Clinical and survival differences during separate COVID-19 surges: Investigating the impact of the Sars-CoV-2 alpha variant in critical care patients.

A number of studies have highlighted physiological data from the first surge in critically unwell Covid-19 patients but there is a paucity of data describing emerging variants of SARS-CoV-2, such as B.1.1.7. We compared ventilatory parameters, biochemical and physiological data and mortality between the first and second COVID-19 surges in the United Kingdom, where distinct variants of SARS-CoV-2 were the dominant stain. We performed a retrospective cohort study investigating critically unwell patients admitted with COVID-19 across three tertiary regional ICUs in London, UK. Of 1782 adult ICU patients screened, 330 intubated and ventilated patients diagnosed with COVID-19 were included. In the second wave where B.1.1.7 variant was the dominant strain, patients were had increased severity of ARDS whilst compliance was greater (p<0.05) and d-dimer lower. The 28-day mortality was not statistically significant (1st wave: 42.2% vs 2nd wave: 39.8%). However, when adjusted for key covariates, the hazard ratio for 28-day mortality in those patients with B.1.1.7 was 3.79 (CI 1.04-13.8; p = 0.043) compared to the original strain. During the second surge in the UK, where the COVID-19 variant B.1.1.7 was most prevalent, significantly more patients presented to critical care with severe ARDS. Furthermore, mortality risk was significantly greater in our ICU population during the second wave of the pandemic in those patients with B.1.1.7. As ICUs are experiencing further waves (particularly by the delta (B.1.617.2) variant), we highlight the urgent need for prospective studies describing immunological and pathophysiological differences across novel emerging variants.

Introduction

An estimated 5% of patients hospitalized with coronavirus disease 2019 (COVID-19) require critical care unit admission, placing significant burden on global healthcare resources [1-3]. Since the outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the UK thus far experienced two discrete surges in cases, in keeping with global trends. Distinction between waves in the pandemic is important given that in the UK, the second peak coincided with the identification of a novel variant of concern: B.1.1.7 (termed the Alpha strain). Whilst there is some debate about whether newly emerging variants have a higher mortality than the original SARS-CoV-2 strain, the alpha strain is understood to exhibit a higher natural reproductive number [4, 5].

A number of studies have highlighted ventilator parameters and outcomes from the first surge in critically unwell COVID-19 patients. There is a paucity of data describing emerging variants of SARS-CoV-2, such as the B.1.1.7 (which subsequently progressed to be the dominant strain in many countries), and this is vital to understand any pathological differences between emerging SARS-CoV-2 variants as the pandemic progresses.

The objective of this study was to examine clinical and survival differences in invasively ventilated patients with COVID-19-related ARDS between waves of the pandemic, to help identify important pathophysiological distinctions between variants.

Materials and methods

This is an observational analysis of all adult patients (≥18 years), performed at three teaching hospitals (within Imperial College NHS trust, London, UK), with the following inclusion criteria in the first 24h after ICU admission: patients sedated and paralysed; requiring invasive mechanical ventilation (with presence of all Berlin definition criteria for ARDS); and confirmed PCR SARS-CoV-2 infection (Fig 1). Physiological or ventilatory variable data was retrospectively collected (at 60minutes following intubation) from patient records. Research ethical approval was not required as this the study was carried out as a service evaluation within the National Health Service (NHS) and recorded under the auspices of the clinical audit office at Imperial College Healthcare NHS Trust and Institutional Data Protection Office.

Fig 1

Study flow diagram demonstrating number of study patients during each wave.

Number of patients receiving high flow nasal oxygen (HFNC), continuous positive airway pressure (CPAP) and non-invasive ventilation (NIV) prior to intubation also recorded.

We defined the first wave as an admission to ICU between 23rd February 2020 to 31st October 2020, whilst the second wave was 1st November 2020 to 23rd February 2021, coinciding with the UK Office of National Statistics epidemiological Data [6]. Investigators collecting data were anonymised to those patients who had S-gene target failure (i.e. likely B.1.1.7 infection) [7]. Patients were excluded if they were intubated at hospitals outside of the study (i.e. transferred in from external ICUs). Study patients were followed for 28 days or until they were discharged from the ICU.

Comparison of continuous data between groups was performed using Student’s T test or Wilcoxon-Mann-Whitney and comparison of categorical data was done using χ2 or Fisher’s exact test. The Kaplan-Meier method and a Cox regression model was used to investigate ICU survival and mortality risk to day 28. The relevant available clinical variable in the adjusted model were sequential organ failure assessment (SOFA) score at ICU admission, sex, and age as previously described [8]. We also accounted for interventions that were used more in the second wave, that may have influenced mortality including application of non-invasive ventilation (NIV) or continuous positive pressure ventilation (CPAP), high flow nasal oxygen, steroids and tocilizumab treatment [9-11]. All analyses were performed using STATA 15 (TX, USA). All statistical tests were two-sided and a p-value <0.05 considered significant; due to the exploratory nature of the study, significance level was not adjusted for multiple comparisons.

