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Erschienen in: Critical Care 1/2020

Open Access 01.12.2020 | Research

A systematic review of biomarkers multivariately associated with acute respiratory distress syndrome development and mortality

verfasst von: Philip van der Zee, Wim Rietdijk, Peter Somhorst, Henrik Endeman, Diederik Gommers

Erschienen in: Critical Care | Ausgabe 1/2020

Abstract

Background

Heterogeneity of acute respiratory distress syndrome (ARDS) could be reduced by identification of biomarker-based phenotypes. The set of ARDS biomarkers to prospectively define these phenotypes remains to be established.

Objective

To provide an overview of the biomarkers that were multivariately associated with ARDS development or mortality.

Data sources

We performed a systematic search in Embase, MEDLINE, Web of Science, Cochrane CENTRAL, and Google Scholar from inception until 6 March 2020.

Study selection

Studies assessing biomarkers for ARDS development in critically ill patients at risk for ARDS and mortality due to ARDS adjusted in multivariate analyses were included.

Data extraction and synthesis

We included 35 studies for ARDS development (10,667 patients at risk for ARDS) and 53 for ARDS mortality (15,344 patients with ARDS). These studies were too heterogeneous to be used in a meta-analysis, as time until outcome and the variables used in the multivariate analyses varied widely between studies. After qualitative inspection, high plasma levels of angiopoeitin-2 and receptor for advanced glycation end products (RAGE) were associated with an increased risk of ARDS development. None of the biomarkers (plasma angiopoeitin-2, C-reactive protein, interleukin-8, RAGE, surfactant protein D, and Von Willebrand factor) was clearly associated with mortality.

Conclusions

Biomarker data reporting and variables used in multivariate analyses differed greatly between studies. Angiopoeitin-2 and RAGE in plasma were positively associated with increased risk of ARDS development. None of the biomarkers independently predicted mortality. Therefore, we suggested to structurally investigate a combination of biomarkers and clinical parameters in order to find more homogeneous ARDS phenotypes.

PROSPERO identifier

PROSPERO, CRD42017078957
Hinweise

Supplementary information

Supplementary information accompanies this paper at https://​doi.​org/​10.​1186/​s13054-020-02913-7.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abkürzungen
AECC
American European Consensus Conference
Ang-2
Angiopoeitin-2
ARDS
Acute respiratory distress syndrome
CRP
C-reactive protein
DAD
Diffuse alveolar damage
IL-8
Interleukin-8
NOS
Newcastle-Ottawa Scale
OR
Odds ratio
RAGE
Receptor for advanced glycation end products
SpD
Surfactant protein D
VWF
Von Willebrand factor

Introduction

The acute respiratory distress syndrome (ARDS) is a major problem in the intensive care unit (ICU) with a prevalence of 10% and an in-hospital mortality rate of 40% [1, 2]. ARDS pathophysiology is based on a triad of alveolar-capillary membrane injury, high permeability alveolar oedema, and migration of inflammatory cells [3]. This triad is not routinely measured in clinical practice. Therefore, arterial hypoxemia and bilateral opacities on chest imaging following various clinical insults are used as clinical surrogates in the American European Consensus Conference (AECC) definition and the newer Berlin definition of ARDS [4, 5].
Histologically, ARDS is characterized by diffuse alveolar damage (DAD). The correlation between a clinical and histological diagnosis of ARDS is poor [6]. Only half of clinically diagnosed patients with ARDS have histological signs of DAD at autopsy [710]. The number of risk factors for ARDS and consequently the heterogeneous histological substrates found in patients with clinical ARDS have been recognized as a major contributor to the negative randomized controlled trial results among patients with ARDS [11].
It has been suggested that the addition of biomarkers to the clinical definition of ARDS could reduce ARDS heterogeneity by the identification of subgroups [1215]. A retrospective latent class analysis of large randomized controlled trials identified two ARDS phenotypes largely based on ARDS biomarkers combined with clinical parameters [16, 17]. These phenotypes responded differently to the randomly assigned intervention arms. Prospective studies are required to validate these ARDS phenotypes and their response to interventions. The set of ARDS biomarkers to prospectively define these phenotypes remains to be established.
Numerous biomarkers and their pathophysiological role in ARDS have been described [12, 18]. In an earlier meta-analysis, biomarkers for ARDS development and mortality were examined in univariate analysis [19]. However, pooling of univariate biomarker data may result in overestimation of the actual effect. For this reason, we conducted a systematic review and included all biomarkers that were multivariately associated with ARDS development or mortality. This study provides a synopsis of ARDS biomarkers that could be used for future research in the identification of ARDS phenotypes.

Methods

This systematic review was prospectively registered in PROSPERO International Prospective Register of Systematic Reviews (PROSPERO identifier CRD42017078957) and performed according to the Transparent Reporting of Systematic Reviews and Meta-analyses (PRISMA) Statement [20]. After the search strategy, two reviewers (PZ, PS, and/or WG) separately performed study eligibility criteria, data extraction, and quality assessment. Any discrepancies were resolved by consensus, and if necessary, a third reviewer was consulted.
We searched for studies that included biomarkers that were associated with ARDS development in critically ill patients at risk for ARDS and mortality in the ARDS population in multivariate analyses adjusted for background characteristics. We did not perform a meta-analysis, because the raw data in all studies was either not transformed or log transformed resulting in varying risk ratios and confidence intervals. In addition, the majority of studies used different biomarker concentration cut-offs, resulting in varying concentration increments for risk ratios. Lastly, the number of days until mortality and variables used in multivariate analysis differed between studies. For these reasons, we limited this study to a systematic review, as the multivariate odds ratios were not comparable and pooling would result in non-informative estimates [21].

Search strategy

We performed a systematic search in Embase, MEDLINE, Web of Science, Cochrane CENTRAL, and Google Scholar from inception until 30 July 2018 with assistance from the Erasmus MC librarian. The search was later updated to 6 March 2020. A detailed description of the systematic search string is presented in Additional file 1. In addition, the reference lists of included studies and recent systematic reviews were screened to identify additional eligible studies.

Study eligibility criteria

All retrieved studies were screened on the basis of title and abstract. Studies that did not contain adult patients at risk for ARDS or with ARDS and any biomarker for ARDS were excluded. The following eligibility criteria were used: human research, adult population, studies in which biomarkers were presented as odds ratios (OR) or risk ratios in multivariate analysis with ARDS development or mortality as outcome of interest, peer-reviewed literature only, and English language. Studies comparing ARDS with healthy control subjects, case series (< 10 patients included in the study), and studies presenting gene expression fold change were excluded.

Data extraction

A standardized form was used for data extraction from all eligible studies. Two clinical endpoints were evaluated in this study: development of ARDS in the at-risk population (patients that did develop ARDS versus critically ill patients that did not) and mortality in the ARDS population (survivors versus non-survivors). The following data were extracted: study design and setting, study population, sample size, the definition of ARDS used in the study, outcome, risk ratio with 95% confidence interval in multivariate analyses, and the variables used in the analyses. In addition, the role of the biomarker in ARDS pathophysiology as reported by the studies was extracted and divided into the following categories: increased endothelial permeability, alveolar epithelial injury, oxidative injury, inflammation, pro-fibrotic, myocardial strain, coagulation, and others. Subsequently, the relative frequency distribution of biomarker roles in ARDS pathophysiology was depicted in a bar chart.

Quality assessment

Methodological quality of the included studies was assessed with the Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomized studies in systematic reviews and meta-analyses [22]. Items regarding patient selection, comparability, and outcome were assessed using a descriptive approach, and a risk-of-bias score, varying between 0 (high risk) and 9 (low risk), was assigned to each study.

Results

Literature search and study selection

A total of 8125 articles were identified by the initial search and 972 by the updated search (Fig. 1). After removal of duplicates and reviewing titles and abstracts, we selected 438 articles for full-text review. A total of 86 studies was eligible for data extraction: 35 for ARDS development and 53 for ARDS mortality.

