# Immune dysregulation through longitudinal lymphocyte trajectories and their clinical determinants in hospitalized COVID-19 patients

José Pedro Cidade, Fabio Silvio Taccone, Luis Felipe Reyes, Laura Merson, Benjamin Lefèvre, Barbara Wanjiru Citarella, Arie Zainul Fatoni, Pedro Póvoa, Sabriya Abdalasalam, Alaa Abdalfattah Abdalhadi, et al.

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## To cite this version:

José Pedro Cidade, Fabio Silvio Taccone, Luis Felipe Reyes, Laura Merson, Benjamin Lefèvre, et al. Immune dysregulation through longitudinal lymphocyte trajectories and their clinical determinants in hospitalized COVID-19 patients. Intensive Care Medicine Experimental, 2026, 14 (1), pp.14-10.1186/s40635-026-00864-x. http://hal.science/hal-05501498

HAL Id: hal-05501498

https://hal.science/hal-05501498v1

Submitted on 10 Feb 2026

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
1 Manuscript Type: ORIGINAL ARTICLE; STROBE statement has been adopted

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4 ORIGINAL ARTICLE

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# Immune Dysregulation through Longitudinal Lymphocyte Trajectories and Their Clinical Determinants in Hospitalized COVID-19 Patients: Insights from the ISARIC Consortium

## 10 Authors

11 *José Pedro Cidade<sup>1,2</sup>, Fabio Silvio Taccone<sup>3</sup>, Luis Felipe Reyes<sup>4,5</sup>, Laura Merson<sup>5,6</sup>, Benjamin Lefevre<sup>7,8</sup>

12 Barbara Wanjiru Citarella<sup>6</sup>, Arie Zainul fatoni<sup>9</sup>, Pedro Póvoa<sup>1,2,10</sup>, and the ISARIC Characterization Group

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14 <sup>1</sup> Intensive Care Unit 4, Department of Intensive Care, São Francisco Xavier Hospital, ULSLO, Lisbon, Portugal

15 <sup>2</sup> Nova Medical School, Clinical Medicine, CHRC, NOVA University of Lisbon, Lisbon, Portugal.

16 <sup>3</sup> Department of Intensive Care Hopital Universitaire de Bruxelles (HUB), Université Libre de Bruxelles (ULB),

17 Brussels, Belgium

18 <sup>4</sup> Unisabana Center for Translational Science, School of Medicine, Chia, Colombia;

19 <sup>5</sup> ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, UK.

20 <sup>6</sup> Public Health Department, Institut Pasteur de Dakar, Dakar, Senegal

21 <sup>7</sup> Université de Lorraine, CHRU Nancy, Service des Maladies Infectieuses et Tropicales, F-54000 Nancy, France

22 <sup>8</sup> Université de Lorraine, Inserm, INSPIIRE, F-54000 Nancy, France

23 <sup>9</sup> Department of Anesthesiology and Intensive Therapy RSUD dr Saiful Anwar Jawa Timur, Brawijaya University

24 Malang East Java

25 <sup>10</sup> Center for Clinical Epidemiology and Research Unit of Clinical Epidemiology, OUH Odense University Hospital,

26 5000 Odense C, Denmark.

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## 28 Corresponding author:

29 José Pedro Cidade;

30 Intensive Care Unit 4, Department of Intensive Care, São Francisco Xavier Hospital, CHLO

31 Estr. Forte do Alto Duque, Piso 1, 1449-005 Lisboa

32 zencidade@gmail.com; ORCID 0000-0002-8359-5171

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## 34 ABSTRACT
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**Objective:** Immune dysregulation plays a pivotal role in the pathophysiology of sepsis and COVID-19, with lymphopenia emerging as a consistent marker of severity and poor prognosis. However, most existing studies have assessed lymphocyte counts at isolated time points, limiting insights into their temporal behavior and prognostic value. The dynamics of lymphocyte recovery or persistence of lymphopenia remain largely unexplored in large populations, as well as the impact of adjunctive therapies such as corticosteroids. Using the multinational ISARIC cohort, we investigated longitudinal lymphocyte trajectories in hospitalized patients and the clinical determinants associated with their evolution over time.

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**Methods:** We conducted a multinational prospective observational cohort study using data from the ISARIC-WHO Clinical Characterization Protocol, which includes standardized data collection from hospitalized adult COVID-19 patients. Patients with confirmed SARS-CoV-2 infection and at least four lymphocyte measurements during the first 28 days of hospitalization were included. We analyzed lymphocyte trajectories, Cox regression survival analyses and multivariable linear regression modelling. We also applied multistate models and joint modeling to assess the association between lymphocyte trajectories and 28-day mortality, incorporating corticosteroid use as a time-varying covariate.

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**Results:** Of 945,317 screened patients, 231,933 hospitalized adults with confirmed COVID-19 and sufficient lymphocyte data were included, with 56.6% classified as lymphopenic. Lymphopenia was independently associated with higher rates of ICU admission, organ support, and in-hospital mortality (OR = 1.52, 95% CI 1.48–1.55), and lower absolute lymphocyte counts were strongly linked to worse survival in adjusted Cox models (HR = 0.75, 95% CI 0.74–0.76). Multistate modeling revealed that lymphopenic patients had a significantly higher daily transition rate to death and a shorter duration in that immune state, while corticosteroid exposure was associated with an increased likelihood of entering and remaining in lymphopenia. Joint modeling identified age, sex, and corticosteroid use as significant predictors of lower lymphocyte trajectories over time, with distinct dynamics between survivors and non-survivors.

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**Conclusion:** Lymphopenia was common and strongly associated with worse outcomes in hospitalized COVID-19 patients, with impaired recovery particularly evident in those receiving corticosteroids. These findings highlight the value of lymphocyte monitoring to inform tailored immunomodulatory strategies in sepsis and severe viral infections.