Results

Of the 1782 ICU admissions screened, 540 were positive for SARS-CoV-2 and 330 patients met inclusion criteria (1st wave: 175 patients; 2nd wave: 155 patients). Patient demographics are described in Table 1. The severity of ARDS (as per Berlin guidelines) increased in Wave 2. However, dynamic lung compliance was higher (1st wave: 27.5ml/cmH2O vs 2nd wave: 35.1ml/cmH2O, p<0.05) and fibrinogen degradation production (d-dimer) was significantly lower (1st wave: 3334ng/ml vs 2nd wave: 2046ng/ml, p<0.05) (Fig 2).

Table 1

Key demographics, ventilation parameters, treatment and disease severity scores, by UK pandemic wave.

		1st Wave	2nd Wave	P value
Demographics		N = 175	N = 155
Age—years		59.2 (10.1)	61.5 (11.2)	0.045
Sex—n (%)	Male	125/175 (72.1%)	87/155 (56.5%)	0.005
BMI—kg/m 2		28.4 (5.5)	29.4 (6.6)	0.133
Ethnicity	White	28/175 (16.0%)	37/155 (23.9%)	0.073
Disease
ARDS Severity at intubation—n (%)	Mild	29/175 (16.6%)	15/155 (9.7%)	0.013
	Moderate	71/175 (40.6%)	63/155 (40.9%)	‥
	Severe	75/175 (42.9%)	75/155 (48.7%)	‥
PF ratio—kPa		18.1 (10.6)	14.1 (6.3)	0.004
C rs −ml/cmH 2 O		27.3 (14.0)	33.8 (22.8)	0.007
D-dimer in first 24hr—ng/ml (IQR)		3334 (1545–9215)	2046 (1076–4580)	0.006
ICU length of stay—days (IQR)		18.7 (16.4)	12.1 (14.0)	0.004
HFNC $ —n (%)		4/175 (2.4%)	22/155 (14.3%)	<0.001
CPAP $ —n (%)		29/175 (17.1%)	30/155 (19.5%)	0.573
NIV $ —n (%)		10/175 (5.9%)	38/155 (24.7%)	<0.001
ICU Mortality—n (%)		73/175 (42.2%)	63/155 (40.9%)	0.810
Disease severity scores
APACHE II		14.9 (5.4)	17.0 (6.9)	0.003
SOFA Score		6.4 (2.9)	6.1 (3.4)	0.313
Treatments
Steroids—n (%)		89/175 (50.9%)	153/155 (99.2%)	<0.001
Tocaluzimab—n (%)		11/175 (6.3%)	49/155 (31.8%)	<0.001
Remdesivir—n (%)		5/175(2.9%)	64/155 (41.6%)	<0.001

Abbreviations: APACHE II, Acute Physiology And Chronic Health Evaluation II; BMI, Body Mass Index; CPAP, Continuous Positive Airways Pressure; Crs, static compliance; HFNC, High Flow Nasal Cannulae; ICU, intensive care unit; IQR, interquartile range; n, number; NIV, Non-invasive ventilation; PF ratio, PaO2/FiO2 ratio; SOFA, Sequential organ failure assessment.

$ denotes treatment administered for >24hrs continuously, prior to mechanical ventilation.

Patient demographics and clinical data are reported as mean (standard deviation), median (inter-quartile range) or as percentages. Comparison of continuous data between groups was done using Student’s T test or Wilcoxon-Mann-Whitney and comparison of categorical data was done using χ2 or Fisher’s exact test as appropriate.

Fig 2

Ventilator, biochemical and physiological data.

A-E) demonstrates the change in static compliance, Pfr, plasma d-dimer, APACHE II and SOFA score respectively. Upper limit for d-dimer concentration is 20,000ng/mL.

We noted in the first wave 95.1% patients accepted for ICU care required intubation and ventilation compared to 82.4% in the second wave (p<0.05) (Fig 1). Importantly, the practice of sustained (>24hrs continuous use) non-invasive ventilation (NIV) and high-flow nasal cannulae (HFNC) prior to intubation and ventilation for patients increased in the second wave.

ICU Mortality during the second surge was not significantly different (1st wave: 42.2% vs 2nd wave: 39.8%, p<0.68) (Table 1) and our data did not demonstrate any survival improvement between pandemic waves (log rank p = 0.630) (Fig 3). A Cox proportional hazard model with the first pandemic wave as the reference group, demonstrated a non-significant adjusted hazard ratio (HR) for ICU mortality of 1.39 in the second pandemic wave (95%CI 0.80–2.40, p = 0.239; Fig 3, Table 2). However, in patients with variant B.1.1.7, ICU survival was found to be poorer compared to the original variant (log rank p = 0.004) with an adjusted HR of 3.79 (CI 1.04–13.8; p = 0.043) (Fig 4, Table 3).

Fig 3

Kaplan-Meier survival demonstrates the proportion of ICU survival by UK pandemic wave.

Single hash mark denotes an ICU discharge (alive). Log-rank p = 0.630 demonstrating no statistical difference in survival between the pandemic waves.