Study characteristics and quality assessment

The study characteristics of the 35 studies for ARDS development are presented in Table 1. A total of 10,667 critically ill patients was at risk for ARDS, of whom 2419 (24.6%) patients developed ARDS. The majority of studies used the Berlin definition of ARDS (21/35), followed by the AECC criteria of ARDS (13/35). The included biomarkers were measured in plasma, cerebrospinal fluid, and bronchoalveolar lavage fluid. In all studies, the first sample was taken within 72 h following ICU admission.
Table 1
Study characteristics for ARDS development
Study
Study design
Study population
ARDS definition
Outcome
Total (n)
ARDS (n)
Age
Gender, male n (%)
Variables in multivariate analysis
Sample moment
Agrawal 2013 [23]
Prospective cohort
Critically ill
AECC
ALI
167
19
69 ± 16
8 (42.1%)
APACHE II score, sepsis
Within 24 h following admission
Ahasic 2012 [24]
Case-control
Critically ill
AECC
ARDS
531
175
60.7 ± 17.6
102 (58.2%)
Age, gender, APACHE III score, BMI, ARDS risk factor
Within 48 h following admission
Aisiku 2016 [25]
RCT (TBI trial)
Critically ill neurotrauma
Berlin
ARDS
200
52
29.0 (19.5 IQR)
50 (96.2%)
Gender, injury severity scale, Glasgow coma scale
Within 24 h following injury
Amat 2000 [26]
Case-control
Critically ill
AECC
ARDS
35
21
54 ± 16
15 (71.4%)
Not specified
At ICU admission
Bai 2017 [27]
Prospective cohort
Critically ill neurotrauma
Berlin
ARDS
50
21
48 (39–57 IQR)
10 (46.7%)
Age, gender, BMI, injury score, blood transfusion, mechanical ventilation, Marshall CT score, Glasgow coma scale
At admission
Bai 2017 [27]
Prospective cohort
Critically ill trauma
Berlin
ARDS
42
16
44 (35–56 IQR)
10 (62.5%)
Age, gender, BMI, injury score, blood transfusion, mechanical ventilation, Marshall CT score, Glasgow coma scale
At admission
Bai 2018 [28]
Prospective cohort
Stroke patients
Berlin
ARDS
384
60
64 (43–72 IQR)
22 (36.7%)
Age, gender, BMI, onset to treatment time, medical history
Within 6 h following stroke
Chen 2019 [29]
Case-control
Critically ill sepsis
Berlin
ARDS
115
57
56.3 ± 10.1
40 (70.2%)
Age, gender, BMI, smoking history, COPD, cardiomyopathy, APACHE II score, SOFA score
Within 24 h following ARDS onset or ICU admission
Du 2016 [30]
Prospective cohort
Cardiac surgery patients
AECC
ALI
70
18
57.7 ± 11.6
12 (66.7%)
Age, medical history, BMI, systolic blood pressure
Within 1 h following surgery
Faust 2020 [31]
Prospective cohort
Critically ill trauma
Berlin
ARDS
224
41
44 (30–60 IQR)
37 (90.2%)
Injury severity score, blunt mechanism, pre-ICU shock
At ED
Faust 2020 [31]
Prospective cohort
Critically ill sepsis
Berlin
ARDS
120
45
62 (52–67 IQR)
15 (33.3%)
Lung source of sepsis, shock, age
At ED
Fremont 2010 [32]
Case-control
Critically ill
AECC
ALI/ARDS
192
107
39 (26–53 IQR)
71 (66.4%)
Not specified
Within 72 h following ICU admission
Gaudet 2018 [33]
Prospective cohort
Critically ill patients
Berlin
ARDS
72
11
56 (51–63 IQR)
8 (72.7%)
Not specified
At inclusion
Hendrickson 2018 [34]
Retrospective cohort
Severe traumatic brain injury
Berlin
ARDS
182
50
44 ± 20
42 (84.0%)
Age, acute injury scale, Glasgow coma scale, vasopressor use
Within 10 min following ED arrival
Huang 2019 [35]
Prospective cohort
Critically ill sepsis
Berlin
ARDS
152
41
63.2 ± 11.0
32 (78.0%)
Age, gender, BMI, smoking history, COPD, cardiomyopathy, APACHE II score, SOFA score
Within 24 h following ICU admission
Huang 2019 [36]
Prospective cohort
Critically ill pancreatitis
Berlin
ARDS
1933
143
49 (42–60 IQR)
87 (60.8%)
Age, gender, aetiology of ARDS, APACHE II score
At admission
Jabaudon 2018 [37]
Prospective cohort
Critically ill
Berlin
ARDS
464
59
62 ± 16
46 (78.0%)
SAPS II, sepsis, shock, pneumonia
Within 6 h following ICU admission
Jensen 2016 [38]
RCT (PASS)
Critically ill
Berlin
ARDS
405
31
NR
NR
Age, gender, APACHE II score, sepsis, eGFR
Within 24 h following admission
Jensen 2016 [38]
RCT (PASS)
Critically ill
Berlin
ARDS
353*
31
NR
NR
Age, gender, APACHE II score, sepsis, eGFR
Within 24 h following admission
Jones 2020 [39]
Prospective cohort
Critically ill sepsis
Berlin
ARDS
672
261
60 (51–69 IQR)
154 (59.0%)
Pulmonary source, APACHE III score
At admission
Jones 2020 [39]
Prospective cohort
Critically ill sepsis
Berlin
ARDS
843
NR
NR
NR
Pulmonary source, APACHE III score
Within 48 h following admission
Komiya 2011 [40]
Cross sectional
Acute respiratory failure
AECC
ALI/ARDS
124
53
78 (69–85 IQR)
34 (64.2%)
Age, systolic blood pressure, VEF, chest X-ray pleural effusion
Within 2 h following emergency department arrival
Lee 2011 [41]
Prospective cohort
Critically ill
AECC
ALI/ARDS
113
50
57.6 ± 19.1
24 (48.0%)
Sepsis, BMI
Within 24 h following ICU admission
Lin 2017 [42]
Retrospective cohort
Critically ill
Berlin
ARDS
212
83
54.3 ± 20.3
53 (63.9%)
CRP, albumin, serum creatinine, APACHE II score
Within 2 h following ICU admission
Liu 2017 [43]
Prospective cohort
Critically ill
AECC
ALI/ARDS
134
19
69 ± 18
10 (52.6%)
APACHE II, sepsis severity
On arrival at ED
Luo 2017 [44]
Retrospective cohort
Severe pneumonia
AECC
ALI/ARDS
157
43
56 ± 19
25 (58.1%)
Lung injury score, SOFA score, PaO2/FiO2, blood urea
Day 1 following admission
Meyer 2017 [45]
Prospective cohort
Critically ill trauma
Berlin
ARDS
198
100
60 ± 14
62 (62.0%)
APACHE III score, age, gender, ethnicity, pulmonary infection
On arrival at ED or ICU
Mikkelsen 2012 [46]
Case-control
Critically ill
AECC
ALI/ARDS
48
24
38 ± 20
22 (91.7%)
APACHE III score
In ED
Osaka 2011 [47]
Prospective cohort
Pneumonia
AECC
ALI/ARDS
27
6
75 (51–92 range)
4 (66.7%)
Not specified
3 to 5 days following admission
Palakshappa 2016 [48]
Prospective cohort
Critically ill
Berlin
ARDS
163
73
58 (52–68 IQR)
42 (57.5%)
APACHE III score, diabetes, BMI, pulmonary sepsis
At ICU admission
Reilly 2018 [49]
Prospective cohort
Critically ill sepsis
Berlin
ARDS
703
289
60 (51–69 IQR)
170 (58.8%)
Pulmonary source, APACHE III score
Within 24 h of ICU admission
Shashaty 2019 [50]
Prospective cohort
Critically ill sepsis
Berlin
ARDS
120
44
61 (50–68 IQR)
NR
Age, transfusion, pulmonary source, shock
At ED
Shashaty 2019 [50]
Prospective cohort
Critically ill trauma
Berlin
ARDS
180
37
41 (25–62 IQR)
NR
Injury severity score, blunt mechanism, transfusion
At presentation
Shaver 2017 [51]
Prospective cohort
Critically ill
AECC
ARDS
280
90
54 (44–64 IQR)
54 (60.0%)
Age, APACHE II, sepsis
Day of inclusion
Suzuki 2017 [52]
Retrospective cohort
Suspected drug-induced lung injury
New bilateral lung infiltration
ALI/ARDS
68
39
72 (65-81IQR)
25 (64.1%)
Gender, age, smoking history, biomarkers
As soon as possible after DLI suspicion
Wang 2019 [53]
Prospective cohort
Critically ill sepsis
Berlin
ARDS
109
32
58 ± 10.7
NR
Age, gender, BMI, smoking history, COPD, cardiomyopathy, APACHE II score, SOFA score
Within 24 h following admission
Ware 2017 [54]
Prospective cohort
Critically ill trauma patients
Berlin
ARDS
393
78
42 (26–55)
56 (71.8%)
Not specified
Within 24 h following inclusion
Xu 2018 [55]
Prospective cohort
Critically ill
Berlin
ARDS
158
45
60.0 ± 17.1
35 (77.8%)
APACHE II score, Lung injury prediction score, biomarkers, sepsis
Within 24 h of ICU admission
Yeh 2017 [56]
Prospective cohort
Critically ill
AECC
ALI/ARDS
129
18
65 ± 18
10 (55.6%)
APACHE II score
On arrival at the ED
Ying 2019 [57]
Prospective cohort
Critically ill pneumonia
Berlin
ARDS
145
37
61.3 ± 10.4
23 (62.2%)
Age, SOFA score, lung injury score, heart rate
At admission
    