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**Keywords** (3-10 keywords): Lymphopenia, Immunity, Sepsis, In-hospital mortality, COVID19, Multistate analysis, Corticosteroids

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Word count (without references): 3223 words

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Tables: 3

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Figures: 5

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**Main manuscript**
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## 2 Introduction

Immune dysfunction has gained prominence as a key area of interest in the pathophysiology of sepsis, with compelling evidence linking the profound derangement in the host inflammatory response to clinical outcomes [1, 2]. Our long-standing view of a universal immune activation response towards infection has been fundamentally redefined. Recent evidence clearly suggests a far more dynamic and heterogeneous biological mechanism, with septic patients transitioning from early hyperinflammation and cytokine release to a state of immune paralysis, usually persisting or fluctuating, in and out of an immunosuppressive phase [3-6]. This complexity is potentially correlated with a wide variability in outcomes among septic patients, prompting increasing interest in immunophenotypes and distinct immune response patterns, despite similar infectious insults [7, 8]. The COVID-19 pandemic intensified focus on this paradigm, revealing striking interindividual differences in disease trajectories despite a common viral insult. This variability has exposed the limitations of our current understanding of immune dynamics over time.

Among the various immunological alterations observed in COVID-19 and other causes of sepsis, lymphopenia has consistently emerged as a unique biological marker of disease severity and poor prognosis [9]. Absolute lymphocyte counts below $1x10^9$ cells/L have been repeatedly associated with increased risk of organ failure, need for organ support, and mortality. However, most existing studies have relied on single time-point measurements, limiting our understanding of the dynamics of lymphocyte recovery or persistence of immune suppression [10-14]. A recent meta-analysis by Elçioğlu et al. demonstrated consistently higher in-hospital mortality rates, both early and late, across fifteen observational studies, arguing that longitudinal immune trajectories may provide greater insight into clinical outcomes [15]. Yet, whether lymphopenia reflects a transient epiphenomenon, a marker of adaptive exhaustion, or a surrogate of immune failure remains unclear, particularly when evaluated outside of its temporal context [16].

Despite consistent associations between lymphopenia and adverse outcomes in critical illness, the temporal dimension of lymphocyte trajectories remains poorly understood. As such, the prognostic implications of lymphocyte dynamics, whether as markers of immune recovery or ongoing dysfunction, remain largely unexplored. In this context, we aimed to assess the prognostic value of lymphocyte trajectories over time. Leveraging the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 database, one of the largest standardized, multinational cohorts of hospitalized patients, we conducted a comprehensive analysis of hospitalized COVID-19 patients with serial lymphocyte counts. We aimed to describe the presence of lymphopenia in these patients and assess its persistence, resolution, or fluctuation over time, exploring how dynamic patterns relate to clinical outcomes, as well as the impact of adjunctive therapies such as corticosteroids.

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## 44 Methods
This study is a prospective observational cohort study of hospitalized patients from five continents. The study Consortium framework is provided by the International Severe Acute Respiratory and Emerging Infection (ISARIC) - World Health Organization (WHO) Clinical Characterization Protocol for Severe Emerging Infections [17, 18]. The protocol, case report forms, consent forms, and study information are available on the ISARIC website (https://isaric.org). This standardized protocol uses tiered data collection tailored to a range of resource settings (15). Investigators from 69 countries collected prospective data using the ISARIC case report form (CRF) built on Research Electronic Data Capture (REDCap, version 8.11.11, Vanderbilt University, Nashville, Tenn.) [19] hosted by the University of Oxford. Other investigators collected data using locally hosted systems and submitted it to the ISARIC dataset for centralized mapping. All investigators retain full rights to their data.

This observational study required no change to clinical management and encouraged patient enrolment in other research projects. The ISARIC-WHO Clinical Characterization Protocol was approved by the World Health Organization Ethics Review Committee (RPC571 and RPC572). Also, local ethics approval was obtained for each participating country and site according to local requirements.

## Study population

We included all adult patients hospitalized with confirmed SARS-CoV-2 infection, defined by clinical and radiological criteria (i.e., symptoms and findings of SARS-CoV-2 pneumonia seen in thoracic diagnostic images) and/or a positive reverse transcription-polymerase chain reaction (RT-PCR) result from a respiratory sample. Eligible patients had to be biochemically characterized during hospitalization, with available data on absolute lymphocyte and leukocyte counts. Patients with >30% missing data in variables deemed critical for analysis were excluded (see the Electronic Supplementary Material [ESM] for further details).

To ensure a meaningful assessment of lymphocyte dynamics, we excluded patients who lacked data for at least four absolute lymphocyte count measurements during the first 28 days of hospitalization. These measurements had to include values from days 0, 1, 3, and 7, ensuring that lymphocyte counts were obtained at standardized time points across all patients. Patients with implausible values for these variables or with missing outcome data were also excluded.

## Data collection and definitions

Demographic, clinical, and biochemical data were extracted from the electronic case report forms (eCRFs), including age, sex, comorbidities, country income classification (per World Bank definitions: https://data.worldbank.org/country), vital signs at admission, laboratory values during hospitalization, need for advanced organ support (mechanical ventilation, vasopressors, renal replacement therapy), systemic complications, and treatments administered during hospitalization. All study variables were predefined in the ISARIC study protocol and case report form completion guide is available online (https://isaric.org).

The resulting analytic cohort is referred to as the Epidemiological cohort. Patients were further stratified based on the development of lymphopenia, defined as at least one absolute lymphocyte count <1×10⁹ cells/L during hospitalization [9, 20]. Patients with
1 no such values were classified in the non-lymphopenia group. For all analyses, day 0

2 was considered the day of hospital admission.

3 **Outcomes**

4 The primary outcome evaluated in this study was 28-day all cause in-hospital mortality.

5 Secondary outcomes included the rate and risk factors for Lymphopenia development,

6 rate of advanced organ support requirements during the hospitalization episode (i.e.

7 mechanical ventilation, vasopressor support or renal replacement therapy), the rate of

8 ICU admission, the ICU and hospital all-cause mortalities and the impact of

9 corticosteroids on lymphocyte trajectories and remaining outcomes.

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11 **Statistical methods**

12 Data was expressed as mean (standard deviation [SD]), if normally distributed, and

13 non-normally distributed variables as median (interquartile range [IQR]). Categorical

14 variables were expressed as numbers and percentages. Chi-square test was used for

15 categorical variables, and t-test and Kruskal-Wallis were used on continuous variables

16 for statistical assessment of outcomes between groups. Initially, we performed

17 univariate analysis of demographic, comorbidities, clinical presentation, biochemical

18 characterization, complications and outcomes between Lymphopenia and non-

19 Lymphopenia cohorts. Absolute lymphocyte counts distribution over time was also

20 obtained to compare the cohorts.