Table 2

Cox proportional risk analysis for mortality of all intubated and ventilated patients across both UK waves of the Sars-Cov-2 pandemic.

Covariates		Hazard ratio (95% CI)	P value
Pandemic Wave
	First Wave	1 (ref)
	Second Wave	1.74 (1.05–2.89)	0.035
Sex
	Male	1 (ref)
	Female	0.67 (0.43–1.06)	0.241
Age		1.04 (1.02–1.06)	< 0.001
Corticosteroid treatment		0.46 (0.27–0.78)	0.016
Tocilizumab treatment		1.36 (0.77–2.38)	0.875
High Flow Nasal Oxygen		1.76 (0.92–3.36)	0.779
NIV/CPAP		0.66 (0.42–1.04)	0.210
SOFA Score		1.04 (0.98–1.11)	0.031

Abbreviations: CI, confidence interval; SOFA, Sequential organ failure assessment.

Fig 4

Kaplan-Meier survival demonstrates the proportion of ICU survival by Variant.

Log-rank p = 0.004 demonstrating that those patients with variant B.1.1.7 have a significant less chance of survival compared to non-B.1.1.7 variants.

Table 3

Cox proportional risk analysis for mortality of all intubated and ventilated patients by B.1.1.7 and the original variant.

Covariates		Hazard ratio (95% CI)
Variant
	Original variant	1 (ref)
	B.1.1.7 variant	3.79 (1.04–13.83)
Pandemic Wave
	First Wave	1 (ref)
	Second Wave	0.39 (0.11–1.31)
Sex
	Male	1 (ref)
	Female	0.93 (0.54–1.60)
Age		1.04 (1.02–1.07)
Corticosteroid treatment		0.55 (0.32–0.95)
APACHE II Score		1.06 (1.00–1.12)
SOFA Score		0.94 (0.85–1.05)

Abbreviations: APACHE II, Acute Physiology And Chronic Health Evaluation II; CI, confidence interval; SOFA, Sequential organ failure assessment. The adjusted for patient-level factors; age, sex, Sequential (Sepsis-Related) Organ Failure Assessment (SOFA) score, APACHE II, Corticosteroid treatment—selected based on our study hypothesis, existing literature [8] and hypothesized associations with mortality.

Discussion

This study compares and contrasts clinical parameters and physiological data in patients ventilated with COVID-19 ARDS across two discrete pandemic waves. We show that patients have more severe ARDS in the second surge yet, dynamic lung compliance and fibrinogen degradation production (d-dimer) (a biomarker linked to increased inflammation, vascular endothelial injury and a proposed predictor of poor outcome in COVID-19 [8, 12]) are improved.

When we adjust for the key variables in our cox proportional risk analysis model, we find that in the second wave of the pandemic (despite use of repurposed therapeutics such as corticosteroids and Tocilizumab) [13-15], there is no improvement in ICU mortality. This was particularly surprising considering the benefit of tocilizumab and corticosteroids in reducing COVID-19 mortality is well established [10, 11]. The ongoing emergence of novel variants of interest is one plausible explanation for the increased risk. We found poorer survival in individuals with B.1.1.7 with a 3.79-fold increase in mortality risk compared to non-B.1.1.7 (albeit with a large confidence interval). This finding contrasts with a previous report where no difference in ICU mortality was found between B.1.1.7 and non-B.1.1.7 COVID-19 patients. However, this study did not adjust for key covariates discussed above and may explain this discrepancy. Indeed, data from the Intensive Care National Audit and Research Centre does suggest a trend towards higher mortality during the second surge in the UK but did not specifically examine the mortality between different variants [16]. Indeed emerging data does also suggest and increased mortality risk from B.1.1.7 consistent with the data presented in this report [17]. It may also be prudent to evaluate whether treatments found to have significant mortality benefit with older COVID-19 variants, continue to be successful as variants mutate and pathophysiology potentially alters.

Given the retrospective nature of this study and the relatively short experience with this novel disease, there are likely to be a host of unknown confounding factors affecting the interpretation of these results. A possible contribution is that by the second wave, experience managing these critically unwell patients had become greater, which dictated changes to practice, for example, increased administration of NIV and HFNC on acute respiratory units/medical wards which in turn are likely to have altered the population referred to ICU. As well as changes to patient selection, frailty and comordities, the timing of mechanical ventilation may also have differed in the disease process. We have included d-dimer in our analysis as it has been shown to correlate with disease severity and is an accurate biomarker for predicting mortality in COVID-19 [8, 12]. However we found that in those patients with B.1.1.7, d-dimer levels are improved compared to non-B.1.1.7 patients, despite the increase in mortality. There are a number of potentially reasons for this including the effect of treatment such as steroids or tocilizumab. Our data suggests that a re-evaluation is required of various biomarkers that were initially thought to be predictive of disease severity as the pandemic progresses, particularly with as newer variants are discovered and novel treatments are introduced into clinical practice. For instance neutrophil/lymphocyte ratio was been associated with disease severity with the original SARS-COV-2 variant due to significant reduction in lymphocytes and increase of neutrophils in patients with severe COVID during this phase of the pandemic. However this has not been replicated with subsequent waves and counts of neutrophils and lymphocytes may not change significantly between different severity groups [18].