Total
10,667
2419
    
      
24.6%
    
*Validating cohort
Some studies included patients from the same cohort
Abbreviations: AECC American European Consensus Conference definition of ARDS, ALI acute lung injury, APACHE acute physiology and chronic health evaluation, ARDS acute respiratory distress syndrome, BMI body mass index, COPD chronic obstructive pulmonary disease, CRP C-reactive protein, DLI drug-induced lung injury, ED emergency department, eGFR estimated glomerular filtration rate, ICU intensive care unit, LVEF left ventricular ejection fraction, SAPS simplified acute physiology score, SOFA sequential organ failure assessment
The study characteristics of the 53 studies for ARDS mortality are presented in Table 2. A total of 15,344 patients with ARDS were included with an observed mortality rate of 36.0%. The AECC definition of ARDS was used in the majority of included studies (39/53). The included biomarkers were measured in plasma, bronchoalveolar lavage fluid, and urine. All samples were taken within 72 h following the development of ARDS.
Table 2
Study characteristics for ARDS mortality
Study
Study design
Setting
ARDS definition
Outcome
Total (n)
Non-survivors (n)
Age
Gender, male n (%)
Variables in multivariate analysis
Sample moment
Adamzik 2013 [58]
Prospective cohort
Single centre
AECC
30 days
47
17
44 ± 13
32 (68. 1%)
SAPS II score, gender, lung injury score, ECMO, CVVHD, BMI, CRP, procalcitonin
Within 24 h following ICU admission
Ahasic 2012 [24]
Prospective cohort
Multicentre
AECC
60 days
175
78
60.7 ± 17.6
102 (58.3%)
Gender, BMI, cirrhosis, Diabetes, need for red cell transfusion, sepsis, septic shock, trauma
Within 48 h following ICU admission
Amat 2000 [26]
Prospective cohort
Two centre
AECC ARDS
1 month after ICU discharge
21
11
54 ± 16
15 (71.4%)
Not specified
Day 0 ICU
Bajwa 2008 [59]
Prospective cohort
Single centre
AECC
60 day
177
70
68.3 ± 15.3
99 (55.9%)
APACHE III score
Within 48 h following ARDS onset
Bajwa 2009 [60]
Prospective cohort
Single centre
AECC
60 days
177
70
62.5 (IQR 29.0)
100 (56.5%)
APACHE III score
Within 48 h following ARDS onset
Bajwa 2013 [61]
RCT (FACTT)
Multicentre
AECC
60 days
826
NR
48 (38–59 IQR)
442 (53.5%)
APACHE III score
Days 0 and 3
Calfee 2008 [62]
RCT (ARMA)
Multicentre
AECC
180 days
676
NR
51 ± 17
282 (41.7%)
Age, gender, APACHE III score, sepsis, or trauma
Day 0
Calfee 2009 [63]
RCT (ARMA)
Multicentre
AECC
Hospital
778
272
51 ± 17
459 (59.0%)
Age, PaO2/FiO2, APACHE III score, sepsis or trauma
Day 0
Calfee 2011 [64]
RCT (ARMA)
Multicentre
AECC
90 days
547
186
50 ± 16
227 (41.5%)
APACHE III score, tidal volume
Day 0
Calfee 2012 [65]
RCT (FACTT)
Multicentre
AECC
90 days
931
261
50 ± 16
498 (53.5%)
Age, APACHE III score, fluid management strategy
Day 0
Calfee 2015 [66]
Prospective cohort
Single centre
AECC
Hospital
100
31
58 ± 11
52 (52.0%)
APACHE III score
Day 2 following ICU admission
Calfee 2015 [66]
RCT (FACTT)
Multicentre
AECC
90 days
853
259
51 ± 15
444 (52.1%)
APACHE III score
Within 48 h following ARDS onset
Cartin-Ceba 2015 [67]
Prospective cohort
Single centre
AECC
In-hospital
100
36
62.5 (51–75 IQR)
54 (54.0%)
Acute physiology score of APACHE III score, DNR status, McCabe score
Within 24 h following diagnosis
Chen 2009 [68]
Prospective cohort
Single centre
*
28 days
59
26
62 ± 19
35 (59.3%)
APACHE II score, biomarkers
Within 24 h following diagnosis
Clark 1995 [69]
Prospective cohort
Single centre
**
Mortality
117
48
43.4 ± 15.4
75 (64.1%)
Lung injury score, risk factor for ARDS, lavage protein concentration
Day 3 following disease onset
Clark 2013 [70]
RCT (FACTT)
Multicentre
AECC
60 days
400
106
47 (37–57 IQR)
210 (52.5%)
Age, gender, ethnicity, baseline serum creatinine, ARDS risk factor
Day 1 following inclusion
Dolinay 2012 [71]
Prospective cohort
Single centre
AECC
In-hospital
28
17
54 ± 14.5
13 (46.4%)
APACHE II score
Within 48 h following ICU admission
Eisner 2003 [72]
RCT (ARMA)
Multicentre
AECC
180 days
565
195
51 ± 17
332 (58.8%)
Ventilation strategy, APACHE III score, PaO2/FiO2, creatinine, platelet count
Day 0 following inclusion
Forel 2015 [73]
Prospective cohort
Multicentrer
Berlin < 200 mmHg
ICU
51
NR (for ICU)
60 ± 13
40 (78.4%)
Lung injury score
Day 3
Forel 2018 [74]
Prospective cohort
Single centre
Berlin < 200 mmHg
60 days
62
21
59 ± 15
47 (75.8%)
Gender, SOFA score, LIS score
Day 3 following onset of ARDS
Guervilly 2011 [75]
Prospective cohort
Single centre
AECC
28 days
52
21
58 ± 17
39 (75.0%)
Not specified
Within 24 h following diagnosis
Kim 2019 [76]
Retrospective cohort
Single centre
Berlin
In-hospital
97
63
67.2 (64.3–70.1)
63 (64.3%)
APACHE II score, SOFA score, SAPS II score
Within 48 h following admission
Lee 2019 [77]
Retrospective cohort
Single centre
Berlin
In-hospital
237
154
69 (61–74 IQR)
166 (70.0%)
Age, diabetes mellitus, non-pulmonary source, APACHE II score, SOFA
Within 24 h following intubation
Lesur 2006 [78]
Prospective cohort
Multicentre
AECC
28 days
78
29
63 ± 16
48 (61.5%)
Age, PaCO2, APACHE II score
Within 48 h following onset of ARDS
Li 2019 [79]
Retrospective cohort
Single centre
Berlin
28 days
224
70
64 (46–77 IQR)
140 (62.5%)
APACHE II score, age, gender, BMI, smoking status, alcohol abusing status, risk factors, comorbidities
Within 24 h following ICU admission
Lin 2010 [80]
Prospective cohort
Single centre
AECC ARDS
28 days
63
27
75 (57–83 IQR)
38 (60.3%)
Age, lung injury score, SOFA score, APACHE II score, CRP, biomarkers
Within 24 h following ARDS onset
Lin 2012 [81]
Prospective cohort
Single centre
AECC
30 days
87
27
61 (56–70 IQR)
42 (48.3%)
APACHE II, Lung injury score, creatinine, biomarkers
At inclusion
Lin 2013 [82]
Prospective cohort
Single centre
AECC
30 days
78
22
63 (54–68 IQR)
45 (57.7%)
Age, APACHE II score, Lung injury score, PaO2/FiO2
Within 10 h following diagnosis
Madtes 1998 [83]
Prospective cohort
Single centre
***
In-hospital
74
33
38 (19–68 Range)
50 (67.6%)
Age, PCP III levels, neutrophils, lung injury score
Day 3 following ARDS onset
McClintock 2006 [84]
RCT (ARMA)
Multicentre
AECC
Mortality
579
NR
51 ± 17
333 (57.5%)
Ventilator group assignment
Day 0 following inclusion
McClintock 2007 [85]
RCT (ARMA)
Multicentre
AECC
Mortality
576
NR
52 ± 17
328 (56.9%)
Gender, ventilator group assignment, eGFR, age, APACHE III score, vasopressor use, sepsis
Day 0 following inclusion
McClintock 2008 [86]
Prospective cohort
Two centre
AECC
In-hospital
50
21
55 ± 16
28 (56.0%)
Age, gender, SAPS II
Within 48 h following diagnosis
Menk 2018 [87]
Retrospective cohort
Single centre
Berlin
ICU
404
182
50 (37–61 IQR)
265 (65.6%)
Age, gender, APACHE II score, SOFA, severe ARDS, peak airway pressure, pulmonary compliance
Within 24 h following admission
Metkus 2017 [88]
RCT (ALVEOLI, FACTT)
Multicentre
AECC
60 days
1057
NR
50.4
549 (51.9%)
Age, gender, trial group assignment
Within 24 h following inclusion
Mrozek 2016 [89]
Prospective cohort
Multicentre
AECC
90 days
119
42
57 ± 17
82 (68.9%)
Age, gender, SAPS II score, PaO2/FiO2, sepsis
Within 24 h following inclusion
Ong 2010 [90]
Prospective cohort
Two centre
AECC
28-day in-hospital
24
NR
51 ± 21
30 (53.6%)
Age, gender, PaO2/FiO2, tidal volume, plateau pressure, APACHE II score
At inclusion
Parsons 2005 [91]
RCT (ARMA)
Multicentre
AECC
180 days or discharge
562
196
NR
NR
Ventilation strategy, APACHE III score, PaO2/FiO2, creatinine, platelet count, vasopressor use
At inclusion
Parsons 2005 [92]
RCT (ARMA)
Multicentre
AECC
In-hospital
781
276
51.6 ± 17.3
319 (40.1%)
Ventilation strategy, APACHE III score, PaO2/FiO2, creatinine, platelet count, vasopressor use
Day 0
Quesnel 2012 [93]
Prospective cohort
Single centre
AECC
28 days
92
37
67 (49–74 IQR)
61 (66.3%)
Age, SAPS II score, malignancy, SOFA score, BAL characteristics
NR
Rahmel 2018 [94]
Retrospective cohort
Single centre
AECC
30 days
119
37
43.7 ± 13.3
71 (59.7%)
Age, SOFA score
Within 24 h following admission
Reddy 2019 [95]
Prospective cohort
Single centre
Berlin
30 days
39
19
55 (47.5-61.5)
25 (64.1%)
Not specified
Within 24 h of ARDS diagnosis
Rivara 2012 [96]
Prospective cohort
Single centre
AECC
60 days
177
70
71.5 (59–80 IQR)
98 (55.4%)
APACHE III score
Within 48 h following diagnosis
Rogers 2019 [97]
RCT (SAILS)
Multicentre
AECC
60 days
683
NR
56 (43–65)
335 (49.0%)
Age, race, APACHE III score, GFR, randomization, shock
Within 48 h following ARDS diagnosis
Sapru 2015 [98]
RCT (FACTT)
Multicentre
AECC
60 days
449
109
49.8 ± 15.6
242 (53.9%)
Age, gender, APACHE III score, pulmonary sepsis, fluid management strategy
Upon inclusion
Suratt 2009 [99]
RCT (ARMA)
Multicentre
AECC
In-hospital
645
222
51 ± 17
381 (59.1%)
Ventilation strategy, age, gender
Day 0
Tang 2014 [100]
Prospective cohort
Multicentre
Berlin
In-hospital
42
20
72.5 ± 10.8
27 (64.3%)
APACHE II score, PaO2/FiO2, CRP, WBC, procalcitonin
Within 24 h following diagnosis
Tsangaris 2009 [101]
Prospective cohort
Single centre
AECC
28 days
52
27
66.1 ± 16.9
32 (59.6%)
APACHE II score, age, genotype
Within 48 h following admission
Tsangaris 2017 [102]
Prospective cohort
Single centre
NR
28 days
53
28
64.6 ± 16.8
33 (62.3%)
Lung injury score
Within 48 h following diagnosis
Tsantes 2013 [103]
Prospective cohort
Single centre
AECC
28 days
69
34
64.4 ± 17.9
43 (62.3%)
Age, gender, APACHE II score, SOFA score, pulmonary parameters, serum lactate
Within 48 h following diagnosis
Tseng 2014 [104]
Prospective cohort
Single centre
AECC ARDS
ICU
56
16
70.6 ± 9.2
31 (55.4%)
APACHE II score, SOFA score, SAPS II score
Day 1 following ICU admission
Wang 2017 [105]
Prospective cohort
Multicentre
Berlin
60 days
167
62
76.5 (19–95 range)
112 (67.1%)
Age, gender, APACHE II score
Day 1 following diagnosis
Wang 2018 [106]
Retrospective cohort
Single centre
AECC
Mortality
247
146
62 (48–73 IQR)
162 (65.6%)
Age, cirrhosis, creatinine, PaO2/FiO2
Within 24 h following diagnosis
Ware 2004 [107]
RCT (ARMA)
Multicentre
AECC
In-hospital
559
193
51 ± 17
332 (59.4%)
Ventilator strategy, APACHE III score, PaO2/FiO2, creatinine, platelet count
Day 0 of inclusion
Xu 2017 [108]
Retrospective cohort
Single centre
Berlin
28 days
63
27
54 (42–67 IQR)
37 (58.7%)
APACHE II score, PaO2/FiO2, procalcitonin
Within 48 following admission
    
