
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12226-8
10.1016/j.heliyon.2024.e36195
e36195
Research Article
The neutrophil-to-lymphocyte ratio levels over time correlate to all-cause hospital mortality in sepsis
Zhang Guyu
Wang Tao
An Le
Hang ChenChen
Wang XingSheng
Shao Fei
Shao Rui
Tang Ziren shaorui@mail.ccmu.edu.cn
⁎
Emergency Medicine Clinical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing, 100020, China
⁎ Corresponding author. shaorui@mail.ccmu.edu.cn
13 8 2024
30 8 2024
13 8 2024
10 16 e3619515 3 2024
5 8 2024
12 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Objective

This research aims to investigate the prognosis value using the time-weighted average neutrophil-to-lymphocyte ratio (TWA-NLR) for predicting all-cause hospital mortality among sepsis patients. Data were analyzed through the use of the eICU Collaborative Research Database (eICU-CRD 2.0) as well as Medical Information Mart for Intensive Care IV 2.2 (MIMIC-IV 2.2).

Methods

Septic patients from both eICU-CRD 2.0 as well as MIMIC-IV 2.2 databases were included. The neutrophil-to-lymphocyte ratios (NLR) were available for analysis, utilizing complete blood counts obtained on days one, four, and seven following ICU admission. The TWA-NLR was computed at the end of the seven days, and patients were then stratified based on TWA-NLR thresholds. 90-day all-cause mortality during hospitalization was the primary objective, with 60-day all-cause hospital mortality as a secondary objective. The correlation between TWA-NLR and sepsis patients' primary outcome was analyzed using univariable and multivariable Cox proportional hazard regressions. A restricted cubic spline (RCS) analysis was conducted in an attempt to confirm this association further, and subgroup analyses were employed to evaluate the correlation across various comorbidity groups.

Results

3921 patients were included from the eICU-CRD 2.0, and the hospital mortality rate was 20.8 %. Both multivariable as well as univariable Cox proportional hazard regression analyses revealed that TWA-NLR was independently correlated with 90-day all-cause hospital mortality, yielding a hazard ratio (HR) of 1.02 (95 % CI 1.01–1.02, P-value<0.01) as well as 1.12 (95 % CI 1.01–1.15, P-value<0.01), respectively. The RCS analysis demonstrated a significant nonlinear relationship between TWA-NLR and 90-day all-cause hospital mortality risk. The study subjects were divided into higher (>10.5) and lower (≤10.5) TWA-NLR cohorts. A significantly decreased incidence of 90-day all-cause hospital mortality (HR = 0.56, 95 % CI 0.48–0.64, P-value<0.01) and longer median survival time (40 days vs 24 days, P-value<0.05) were observed in the lower TWA-NLR cohort. However, septic patients with chronic pulmonary (interaction of P-value = 0.009) or renal disease (interaction of P-value = 0.008) exhibited significant interactive associations between TWA-NLR and 90-day all-cause hospital mortality, suggesting the predictive power of TWA-NLR may be limited in these subgroups. The MIMIC-IV 2.2 was utilized as a validation cohort and exhibited a similar pattern.

Conclusion

Our findings suggest that TWA-NLR is a powerful and independent prognostic indicator for 90-day all-cause hospital mortality among septic patients, and the TWA-NLR cutoff value may prove a useful method for identifying high-risk septic patients.

Keywords

Sepsis
Neutrophil-to-lymphocyte ratio
Prediction
Restricted cubic spline
Intensive care unit
==== Body
pmc1 Introduction

Sepsis, a serious medical situation triggered by a variety of infections, leads to unregulated systemic production of excessive amounts of inflammatory mediators. Despite advancements in medicine and a deeper understanding of its underlying pathophysiology, sepsis persists as a leading reason for ICU admissions, with an estimated thirty million fatalities occurring annually [[1], [2], [3]]. The third international consensus has defined that sepsis as well as septic shock as rapidly progressive state of inflammation accompanied by immunosuppression [4]. Lymphocytes, comprising 20–40 % of the leukocytes, play a pivotal function in the adaptive immunity during sepsis [5]. Patients with higher lymphocyte counts during sepsis have been shown to experience more favorable outcomes [6].

Conversely, sepsis-induced lymphocyte apoptosis and impaired proliferation lead to lymphopenia, which correlates with increased mortality rates in ICU settings [[7], [8], [9], [10], [11]]. Recently, attention has turned to composite biomarkers that reflect the balance between different immune cell populations, such as the platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and neutrophil-to-lymphocyte ratio (NLR) [12,13]. Derived from white blood cell counts, the NLR has been identified as a powerful biomarker for systemic inflammatory and immune reactions for sepsis [14]. This easily accessible biomarker reflects the immunological dynamics of sepsis and demonstrates superior prognostic value compared to lymphocyte counts alone [15,16]. A positive correlational relationship between NLR and thirty-day mortality in bloodstream infections has been demonstrated, and a meta-analysis revealed that non-survivors exhibited elevated NLR levels relative to survivors of sepsis [17,18].

Despite these promising findings, the NLR over time and its relationship to hospital mortality in sepsis remains understudied. Most investigations have relied on single time-point NLR measurements, typically within the initial 24 h following ICU admission [19]. This approach may not reflect the dynamical nature of the immune reaction in sepsis, potentially limiting the prognostic accuracy of NLR.

To address this gap, our research is designed to verify the effect of a time-varying NLR upon hospital mortality among sepsis patients. We introduce the concept of time-weighted average neutrophil-to-lymphocyte ratios (TWA-NLR), which allows for a more comprehensive assessment of NLR fluctuations throughout the ICU stay. By exploring the potential relationship between TWA-NLR levels and 90-day all-cause hospital mortality among septic patients, we seek to enhance the prognostic utility of this biomarker and potentially improve patient risk stratification and management strategies. This novel approach may provide clinicians with a more tailored tool for monitoring sepsis progression and predicting outcomes, ultimately contributing to more effective and personalized treatment protocols in the critical care setting for sepsis patients.

2 Methods

2.1 Participants

Participants, age above or equal to 18 years, were recruited from the eICU-CRD 2.0 and MIMIC-IV 2.2 databases were included. Inclusion criteria were: 1) A confirmed or suspected infection, as well as a Sequential Organ Failure Assessment (SOFA) score of two or greater in accordance with the Sepsis-3.0 criteria [4]. 2) Complete blood count of peripheral blood documentation on the first, fourth, and seventh days of ICU admission. 3) An ICU stay duration of at least seven days.