There are some limitations to our report. Firstly, the ventilator settings were not protocolised instead judged to be most appropriate by the senior clinician following intubation. In addition, some patients may have deteriorated post hospital discharge (e.g. at home) and whilst this is likely to be similar in both waves, their outcome is not captured. Thirdly, whilst we considered the covariates to include in our analysis, we did not account for other co-morbidities such as cardiovascular disease [19], cancer [20] and chronic kidney disease [21], all of which have been associated with worse outcomes in COVID-19. Fourthly, scoring systems based upon computer tomography (CT) have been used to characterize COVID-19 severity and outcomes (e.g. requirement of mechanical ventilation/mortality) in patients, particularly on presentation to hospital [22]. CT scores were not included in this study as all patients were already intubated and ventilated. Furthermore, we are unable to comment if ventilatory data at time of intubation correlated with CT score as most patients had CT scans when clinically stable to undertake transfer to radiology departments, rather than specifically at the onset of mechanical ventilation. Finally, cases of SARS-CoV-2 with an S gene target failure were identified as highly suspicious of B.1.1.7 and we did not perform complete genomic sequencing on all patient samples. However S gene target failure has been shown to be a reliably distinguish variant B.1.1.7 from the original SARS-CoV-2 strain and has been used as such in a number of studies [23, 24].

Nevertheless, this study does yield important insights into the changing characteristics and mortality within the ICU population across the first two waves of the pandemic. Although mortality risk in patients with COVID-19 is tempered by corticosteroids and tocilizumab, our data highlight that the alpha variant may result in a higher risk of death compared to the original variant. As critical care units experience further waves (particularly those affected by the delta (B.1.617.2) variant), we highlight the urgent need for further controlled prospective studies describing the immunological and pathophysiological differences across novel emerging variants, which will improve mechanistic understanding of COVID-19 and potentially identify novel therapeutic targets.

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20 Dec 2021

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Reviewer #1: This is a retrospective cohort study, as such, the results are primarily hypothesis-generating. Nevertheless, it is interesting that there is no difference in mortality between the two waves even though the patients in the second wave have largely received treatment that has been documented to have an effect on survival. This may indicate that the characteristics of the virus could be of importance. The article describes examples of what are called key biomarkers. Here it is appropriate to remind that our designation of what are important markers, is largely influenced by what we can measure more than by the fact that it has a decisive role in the process we seek to describe.

The actual division into two distinct virus variants is not based on a complete gene sequencing, but a so-called S gene target failure. How precise this division is, is not explained. Can the authors comment on this?

The authors address a number of important limitations of this type of study, not least it is pointed out that it is to be expected that there are likely to be a host of unknown confounding factors affecting the interpretation of these results.

However, this work contains observations that will be very interesting to clarify further, and as the authors say, it will be necessary to carry out controlled prospective studies in order to be able to determine the significance of different virus variants on the course of the disease and death.

Reviewer #2: Although the study addressed an interesting topic in the COVID-19 periods, the study design preclude the conclusion to be drawn with current analysis.

1. while the authors aimed to compare difference in two variants of the virus, the difference in current analysis can be due to other confounding factors that cannot be fully excluded.

2. The 2d wave patients showed more severe illness than at baseline than the 1st wave, which cannot be fully due to the virus variants. Other factors such as availability of ICU beds, older age and more comorbidity burden can cause this phenomenon.

3. It is interesting to note that nearly all patients in the 2d wave were treated with steroids while the usefulness of steoids were not confirmed. you need discuss this issue since there has been many literature on this issue (Crit Care. 2020 Dec 18;24(1):698. doi: 10.1186/s13054-020-03429-w. Ann Intensive Care. 2021 Nov 26;11(1):159. doi: 10.1186/s13613-021-00951-0.).

4. "1st wave: 42.2% vs 2nd wave: 39.8%, p<0.68"--such reporting of p<0.68 is not meaningful in the text.

5. I also suggest to report p value in table 2.

6. The confidence interval for B.1.1.7 variant in table 3 is very large, should interpret with caution.

7. There is no description of how variables were selected to enter into the multiple regression model, you need to clarify. there are many methods such as purposeful selection, stepwise and so on. But the authors fail to prespecify the model.

Reviewer #3: Author present their work as "Clinical and Survival differences during separate COVID-19 surges: Investigating the

impact of the Sars-CoV-2 alpha variant in critical care patients"

The manuscript is more of audit analysis of retrospective data.

Many similar articles now have been already published in recent times

1. Tracking the Emergence of SARS-CoV-2 Alpha Variant in the United Kingdom NEJM

2. The Disease Severity and Clinical Outcomes of the SARS-CoV-2 Variants of Concern by Lin et al.- Frontiers in Public Health

3. Mortality and critical care unit admission associated with the SARS-CoV-2 lineage B.1.1.7 in England: an observational cohort study. Lancet Infect Dis. 2021 by Patone et al.