Total
15,344
3914
    
      
36.0%
    
*Respiratory failure requiring positive pressure ventilation, PF ratio < 200 mmHg, bilateral pulmonary infiltration on chest X-ray, no clinical evidence of left atrial hypertension
**PF ratio < 150 mmHg, PF < 200 mmHg with 5 PEEP, diffuse parenchymal infiltrates, pulmonary artery wedge pressure < 18 mmHg, no clinical evidence of congestive heart failure
***PF ratio < 150 mmHg, PF ratio < 200 mmHg with 5 cmH2O PEEP, diffuse parenchymal infiltrates, pulmonary artery wedge pressure < 18 mmHg, or no clinical evidence of congestive heart failure
Some studies included patients from the same cohort
Abbreviations: AECC American European Consensus Conference definition of ARDS, APACHE acute physiology and chronic health evaluation, ARDS acute respiratory distress syndrome, BAL bronchoalveolar lavage, BMI body mass index, CRP C-reactive protein, CVVHD continuous veno-venous haemodialysis, DNR do not resuscitate, ECMO extra corporeal membrane oxygenation, eGFR estimated glomerular filtration rate, FiO2 fraction of inspired oxygen, ICU intensive care unit, PCP procollagen, No. number, SAPS simplified acute physiology score, SOFA sequential organ failure assessment, WBC white blood cell count
The median quality of the included publications according to the NOS was 7 (range 4–9) for ARDS development and 8 (range 5–9) for ARDS mortality (Additional file 2).