The following exclusion criteria were applied: 1) An ICU duration of fewer than seven days or more than 90 days; 2) Presence of human immunodeficiency virus (HIV) infection, rheumatic disorders, metastatic tumors, cancer, and hematological diseases such as aplastic anemia; 3) Missing lymphocyte data on the first, fourth, and seventh days following ICU admission; 4) The initial ICU admission of patients with a history of multiple hospitalizations was selected for analysis in this research.

2.2 Extraction of data

The subsequent clinical information was obtained by means of Structured Query Language (SQL) statements:1) Biochemistry results within the first 24 h: blood glucose, albumin, creatinine, glucose, hemoglobin, lactate, potassium, sodium, blood urea nitrogen (BUN), calcium, aspartate aminotransferase (AST), alanine aminotransferase (ALT). 2) The first 24 h of demographic and vital parameters: heart rate, temperature (°C), respiratory rate, sex, age, diastolic blood pressure, sofa score, oasis score, systolic blood pressure, and BMI. 3) Analysis of blood gases within the first 24 h: the potential of Hydrogen (pH), the fraction of inspired Oxygen (FiO2), arterial partial pressure of oxygen (PaO2), and arterial partial pressure of carbon dioxide (PaCO2). 4) Details of the ICU: the duration of ICU stays and the survival status of patients. 5) Comorbid conditions and treatments: renal replacement therapy, mechanical ventilation, congestive heart failure, chronic pulmonary, liver disease, myocardial infarction, and renal disease. 6) Blood cell counts: White blood count (WBC), lymphocytes, neutrophils, platelets, and monocytes were extracted on the first, fourth, and seventh days following admittance to the ICU. 7) The derived inflammatory indicators: PLR was calculated from the platelet to lymphocyte count ratio, LMR was from the ratio of the lymphocyte to monocyte count, and NLR was from the neutrophil to lymphocyte count ratio. 8) The TWA values of WBC, platelets, neutrophils, lymphocytes, monocytes, PLR, NLR, and LMR were computed as the ratio of their area under the curve to the number of days (seven days). The average value was used if a variable was recorded multiple times on the same day. The study's primary objective was the measurement of 90-day all-cause hospital death, while the secondary objective focused on 60-day hospital all-cause death.

2.3 Statistical analysis of data

The continuous data were presented as w5ell as compared in two ways: either as the mean ± standard deviation (SD) or as the median (interquartile range). For normally distributed variables, the Student's t-test was employed for comparison. Conversely, for variables that were not normally distributed, the Mann-Whitney U test was applied. Categorical variables were expressed in terms of proportions and evaluated by the Chi-square or Fisher's exact tests. The independent prognostic effect of TWA-NLR on hospital mortality was assessed through univariable as well as multivariable Cox proportional hazard model analyses, employing the R package ‘survival’. Results were presented in hazard ratios (HR) and 95 % confidence intervals (CI). Spearman correlation analyses were conducted using the ‘corrplot’ package to assess correlation coefficients. To further investigate the relationships between TWA-NLR and hospital mortality, clinically relevant and prognosis-associated variables such as time-weighted average white blood cell count (TWA-WBC), gender, age, BUN, and lactate were incorporated into the restricted cubic spline (RCS) model using the 'rms' package in R. The TWA-NLR was calculated to capture fluctuations in NLR during the ICU stay. Cutoff values for TWA-NLR were determined using maximally selected rank statistics with the ‘maxstat’ package [20]. Kaplan-Meier survival analysis was conducted to assess survival probabilities across two TWA-NLR level groups via the ‘survminer’ package. To verify the consistency of TWA-NLR's prognostic effect, we conducted subgroup analyses across various subgroups, including age, invasive ventilation, diabetes, cerebrovascular disease, renal disease, congestive heart failure, liver disease, renal replacement therapy, and chronic pulmonary disease. Variables missing for over 35 % were not considered in the analysis (Fig. S1). The rest 26 candidate predictors obtained during admission to ICU were chosen for further analysis. Missing values for these selected variables were imputed using the multiple imputations by predictive mean matching (PMM) through the package ‘mice’. Package ‘timeROC’ was used to calculate the accuracy of survival outcome prediction by TWA-NLR [21].

All analytical procedures were carried out by R programming language, version 4.1.3 (Beijing, China). We regard a two-tailed P-value of smaller than 0.05 to be meaningful for all analyses.

3 Results

3.1 Demographics and clinical features

Our study enrolled 3,921 patients from eICU-CRD 2.0 (Fig. 1) and 1,714 patients from MIMIC-IV 2.2 (Fig. S2), meeting our inclusion criteria. As illustrated in Table 1, the survival cohort demonstrated reduced levels of TWA-WBC, time-weighted average neutrophils (TWA-neutrophils), pH, BUN, potassium, AST, respiratory rate, TWA-NLR, Fio2, and were younger. Conversely, survivors showed higher levels of time-weighted average lymphocytes (TWA-lymphocytes), time-weighted average platelets (TWA-platelets), time-weighted lymphocyte-to-monocyte ratio (TWA-LMR), albumin, and hemoglobin. Additionally, this group demonstrated lower incidences of comorbid conditions, such as congestive heart failure (17.5 % vs. 22.1 %, P-value = 0.003), renal diseases (15.5 % vs. 21.3 %, P-value<0.001), liver disease (3.51 % vs. 7.11 %, P-value<0.001), and cerebrovascular disease (9.92 % vs. 12.4 %, P-value<0.001). Furthermore, the likelihood of requiring renal replacement therapy was greater in the non-survival group (24.5 % vs. 18.3 %, P-value<0.001). The length of stay in the ICU or the first-day WBC, lymphocytes, neutrophils, monocytes, and platelets levels were not significantly different.Fig. 1 A flow chart illustrating the regulatory model of patient enrollment and analysis workflow in the eICU-CRD 2.0 database.

Fig. 1

Table 1 Baseline characteristics of sepsis patients in eICU-CRD.