21 Survival analysis was performed using Kaplan-Meier curves with log-rank tests to

22 compare 28-day survival across lymphocyte-defined groups. Multivariable Cox

23 proportional hazards models evaluated the association between continuous

24 lymphocyte counts and mortality, adjusting for age, sex, corticosteroid treatment, and

25 organ support interventions (vasopressors/inotropes, renal replacement therapy,

26 invasive and non-invasive ventilation). Proportional hazards assumptions were verified.

27 Additionally, we conducted a multivariable logistic regression to assess associations

28 between lymphopenia status (based on longitudinal lymphocyte thresholds) and all-

29 cause in-hospital mortality, adjusting for the same covariates. Model discrimination and

30 calibration were evaluated.

31 To analyze transitions between the non-lymphopenic and lymphopenic states and the risk of

32 death, we fitted a continuous-time, time-homogeneous multistate Markov model using Aalen-

33 Johansen estimation. This approach allowed estimation of transition probabilities over time,

34 capturing competing risks and intermediate transitions, and was adjusted for age and gender.

35 An additional analysis including corticosteroid therapy as a covariate was performed

36 subsequently. A diagram of the multistate analysis is presented in the Methods section of the

37 ESM.

38 To jointly assess lymphocyte dynamics and mortality in relation to corticosteroid

39 treatment, a joint modeling approach combined a linear mixed-effects model for

40 longitudinal lymphocyte counts with a Cox survival model. Corticosteroid treatment was

41 included as a time-varying covariate. Models were adjusted for relevant clinical

42 variables, organ support requirements (respiratory, hemodynamic and renal) and

43 demographic factors.
1 Statistical analyses were performed using R version 4.4.0 (R Foundation for Statistical
2 Computing, Vienna, Austria) with packages *survival*, *Ime4* and *JMbayes2*. A two-sided
3 p-value < 0.05 was considered statistically significant.

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## 5 Results

6 A total of 945,317 patients were initially screened for inclusion. Among these, 686,933 were
7 excluded due to the absence of valid absolute lymphocyte and leukocyte determinations, high
8 rate of missing values or absence of confirmation of required COVID19 infection criteria. After
9 applying exclusion criteria, 26,451 patients were removed, resulting in a final cohort of 231,933
10 patients (as depicted in Figure 1). Of these, 100,698 patients (43.4%) were classified into the
11 non-Lymphopenia group, while 131,235 patients (56.6%) were categorized in the Lymphopenia
12 group.

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14 *Patients' demographic, treatment and outcome characteristics*

15 Patient characteristics are detailed in Table 1. The groups presented significantly different
16 demographic and clinical characteristics, with Lymphopenia group being composed of older
17 patients, with a male preponderance and consistently higher rates of comorbidities and
18 complications during Hospital stay. Patients in the Lymphopenia group also required higher
19 rates of ICU admission and longer lengths of respiratory support (invasive mechanical
20 ventilation) along with longer lengths of ICU and Hospital stay. We also documented higher all-
21 cause mortality rates, either at ICU or in-Hospital, in the Lymphopenia group when compared
22 to non-Lymphopenia group patients (Table 1). Figure 2 illustrates the longitudinal distribution
23 of absolute lymphocyte serum values across Day 0 to Day 28 stratified by lymphopenia status.
24 Patients classified in the Lymphopenia group consistently exhibited significantly lower
25 lymphocyte counts at every measured time point compared to the non-Lymphopenia group (p
26 < 0.05 for all days). This persistent lymphopenia was evident despite the general upward trend
27 in lymphocyte values observed over time in both groups.

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29 *Primary Outcomes and Survival Analysis*

30 The adjusted multivariable Cox proportional hazards regression demonstrated that lower
31 lymphocyte counts were independently associated with increased risk of death (HR = 1.33 per
32 1x10⁹ cells/L, 95% CI 1.28–1.38, *p* < 0.001). Increasing age (HR = 1.05 per year, 95% CI 1.05–
33 1.06, *p* < 0.001), male sex (HR = 1.22, 95% CI 1.20–1.24, *p* < 0.001), corticosteroid use
34 (HR = 1.05, 95% CI 1.03–1.07, *p* < 0.001) and vasopressor, mechanical ventilation and renal
35 replacement therapy requirements (all p<0.001) were also significantly associated with
36 increased hazard of death. A forest plot of hazard ratios is presented in Figure 1ESM. These
37 results were consistent with the survival estimates by lymphocyte status, illustrated with a
38 Kaplan–Meier curve in Figure 3, with differences in survival confirmed by the log-rank test
39 (*p* < 0.001).

40 We also performed a multivariable logistic regression to identify independent predictors
41 associated with the all-cause in-hospital mortality (Table 2). After adjustment for key
42 confounders, lymphopenia status was independently associated with a significantly increased
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odds of the adverse outcome (OR 1.52, 95% CI 1.48–1.55, *p* < 0.001). Other significant

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predictors included advancing age (OR 1.07 per year increase, 95% CI 1.07–1.07, *p* < 0.001) and

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male sex (OR 1.29, 95% CI 1.26–1.32, *p* < 0.001). Use of corticosteroids (OR 1.20, 95% CI 1.17–

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1.23, *p* < 0.001) and organ support requirements were also linked to higher odds of the

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outcome.

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Estimated transition intensities between states of our multistate Markov model are depicted in

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Table 3. Patients in the non-Lymphopenia state transitioned to the Lymphopenia state at a rate

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of 1.2% per day (95% CI: 1.18–1.23) and to death at a rate of 2.0% per day (95% CI: 1.99–2.07).

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From the Lymphopenia state, the transition intensity back to the non-Lymphopenia state was

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1.6% per day (95% CI: 1.54–1.61), while the transition to death occurred at a markedly higher

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rate of 4.3% per day (95% CI: 4.26–4.36). All transitions were statistically significant (*p* < 0.001).