Biomarkers associated with ARDS development in the at-risk population

A total of 37 biomarkers in plasma, 7 in cerebrospinal fluid, and 1 in bronchoalveolar lavage fluid were assessed in multivariate analyses (Table 3). Five studies examined angiopoeitin-2 (Ang-2) and seven studies examined receptor for advanced glycation end products (RAGE). In all studies, high plasma levels of Ang-2 and RAGE were significantly associated with an increased risk of ARDS development in the at-risk population. Similar results were seen for surfactant protein D (SpD) in plasma in all three studies that assessed SpD. In contrast, biomarkers for inflammation as C-reactive protein (CRP), procalcitonin, interleukin-6, and interleukin-8 were not clearly associated with ARDS development. The majority of biomarkers in plasma are surrogates for inflammation in ARDS pathophysiology (Fig. 2).
Table 3
Risk ratios for ARDS development in the at-risk population
 
Reference
Biomarker role in ARDS
Sample size
Risk ratio (95% CI)
Cut-off
Comment
Biomarkers in plasma
      
 Adiponectin
Palakshappa 2016 [48]
Anti-inflammatory
163
1.12 (1.01–1.25)
Per 5 mcg/mL
 
 Angiopoietin-2
Agrawal 2013 [23]
Increased endothelial permeability
167
1.8 (1.0–3.4)
Per log10
 
 Angiopoietin-2
Fremont 2010 [32]
Increased endothelial permeability
192
2.20 (1.19–4.05)
Highest vs lowest quartile
 
 Angiopoietin-2
Reilly 2018 [49]
Increased endothelial permeability
703
1.49 (1.20–1.77)
Per log increase
 
 Angiopoietin-2
Ware 2017 [54]
Increased endothelial permeability
393
1.890 (1.322–2.702)
1st vs 4th quartile
 
 Angiopoietin-2
Xu 2018 [55]
Increased endothelial permeability
158
1.258 (1.137–1.392)
  
 Advanced oxidant protein products
Du 2016 [30]
Oxidative injury
70
1.164 (1.068–1.269)
  
 Brain natriuretic peptide
Fremont 2010 [32]
Myocardial strain
192
0.45 (0.26–0.77)
Highest vs lowest quartile
 
 Brain natriuretic peptide
Komiya 2011 [40]
Myocardial strain
124
14.425 (4.382–47.483)
> 500 pg/mL
Outcome is CPE
 Club cell secretory protein
Jensen 2016 [38]
Alveolar epithelial injury
405
2.6 (0.7–9.7)
≥ 42.8 ng/mL
Learning cohort
 Club cell secretory protein
Jensen 2016 [38]
Alveolar epithelial injury
353
0.96 (0.20–4.5)
≥ 42.8 ng/mL
Validating cohort
 Club cell secretory protein
Lin 2017 [42]
Alveolar epithelial injury
212
1.096 (1.085–1.162)
  
 C-reactive protein (CRP)
Bai 2018 [28]
Inflammation
384
1.314 (0.620–1.603)
  
 C-reactive protein (CRP)
Chen 2019 [29]
Inflammation
115
0.994 (0.978–1.010)
  
 C-reactive protein (CRP)
Huang 2019 [35]
Inflammation
152
1.287 (0.295–5.606)
≥ 90.3 mg/L
 
 C-reactive protein (CRP)
Huang 2019 [36]
Inflammation
1933
1.008 (1.007–1.010)
  
 C-reactive protein (CRP)
Komiya 2011 [40]
Inflammation
124
0.106 (0.035–0.323)
> 50 mg/L
Outcome is CPE
 C-reactive protein (CRP)
Lin 2017 [42]
Inflammation
212
1.007 (1.001–1.014)
  
 C-reactive protein (CRP)
Osaka 2011 [47]
Inflammation
27
1.029 (0.829–1.293)
Per 1 mg/dL increase
 
 C-reactive protein (CRP)
Wang 2019 [53]
Inflammation
109
1.000 (0.992–1.008)
  
 C-reactive protein (CRP)
Ying 2019 [57]
Inflammation
145
1.22 (0.95–1.68)
  
 Free 2-chlorofatty acid
Meyer 2017 [45]
Oxidative injury
198
1.62 (1.25–2.09)
Per log10
 
 Total 2-chlorofatty acid
Meyer 2017 [45]
Oxidative injury
198
1.82 (1.32–2.52)
Per log10
 
 Free 2-chlorostearic acid
Meyer 2017 [45]
Oxidative injury
198
1.82 (1.41–2.37)
Per log10
 
 Total 2-chlorostearic acid
Meyer 2017 [45]
Oxidative injury
198
1.78 (1.31–2.43)
Per log10
 
 Endocan
Gaudet 2018 [33]
Leukocyte adhesion inhibition
72
0.001 (0–0.215)
> 5.36 ng/mL
 
 Endocan
Mikkelsen 2012 [46]
Leukocyte adhesion inhibition
48
0.69 (0.49–0.97)
1 unit increase
 
 Endocan
Ying 2019 [57]
Leukocyte adhesion modulation
145
1.57 (1.14–2.25)
  
 Fibrinogen
Luo 2017 [44]
Coagulation
157
1.893 (1.141–3.142)
  
 Glutamate
Bai 2017 [27]
Non-essential amino acid, neurotransmitter
50
2.229 (1.082–2.634)
  
 Glutamate
Bai 2017 [27]
Non-essential amino acid, neurotransmitter
42
0.996 (0.965–1.028)
  
 Glutamate
Bai 2018 [28]
Non-essential amino acid
384
3.022 (2.001–4.043)
  
 Growth arrest-specific gene 6
Yeh 2017 [56]
Endothelial activation
129
1.6 (1.3–2.6)
  
 Insulin-like growth factor 1
Ahasic 2012 [24]
Pro-fibrotic
531
0.58 (0.42–0.79)
Per log10
 
 IGF binding protein 3
Ahasic 2012 [24]
Pro-fibrotic
531
0.57 (0.40–0.81)
Per log10
 
 Interleukin-1 beta
Aisiku 2016 [25]
Pro-inflammatory
194
0.98 (0.73–1.32)
  
 Interleukin-1 beta
Chen 2019 [29]
Pro-inflammatory
115
1.001 (0.945–1.061)
  
 Interleukin-1 beta
Huang 2019 [35]
Pro-inflammatory
152
0.666 (0.152–2.910)
≥ 11.3 pg/mL
 
 Interleukin-1 beta
Wang 2019 [53]
Pro-inflammatory
109
1.021 (0.982–1.063)
  
 Interleukin-6
Aisiku 2016 [25]
Pro-inflammatory
195
1.24 (1.05–1.49)
  
 Interleukin-6
Bai 2018 [28]
Pro-inflammatory
384
1.194 (0.806–1.364)
  
 Interleukin-6
Chen 2019 [29]
Pro-inflammatory
115
0.998 (0.993–1.003)
  
 Interleukin-6
Huang 2019 [35]
Pro-inflammatory
152
0.512 (0.156–1.678)
≥ 63.7 pg/mL
 
 Interleukin-6
Yeh 2017 [56]
Pro-inflammatory
129
1.4 (0.98–1.7)
  
 Interleukin-8
Agrawal 2013 [23]
Pro-inflammatory
167
1.3 (0.97–1.8)
Per log10
 
 Interleukin-8
Aisiku 2016 [25]
Pro-inflammatory
194
1.26 (1.04–1.53)
  
 Interleukin-8
Chen 2019 [29]
Pro-inflammatory
115
1.000 (0.996–1.003)
  
 Interleukin-8
Fremont 2010 [32]
Pro-inflammatory
192
1.81 (1.03–3.17)
Highest vs lowest quartile
 
 Interleukin-8
Liu 2017 [43]
Pro-inflammatory
134
1.4 (0.98–1.7)
Per log10
 
 Interleukin-8
Yeh 2017 [56]
Pro-inflammatory
129
1.4 (0.92–1.7)
  
 Interleukin-10
Aisiku 2016 [25]
Anti-inflammatory
195
1.66 (1.22–2.26)
  
 Interleukin-10
Chen 2019 [29]
Anti-inflammatory
115
1.003 (0.998–1.018)
  
 Interleukin-10
Fremont 2010 [32]
Anti-inflammatory
192
2.02 (0.96–4.25)
Highest vs lowest quartile
 
 Interleukin-12p70
Aisiku 2016 [25]
Pro-inflammatory
194
1.18 (0.82–1.69)
  
 Interleukin-17
Chen 2019 [29]
Pro-inflammatory
115
1.003 (1.000–1.007)
 
Not significant
 Interleukin-17
Huang 2019 [35]
Pro-inflammatory
152
0.644 (0.173–2.405)
≥ 144.55 pg/mL
 
 Interleukin-17
Wang 2019 [53]
Pro-inflammatory
109
1.001 (0.997–1.004)
  