The intent is good however the methodology is unclear. Inclusion criteria says “requiring invasive mechanical ventilation” whereas Fig 1 Flowchart includes NIV patients along with HFNC patients. On one hand authors say “Physiological or ventilatory variable data” and on other hand ventilator settings and protocol were probably different which can be responsible for variations in observations.

It is agreed about predominant variant; however was the alpha variant confirmed ? It is listed as limitation but should have confirmed for research article. Most data is on presumption of SARS-CoV-2 lineage B.1.1.7.

“with B.1.1.7. As ICUs are experiencing further waves (particularly by the delta (B.1.617.2) variant”

It appears Delta variant is on the wane, Which variants are now of concern or active in UK ?

There are many inflammatory variables studied. Why particular two variables selected- what about TLC, N/L ratio, CRP, IL-6 ? These markers also have shown co-relation in other studies.

What was the criteria of prescribing Toculizumab ?

The ventilator settings may also be dependent on amount of lung parenchyma involvement i.e CT Score. Any correlation with CT score ?

The discussion is not up to the mark. It needs to be worked upon with inputs from similar articles.

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15 Feb 2022

Responses to Reviewers

We would like to thank the editor for the opportunity to respond to the reviewers’ comments and are very grateful to the reviewers for their time in evaluating our paper. We have provided responses below to all of the concerns raised.

Editor's comments:

Comment 1: ‘Please follow the PLoS One's guidelines for manuscript preparation.’

Response 1: We have now amended the manuscript in line with PLoS One’s guidelines

Comment 2: ‘Please cite at least 5 articles from PLoS One’.

Response 2: We have added 5 Citations relating to PlosOne articles

Comment 3: ‘Please revise the manuscript per the following reviewers' comments’.

Response 3: Thank you, we have carefully considered all the comments made by the reviewers which we thank for being constructive. Please see our point by point responses below.

Reviewer #1

We thank the reviewer for his/her time and detailed, helpful comments and below are point-by-point responses to each of these. We have implemented the majority of the suggestions made by this reviewer, and provided explanations where we do not agree.

Comment 1: ‘This is a retrospective cohort study, as such, the results are primarily hypothesis-generating. Nevertheless, it is interesting that there is no difference in mortality between the two waves even though the patients in the second wave have largely received treatment that has been documented to have an effect on survival. This may indicate that the characteristics of the virus could be of importance. The article describes examples of what are called key biomarkers. Here it is appropriate to remind that our designation of what are important markers, is largely influenced by what we can measure more than by the fact that it has a decisive role in the process we seek to describe.’

Response 1: We appreciate these comments and review of the article.

Comment 2: ‘The actual division into two distinct virus variants is not based on a complete gene sequencing, but a so-called S gene target failure. How precise this division is, is not explained. Can the authors comment on this?’

Response 2: We thank the reviewer for this point. It has been shown comprehensively that S gene target failure (SGTF) was a reliable marker of B.1.1.7 (Brown et al JAMA 2021; doi 10.1001/jama.2021.5607). Indeed SGTF had been consistently used to identify B.1.1.7 variant in a number of high profile studies (e.g. Walker et al NEJM 2021; doi 10.1056/NEJMc2103227) and therefore we feel that this is a precise marker to differentiate alpha variant from the original SARS-CoV-2 variant.

We have added the following phrase to the discussion/limitations:

‘However S gene target failure has been shown to be a reliably distinguish variant B.1.1.7 from the original SARS-CoV-2 strain and has been used as such in a number of studies.’

Comment 3: ‘The authors address a number of important limitations of this type of study, not least it is pointed out that it is to be expected that there are likely to be a host of unknown confounding factors affecting the interpretation of these results. However, this work contains observations that will be very interesting to clarify further, and as the authors say, it will be necessary to carry out controlled prospective studies in order to be able to determine the significance of different virus variants on the course of the disease and death.’

Response 3: Thank you for your comments regarding our discussion and conclusion. We agree and have added “controlled” to the relevant sentence in the discussion to better emphasise this point.

“we highlight the urgent need for further controlled prospective studies describing the immunological and pathophysiological differences across novel emerging variants, which will improve mechanistic understanding of COVID-19 and potentially identify novel therapeutic targets.”

Reviewer #2

We thank the reviewer for his/her time and detailed, helpful comments and below are point-by-point responses to each of these. We have implemented the majority of the suggestions made by this reviewer, and provided explanations where we do not agree.

Comment 1: ‘while the authors aimed to compare difference in two variants of the virus, the difference in current analysis can be due to other confounding factors that cannot be fully excluded.’

Response 1: We agree with that the nature of the study design does not preclude unknown confounding factors. Variables included in our cox regression were decided a priori to allow adjustment for known confounding factors based on the existing literature (PMID: 32861276). We acknowledge this with the sentence:

“there are likely to be a host of unknown confounding factors affecting the interpretation of these results”

Comment 2: ‘The 2nd wave patients showed more severe illness than at baseline than the 1st wave, which cannot be fully due to the virus variants. Other factors such as availability of ICU beds, older age and more comorbidity burden can cause this phenomenon.’