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Mean expected duration spent in a state before transitioning were 30.9 days for the non-

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Lymphopenia state and 17.1 days for the Lymphopenia state. These estimates suggest differing

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stability across immune states, with shorter durations observed in lymphopenia patients. In a

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covariate-adjusted multistate model including corticosteroid exposure, corticosteroid use was

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associated with increased hazard of transitioning from the non-Lymphopenia state to

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Lymphopenia (HR = 1.13, 95% CI: 1.08–1.18) and from non-Lymphopenia to death (HR = 1.16,

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95% CI: 1.12–1.20). Conversely, corticosteroid use was associated with a reduced hazard of

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transitioning from the Lymphopenia to the non-Lymphopenia state (HR = 0.71, 95% CI: 0.68–

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0.74), with no significant effect on transitions from Lymphopenia to death (HR = 0.94, 95% CI:

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0.92–1.06) (Figure 4, Figure 2ESM).

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Longitudinal absolute lymphocyte serum count trajectories and Impact of
 corticosteroids

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Using a joint modeling approach, our data revealed multiple significant predictors of

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lymphocyte trajectories. Increasing age and male sex were associated with lower lymphocyte

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counts, corresponding to relative decreases of approximately 13% per scaled year of age (95%

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CI: 11% to 15%) and 11% lower counts in males compared to females (9% to 14% decrease;

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both *p* < 0.001). Corticosteroid treatment was linked to a significant 10% reduction in

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lymphocyte counts over time (95% CI: 7% to 13% decrease, *p* < 0.001). Additional factors such

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as inotropes/vasopressors (approximately 10% decrease), renal replacement therapy

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(approximately 12% increase), and non-invasive ventilation (approximately 15% decrease) also

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showed significant effects on lymphocyte trajectories. Among survivors, lymphocyte values

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were significantly influenced by time since admission, corticosteroid treatment, and their

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interaction (*p* < 0.005 for all), indicating that corticosteroids modulate lymphocyte dynamics

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over time in this group. In non-survivors, corticosteroid treatment and its interaction with time

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were not significant, though lymphocyte counts still changed significantly over time (*p* =

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0.0007). Overall, survivors and non-survivors differed significantly in lymphocyte counts, with

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the interaction between time and outcome being more pronounced in patients not receiving

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corticosteroids (Figure 5).

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Discussion

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Our findings robustly position lymphopenia as a prevalent and clinically meaningful

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marker of immune dysregulation in hospitalized patients with COVID-19. This study
also documents that the presence and persistence of lymphopenia were independently associated with a markedly increased risk of adverse outcomes, including ICU admission, requirement of organ support, and higher in-hospital mortality. It also supports that patients who developed progressive or sustained lymphopenia, largely regardless of their initial lymphocyte count at admission, faced significantly higher mortality risk. These results suggest lymphopenia not merely as an epiphenomenon of critical illness but as a dynamic, prognostically relevant immunological signature.

Lymphopenia has long been proposed as a marker of immune dysregulation in sepsis and severe viral infections, despite the absence of definitive large-scale evidence support [21]. Our findings confirm and extend prior observations by providing multivariable-adjusted analyses that consistently link lower lymphocyte counts with increased hazard of in-hospital complications (across nearly every organ system), ICU admission rates, organ support requirements, and death. Although with a pragmatic and far less restrictive definition of lymphopenia status, demanding a single measurement during the first 28 days of hospital stay, our results show that over half of COVID-19 hospitalized patients (56%) met criteria for lymphopenia, exceeding prevalence estimates from earlier studies [14, 22, 23].

Patients who exhibit lymphopenia had significantly higher risk of adverse outcomes, even after adjustment for major clinical and treatment-related determinants, with 52% higher odds of in-hospital death compared to non-lymphopenia patients. These findings are consistent with previous studies, suggesting a potential dose-response relationship between lymphocyte depletion and poor prognosis [15, 24, 25]. These data support the inclusion of immune monitoring, specifically lymphocyte kinetics, as a critical parameter in the clinical assessment of hospitalized patients, comparable to the monitoring of other organ systems.

Our multistate model further reveals the temporal instability of immune states, showing that patients transitioning into lymphopenia face a markedly higher daily risk of death, while recovery from this state, or its complete absence, is associated with a survival advantage. These findings offer compelling support for the hypothesis that restoration of immune competence, supported by lymphocyte recovery, may represent a potentially critical inflection point in the trajectory of these hospitalized patients. Consistent with prior evidence, this dynamic is likely not limited to COVID-19, but may extend to other etiologies of sepsis, given the well-documented role of immune recovery in various infectious contexts [26-28].

Our data also reinforces the paradoxical role of corticosteroids, which were independently associated with both increased risk of transitioning into lymphopenia, impaired lymphocyte recovery and higher mortality. Considering their immunosuppressive properties, our results may reflect a potential unintended cost on immune resilience, particularly in patients with already compromised adaptive responses [28, 29]. Notably, in survivors, corticosteroids exerted dynamic modulatory effects on lymphocyte trajectories. In contrast, in non-survivors, these modulatory effects appeared blunted, suggesting differential responsiveness based on host immune trajectory and possibly timing of administration. These results are consistent with recent data from several studies challenging the “one-size-fits-all” approach to corticosteroid therapy, specifically in COVID-19, and more broadly in sepsis [30-32]. A recent meta-analysis argues that the observed survival benefit of corticosteroids in COVID19 appeared to be largely driven by the RECOVERY trial, as the effect was no longer statistically significant when this study was excluded [33, 34]. Another meta-analysis by Xiangrong Ye et al demonstrated no significant difference in 28-day
1 mortality among hospitalized COVID-19 patients receiving corticosteroids [35].

2 However, subgroup analyses suggested potential clinical benefit in moderate-to-severe

3 cases, particularly in selected populations, thereby advocating for a personalized

4 therapeutic approach aimed at minimizing adverse immunological consequences.

5 Importantly, the absence of data on steroid treatment and lymphocyte trajectories in

6 those analyses may limit the interpretation of these findings even on selected

7 populations.

8 Collectively, all this data contributes to the growing body of evidence framing immune

9 paralysis and dysfunction as a clinically relevant, and often underutilized, readily

10 accessible surrogate for immune trajectory. This is particularly important in contexts

11 where advanced immunophenotyping is unavailable [36]. Cautiously, our results also

12 argue against a reductionist interpretation: lymphocyte counts alone cannot fully

13 characterize the complexity of host immune response in sepsis and severe viral

14 infections. Nevertheless, lymphocyte trajectories may serve as valuable biomarkers

15 when interpreted within a broader immunological context, including patient age,

16 comorbidities, disease phase, and therapeutic exposures, namely corticosteroids.