 Leukotriene B4
Amat 2000 [26]
Pro-inflammatory
35
14.3 (2.3–88.8)
> 14 pmol/mL
 
 Microparticles
Shaver 2017 [51]
Coagulation
280
0.693 (0.490–0.980)
Per 10 μM
 
 Mitochondrial DNA
Faust 2020 [31]
Damage-associated molecular pattern
224
1.58 (1.14–2.19)
 
48 h plasma
 Mitochondrial DNA
Faust 2020 [31]
Damage-associated molecular pattern
120
1.52 (1.12–2.06)
Per log copies per microlitre
48 h plasma
 Myeloperoxidase
Meyer 2017 [45]
Pro-inflammatory
198
1.28 (0.89–1.84)
Per log10
 
 Nitric oxide
Aisiku 2016 [25]
Oxidative injury
193
1.60 (0.98–2.90)
  
 Parkinson disease 7
Liu 2017 [43]
Anti-oxidative injury
134
1.8 (1.1–3.5)
Per log10
 
 Pre B cell colony enhancing factor
Lee 2011 [41]
Pro-inflammatory
113
0.78 (0.43–1.41)
Per 10 fold increase
 
 Procalcitonin
Bai 2018 [28]
Inflammation
384
1.156 (0.844–1.133)
  
 Procalcitonin
Chen 2019 [29]
Inflammation
115
1.020 (0.966–1.077)
  
 Procalcitonin
Huang 2019 [35]
Inflammation
152
2.506 (0.705–8.913)
≥ 13.2 ng/mL
 
 Procalcitonin
Huang 2019 [36]
Inflammation
1933
1.008 (1.000–1.016)
 
Not significant
 Procalcitonin
Wang 2019 [53]
Inflammation
109
1.019 (0.981–1.058)
  
 Procollagen III
Fremont 2010 [32]
Pro-fibrotic
192
2.90 (1.61–5.23)
Highest vs lowest quartile
 
 Receptor for advanced glycation end products
Fremont 2010 [32]
Alveolar epithelial injury
192
3.33 (1.85–5.99)
Highest vs lowest quartile
 
 Receptor for advanced glycation end products
Jabaudon 2018 [37]
Alveolar epithelial injury
464
2.25 (1.60–3.16)
Per log10
Baseline
 Receptor for advanced glycation end products
Jabaudon 2018 [37]
Alveolar epithelial injury
464
4.33 (2.85–6.56)
Per log10
Day 1
 Receptor for advanced glycation end products
Jones 2020 [39]
Alveolar epithelial injury
672
1.73 (1.35–2.21)
 
European ancestry
 Receptor for advanced glycation end products
Jones 2020 [39]
Alveolar epithelial injury
672
2.05 (1.50–2.83)
 
African ancestry
 Receptor for advanced glycation end products
Jones 2020 [39]
Alveolar epithelial injury
843
2.56 (2.14–3.06)
 
European ancestry
 Receptor for advanced glycation end products
Ware 2017 [54]
Alveolar epithelial injury
393
2.382 (1.638–3.464)
1st vs 4th quartile
 
 Receptor interacting protein kinase-3
Shashaty 2019 [50]
Increased endothelial permeability
120
1.30 (1.03–1.63)
Per 0.5 SD
 
 Receptor interacting protein kinase-3
Shashaty 2019 [50]
Increased endothelial permeability
180
1.83 (1.35–2.48)
Per 0.5 SD
 
 Soluble endothelial selectin
Osaka 2011 [47]
Pro-inflammatory
27
1.099 (1.012–1.260)
Per 1 ng/mL increase
 
 Soluble urokinase plasminogen activator receptor
Chen 2019 [29]
Pro-inflammatory
115
1.131 (1.002–1.277)
  
 Surfactant protein D
Jensen 2016 [38]
Alveolar epithelial injury
405
3.4 (1.0–11.4)
≥ 525.6 ng/mL
Learning cohort
 Surfactant protein D
Jensen 2016 [38]
Alveolar epithelial injury
353
8.4 (2.0–35.4)
≥ 525.6 ng/mL
Validating cohort
 Surfactant protein D
Suzuki 2017 [52]
Alveolar epithelial injury
68
5.31 (1.40–20.15)
Per log10
 
 Tissue inhibitor of matrix metalloproteinase 3
Hendrickson 2018 [34]
Decreases endothelial permeability
182
1.4 (1.0–2.0)
1 SD increase
 
 Tumour necrosis factor alpha
Aisiku 2016 [25]
Pro-inflammatory
195
1.03 (0.71–1.51)
  
 Tumour necrosis factor alpha
Chen 2019 [29]
Pro-inflammatory
115
1.002 (0.996–1.009)
  
 Tumour necrosis factor alpha
Fremont 2010 [32]
Pro-inflammatory
192
0.51 (0.27–0.98)
Highest vs lowest quartile
 
 Tumour necrosis factor alpha
Huang 2019 [35]
Pro-inflammatory
152
3.999 (0.921–17.375)
≥ 173.0 pg/mL
 
 Tumour necrosis factor alpha
Wang 2019 [53]
Pro-inflammatory
109
1.000 (0.995–1.005)
  
Biomarkers in CSF
      
 Interleukin-1 beta
Aisiku 2016 [25]
Pro-inflammatory
174
1.11 (0.80–1.54)
  
 Interleukin-6
Aisiku 2016 [25]
Pro-inflammatory
174
1.06 (0.95–1.19)
  
 Interleukin-8
Aisiku 2016 [25]
Pro-inflammatory
173
1.01 (0.92–1.12)
  
 Interleukin-10
Aisiku 2016 [25]
Anti-inflammatory
174
1.33 (1.00–1.76)
  
 Interleukin-12p70
Aisiku 2016 [25]
Pro-inflammatory
173
1.52 (1.04–2.21)
  
 Nitric oxide
Aisiku 2016 [25]
Oxidative injury
172
1.66 (0.70–3.97)
  
 Tumour necrosis factor alpha
Aisiku 2016 [25]
Pro-inflammatory
174
1.43 (0.97–2.14)
  
Biomarkers in BALF
      
 Soluble trombomodulin
Suzuki 2017 [52]
Endothelial injury
68
7.48 (1.60–34.98)
  
Abbreviations: CPE cardiopulmonary effusion, CSF cerebrospinal fluid, BALF bronchoalveolar lavage fluid, SD standard deviation

Biomarkers associated with mortality in the ARDS population

A total of 49 biomarkers in plasma, 8 in bronchoalveolar lavage fluid, and 3 in urine were included in this study (Table 4). Ang-2, CRP, interleukin-8 (IL-8), RAGE, SpD, and Von Willebrand factor (VWF) in plasma were assessed in four or more studies. However, none of these biomarkers was associated with ARDS mortality in all four studies. Similarly to biomarkers in ARDS development, the majority of biomarkers for ARDS mortality in plasma had a pathophysiological role in inflammation (Fig. 2). The majority of biomarkers measured in bronchoalveolar lavage fluid had a pro-fibrotic role in ARDS pathophysiology.
Table 4
Risk ratios for ARDS mortality in the ARDS population
 
Reference
Biomarker role in ARDS
Sample size
Risk ratio (95% CI)
Cut-off
Comment
Biomarkers in plasma
      
 Activin-A
Kim 2019 [76]
Pro-fibrotic
97
2.64 (1.04–6.70)
  
 Angiopoietin-1/angiopoietin-2 ratio
Ong 2010 [90]
Modulates endothelial permeability
24
5.52 (1.22–24.9)
  
 Angiopoietin-2
Calfee 2012 [65]
Increased endothelial permeability
931
0.92 (0.73–1.16)
Per log10
Infection-related ALI
 Angiopoietin-2
Calfee 2012 [65]
Increased endothelial permeability
931
1.94 (1.15–3.25)
Per log10
Noninfection-related ALI
 Angiopoietin-2
Calfee 2015 [66]
Increased endothelial permeability
100
2.54 (1.38–4.68)
Per log10
Single centre
 Angiopoietin-2
Calfee 2015 [66]
Increased endothelial permeability
853
1.43 (1.19–1.73)
per log10
Multicentre
 Angiotensin 1–9
Reddy 2019 [95]
Pro-fibrotic
39
2.24 (1.15–4.39)
Concentration doubled (in Ln)
 
 Angiotensin 1–10
Reddy 2019 [95]
Pro-fibrotic
39
0.36 (0.18–0.72)
Concentration doubled (in Ln)
 