Table 1	Survival	Non-Survival	P value	
N = 3105	N = 816	
Age (years)	60.9 ± 15.8	66.7 ± 14.5	<0.001	
Gender			0.958	
Female	1364 (43.9 %)	357 (43.8 %)		
Male	1741 (56.1 %)	459 (56.2 %)		
BMI	28.7 [23.9–35.3]	28.1 [23.3–34.5]	0.880	
TWA-WBC (109/L)	12.9 ± 5.52	14.5 ± 6.45	<0.001	
TWA-lymphocytes (109/L)	1.15 ± 0.56	1.04 ± 0.56	<0.001	
TWA-neutrophils (109/L)	10.5 ± 4.94	12.1 ± 5.63	<0.001	
TWA-monocytes (109/L)	0.83 ± 0.39	0.84 ± 0.43	0.708	
TWA-platelets (109/L)	206 (89.0)	176 (90.0)	<0.001	
TWA-NLR	11.8 ± 9.71	15.8 ± 11.7	<0.001	
TWA-LMR	1.90 ± 1.38	1.73 ± 1.35	0.002	
TWA-PLR	211 [144–314]	214 [133–332]	0.520	
WBC (109/L)	16.6 ± 9.50	17.2 ± 9.67	0.094	
First-day neutrophils (109/L)	13.8 ± 8.28	14.3 ± 8.06	0.147	
First-day lymphocytes (109/L)	1.94 ± 1.80	2.13 ± 3.72	0.163	
First-day monocytes (109/L)	1.27 ± 1.06	1.28 ± 1.04	0.846	
First-day platelets (109/L)	243 ± 125	234 ± 130	0.094	
Sofa score	9.27 ± 3.41	11.4 ± 4.01	<0.001	
Oasis score	34.4 ± 9.78	36.4 ± 9.91	<0.001	
Heart rate	116 ± 23.9	116 ± 24.1	0.788	
Respiratory rate	31.3 ± 9.03	32.4 ± 8.80	0.001	
Systolic blood pressure (mmHg)	149 [130–168]	146 [130–166]	0.187	
Diastolic blood pressure (mmHg)	89.0 [76.0–104]	89.0 [76.0–102]	0.850	
Mean blood pressure (mmHg)	109 [95.3–125]	108 [95.3–123]	0.462	
Temperature (°C)	37.8 ± 0.97	37.7 ± 0.98	0.036	
Liver disease			<0.001	
No	2996 (96.5 %)	758 (92.9 %)		
Yes	109 (3.51 %)	58 (7.11 %)		
Renal disease			<0.001	
No	2625 (84.5 %)	642 (78.7 %)		
Yes	480 (15.5 %)	174 (21.3 %)		
Diabetes			0.818	
No	2123 (68.4 %)	562 (68.9 %)		
Yes	982 (31.6 %)	254 (31.1 %)		
Myocardial infarct			0.772	
No	2862 (92.2 %)	749 (91.8 %)		
Yes	243 (7.83 %)	67 (8.21 %)		
Congestive heart failure			0.003	
No	2562 (82.5 %)	636 (77.9 %)		
Yes	543 (17.5 %)	180 (22.1 %)		
Cerebrovascular disease			0.048	
No	2797 (90.1 %)	715 (87.6 %)		
Yes	308 (9.92 %)	101 (12.4 %)		
Chronic pulmonary disease			0.058	
No	2511 (80.9 %)	635 (77.8 %)		
Yes	594 (19.1 %)	181 (22.2 %)		
Fio2 (%)	0.75 ± 0.27	0.78 ± 0.27	0.038	
Pao2 (mmHg)	168 ± 108	172 ± 109	0.314	
Paco2 (mmHg)	50.6 ± 20.2	49.8 ± 19.7	0.262	
pH	7.40 ± 0.09	7.41 ± 0.09	0.028	
Albumin (g/dl)	2.95 ± 0.74	2.82 ± 0.74	<0.001	
Creatinine (mg/dl)	2.10 ± 2.03	2.21 ± 2.05	0.165	
Glucose (mg/dl)	193 ± 117	195 ± 111	0.657	
Hemoglobin (g/dl)	11.9 ± 2.52	11.6 ± 2.31	<0.001	
Lactate (mmol/L)	3.01 ± 2.76	3.82 ± 3.41	<0.001	
Potassium (mEq/L)	4.53 ± 0.82	4.61 ± 0.83	0.011	
Sodium (mEq/L)	140 ± 6.05	141 ± 6.36	0.516	
BUN (mg/dl)	36.7 ± 27.4	40.6 ± 27.8	<0.001	
ALT (IU/L)	31.0 [19.0–61.0]	32.0 [19.0–65.0]	0.337	
AST (IU/L)	40.0 [24.0–89.0]	47.0 [27.0–109]	<0.001	
Calcium (mg/dl)	8.62 ± 0.93	8.65 ± 1.02	0.487	
Renal replacement therapy			<0.001	
No	2537 (81.7 %)	616 (75.5 %)		
Yes	568 (18.3 %)	200 (24.5 %)		
Invasive ventilation			0.232	
No	429 (13.8 %)	99 (12.1 %)		
Yes	2676 (86.2 %)	717 (87.9 %)		
The length of ICU stays (Days)	11.0 [8.50–15.8]	11.3 [8.75–16.0]	0.348	
TWA: Time weighted average; WBC: white blood count.

To investigate further the effect of TWA-NLR on ICU mortality, septic patients were stratified in two cohorts based on their TWA-NLR cutoff levels (Fig. S3). As illustrated in Table 2, the group with higher TWA-NLR group exhibited increased mortality rates (28.0 % vs. 14.6 %, P-value<0.001) as well as prolonged ICU stays (11.2 days vs 10.9 days, P-value = 0.004). Additionally, this group exhibited a higher rate of renal disease (18.2 % vs. 15.4 %, P-value = 0.021) as well as chronic pulmonary disease (25.4 % vs. 14.9 %, P-value<0.001). Simultaneously, the patients with higher TWA-NLR exhibited lower TWA-lymphocytes and TWA-platelets, and were more frequently subjected to renal replacement therapy (23.9 % vs. 15.9 %, P-value<0.001).Table 2 Baseline characteristics of sepsis patients according to TWA-NLR cutoff value in eICU-CRD.