17 Our study benefits from several strengths. It is based on an unprecedented sample

18 size, with granular temporal resolution of both laboratory and clinical variables. The

19 application of advanced statistical methodologies, including multistate modeling and

20 latent class mixed models to capture dynamic immunological transitions and

21 associations between lymphocyte dynamics, treatment exposures, and outcomes.

22 Furthermore, it uses a large cohort with pragmatic data capturing across multiple

23 countries that represents real-world reported observations.

24 However, some limitations merit consideration. First, the observational nature of the

25 study precludes definitive causal inferences, and residual confounding cannot be fully

26 excluded. Second, missing laboratory data led to the exclusion of a substantial number

27 of patients, possibly limiting the power and generalizability of some of our more

28 sophisticated analyses, such as LCMM. Also, most patients in this cohort were from

29 high-income countries, which may further limit generalizability. Additionally, we cannot

30 exclude that lymphocyte measurements may have been performed more frequently in

31 patients with severe disease, which could introduce selection bias; however, our

32 inclusion criteria requiring measurements on days 0, 1, 3, and 7 were applied

33 consistently across all patients, in attempt to reduce this impact, although not fully

34 eliminating this potential bias. To ensure the integrity of the dataset, we opted not to

35 impute missing values in the primary analyses; sensitivity analyses using median

36 imputation and last observation carried forward confirmed that our main findings were

37 robust and did not materially change. Despite this, our final analytical cohort remains

38 among the largest of its kind. Third, we agree that the definition of lymphopenia

39 employed, while intentionally inclusive, may lack sensitivity and specificity. We aimed to

40 capture all plausible immune states consistent with lymphopenia in every patient.

41 Additionally, we state that lymphocyte count is a crude proxy for immune function, and

42 more refined markers (e.g., CD4+ T cells absolute count, mHLA-DR expression, PD-1

43 and PD-L1 serum levels or Ex-vivo LPS-induced tests) were not available. Lastly, our

44 data reflect the COVID-19 era, and thus, extrapolation to non-COVID sepsis should be

45 done with caution, though we emphasize that the underlying immunopathological

46 mechanisms are likely to share significant overlap.

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## 1 Conclusion

In this study, Lymphopenia was common in hospitalized COVID-19 patients and independently associated with worse clinical outcomes, with persistent or worsening lymphopenia identifying those at highest risk. Monitoring lymphocyte dynamics provides an accessible window into immune function and prognosis, particularly in resource-limited settings. Furthermore, corticosteroid exposure was associated with impaired lymphocyte recovery, urging the need for a more nuanced, immune-informed approach to immunomodulatory therapies, especially in patients with signs of adaptive immune suppression. These findings support immune-informed management strategies in sepsis and severe viral infections and highlight the potential for personalized therapeutic approaches based on immune trajectory.

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## 13 Declarations

### 14 Ethics approval and consent to participate

This observational study required no change to clinical management. The ISARIC-WHO Clinical Characterisation Protocol was approved by the World Health Organization Ethics Review Committee (RPC571 and RPC572 on 25 April 2013). Institutional approval was additionally obtained by participating sites including the South Central Oxford C Research Ethics Committee in England (Ref 13/SC/0149) and the Scotland A Research Ethics Committee (Ref 20/SS/0028) for the United Kingdom, representing the majority of the data. Requirement for consent was waived by the confidentiality advisory group of UK Health Regulations Authority and approved by the sponsor.

Other institutional and national approvals were obtained by participating sites as per local requirements. Regionally appropriate decisions regarding a waiver or requirement of patient consent and/or assent were made by each committee and implemented at the sites.

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### 26 Data sharing statement

The data that underpin this analysis are highly detailed clinical data on individuals hospitalised with COVID-19. Due to the sensitive nature of this data and the associated privacy concerns, they are available via a governed data access mechanism following review of a data access committee. Data can be requested via the IDDO COVID-19 Data Sharing Platform (http://www.iddo.org/covid-19). The Data Access Application, Terms of Access and details of the Data Access Committee are available on the website. Briefly, the requirements for access are a request from a qualified researcher working with a legal entity who has a health and/or research remit; a scientifically valid reason for data access which adheres to appropriate ethical principles. The full terms are at: https://www.iddo.org/document/covid-19-data-access-guidelines. A small subset of sites who contributed data to this analysis have not agreed to pooled data sharing as above. In the case of requesting access to these data, please contact the corresponding author in the first instance who will look to facilitate access.

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### 40 Conflict-of-interest statement

The authors state their conflicts of interest through the COI document provided by the journal, dated and signed individually by every individual author

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## 2 Funding

The ISARIC Clinical Characterisation Group is supported by the Wellcome Trust [303666/Z/23/Z]; UK

International Development [301542-403]; and the Gates Foundation [INV-063472]. The funders had

no role in study design, data collection and analysis, decision to publish, or preparation of the

manuscript.

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## 9 STROBE statement

The authors have read the STROBE Statement—checklist of items, and the manuscript was

prepared and revised according to this statement

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## 13 Authors' Contributions

José Pedro Cidade: Writing – review & editing, Writing – original draft, Visualization, Software,

Project administration, Methodology, Investigation, Formal analysis, Data curation,

Conceptualization. Fabio Silvio Taccone: Writing – review & editing, Writing – original draft,

Investigation. Luis Felipe Reyes: Writing – review & editing, Writing – original draft, Investigation.

Laura Merson: Writing – review & editing, Writing – original draft, Investigation. Benjamin Lefevre:

Writing – review & editing, Writing – original draft, Investigation. Barbara Wanjiru Citarella: Writing

– review & editing, Writing – original draft, Investigation, Data curation. Arie Zainul fatoni: Writing

– review & editing. Pedro Póvoa: Writing – review & editing, Writing – original draft, Visualization,

Project administration, Methodology, Investigation, Supervision, Conceptualization.