Response 2: Thank you for this comment and we agree with this interesting observation. We included covariates such as SOFA score and use of NIV/CPAP or HRNC oxygen support prior to intubation, but have amended our discussion to acknowledge further factors which may have accounted for our observation.

“As well as changes to patient selection, frailty and co-morbidities, the timing of mechanical ventilation may also have differed in the disease process.”

Comment 3: ‘It is interesting to note that nearly all patients in the 2d wave were treated with steroids while the usefulness of steroids were not confirmed. you need discuss this issue since there has been many literature on this issue (Crit Care. 2020 Dec 18;24(1):698. doi: 10.1186/s13054-020-03429-w. Ann Intensive Care. 2021 Nov 26;11(1):159. doi: 10.1186/s13613-021-00951-0.).’

Response 3: Thank you for this comment and including two interesting studies on the subject of corticosteroid use in the ICU setting. The second wave of the pandemic in the UK coincided with a change to UK Covid-19 treatment guidelines which stipulated that dexamethasone should be offered to all hospitalised patients requiring oxygen supplementation. Patients in the first wave were treated off license or as part of open label clinical trials such as “RECOVERY”. However, we very respectfully disagree with the reviewer. Our data does not question the usefulness of corticosteroids, rather demonstrate that despite their use, mortality in the second wave remains very high. Our data suggest that this is because of an increased risk of mortality with B.1.1.7 and we have discussed in the discussion:

‘The ongoing emergence of novel variants of interest is one plausible explanation for the increased risk. We found poorer survival in individuals with B.1.1.7 with a 3.79-fold increase in mortality risk compared to non-B.1.1.7’

However, in light of the reviewer’s comments, we have added in the final paragraph:

“we highlight the urgent need for further controlled prospective studies describing the immunological and pathophysiological differences across novel emerging variants, which will improve mechanistic understanding of COVID-19 and potentially identify novel therapeutic targets.”

Comment 4: ‘1st wave: 42.2% vs 2nd wave: 39.8%, p<0.68"--such reporting of p<0.68 is not meaningful in the text’.

Response 4: Thank you we have removed this.

Comment 5: I also suggest to report p value in table 2.

Response 5: Thank you we have made this addition to table 2.

Comment 6: ‘The confidence interval for B.1.1.7 variant in table 3 is very large, should interpret with caution’

Response 6: Thank you for this comment and we will draw attention to this in our discussion.

“We found poorer survival in individuals with B.1.1.7 with a 3.79-fold increase in mortality risk compared to non-B.1.1.7 (albeit with a large confidence interval).”

Comment 7: ‘There is no description of how variables were selected to enter into the multiple regression model, you need to clarify. there are many methods such as purposeful selection, stepwise and so on. But the authors fail to prespecify the model.’

Response 7: As mentioned in the Response to comment 1, variables included in our cox regression were decided a priori to allow adjustment for known confounding factors based on the existing literature (PMID: 32861276). In acknowledgement of the reviewer’s comments, we have added the following section into the methods:

‘The relevant available clinical variable in the adjusted model were sequential organ failure assessment (SOFA) score at ICU admission, sex, and age as previously described. We also accounted for interventions that were used more in the second wave, that may have influenced mortality including application of non invasive ventilation (NIV) or continuous positive pressure ventilation (CPAP), high flow nasal oxygen steroids and tocilizumab treatment’

Reviewer #3:

We thank the reviewer for his/her time and detailed, helpful comments and below are point-by-point responses to each of these. We have implemented the majority of the suggestions made by this reviewer, and provided explanations where we do not agree.

Comment 1: ‘Author present their work as "Clinical and Survival differences during separate COVID-19 surges: Investigating the impact of the Sars-CoV-2 alpha variant in critical care patients"

The manuscript is more of audit analysis of retrospective data.

Many similar articles now have been already published in recent times

1. Tracking the Emergence of SARS-CoV-2 Alpha Variant in the United Kingdom NEJM

2. The Disease Severity and Clinical Outcomes of the SARS-CoV-2 Variants of Concern by Lin et al.- Frontiers in Public Health

3. Mortality and critical care unit admission associated with the SARS-CoV-2 lineage B.1.1.7 in England: an observational cohort study. Lancet Infect Dis. 2021 by Patone et al.’

Response 1: We thank the reviewer for their comment and have stated in the methods that the report was a carried out as a service evaluation. We have reference these papers in the discussion.

Comment 2: ‘The intent is good however the methodology is unclear. Inclusion criteria says “requiring invasive mechanical ventilation” whereas Fig 1 Flowchart includes NIV patients along with HFNC patients. On one hand authors say “Physiological or ventilatory variable data” and on other hand ventilator settings and protocol were probably different which can be responsible for variations in observations.’

Response 2: We sincerely apologise for the confusion and have made the legend for Figure 1 clearer. The flow chart mentions NIV and HFNC but this is to explain the percentage of patients who received these therapies prior to intubation. By the second wave, there was an increased administration of NIV and HFNC on acute respiratory units/medical wards which likely altered the population referred to ICU. Our intention is to recognise and account for this as could well have affected ICU mortality.