 Angiotensin converting enzyme
Tsantes 2013 [103]
Endothelial permeability, pro-fibrotic
69
1.06 (1.02–1.10)
Per 1 unit increase
28-day mortality
 Angiotensin converting enzyme
Tsantes 2013 [103]
Endothelial permeability, pro-fibrotic
69
1.04 (1.01–1.07)
Per 1 unit increase
90-day mortality
 NT-pro brain natriuretic peptide
Bajwa 2008 [59]
Myocardial strain
177
2.36 (1.11–4.99)
≥ 6813 ng/L
 
 NT-pro brain natriuretic peptide
Lin 2012 [81]
Myocardial strain
87
2.18 (1.54–4.46)
Per unit
 
 Club cell secretory protein
Cartin-Ceba 2015 [67]
Alveolar epithelial injury
100
1.09 (0.60–2.02)
Per log10
 
 Club cell secretory protein
Lesur 2006 [78]
Alveolar epithelial injury
78
1.37 (1.25–1.83)
Increments of 0.5
 
 Copeptin
Lin 2012 [81]
Osmo-regulatory
87
4.72 (2.48–7.16)
Per unit
 
 C-reactive protein (CRP)
Adamzik 2013 [58]
Inflammation
47
1.01 (0.9–1.1)
Per log10
 
 C-reactive protein (CRP)
Bajwa 2009 [60]
Inflammation
177
0.67 (0.52–0.87)
Per log10
 
 C-reactive protein (CRP)
Lin 2010 [80]
Inflammation
63
2.316 (0.652–8.226)
  
 C-reactive protein (CRP)
Tseng 2014 [104]
Inflammation
56
1.265 (0.798–2.005)
 
Day 3
 D-dimer
Tseng 2014 [104]
Coagulation
56
1.211 (0.818–1.793)
  
 Decoy receptor 3
Chen 2009 [68]
Immunomodulation
59
4.02 (1.20–13.52)
> 1 ng/mL
Validation cohort
 Endocan
Tang 2014 [100]
Leukocyte adhesion inhibition
42
1.374 (1.150–1.641)
> 4.96 ng/mL
 
 Endocan
Tsangaris 2017 [102]
Leukocyte adhesion inhibition
53
3.36 (0.74–15.31)
> 13 ng/mL
 
 Galectin 3
Xu 2017 [108]
Pro-fibrotic
63
1.002 (0.978–1.029)
Per 1 ng/mL
 
 Granulocyte colony stimulating factor
Suratt 2009 [99]
Inflammation
645
1.70 (1.06–2.75)
Quartile 4 vs quartile 2
 
 Growth differentiation factor-15
Clark 2013 [70]
Pro-fibrotic
400
2.86 (1.84–4.54)
Per log10
 
 Heparin binding protein
Lin 2013 [82]
Inflammation, endothelial permeability
78
1.52 (1.12–2.85)
Per log10
 
 High mobility group protein B1
Tseng 2014 [104]
Pro-inflammatory
56
1.002 (1.000–1.004)
 
Day 1
 High mobility group protein B1
Tseng 2014 [104]
Pro-inflammatory
56
0.990 (0.968–1.013)
 
Day 3
 Insulin-like growth factor
Ahasic 2012 [24]
Pro-fibrotic
175
0.70 (0.51–0.95)
Per log10
 
 IGF binding protein 3
Ahasic 2012 [24]
Pro-fibrotic
175
0.69 (0.50–0.94)
Per log10
 
 Intercellular adhesion molecule-1
Calfee 2009 [63]
Pro-inflammatory
778
1.22 (0.99–1.49)
Per log10
 
 Intercellular adhesion molecule-1
Calfee 2011 [64]
Pro-inflammatory
547
0.74 (0.59–0.95)
Per natural log
 
 Intercellular adhesion molecule-1
McClintock 2008 [86]
Pro-inflammatory
50
5.8 (1.1–30.0)
Per natural log
 
 Interleukin-1 beta
Lin 2010 [80]
Pro-inflammatory
63
1.355 (0.357–5.140)
Per log 10
 
 Interleukin-6
Calfee 2015 [66]
Pro-inflammatory
100
1.81 (1.34–2.45)
Per log10
Single centre
 Interleukin-6
Calfee 2015 [66]
Pro-inflammatory
853
1.24 (1.14–1.35)
Per log10
Multicentre
 Interleukin-6
Parsons 2005 [92]
Pro-inflammatory
781
1.18 (0.93–1.49)
Per log10
 
 Interleukin-8
Amat 2000 [26]
Pro-inflammatory
21
0.09 (0.01–1.35)
> 150 pg/mL
 
 Interleukin-8
Calfee 2011 [64]
Pro-inflammatory
547
1.36 (1.15–1.62)
Per natural log
 
 Interleukin-8
Calfee 2015 [66]
Pro-inflammatory
100
1.65 (1.25–2.17)
Per log10
Single centre
 Interleukin-8
Calfee 2015 [66]
Pro-inflammatory
853
1.41 (1.27–1.57)
Per log10
Multicentre
 Interleukin-8
Cartin-Ceba 2015 [67]
Pro-inflammatory
100
1.08 (0.72–1.61)
Per log10
 
 Interleukin-8
Lin 2010 [80]
Pro-inflammatory
63
0.935 (0.280–3.114)
Per log 10
 
 Interleukin-8
McClintock 2008 [86]
Pro-inflammatory
50
2.0 (1.1–4.0)
Per natural log
 
 Interleukin-8
Parsons 2005 [92]
Pro-inflammatory
780
1.73 (1.28–2.34)
Per log10
 
 Interleukin-8
Tseng 2014 [104]
Pro-inflammatory
56
1.039 (0.955–1.130)
 
Day 1
 Interleukin-8
Tseng 2014 [104]
Pro-inflammatory
56
1.075 (0.940–1.229)
 
Day 3
 Interleukin-10
Parsons 2005 [92]
Anti-inflammatory
593
1.23 (0.86–1.76)
Per log10
 
 Interleukin-18
Dolinay 2012 [71]
Pro-inflammatory
28
1.60 (1.17–2.20)
Per 500 pg/mL increase
 
 Interleukin-18
Rogers 2019 [97]
Pro-inflammatory
683
2.2 (1.5–3.1)
≥ 800 pg/mL
 
 Leukocyte microparticles
Guervilly 2011 [75]
Immunomodulation
52
5.26 (1.10–24.99)
< 60 elements/μL
 
 Leukotriene B4
Amat 2000 [26]
Pro-inflammatory
21
22.5 (1.1–460.5)
> 14 pmol/mL
 
 Neutrophil elastase
Wang 2017 [105]
Pro-inflammatory
167
1.76 (p value 0.002)
1 SD change
Day 1
 Neutrophil elastase
Wang 2017 [105]
Pro-inflammatory
167
1.58 (p value 0.06)
1 SD change
Day 3
 Neutrophil elastase
Wang 2017 [105]
Pro-inflammatory
167
1.70 (p value 0.001)
1 SD change
Day 7
 Neutrophil to lymphocyte ratio
Li 2019 [79]
Pro-inflammatory
224
5.815 (1.824–18.533)
First–fourth quartile
 
 Neutrophil to lymphocyte ratio
Wang 2018 [106]
Pro-inflammatory
247
1.011 (1.004–1.017)
Per 1% increase
 
 Neutrophil to lymphocyte ratio
Wang 2018 [106]
Pro-inflammatory
247
1.532 (1.095–2.143)
> 14
 
 Nucleated red blood cells
Menk 2018 [87]
Erythrocyte progenitor cell, pro-inflammatory
404
3.21 (1.93–5.35)
> 220/μL
 
 Peptidase inhibitor 3
Wang 2017 [105]
Anti-inflammatory
167
0.50 (p value 0.003)
1 SD change
Day 1
 Peptidase inhibitor 3
Wang 2017 [105]
Anti-inflammatory
167
0.43 (p value 0.001)
1 SD change
Day 3
 Peptidase inhibitor 3
Wang 2017 [105]
Anti-inflammatory
167
0.70 (p value 0.18)
1 SD change
Day 7
 Plasminogen activator inhibitor 1
Cartin-Ceba 2015 [67]
Coagulation
100
0.96 (0.62–1.47)
Per log10
 