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## 24 References

[1] Wang Z, Zhang W, Chen L, et al. Lymphopenia in sepsis: a narrative review. Crit Care

2024;28(1):315.10.1186/s13054-024-05099-4

[2] Cilloniz C, Peroni HJ, Gabarrús A, et al. Lymphopenia Is Associated With Poor

Outcomes of Patients With Community-Acquired Pneumonia and Sepsis. Open Forum

Infect Dis 2021;8(6):ofab169.10.1093/ofid/ofab169

[3] Finfer S, Venkatesh B, Hotchkiss RS, et al. Lymphopenia in sepsis-an acquired

immunodeficiency? Immunol Cell Biol 2023;101(6):535-44.10.1111/imcb.12611

[4] Willmann K, Moita LF. Physiologic disruption and metabolic reprogramming in infection

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## Tables and Figures

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Table 1. Demographic, clinical, treatment and outcome characteristics Stratified by Lymphopenia Groups
<table><thead><tr><th>Variable</th><th>Non-Lymphopenia (n=100,698)</th><th>Lymphopenia (n=131,235)</th><th>p-value</th></tr></thead><tbody><tr><td colspan="4"><strong>DEMOGRAPHIC VARIABLES</strong></td></tr><tr><td>Age, years (median [IQR])</td><td>60.0 [43.0, 76.0]</td><td>69.0 [55.0, 80.0]</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Gender, males (%)</td><td>51,834 (51.5)</td><td>79,285 (60.4)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Height (cm), Median [IQR]</td><td>168.0 [161.0, 175.0]</td><td>170.0 [163.0, 175.0]</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Weight (kg), Median [IQR]</td><td>75.0 [54.0, 90.0]</td><td>76.0 [60.0, 90.0]</td><td>0.410</td></tr><tr><td colspan="4"><strong>COMORBIDITIES</strong></td></tr><tr><td>HIV/AIDS</td><td>653 (0.8%)</td><td>562 (0.5%)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Asthma</td><td>12,102 (13.6%)</td><td>15,338 (13.0%)</td><td></td></tr><tr><td>Chronic Cardiac Disease</td><td>19,958 (21.9%)</td><td>33,659 (27.7%)</td><td></td></tr><tr><td>Chronic Hematological Disease</td><td>2,782 (3.1%)</td><td>5,216 (4.5%)</td><td></td></tr><tr><td>Chronic Kidney Disease</td><td>9,905 (11.1%)</td><td>18,518 (15.5%)</td><td></td></tr><tr><td>Chronic Neurological Disorder</td><td>8,592 (9.6%)</td><td>11,610 (9.9%)</td><td></td></tr><tr><td>Chronic Pulmonary Disease</td><td>11,301 (12.4%)</td><td>19,704 (16.3%)</td><td></td></tr><tr><td>Dementia</td><td>7,884 (8.8%)</td><td>11,635 (9.8%)</td><td></td></tr><tr><td>Diabetes</td><td>23,275 (25.8%)</td><td>31,204 (26.1%)</td><td></td></tr><tr><td>Hypertension</td><td>31,899 (39.0%)</td><td>48,248 (46.1%)</td><td></td></tr><tr><td>Immunosuppression</td><td>1,272 (3.1%)</td><td>3,069 (5.1%)</td><td></td></tr><tr><td>Liver Disease</td><td>2,768 (3.0%)</td><td>3,897 (3.2%)</td><td></td></tr><tr><td>Malignant Neoplasm</td><td>6,119 (6.8%)</td><td>12,958 (10.8%)</td><td></td></tr><tr><td>Malnutrition</td><td>1,426 (1.7%)</td><td>2,089 (1.9%)</td><td></td></tr><tr><td>Obesity</td><td>13,738 (16.8%)</td><td>17,678 (16.5%)</td><td></td></tr><tr><td>Other Comorbidities</td><td>35,202 (45.4%)</td><td>50,732 (48.9%)</td><td></td></tr><tr><td>Rare Diseases/Inborn Errors of Metabolism</td><td>295 (0.7%)</td><td>320 (0.5%)</td><td></td></tr><tr><td>Rheumatologic Disorder</td><td>8,254 (9.3%)</td><td>12,635 (10.8%)</td><td></td></tr><tr><td>Smoking</td><td>22,382 (38.9%)</td><td>32,512 (43.6%)</td><td></td></tr><tr><td>Transplantation</td><td>468 (1.1%)</td><td>1,217 (2.0%)</td><td></td></tr><tr><td>Tuberculosis</td><td>46 (0.5%)</td><td>71 (0.5%)</td><td></td></tr><tr><td>Pregnancy</td><td>2,471 (12.7%)</td><td>1,648 (11.0%)</td><td></td></tr><tr><td colspan="4"><strong>SYMPTOMS AT HOSPITAL ADMISSION</strong></td></tr><tr><td>Abdominal Pain (%)</td><td>7822 (10.0)</td><td>9510 (9.3)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Altered Consciousness/Confusion (%)</td><td>13259 (17.7)</td><td>22477 (21.2)</td><td></td></tr><tr><td>Asymptomatic (%)</td><td>2386 (4.6)</td><td>1973 (2.7)</td><td></td></tr><tr><td>Bleeding (%)</td><td>1407 (1.8)</td><td>1735 (1.7)</td><td></td></tr><tr><td>Chest Pain (%)</td><td>13443 (17.0)</td><td>14960 (14.4)</td><td></td></tr><tr><td>Conjunctivitis (%)</td><td>327 (0.4)</td><td>374 (0.4)</td><td></td></tr><tr><td>Cough (%)</td><td>49945 (58.7)</td><td>76617 (67.1)</td><td></td></tr><tr><td>Diarrhoea (%)</td><td>12996 (16.2)</td><td>19947 (18.8)</td><td></td></tr><tr><td>Ear Pain (%)</td><td>305 (0.5)</td><td>312 (0.4)</td><td></td></tr><tr><td>Fatigue/Malaise (%)</td><td>29943 (38.7)</td><td>46663 (45.7)</td><td></td></tr><tr><td>Headache (%)</td><td>9787 (13.1)</td><td>11586 (11.8)</td><td></td></tr><tr><td>History of Fever (%)</td><td>44881 (53.2)</td><td>73516 (64.8)</td><td></td></tr><tr><td>Lost/Altered Sense of Smell (%)</td><td>5255 (8.1)</td><td>6014 (7.5)</td><td></td></tr><tr><td>Lost/Altered Sense of Taste</td><td>5801 (9.2)</td><td>7420 (9.5)</td><td></td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>(%)</td><td></td><td></td></tr><tr><td>Lymphadenopathy (%)</td><td>366 (0.5)</td><td>486 (0.5)</td></tr><tr><td>Muscle Aches/Joint Pain (%)</td><td>13836 (18.6)</td><td>19434 (19.8)</td></tr><tr><td>Runny Nose (%)</td><td>2710 (3.8)</td><td>2606 (2.8)</td></tr><tr><td>Seizures (%)</td><td>1038 (1.4)</td><td>1084 (1.1)</td></tr><tr><td>Severe Dehydration (%)</td><td>4308 (11.1)</td><td>7032 (13.9)</td></tr><tr><td>Shortness of Breath (%)</td><td>50301 (58.9)</td><td>81392 (70.6)</td></tr><tr><td>Skin Rash (%)</td><td>1639 (2.2)</td><td>2362 (2.4)</td></tr><tr><td>Sore Throat (%)</td><td>5747 (7.9)</td><td>6506 (6.9)</td></tr><tr><td>Vomiting/Nausea (%)</td><td>15177 (19.0)</td><td>20961 (19.9)</td></tr><tr><td>Wheezing (%)</td><td>4550 (6.0)</td><td>7141 (7.3)</td></tr></tbody></table>