‘Fig 1: Study flow diagram demonstrating number of study patients during each wave. Number of patients receiving high flow nasal oxygen (HFNC), continuous positive airway pressure (CPAP) and non-invasive ventilation (NIV) prior to intubation also recorded.’

Comment 3: ‘It is agreed about predominant variant; however was the alpha variant confirmed ? It is listed as limitation but should have confirmed for research article. Most data is on presumption of SARS-CoV-2 lineage B.1.1.7.’

Response 3: We thank the reviewer for this point. It has been shown comprehensively that S gene target failure (SGTF) was a reliable marker of B.1.1.7 (Brown et al JAMA 2021; doi 10.1001/jama.2021.5607). Indeed SGTF had been consistently used as a measure of B.1.1.7 variant in a number of high profile studies (e.g. Walker et al NEJM 2021; doi 10.1056/NEJMc2103227) and therefore we feel that this is a precise marker to differentiate alpha variant from the original SARS-CoV-2 variant.

Comment 4: ‘with B.1.1.7. As ICUs are experiencing further waves (particularly by the delta (B.1.617.2) variant”. It appears Delta variant is on the wane, Which variants are now of concern or active in UK?’

Response 4: Omicron is the spreading rapidly within the UK and is the active variant.

Comment 5: ‘There are many inflammatory variables studied. Why particular two variables selected- what about TLC, N/L ratio, CRP, IL-6 ? These markers also have shown co-relation in other studies.’

Response 5: We thank the reviewer for these helpful comments. We chose PF ratio and dynamic compliance as these are easy to measure bedside tests that accurately describe the severity of ARDS and extent of disease burden on the lungs.

D-dimer was chosen as a measure of hypercoagulability and a systematic review (Varikusuvu et al 2021) of 113 studies demonstrated that D-dimer was accurate for predicting disease progression in COVID-19. Indeed an article published in this journal (Poudel et al) demonstrated that d-dimer on admission is an accurate biomarker for predicting mortality in COVID.

We did not include the variables suggested by the reviewer (total lymphocyte, neutrophil/lymphocyte (N/L ratio), CRP and IL-6) as we were not convinced of the diagnostic value of these variables. For example, it has been found that NLR correlated with disease severity during the 1st wave in COVID-19 patients due to the significant reduction of lymphocyte and the significant increase of neutrophil in severe COVID-19 patients. However, already published data confirms that this was not replicated in the 2nd wave and the number of lymphocytes and neutrophils did not change significantly between patients of different severity subgroups. Gelzo et al (2021 Frontiers in Immunology) hypothesise that this difference was likely because of the steroid therapy between the first and second wave. Furthermore, Gelzo as shows that biomarkers such as serum interleukin IL-6, which gradually increased with disease severity in patients of the 1st wave, did not correlate with disease severity in the 2nd wave when alpha variant was dominant. There has been a significant amount of debate about Il-6 levels and a review demonstrated that its levels are much lower than hypo or hyperinflammatory phenotypes of ARDS (Sinha et al 2020).

Finally CRP is known to dramatically supressed in patients who have been given tocilizumab (Hofmaenner et al 2021) and have very little and have very little diagnostic yield. Therefore we are unsure of the relevance of reporting it in our study as it may provide misleading results.

However in acknowledgement of the reviewers comments, we have made the following changes in the discussion:

- We have removed the term ‘when we adjust for key biomarker’ and changed to ‘when we adjust for the key variable in our cox proportional; risk analysis model’ (line 186)

- ‘We have included d-dimer in our analysis as it has been shown to correlate with disease severity and is an accurate biomarker for predicting mortality in COVID-19. However we found that in those patients with B.1.1.7, d-dimer levels are improved compared to non-B.1.1.7 patients, despite the increase in mortality. There are a number of potentially reasons for this including the effect of treatment such as steroids or tocilizumab. Our data suggests that a re-evaluation is required of various biomarkers that were initially thought to be predictive of disease severity as the pandemic progresses, particularly with as newer variants are discovered and novel treatments are introduced into clinical practice. For instance neutrophil/lymphocyte ratio was been associated with disease severity with the original SARS-COV-2 variant due to significant reduction in lymphocytes and increase of neutrophils in patients with severe COVID during this phase of the pandemic. However this has not been replicated with subsequent waves and counts of neutrophils and lymphocytes may not change significantly between different severity groups.’

Comment 6: ‘What was the criteria of prescribing Toculizumab?’

Response 6: The REMAP CAP trial reported their results of tocilizumab in Jan 2021 and prior to then, patients were prescribed it as part of the trial. Thereafter, each patient was discussed on a case by case bass by the Imperial College Healthcare NHS Trust independent COVID-19 Treatment Group (made up of specialists in the field).

Comment 7: The ventilator settings may also be dependent on amount of lung parenchyma involvement i.e CT Score. Any correlation with CT score ?