Table 2	Total	Higher NLR	Lower NLR	P value	
N = 3921	N = 1808	N = 2113	
Age	64.0 [53.0–74.0]	66.0 [56.0–76.0]	61.0 [50.0–71.0]	<0.001	
Gender				0.174	
Female	1721 (43.9 %)	772 (42.7 %)	949 (44.9 %)		
Male	2200 (56.1 %)	1036 (57.3 %)	1164 (55.1 %)		
BMI	28.5 [23.8–35.1]	27.9 [23.4–34.2]	29.1 [24.1–36.0]	<0.001	
TWA-WBC (109/L)	12.2 [9.31–16.0]	14.4 [11.2–18.6]	10.8 [8.30–13.6]	<0.001	
TWA-lymphocytes (109/L)	1.04 [0.72–1.43]	0.77 [0.55–1.04]	1.29 [1.00–1.69]	<0.001	
TWA-neutrophils (109/L)	9.90 [7.21–13.3]	12.5 [9.52–16.2]	8.17 [6.12–10.6]	<0.001	
TWA-monocytes (109/L)	0.78 [0.55–1.05]	0.75 [0.51–1.05]	0.79 [0.58–1.05]	<0.001	
TWA-platelets (109/L)	193 [134–255]	185 [123–245]	201 [144–262]	<0.001	
TWA-LMR	1.54 [1.08–2.19]	1.23 [0.88–1.72]	1.80 [1.34–2.56]	<0.001	
TWA-PLR	212 [141–317]	290 [188–426]	173 [121–237]	<0.001	
WBC (109/L)	14.9 [10.4–21.0]	16.8 [12.0–23.5]	13.3 [9.60–18.8]	<0.001	
First-day neutrophils (109/L)	12.3 [8.35–17.8]	14.4 [9.93–20.2]	10.7 [7.37–15.5]	<0.001	
First-day lymphocytes (109/L)	1.41 [0.87–2.38]	1.12 [0.70–1.94]	1.67 [1.08–2.74]	<0.001	
First-day monocytes (109/L)	1.05 [0.66–1.59]	1.05 [0.62–1.63]	1.04 [0.68–1.55]	0.896	
First-day platelets (109/L)	219 [161–296]	219 [161–303]	219 [160–292]	0.519	
Sofa score	9.00 [7.00–12.0]	10.0 [7.00–13.0]	9.00 [7.00–11.0]	<0.001	
Oasis Score	35.0 [28.0–42.0]	35.0 [28.0–42.0]	35.0 [27.0–41.0]	0.003	
Heart rate	115 [98.0–132]	116 [99.0–132]	115 [98.0–131]	0.065	
Respiratory rate	30.0 [25.0–37.0]	31.0 [25.0–37.0]	30.0 [25.0–36.0]	0.014	
Systolic blood pressure (mm Hg)	148 [130–168]	146 [129–168]	150 [131–169]	0.013	
Diastolic blood pressure (mm Hg)	89.0 [76.0–103]	88.0 [74.0–102]	90.0 [77.0–104]	0.002	
Mean blood pressure (mm Hg)	109 [95.3–124]	108 [94.0–123]	110 [96.3–125]	0.003	
Temperature (°C)	37.6 [37.1–38.4]	37.5 [37.1–38.3]	37.7 [37.2–38.5]	<0.001	
Liver disease				0.234	
No	3754 (95.7 %)	1723 (95.3 %)	2031 (96.1 %)		
Yes	167 (4.26 %)	85 (4.70 %)	82 (3.88 %)		
Renal disease				0.021	
No	3267 (83.3 %)	1479 (81.8 %)	1788 (84.6 %)		
Yes	654 (16.7 %)	329 (18.2 %)	325 (15.4 %)		
Diabetes				0.391	
No	2685 (68.5 %)	1251 (69.2 %)	1434 (67.9 %)		
Yes	1236 (31.5 %)	557 (30.8 %)	679 (32.1 %)		
Myocardial infarct				<0.001	
No	3611 (92.1 %)	1634 (90.4 %)	1977 (93.6 %)		
Yes	310 (7.91 %)	174 (9.62 %)	136 (6.44 %)		
Congestive heart failure				0.212	
No	3198 (81.6 %)	1459 (80.7 %)	1739 (82.3 %)		
Yes	723 (18.4 %)	349 (19.3 %)	374 (17.7 %)		
Cerebrovascular disease				0.396	
No	3512 (89.6 %)	1628 (90.0 %)	1884 (89.2 %)		
Yes	409 (10.4 %)	180 (9.96 %)	229 (10.8 %)		
Chronic pulmonary disease				<0.001	
No	3146 (80.2 %)	1348 (74.6 %)	1798 (85.1 %)		
Yes	775 (19.8 %)	460 (25.4 %)	315 (14.9 %)		
Fio2 (%)	0.95 [0.50–1.00]	1.00 [0.50–1.00]	0.80 [0.50–1.00]	0.017	
Pao2 (mmHg)	131 [89.0–215]	131 [89.0–215]	131 [89.0–214]	0.894	
Paco2 (mmHg)	45.3 [37.5–57.0]	46.0 [38.0–58.0]	44.6 [37.4–56.5]	0.045	
pH	7.41 [7.35–7.46]	7.40 [7.34–7.46]	7.41 [7.36–7.46]	<0.001	
Albumin (g/dl)	2.90 [2.40–3.40]	2.80 [2.30–3.40]	3.00 [2.50–3.50]	<0.001	
Creatinine (mg/dl)	1.42 [0.90–2.55]	1.60 [1.00–2.80]	1.30 [0.83–2.36]	<0.001	
Glucose (mg/dl)	163 [127–222]	170 [132–224]	157 [123–219]	<0.001	
Hemoglobin (g/dl)	11.7 [10.0–13.6]	11.6 [10.0–13.5]	11.8 [10.0–13.7]	0.147	
Lactate (mmol/L)	2.10 [1.40–3.90]	2.30 [1.40–4.20]	2.10 [1.30–3.70]	<0.001	
Potassium (mEq/L)	4.40 [4.00–5.00]	4.50 [4.00–5.00]	4.30 [3.90–4.90]	<0.001	
Sodium (mEq/L)	140 [137–144]	140 [137–143]	140 [137–144]	<0.001	
Bun (mg/dl)	30.0 [18.0–49.0]	34.0 [21.0–54.0]	26.0 [16.0–43.0]	<0.001	
ALT (IU/L)	31.0 [19.0–62.0]	32.0 [19.0–65.0]	31.0 [19.0–59.0]	0.426	
AST (IU/L)	41.0 [24.0–92.0]	43.0 [25.0–100]	40.0 [24.0–86.0]	0.007	
Calcium (mg/dl)	8.60 [8.00–9.20]	8.60 [8.00–9.10]	8.60 [8.10–9.20]	0.129	
Renal replacement therapy				<0.001	
No	3153 (80.4 %)	1375 (76.1 %)	1778 (84.1 %)		
Yes	768 (19.6 %)	433 (23.9 %)	335 (15.9 %)		
Invasive ventilation				0.060	
No	528 (13.5 %)	264 (14.6 %)	264 (12.5 %)		
Yes	3393 (86.5 %)	1544 (85.4 %)	1849 (87.5 %)		
The length of ICU stays (Days)	11.0 [8.54–15.8]	11.2 [8.75–16.4]	10.9 [8.38–15.5]	0.004	
Survival status				<0.001	
Death	816 (20.8 %)	507 (28.0 %)	309 (14.6 %)		
Survived	3105 (79.2 %)	1301 (72.0 %)	1804 (85.4 %)		
TWA: Time weighted average; WBC: white blood count.