<table><thead><tr><th>COMPLICATIONS DURING HOSPITAL STAY</th><th></th><th>&lt;0.001</th></tr></thead><tbody><tr><td>Acute Kidney Injury (%)</td><td>10223 (12.4)</td><td>20689 (18.4)</td></tr><tr><td>Anemia (%)</td><td>8833 (10.7)</td><td>18178 (16.3)</td></tr><tr><td>ARDS (%)</td><td>7481 (9.1)</td><td>18587 (16.8)</td></tr><tr><td>Bacteremia (%)</td><td>2370 (2.9)</td><td>6134 (5.5)</td></tr><tr><td>Bacterial Pneumonia (%)</td><td>8590 (10.6)</td><td>17623 (16.2)</td></tr><tr><td>Bleeding (%)</td><td>17 (0.8)</td><td>13 (0.9)</td></tr><tr><td>Cardiac Arrest (%)</td><td>1549 (1.9)</td><td>3731 (3.4)</td></tr><tr><td>Cardiac Arrhythmia (%)</td><td>4450 (5.6)</td><td>8883 (8.4)</td></tr><tr><td>Cardiac Ischemia (%)</td><td>901 (1.1)</td><td>1690 (1.5)</td></tr><tr><td>Cardiomyopathy (%)</td><td>273 (0.4)</td><td>593 (0.6)</td></tr><tr><td>Coagulation Disorder (%)</td><td>2673 (3.3)</td><td>5628 (5.1)</td></tr><tr><td>Congestive Heart Failure (%)</td><td>1818 (2.2)</td><td>3629 (3.3)</td></tr><tr><td>Cryptogenic Organizing Pneumonia (%)</td><td>250 (0.3)</td><td>644 (0.6)</td></tr><tr><td>Deep Vein Thrombosis (%)</td><td>281 (0.7)</td><td>445 (0.8)</td></tr><tr><td>DIC (%)</td><td>2 (0.8)</td><td>0 (0.0)</td></tr><tr><td>Endocarditis (%)</td><td>48 (0.4)</td><td>86 (0.4)</td></tr><tr><td>GI Bleeding (%)</td><td>919 (1.1)</td><td>1538 (1.4)</td></tr><tr><td>Hemodynamic Decompensation (%)</td><td>159 (8.1)</td><td>178 (12.8)</td></tr><tr><td>Hyperglycemia (%)</td><td>8259 (10.1)</td><td>15559 (14.1)</td></tr><tr><td>Liver Dysfunction (%)</td><td>4902 (6.0)</td><td>9333 (8.4)</td></tr><tr><td>Meningitis/Encephalitis (%)</td><td>172 (0.2)</td><td>249 (0.2)</td></tr><tr><td>Myocardial Infarction (%)</td><td>98 (0.9)</td><td>217 (1.2)</td></tr><tr><td>Neurological Complication (%)</td><td>1351 (2.5)</td><td>2136 (2.8)</td></tr><tr><td>Other Complications (%)</td><td>17013 (23.1)</td><td>27324 (26.8)</td></tr><tr><td>Pleural Effusion (%)</td><td>3227 (3.9)</td><td>6809 (6.1)</td></tr><tr><td>Pneumothorax (%)</td><td>754 (0.9)</td><td>2121 (1.9)</td></tr><tr><td>Pulmonary Embolism (%)</td><td>1736 (3.5)</td><td>2929 (4.1)</td></tr><tr><td>Pulmonary Embolism/DVT (%)</td><td>34 (12.5)</td><td>86 (15.0)</td></tr><tr><td>Rhabdomyolysis (%)</td><td>341 (0.4)</td><td>762 (0.7)</td></tr><tr><td>Seizures (%)</td><td>850 (1.0)</td><td>961 (0.9)</td></tr><tr><td>Stroke (%)</td><td>1130 (1.4)</td><td>1460 (1.3)</td></tr><tr><td>Supraventricular Arrhythmia (%)</td><td>45 (2.2)</td><td>61 (4.3)</td></tr><tr><td>Thromboembolism (%)</td><td>9 (0.8)</td><td>38 (1.9)</td></tr><tr><td>Thrombosis (%)</td><td>84 (4.1)</td><td>109 (7.7)</td></tr><tr><td>VAP (%)</td><td>99 (36.4)</td><td>264 (46.2)</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Viral Pneumonia (%)</td><td>38493 (47.1)</td><td>66165 (59.3)</td><td></td></tr><tr><td colspan="4"><strong>MAIN OUTCOMES</strong></td></tr><tr><td>ICU Admission</td><td>17,183 (17.6%)</td><td>35,281 (27.7%)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Duration of IMV, days (median [IQR])</td><td>6.0 [3.0, 12.0]</td><td>7.0 [4.0, 14.0]</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Duration of Prone Ventilation, days (median [IQR])</td><td>4.0 [2.0, 8.0]</td><td>4.0 [2.0, 7.0]</td><td>0.055</td></tr><tr><td>Duration of HFNC, days (median [IQR])</td><td>4.0 [2.0, 7.0]</td><td>5.0 [3.0, 7.0]</td><td>0.226</td></tr><tr><td>Duration of ECMO, days (median [IQR])</td><td>9.0 [3.0, 11.5]</td><td>8.0 [4.0, 13.8]</td><td>0.756</td></tr><tr><td>Duration from Admission to ICU, days (median [IQR])</td><td>1.0 [0.0, 3.0]</td><td>1.0 [0.0, 3.0]</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Duration from Admission to IMV, days (median [IQR])</td><td>1.0 [0.0, 5.0]</td><td>2.0 [0.0, 5.0]</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Duration of ICU (median [IQR])</td><td>7.0 [3.0, 13.0]</td><td>9.0 [4.0, 17.0]</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Duration of Hospital Stay (median [IQR])</td><td>7.0 [3.0, 13.0]</td><td>9.0 [4.0, 16.0]</td><td><strong>&lt;0.001</strong></td></tr><tr><td colspan="4"><strong>In-Hospital outcome</strong></td></tr><tr><td>Death</td><td>12,654 (12.6)</td><td>32,653 (24.9)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Discharge</td><td>74,361 (73.9)</td><td>79,969 (61.0)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Ongoing Care</td><td>1,593 (1.6)</td><td>2,968 (2.3)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Transferred</td><td>4,975 (4.9)</td><td>6,684 (5.1)</td><td>0.124</td></tr></tbody></table>

