data set is that the deidentified data of this biobank lacks certain parameters, such as common lab parameters like complete blood count, CRP or fibrinogen. While privacy issues limit our ability to obtain that data for this study, we have updated our Discussion to include this avenue for future research.

2) Please state when the patient-derived blood samples were obtained. This is critical information and should be described here. Based on your use of "presenting" in the title and other places in the text, we are quite sure that the patient samples were collected on their arrival in the Emergency Dept of the Rhode Island Hospital, which would also have to be the case if the samples from all of the subjects (other than the normal subjects whose samples were purchased) were supposed to have been drawn at the same clinically-relevant point-in-time, i.e. as some of SARS-CoV-2 infected patients were asymptomatic and not admitted to the hospital, etc. Please also mention whether all of the patients were unrelated or if any were related, as this would then require accounting for the non-independence of the data generated from them, although this is mainly important for genetic association studies.

In the Materials and methods section we have clarified when the patient-derived blood samples were obtained. As intuited by the reviewers, all samples (other than purchased normal samples) were indeed obtained on the patient’s arrival to the emergency department of Rhode Island Hospital. Unfortunately, due to the limited data present in the deidentified data set, we are unable to determine if any of the patients were related.

3) Please describe the centrifugation parameters employed including the g-force and length of time of the spin and mention what was the exact sample that was obtained. Was it, for example, citrated plasma samples used for coagulation studies? If so, why were the tubes needing to be spun again as whole blood samples centrifuged to remove aliquots of citrated plasma are spun at high speed two times to ensure platelet poor plasma which would also ensure cellular debris would have been removed.

The centrifugation parameters including g-force (14,000 rpm) and length of spin (10 minutes) have been added to the Materials and methods section. Although samples were platelet-poor plasma, and thus should already have been free of debris, the Luminex operating instructions suggest that all samples, regardless of their initial processing, be spun again immediately before being run to ensure they are free of debris and prevent potential clogs of the instrument.

4) The text of the manuscript, both in the Materials and methods section and elsewhere, creates the impression you performed a multiplex assay to measure the levels of the 15 cytokines listed in each study subject's plasma sample. But here, in the Materials and methods section, you mention (for the first time) that it is each subject's "production of" cytokines in the culture supernatant" which was used for the multiplex measurement. Please provide details as to: (i) what blood cell type or types (e.g., PBMCs) were used; (ii) how you isolated the blood cell(s); (iii) how you cultured them, i.e. in which medium and under what atmosphere they were cultured, and for how long, etc.; and (iv) how the conditioned medium was collected, processed and assayed.

Thank you for pointing out this inaccuracy in the Materials and methods section. The multiplex assay measured the levels of the 15 cytokines on each study subjects’ plasma sample. The Materials and methods have been updated to accurately reflect the experiment performed.

5) Please explain briefly if and how in these statistical analyses you accounted for the issue of multiple testing (i.e., for the multiple cytokine measures).

A Sidak correction was applied to correct for multiple testing for the multiple cytokine measures. The text and figures have been updated to reflect this.

6) It is important to include information on the well-known race- and ethnicity-based disparities in case fatality rates (CFRs) in African Americans and Mexican Americans. Please also include it as one of your "clinical variables" in your analysis. If you did incorporate it in your analysis, please add text to discuss it. If you did not, please discuss why not?

Race was included as a clinical variable in the mutual information algorithm. Unfortunately, the small sample size of our data set does not give it the power to identify race-based differences in COVID-19 severity. Indeed, the mutual information algorithm found the information provided by race was redundant after considering other factors such as age, symptoms, insurance status, number of abnormal vital signs, and chest x-ray abnormalities. We have updated our Discussion section to include discussion of race and COVID-19. In Author response image 1 is a race/ethnicity scatter plot if it is of interest to the reviewers.

Author response image 1

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7) Figure 3 is cited in the text of the manuscript proper before Figure 2 is cited. Switch the numbering please.

The numbering of these two figures has been corrected.

8) In the legend for Figure 3B the labels for the y-axis ("years") and x-axis ("M-CSF concentration in pg/mL") are reversed to what is in the actual figure. Please correct this discrepancy.

This discrepancy has been corrected.

9) It would be helpful to also show in Figure 3C the ROC curve(s) for predicting CSS 4-5 using age alone, and possibly, age together with only M-CSF levels.

The ROC curve for predicting CSS 4-5 found in Figure 2 has been updated to include a comparison curve predicting CSS 4-5 using age alone. This age ROC curve has an AUC of 0.74.

10) Please correct the stylistic points and/or typos which we have highlighted yellow and/or struck through in the marked up word version of your manuscript.

Stylistic points and typos have been addressed.

Author details

1. Kelsey E Huntington

   1. Brown Experimentalists Against COVID-19 (BEACON) Group, Brown University, Providence, United States
   2. Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Warren Alpert Medical School, Brown University, Providence, United States
   3. The Joint Program in Cancer Biology, Brown University and Lifespan Health System, Providence, United States
   4. Cancer Center at Brown University, Warren Alpert Medical School, Brown University, Providence, United States
   5. Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, United States
   6. Pathobiology Graduate Program, Warren Alpert Medical School, Brown University, Providence, United States
   7. Department of Molecular Microbiology and Immunology, Brown University, Providence, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Anna D Louie

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5810-8220

2. Anna D Louie

   1. Brown Experimentalists Against COVID-19 (BEACON) Group, Brown University, Providence, United States
   2. Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Warren Alpert Medical School, Brown University, Providence, United States
   3. The Joint Program in Cancer Biology, Brown University and Lifespan Health System, Providence, United States
   4. Cancer Center at Brown University, Warren Alpert Medical School, Brown University, Providence, United States
   5. Department of Surgery, Lifespan Health System and Warren Alpert Medical School, Brown University, Providence, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Kelsey E Huntington

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8394-6106

3. Chun Geun Lee

   1. The Joint Program in Cancer Biology, Brown University and Lifespan Health System, Providence, United States
   2. Cancer Center at Brown University, Warren Alpert Medical School, Brown University, Providence, United States
   3. Department of Molecular Microbiology and Immunology, Brown University, Providence, United States

   Contribution

   Conceptualization, Resources, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

4. Jack A Elias

   1. The Joint Program in Cancer Biology, Brown University and Lifespan Health System, Providence, United States
   2. Cancer Center at Brown University, Warren Alpert Medical School, Brown University, Providence, United States
   3. Department of Molecular Microbiology and Immunology, Brown University, Providence, United States

   Contribution

   Conceptualization, Resources, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

5. Eric A Ross

   Biostatistics and Bioinformatics Facility, Fox Chase Cancer Center, Temple University Health System, Philadelphia, United States

   Contribution

   Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. Wafik S El-Deiry

   1. Brown Experimentalists Against COVID-19 (BEACON) Group, Brown University, Providence, United States
   2. Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Warren Alpert Medical School, Brown University, Providence, United States
   3. The Joint Program in Cancer Biology, Brown University and Lifespan Health System, Providence, United States
   4. Cancer Center at Brown University, Warren Alpert Medical School, Brown University, Providence, United States
   5. Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, United States
   6. Pathobiology Graduate Program, Warren Alpert Medical School, Brown University, Providence, United States
   7. Hematology-Oncology Division, Department of Medicine, Lifespan Health System and Warren Alpert Medical School, Brown University, Providence, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   wafik@brown.edu

   Competing interests

   Senior Editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-9577-8266

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