3.2 Associations between TWA-NLR and hospital mortality risk

In our univariable Cox regression analysis, several variables were initially identified as significantly associated with 90-day hospital mortality (Table 3). These prognostic factors included age, TWA-WBC, TWA-lymphocytes, TWA-neutrophils, TWA-NLR, TWA-LMR, albumin, creatinine, hemoglobin, lactate, potassium, and BUN. As depicted in Fig. 2, there were strong positive correlations among TWA-WBC, TWA-neutrophils, and TWA-monocytes. Conversely, a negative correlation was observed between TWA-NLR and TWA-lymphocytes. Additionally, BUN and creatinine were found to have a significant positive correlation. To mitigate the potential for collinearity in the multivariable Cox regression analysis, we selectively included factors such as TWA-WBC, TWA-NLR, BUN, age, TWA-LMR, albumin, hemoglobin, lactate, and potassium. Finally, the refined model, using a restricted cubic spline (RCS) approach, incorporated covariates such as TWA-WBC (hazard ratio [HR] 1.02, 95 % CI 1.01–1.04, P-value = 0.008), BUN (HR 1.08, 95 % CI 1.06–1.19, P-value = 0.009), age (HR 1.03, 95 % CI 1.02–1.04, P-value<0.001), hemoglobin (HR 0.96, 95 % CI 0.93–0.98, P-value = 0.009), as well as lactate (HR 1.04, 95 % CI 1.02–1.06, P-value<0.001). After adjusting for these factors, TWA-NLR continued to be a significant predictable element for mortality (HR 1.12, 95 % CI 1.01–1.15, P-value<0.001). The adjusted RCS model showed a significant nonlinear association between TWA-NLR and 90-day hospital mortality (Fig. 3A). Interestingly, in patients receiving renal replacement therapy, we observed a linear correlation between TWA-NLR and hospital mortality (Fig. 3B). This finding suggests that decreasing TWA-NLR levels correlates with improved prognosis in this specific patient subgroup, emphasizing the potential value of TWA-NLR in clinical assessment for these patients.Table 3 Cox regression analysis of the variables.

Table 3Variables	Univariable	Multivariable	
HR	95 % CI	P value	HR	95 % CI	P value	
Age	1.03	1.02–1.03	<0.001	1.03	1.02–1.04	<0.001	
TWA-Wbc	1.03	1.02–1.04	<0.001	1.02	1.01–1.04	0.008	
TWA-lymphocyte	0.76	0.66–0.87	<0.001				
TWA-neutrophils	1.04	1.02–1.05	<0.001				
TWA-monocytes	1.03	0.87–1.21	0.76				
TWA-platelets	0.98	0.96–1.1	0.56				
TWA-NLR	1.02	1.01–1.02	<0.001	1.12	1.01–1.15	<0.001	
TWA-LMR	0.92	0.86–0.97	0.005	0.97	0.92–1.03	0.3	
TWA-PLR	0.91	0.9–1.13	0.1				
Fio2	0.99	0.76–1.28	0.92				
Pao2	0.86	0.85–1.15	0.053				
Paco2	1.00	0.99–1.00	0.24				
Ph	2.04	0.92–4.52	0.080				
Albumin	0.88	0.80–0.96	0.005	0.95	0.86–1.05	0.3	
Creatinine	1.03	1.00–1.07	0.028				
Glucose	0.93	0.9–1.06	0.082				
Hemoglobin	0.95	0.92–0.98	<0.001	0.96	0.93–0.98	0.009	
Lactate	1.06	1.04–1.08	<0.001	1.04	1.02–1.06	<0.001	
Potassium	1.09	1.01–1.18	0.035	0.99	0.90–1.08	0.83	
Sodium	1.00	0.99–1.01	0.67				
BUN	1.12	1.03–1.15	0.01	1.08	1.06–1.19	0.009	
ALT	0.98	0.85–1.15	0.79				
AST	1.05	0.91–1.13	0.38				
Calcium	1.02	0.95–1.09	0.53				
Spo2	0.99	0.94–1.05	0.75				
BMI	1.12	0.98–1.23	0.051				
TWA-NLR category							
Higher TWA-NLR				Ref			
Lower TWA-NLR				0.56	0.48–0.64	<0.001	
TWA: Time weighted average.

Fig. 2 Analysis of the correlation between clinical variables in the eICU-CRD 2.0 database.

Fig. 2

Fig. 3 The restricted cubic spline of TWA-NLR and the risk of all-cause mortality within 90 Days of hospitalization (A); Subgroup for renal replacement therapy (B) in the eICU-CRD 2.0 database.