1 Legend: Data is median [IQR] or n (%). Abbreviations: DIC = Disseminated intravascular coagulation disorder, GI = Gastrointestinal, VAP = Ventilator-acquired pneumonia, ICU = intensive care unit, IMV =

2 Invasive mechanical ventilation, HFNC = High-flow nasal cannula, ECMO = extracorporeal membrane oxygenation

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<table><caption>Table 2. Multivariable Logistic Regression Predicting All-cause in-hospital mortality</caption><thead><tr><th>Variable</th><th>Odds Ratio (OR)</th><th>95% Confidence Interval</th><th>p-value</th></tr></thead><tbody><tr><td>Lymphopenia group (yes)</td><td>1.52</td><td>1.48 – 1.55</td><td>&lt;0.001</td></tr><tr><td>Age (years, continuous)</td><td>1.07</td><td>1.07 – 1.07</td><td>&lt;0.001</td></tr><tr><td>Gender (Male)</td><td>1.29</td><td>1.26 – 1.32</td><td>&lt;0.001</td></tr><tr><td>Corticosteroid treatment (yes)</td><td>1.20</td><td>1.17 – 1.23</td><td>&lt;0.001</td></tr></tbody></table>
<table><caption>Table 3. Estimated Transition Intensities Between Immune States per day</caption><tr><td>Inotropes/Vasopressors requirement (yes)</td><td>1.39</td><td>1.31 – 1.47</td><td>&lt;0.001</td></tr><tr><td>Renal replacement therapy requirement (yes)</td><td>2.55</td><td>2.38 – 2.73</td><td>&lt;0.001</td></tr><tr><td>Invasive mechanical ventilation requirement (yes)</td><td>3.51</td><td>3.33 – 3.70</td><td>&lt;0.001</td></tr><tr><td>Non-invasive ventilation requirement (yes)</td><td>1.91</td><td>1.84 – 1.99</td><td>&lt;0.001</td></tr></table>

<table><thead><tr><th>From → To</th><th>Non-lymphopenic</th><th>Lymphopenic</th><th>Death</th></tr></thead><tbody><tr><td>Non-lymphopenic</td><td>—</td><td>1.205 (1.177–1.233)</td><td>2.030 (1.995–2.066)</td></tr><tr><td>Lymphopenic</td><td>1.574 (1.544–1.605)</td><td>—</td><td>4.311 (4.262–4.362)</td></tr><tr><td>Death (absorbing state)</td><td>—</td><td>—</td><td>—</td></tr></tbody></table>
1 Values represent estimated transition intensities (hazard rates per day) with 95% confidence intervals.

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1 Figures:

<figure><img src="image_3.png" /></figure>

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3 **Figure 1.** Patient selection Diagram using STROBE requirements

4 * Considered critical variables are further characterized in the ESM; # Day 0 was defined as the

5 day of hospital admission. Required days D0, D1, D3 and D7 correspond to days after hospital

6 admission.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Comparison of absolute lymphocyte serum levels between patients in Lymphopenia group and non-Lymphopenia group</figcaption></figure>

\#Values of absolute lymphocyte serum levels are presented in x10⁹ cells/L

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<figure><img src="image_5.png" /><figcaption>**Figure 3.** Kaplan-Meier survival curves and analysis of cumulative survival, including log-rank test, between Lymphopenia and non-Lymphopenia groups</figcaption></figure>

<figure><img src="image_6.png" /></figure>
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**Figure 4.** Modified alluvial plot depicting transition rates of Lymphocyte status and mortality outcomes over time, in patients untreated and treated with corticosteroids.

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Impact of Corticosteroid Treatment on Lymphocyte Values
Comparison between Survivors and Non-Survivors

<figure><img src="image_7.png" /></figure>

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**Figure 5.** Joint model of a linear mixed model and Cox regression model, depicting the impact of corticosteroid therapy on predicted lymphocyte counts over time, in surviving and non-surviving patients

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## Electronic Supplementary Material

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# Title: Immune Dysregulation through Longitudinal Lymphocyte Trajectories and Their Clinical Determinants in Hospitalized COVID-19 Patients: Insights from the ISARIC Consortium

15 *José Pedro Cidade<sup>1,2</sup>, Fabio Silvio Taccone<sup>3</sup>, Luis Felipe Reyes<sup>4,5</sup>, Laura Merson<sup>5,6</sup>, Benjamin Lefevre<sup>7</sup>,

16 Barbara Wanjiru Citarella<sup>6</sup>, Arie Zainul fatoni<sup>8</sup>, Pedro Póvoa<sup>1,2,9</sup>, and the ISARIC Characterization Group

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### Hazard Ratios from Cox Regression Analysis

<figure><img src="image_8.png" /></figure>

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1 **Figure 1 ESM.** Forest plot of the Hazard ratios of in-hospital all-cause mortality from Cox

2 regression analysis

3 *Cont stands for continuous variable

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<figure><img src="image_9.png" /></figure>

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7 **Figure 2ESM.** Forest plot of the Hazard ratios of transition between states of Non-

8 Lymphopenia, Lymphopenia and Death, with corticosteroid therapy

9