Response 7: This is a very interesting comment and we thank the reviewer for this. However sadly we were unable to do this. For our patients, there was no protocol for when precisely a patient should have CT imaging of their lungs (meaning that patients had scans performed at different times during their disease course). For instance most patients had CT scans when clinically stable to undertake the transfer to radiology department. When patients were intubated, they were often very unstable and not able to tolerate this transfer (and therefore no accompanying CT scan for that day). As a ventilatory data was collected at time of intubation, we are unable to comment if the ventilatory data at time of intubation correlated with CT score. In addition, not all our patients had CT scans, particularly if they were too unwell to transfer for a CT scan and subsequently died (particularly during the first wave).

Comment 8:. The discussion is not up to the mark. It needs to be worked upon with inputs from similar articles.

Response 8: We thank the reviewer for pointing this out. We have now made a number of changes to the discussion, which we hope brings it up to expected standard. We have also referenced all the aforementioned papers discussed by the reviewer, which have similar inputs.

Submitted filename: PLOS ONE-response to reviewers-transfer-R1.docx

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21 Apr 2022

4 May 2022

Responses to Reviewers

We would like to thank the editor for the opportunity to respond to the reviewers’ comments and are very grateful to the reviewers for their time in evaluating our paper. We have provided responses below to all of the concerns raised.

Editor's comments:

Comment 1: ‘Please find the reviewer's 2 comments for the remaining revision.’

Response 1: We have responded to all of reviewer’s 2 comennts for the remaining revision

Comment 2: ‘Please prepare the tables per the standard "AMA Manual of Style".

Response 2: We have prepared the tables as per the requested standard

Comment 3: ‘There is strong evidence denoting the importance of comorbidities on the survival of patients with COVID-19, such as cardiovascular disease (https://academic.oup.com/eurjpc/article/28/14/1599/6145858), cancer (https://brieflands.com/articles/ijcm-110907.html, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184167/), chronic kidney disease (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254525#:~:text=The%20crude%20mortality%20rate%20among,19%20in%20the%20PIRP%20cohort.), among others. I found that the authors have not evaluated this factor. I suggest considering this factor in the analysis; if the data are not available, please address it in the limitations by using the mentioned references’

Response 3: Thank you for this comment and we have made the following change to our discussion and limitations:

‘Thirdly, whilst we considered the covariates to include in our analysis, we did not account for other co-morbidities such as cardiovascular disease,19 cancer20 and chronic kidney disease,21 all of which have been associated with worse outcomes in COVID-19.’

Reviewer #2

We thank the reviewer for his/her time and detailed, helpful comments and below are point-by-point responses to each of these. We have implemented the majority of the suggestions made by this reviewer, and provided explanations where we do not agree.

Comment 1: ‘Though the authors have improved the manuscript draft; There are still few confounding variables which could have impacted the outcome in second wave that needs to be evaluated and mentioned in the limitations. (e.g., CT score, day of presentation)’

Response 1: We thank the reviewer for this comment and have made the following changes to the discussion:

Fourthly, scoring systems based upon computer tomography (CT) have been used to characterize COVID-19 severity and outcomes (e.g. requirement of mechanical ventilation/mortality) in patients, particularly on presentation to hospital.22 CT scores were not included in this study as all patients were already intubated and ventilated. Furthermore, we are unable to comment if ventilatory data at time of intubation correlated with CT score as most patients had CT scans when clinically stable to undertake transfer to radiology departments, rather than specifically at the onset of mechanical ventilation.

Comment 2: ‘The authors mention "Although mortality risk in patients with COVID-19 is tempered by corticosteroids and tocilizumab" however fail to address this in the discussion.

Steroids – (50.9%) 1st wave vs (99.2%) of second wave

Tocilizumab –(6.3%) in 1st wave and (31.8%) of second wave

Remdesivir – (2.9%) in 1st wave and (41.6%) of second wave.?’

Response 2: We thank the reviewer for this comment and have made the following changes to the discussion:

We have added the following phrase to the discussion/limitations:

When we adjust for the key variables in our cox proportional risk analysis model, we find that in the second wave of the pandemic (despite use of repurposed therapeutics such as corticosteroids and Tocilizumab),13-15 there is no improvement in ICU mortality. This was particularly surprising considering the benefit of tocilizumab and corticosteroids in reducing COVID-19 mortality is well established.10,11

It may also be prudent to evaluate whether treatments found to have significant mortality benefit with older COVID-19 variants, continue to be successful as variants mutate and pathophysiology potentially alters.

Comment 3: ‘The discussion still needs improvement based on the above facts.’

Response 3: Thank you for your comments regarding our discussion and conclusion. We agree and hope your suggested changes have imporeved the manuscript

Submitted filename: PLOS ONE - Responses to Reviewers - R2 -final.docx

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18 May 2022

Clinical and Survival differences during separate COVID-19 surges: Investigating the impact of the Sars-CoV-2 alpha variant in critical care patients

PONE-D-21-31797R2

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23 Jun 2022

PONE-D-21-31797R2

Clinical and Survival differences during separate COVID-19 surges: Investigating the impact of the Sars-CoV-2 alpha variant in critical care patients

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