Fig. 3

The Kaplan-Meier survival analysis, as illustrated in Fig. 4, demonstrates significant differences in hospital mortality rates at 60 (Figs. 4B) and 90 days (Fig. 4A) between two patient groups stratified by their TWA-NLR levels. The group with lower TWA-NLR demonstrated a significantly longer median survival time of 40 days compared to 24 days in the higher TWA-NLR group (Fig. 4A). This trend was similarly observed in the MIMIC-IV 2.2 database, where the lower TWA-NLR group displayed a longer median survival time (44 days vs. 24 days, P-value<0.001), reinforcing the survival benefit associated with lower TWA-NLR level (Fig. S4A). Moreover, follow-up data from the MIMIC-IV 2.2 revealed that the lower TWA-NLR group had a higher out-of-hospital survival rate and a longer median survival time (23 days vs. 19 days, P-value<0.001) (Fig. S4B). These findings emphasize the prognostic significance of TWA-NLR in evaluating the outcomes of patients in the hospital as well as after hospital discharge.Fig. 4 Kaplan–Meier survival analysis curves for 90-day all-cause hospital mortality (A) and sixty days (B) in the eICU-CRD 2.0 database.

Fig. 4

3.3 The prognostic capacity of TWA-NLR for all-cause hospital mortality among sepsis and subgroup analyses

To evaluate the prediction ability of the TWA-NLR for 90-day hospital mortality, a time-dependent receiver operating characteristic (ROC) curve analysis was conducted. TWA-NLR showed an area under the curve (AUC) that was 0.69, surpassing TWA-neutrophils (AUC: 0.57) and TWA-lymphocytes (AUC: 0.51) (Fig. 5A). The predictive model was further enhanced by adjusting for TWA-WBC, age, hemoglobin, lactate, and blood urea nitrogen (BUN), improving the AUC to 0.74. This adjusted model surpassed the Oxford Acute Severity of Illness Score (OASIS) (AUC: 0.66) as well as SOFA score (AUC: 0.65) (Fig. 5B). Similar patterns were observed on the MIMIC-IV 2.2 database (Figs. S5A–B).Fig. 5 Time-dependent ROC curves and time-dependent AUC values of the adjusted TWA-NLR (A) and model (B) for predicting 90-day all-cause hospital mortality in the eICU-CRD 2.0 database.

Fig. 5

We performed an extensive subgroup analysis to assess the consistency in prognostic value of TWA-NLR across diverse patient subgroups. These subgroups included age, gender, use of invasive ventilation therapy, renal replacement therapy requirement, liver disease, diabetes, or cerebrovascular disease. As detailed in Table 4, our analysis revealed that most interactions were not statistically significant (interaction of P-value>0.05), affirming TWA-NLR's uniform prognostic significance across these subgroups. However, significant interactions were observed in patients with renal disease (interaction of P-value = 0.008) and chronic pulmonary disease (interaction of P-value = 0.009), where TWA-NLR did not significantly predict hospital mortality, resulting in HR values of 0.83 (95 % CI 0.61–1.14, P-value = 0.248) for pulmonary disease and 0.80 (95 % CI 0.59–1.08, P-value = 0.147) for renal disease. Further analysis by restricted cubic spline analysis confirmed the lack of a significant association between TWA-NLR and 90-day hospital mortality in chronic pulmonary disease (Fig. 6A) and renal disease subgroups (Fig. 6B), suggesting that TWA-NLR's utility might be limited in specific chronic conditions.Table 4 Subgroup analysis of the associations between NLR and mortality.

Table 4Characteristics	ALL-cause mortality	P interaction	
Higher TWA-NLR	Lower TWA-NLR	
HR (95%CI)	P value	
Age				0.934	
 ≤60	Ref	0.6 (0.46–0.78)	<0.001		
 >60	Ref	0.6 (0.51–0.72)	<0.001		
Gender				0.628	
 Female		0.57 (0.46–0.71)	<0.001		
 Male		0.54 (0.44–0.65)	<0.001		
Invasive ventilation				0.589	
 No	Ref	0.51 (0.33–0.78)	<0.001		
 Yes	Ref	0.57 (0.49–0.66)	<0.001		
Renal replacement therapy				0.258	
 No	Ref	0.53 (0.45–0.62)	<0.001		
 Yes	Ref	0.64 (0.48–0.87)	<0.001		
Diabetes				0.44	
 No	Ref	0.54 (0.45–0.64)	<0.001		
 Yes	Ref	0.62 (0.48–0.79)	<0.001		
Renal disease				0.008	
 No	Ref	0.51 (0.44–0.6)	<0.001		
 Yes	Ref	0.80 (0.59–1.08)	0.147		
Liver disease				0.67	
 No	Ref	0.56 (0.48–0.65)	<0.001		
 Yes	Ref	0.66 (0.38–1.15)	0.142		
Cerebrovascular disease				0.73	
 No	Ref	0.55 (0.47–0.64)	<0.01		
 Yes	Ref	0.60 (0.41–0.90)	0.012		
Chronic pulmonary disease				0.009	
 No	Ref	0.52 (0.45–0.61)	<0.01		
 Yes	Ref	0.83 (0.61–1.14)	0.248		
TWA: Time weighted average.

Fig. 6 Restricted cubic spline curve for chronic pulmonary disease subgroup (A) and renal disease (B) in the eICU-CRD 2.0 database.

Fig. 6

4 Discussion

Sepsis is defined by systemic organ dysfunction and exaggeration of the immunological response to host infection, often leading to metabolic disturbances, severe immunosuppression, and alterations in lymphocyte distribution within lymphoid organs [22]. Initially, neutrophil and lymphocyte counts typically increase in response to microbial invasion. Neutrophils migrate toward the infection site as sepsis progresses, whereas lymphocyte levels decline due to immunosuppression. The variability in neutrophil counts and delayed lymphocyte decrease provide limited predictive value for sepsis outcomes [23,24]. Instead, the NLR has emerged as a valuable biomarker in adult sepsis, reflecting both innate as well as adaptive immunity balance and capturing the dynamics involved in the immune responses [25,26]. Recent studies have identified NLR as a more reliable predictor of patient survival than individual neutrophils or lymphocytes [27], and it has shown superior predictive value for mortality compared to other inflammation-related biomarkers like procalcitonin (PCT) and C-reactive protein (CRP) [28]. When combined with other inflammatory biomarkers, NLR may improve mortality predictions in sepsis patients [29,30]. The prognostic impact of NLR is mainly assessed through a single measured value within the first 24 h of ICU admission [31,32]. This single measurement fails to capture the fluctuating nature of sepsis, thereby limiting its diagnostic and prognostic utility [33]. To overcome this limitation, we employed a longitudinal approach using time-weighted averages [34,35] to monitor inflammatory markers such as WBC, platelets, neutrophils, lymphocytes, and monocytes at admission and subsequently every 72 h for up to seven days. This methodology enabled us to track changes in these markers over time and their correlations with patient outcomes, offering a more detailed view of sepsis progression. Our results indicate a strong statistical association between elevated TWA-NLR levels and increased mortality rates and extended ICU stays. Conversely, lower TWA-NLR values correlated with significantly longer median survival times. The prognostic value of TWA-NLR proved consistent across various patient subgroups, demonstrating its robustness as a prognostic indicator across a diverse patient population. However, we noted significant interactions in patients with chronic pulmonary or renal diseases, indicating that these conditions might influence the correlation among TWA-NLR and mortality. In addition, the adjusted model outperformed traditional score systems, such as the OASIS and SOFA, in predicting 90-day in-hospital mortality. External validation with the MIMIC-IV 2.2 database further confirmed the reliability of our predictive model.

Several limitations of this study warrant consideration. First, the retrospective design and utilization of observational databases such as MIMIC-IV 2.2 and eICU-CRD 2.0 inherently introduce potential biases. Secondly, inadequate documentation of inflammatory biomarkers limits comprehensive analysis. Thirdly, the associations identified between TWA-NLR and mortality do not imply causation. To better understand this relationship, future research should aim to conduct prospective studies or randomized controlled trials.

5 Conclusion

In summary, our results reveal a substantial and independent correlation between increased TWA-NLR and the incidence of 90-day in-hospital mortality in sepsis patients. Notably, TWA-NLR demonstrates potential predictive capabilities for in-hospital as well as out-of-hospital mortality. These findings indicate that TWA-NLR may serve as a convenient and reliable diagnostic tool to help identify high-risk sepsis, allowing more specific and effective management of sepsis.

Funding

The study was funded by High-Level Public Health Technical Talent Building Program (Discipline Leader-01-01 ).

Data and code statement

All the data present in our articles has been stored in the MIMIC-IV 2.2 (https://mimic.mit.edu/) and eICU-CRD 2.0 (https://eicu-crd.mit.edu/) database, which are freely available for analysis and download. Access to the MIMIC-IV 2.2 and eICU-CRD 2.0 databases involved successful completion of a qualifying exam and approval (certification number: 55849941). The related code and the data extracted are accessible from the corresponding author upon appropriate demand.

Ethics approval and consent to participate

The research was carried out following the ethical guidelines of the 1964 statement of Helsinki and its subsequent revisions or equivalent ethical guidelines. It is important to point out that the eICU-CRD 2.0 as well as MIMIC-IV 2.2 database received approval from the Beth Israel Deaconess Medical Center (2001-P-001699/14) as well as the Massachusetts Institute of Technology (0403000206). The eICU-CRD 2.0 as well as MIMIC-IV 2.2 database contain copies of the data utilized in this research. As a result, no ethical approval or informed consent did not need to be obtained for this research, and the study followed STROBE guidelines.

Consent for publication

Not applicable.

CRediT authorship contribution statement

Guyu Zhang: Writing – original draft. Tao Wang: Investigation. Le An: Software. ChenChen Hang: Data curation. XingSheng Wang: Methodology. Fei Shao: Validation. Rui Shao: Funding acquisition. Ziren Tang: Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the supplementary data to this article:Multimedia component 1

Multimedia component 1

figs1

figs2

figs3

figs4

figs5

Acknowledgments

We would to thank all those involved in the Emergency Medicine Clinical Research Center, Beijing Chao Yang Hospital, Capital Medical University.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36195.
==== Refs
References

1 Fleischmann C. Scherag A. Adhikari N.K. Hartog C.S. Tsaganos T. Schlattmann P. Angus D.C. Reinhart K. Assessment of global incidence and mortality of hospital-treated sepsis. Current estimates and limitations Am. J. Respir. Crit. Care Med. 193 3 2016 259 272 26414292
2 Denstaedt S.J. Singer B.H. Standiford T.J. Sepsis and nosocomial infection: patient characteristics, mechanisms, and modulation Front. Immunol. 9 2018 2446 30459764
3 Gameiro J. Fonseca J.A. Jorge S. Gouveia J. Lopes J.A. Neutrophil, lymphocyte and platelet ratio as a predictor of mortality in septic-acute kidney injury patients Nefrologia 40 4 2020 461 468 31948827
4 Singer M. Deutschman C.S. Seymour C.W. Shankar-Hari M. Annane D. Bauer M. Bellomo R. Bernard G.R. Chiche J.D. Coopersmith C.M. Hotchkiss R.S. Levy M.M. Marshall J.C. Martin G.S. Opal S.M. Rubenfeld G.D. van der Poll T. Vincent J.L. Angus D.C. The third international consensus definitions for sepsis and septic shock (Sepsis-3) JAMA 315 8 2016 801 810 26903338
5 de Pablo R. Monserrat J. Prieto A. Alvarez-Mon M. Role of circulating lymphocytes in patients with sepsis BioMed Res. Int. 2014 2014 671087
6 Polilli E. Esposito J.E. Frattari A. Trave F. Sozio F. Ferrandu G. Di Iorio G. Parruti G. Circulating lymphocyte subsets as promising biomarkers to identify septic patients at higher risk of unfavorable outcome BMC Infect. Dis. 21 2021 1 7 33390160
7 Girardot T. Rimmelé T. Venet F. Monneret G. Apoptosis-induced lymphopenia in sepsis and other severe injuries Apoptosis 22 2 2017 295 305 27812767
8 Jiang W. Zhong W. Deng Y. Chen C. Wang Q. Zhou M. Li X. Sun C. Zeng H. Evaluation of a combination "lymphocyte apoptosis model" to predict survival of sepsis patients in an intensive care unit BMC Anesthesiol. 18 1 2018 89 30021561
9 Liu D. Huang S.Y. Sun J.H. Zhang H.C. Cai Q.L. Gao C. Li L. Cao J. Xu F. Zhou Y. Guan C.X. Jin S.W. Deng J. Fang X.M. Jiang J.X. Zeng L. Sepsis-induced immunosuppression: mechanisms, diagnosis and current treatment options Mil Med Res 9 1 2022 56 36209190
10 Cao C. Yu M. Chai Y. Pathological alteration and therapeutic implications of sepsis-induced immune cell apoptosis Cell Death Dis. 10 10 2019 782 31611560
11 Finfer S. Venkatesh B. Hotchkiss R.S. Sasson S.C. Lymphopenia in sepsis-an acquired immunodeficiency? Immunol. Cell Biol. 101 6 2022 535 544 36468797
12 Shi Y. Yang C. Chen L. Cheng M. Xie W. Predictive value of neutrophil-to-lymphocyte and platelet ratio in in-hospital mortality in septic patients Heliyon 8 11 2022 e11498
13 Zheng R. Shi Y.-Y. Pan J.-Y. Qian S.-Z. Decrease in the platelet-to-lymphocyte ratio in days after admission for sepsis correlates with in-hospital mortality Shock 59 4 2023 553 559 36802214
14 Zahorec R. Neutrophil-to-lymphocyte ratio, past, present and future perspectives Bratisl. Lek. Listy 122 7 2021 474 488 34161115
15 Zhang X. Wei R. Wang X. Zhang W. Li M. Ni T. Weng W. Li Q. The neutrophil-to-lymphocyte ratio is associated with all-cause and cardiovascular mortality among individuals with hypertension Cardiovasc. Diabetol. 23 1 2024 117 38566082
16 Wen X. Zhang Y. Xu J. Song C. Shang Y. Yuan S. Zhang J. The early predictive roles of NLR and NE% in in-hospital mortality of septic patients Heliyon 10 4 2024 e26563
17 Wang X. Li M. Yang Y. Shang X. Wang Y. Li Y. Clinical significance of inflammatory markers for evaluating disease severity of mixed-pathogen bloodstream infections of both Enterococcus spp. and Candida spp Heliyon 10 5 2024 e26873
18 Russell C.D. Parajuli A. Gale H.J. Bulteel N.S. Schuetz P. de Jager C.P.C. Loonen A.J.M. Merekoulias G.I. Baillie J.K. The utility of peripheral blood leucocyte ratios as biomarkers in infectious diseases: a systematic review and meta-analysis J. Infect. 78 5 2019 339 348 30802469
19 Martins E.C. Silveira L.D.F. Viegas K. Beck A.D. Fioravantti Junior G. Cremonese R.V. Lora P.S. Neutrophil-lymphocyte ratio in the early diagnosis of sepsis in an intensive care unit: a case-control study Rev Bras Ter Intensiva 31 1 2019 64 70 30916236
20 Yang J. Guo X. Hao J. Dong Y. Zhang T. Ma X. The prognostic value of blood-based biomarkers in patients with testicular diffuse large B-cell lymphoma Front. Oncol. 9 2019 1392 31921649
21 Díaz-Coto S. Martínez-Camblor P. Pérez-Fernández S. smoothROCtime: an R package for time-dependent ROC curve estimation Comput. Stat. 35 2020 1231 1251
22 Venet F. Monneret G. Advances in the understanding and treatment of sepsis-induced immunosuppression Nat. Rev. Nephrol. 14 2 2018 121 137 29225343
23 Jensen I.J. Sjaastad F.V. Griffith T.S. Badovinac V.P. Sepsis-induced T cell immunoparalysis: the ins and outs of impaired T cell immunity J. Immunol. 200 5 2018 1543 1553 29463691
24 Jarczak D. Kluge S. Nierhaus A. Sepsis-pathophysiology and therapeutic concepts Front. Med. 8 2021 628302
25 Buonacera A. Stancanelli B. Colaci M. Malatino L. Neutrophil to lymphocyte ratio: an emerging marker of the relationships between the immune system and diseases Int. J. Mol. Sci. 23 7 2022
26 Drăgoescu A.N. Pădureanu V. Stănculescu A.D. Chiuțu L.C. Tomescu P. Geormăneanu C. Pădureanu R. Iovănescu V.F. Ungureanu B.S. Pănuș A. Drăgoescu O.P. Neutrophil to lymphocyte ratio (NLR)-A useful tool for the prognosis of sepsis in the ICU Biomedicines 10 1 2021
27 Kumarasamy C. Sabarimurugan S. Madurantakam R.M. Lakhotiya K. Samiappan S. Baxi S. Nachimuthu R. Gothandam K.M. Jayaraj R. Prognostic significance of blood inflammatory biomarkers NLR, PLR, and LMR in cancer—a protocol for systematic review and meta-analysis Medicine 98 24 2019 e14834
28 Liu Y. Zheng J. Zhang D. Jing L. Neutrophil-lymphocyte ratio and plasma lactate predict 28-day mortality in patients with sepsis J. Clin. Lab. Anal. 33 7 2019 e22942
29 Liu S. Wang X. She F. Zhang W. Liu H. Zhao X. Effects of neutrophil-to-lymphocyte ratio combined with interleukin-6 in predicting 28-day mortality in patients with sepsis Front. Immunol. 12 2021 639735
30 Zhao C. Wei Y. Chen D. Jin J. Chen H. Prognostic value of an inflammatory biomarker-based clinical algorithm in septic patients in the emergency department: an observational study Int. Immunopharm. 80 2020 106145
31 Ni J. Wang H. Li Y. Shu Y. Liu Y. Neutrophil to lymphocyte ratio (NLR) as a prognostic marker for in-hospital mortality of patients with sepsis: a secondary analysis based on a single-center, retrospective, cohort study Medicine 98 46 2019 e18029
32 Li T. Dong G. Zhang M. Xu Z. Hu Y. Xie B. Wang Y. Xu B. Association of neutrophil-lymphocyte ratio and the presence of neonatal sepsis J Immunol Res 2020 2020 7650713
33 Wu H. Cao T. Ji T. Luo Y. Huang J. Ma K. Predictive value of the neutrophil-to-lymphocyte ratio in the prognosis and risk of death for adult sepsis patients: a meta-analysis Front. Immunol. 15 2024 1336456
34 Xiao W. Liu W. Zhang J. Liu Y. Hua T. Yang M. The association of diastolic arterial pressure and heart rate with mortality in septic shock: a retrospective cohort study Eur. J. Med. Res. 27 1 2022 285 36496399
35 Zhu Z. Zhou M. Wei Y. Chen H. Time-varying intensity of oxygen exposure is associated with mortality in critically ill patients with mechanical ventilation Crit. Care 26 1 2022 239 35932009