 Plasminogen activator inhibitor 1 (activity)
Tsangaris 2009 [101]
Coagulation
52
1.30 (0.84–1.99)
Per 1 unit increase
 
 Procalcitonin
Adamzik 2013 [58]
Inflammation
47
1.01 (0.025–1.2)
Per log10
 
 Procalcitonin
Rahmel 2018 [94]
Inflammation
119
0.999 (0.998–1.001)
  
 Protein C
McClintock 2008 [86]
Coagulation
50
0.5 (0.2–1.0)
Per natural log
 
 Protein C
Tsangaris 2017 [102]
Coagulation
53
3.58 (0.73–15.54)
< 41.5 mg/dL
 
 Receptor for advanced glycation end products
Calfee 2008 [62]
Alveolar epithelial injury
676
1.41 (1.12–1.78)
Per log10
Tidal volume 12 mL/kg
 Receptor for advanced glycation end products
Calfee 2008 [62]
Alveolar epithelial injury
676
1.03 (0.81–1.31)
Per log10
Tidal volume 6 mL/kg
 Receptor for advanced glycation end products
Calfee 2015 [66]
Alveolar epithelial injury
100
1.98 (1.18–3.33)
Per log10
Single centre
 Receptor for advanced glycation end products
Calfee 2015 [66]
Alveolar epithelial injury
853
1.16 (1.003–1.34)
Per log10
Multicentre
 Receptor for advanced glycation end products
Cartin-Ceba 2015 [67]
Alveolar epithelial injury
100
0.81 (0.50–1.30)
Per log10
 
 Receptor for advanced glycation end products
Mrozek 2016 [89]
Alveolar epithelial injury
119
3.1 (1.1–8.9)
 
 Soluble suppression of tumourigenicity-2
Bajwa 2013 [61]
Myocardial strain and inflammation
826
1.47 (0.99–2.20)
≥ 534 ng/mL (day 0)
Day 0
 Soluble suppression of tumourigenicity-2
Bajwa 2013 [61]
Myocardial strain and inflammation
826
2.94 (2.00–4.33)
≥ 296 ng/mL (day 3)
Day 3
 Soluble triggering receptor expressed on myeloid cells-1
Lin 2010 [80]
Pro-inflammatory
63
6.338 (1.607–24.998)
Per log 10
 
 Surfactant protein-A
Eisner 2003 [72]
Alveolar epithelial injury
565
0.92 (0.68–1.27)
Per 100 ng/mL increment
 
 Surfactant protein D
Calfee 2011 [64]
Alveolar epithelial injury
547
1.55 (1.27–1.88)
Per natural log
 
 Surfactant protein D
Calfee 2015 [66]
Alveolar epithelial injury
100
1.33 (0.82–2.14)
Per log10
Single centre
 Surfactant protein D
Calfee 2015 [66]
Alveolar epithelial injury
853
1.09 (0.95–1.24)
Per log10
Multicentre
 Surfactant protein D
Eisner 2003 [72]
Alveolar epithelial injury
565
1.21 (1.08–1.35)
Per 100 ng/mL increment
 
 Thrombin–antithrombin III complex
Cartin-Ceba 2015 [67]
Coagulation
100
1.05 (0.53–2.05)
Per log10
 
 High sensitivity troponin I
Metkus 2017 [88]
Myocardial injury
1057
0.94 (0.64–1.39)
1st, 5th quintile
 
 Cardiac troponin T
Rivara 2012 [96]
Myocardial injury
177
1.44 (1.14–1.81)
Per 1 ng/mL increase
 
 Trombomodulin
Sapru 2015 [98]
Coagulation
449
2.40 (1.52–3.83)
Per log10
Day 0
 Trombomodulin
Sapru 2015 [98]
Coagulation
449
2.80 (1.69–4.66)
Per log10
Day 3
 Tumour necrosis factor alpha
Lin 2010 [80]
Pro-inflammatory
63
3.691 (0.668–20.998)
Per log 10
 
 Tumour necrosis factor receptor-1
Calfee 2011 [64]
Pro-inflammatory
547
1.58 (1.20–2.09)
Per natural log
 
 Tumour necrosis factor receptor-1
Parsons 2005 [91]
Pro-inflammatory
562
5.76 (2.63–12.6)
Per log10
 
 Tumour necrosis factor receptor-2
Parsons 2005 [91]
Pro-inflammatory
376
2.58 (1.05–6.31)
Per log10
 
 Uric acid
Lee 2019 [77]
Antioxidant
237
0.549 (0.293–1030)
≥ 3.00 mg/dL
 
 Von Willebrand factor
Calfee 2011 [64]
Endothelial activation, coagulation
547
1.57 (1.16–2.12)
Per natural log
 
 Von Willebrand factor
Calfee 2012 [65]
Endothelial activation, coagulation
931
1.51 (1.20–1.90)
Per log10
 
 Von Willebrand factor
Calfee 2015 [66]
Endothelial activation, coagulation
853
1.83 (1.46–2.30)
Per log10
Multicentre
 Von Willebrand factor
Cartin-Ceba 2015 [67]
Endothelial activation, coagulation
100
2.93 (0.90–10.7)
Per log10
 
 Von Willebrand factor
Ware 2004 [107]
Endothelial activation, coagulation
559
1.6 (1.4–2.1)
Per SD increment
 
Biomarkers in BALF
 Angiopoietin-2
Tsangaris 2017 [102]
Increased endothelial permeability
53
11.18 (1.06–117.48)
> 705 pg/mL
 
 Fibrocyte percentage
Quesnel 2012 [93]
Pro-fibrotic
92
6.15 (2.78–13.64)
> 6%
 
 Plasminogen activator inhibitor 1 (activity)
Tsangaris 2009 [101]
Coagulation
52
0.37 (0.06–2.35)
Per 1 unit increase
 
 Procollagen III
Clark 1995 [69]
Pro-fibrotic
117
3.6 (1.2–10.7)
≥ 1.75 U/mL
 
 Procollagen III
Forel 2015 [73]
Pro-fibrotic
51
5.02 (2.06–12.25)
≥ 9 μg/L
 
 Transforming growth factor alpha
Madtes 1998 [83]
Pro-fibrotic
74
2.3 (0.7–7.0)
> 1.08 pg/mL
 
 Transforming growth factor beta 1
Forel 2018 [74]
Pro-fibrotic
62
1003 (0.986–1.019)
  
 T regulatory cell/CD4+ lymphocyte ratio
Adamzik 2013 [58]
Immunomodulation
47
6.5 (1.7–25)
≥ 7.4%
 
Biomarkers in urine
 Desmosine-to-creatinine ratio
McClintock 2006 [84]
Alveolar epithelial injury (elastin breakdown)
579
1.36 (1.02–1.82)
Per log10
 
 Nitric oxide
McClintock 2007 [85]
Oxidative injury
576
0.33 (0.20–0.54)
Per log10
 
 Nitric oxide-to-creatinine ratio
McClintock 2007 [85]
Oxidative injury
576
0.43 (0.28–0.66)
Per log10
 
Abbreviations: ALI acute lung injury, BALF bronchoalveolar lavage fluid, SD standard deviation

Discussion

In the current systematic review, we present a synopsis of biomarkers for ARDS development and mortality tested in multivariate analyses. We did not perform a meta-analysis because of severe data heterogeneity between studies. Upon qualitative inspection, we found that high levels of Ang-2 and RAGE were associated with ARDS development in the at-risk population. None of the biomarkers assessed in four or more studies was associated with an increased mortality rate in all studies. The majority of plasma biomarkers for both ARDS development and mortality are surrogates for inflammation in ARDS pathophysiology.
Previously, Terpstra et al. [19] calculated univariate ORs from absolute biomarker concentrations and performed a meta-analysis. They found that 12 biomarkers in plasma were associated with mortality in patients with ARDS. However, a major limitation of their meta-analysis is that these biomarkers were tested in univariate analyses without considering confounders as disease severity scores. Given the high univariate ORs as compared to the multivariate ORs found in this systematic review, the performance of these biomarkers is likely to be overestimated. Jabaudon et al. [109] found in an individual patient data meta-analysis that high concentrations of plasma RAGE were associated with 90-day mortality independent of driving pressure or tidal volume. However, they could not correct for disease severity score as these differed between studies. Unfortunately, we were unable to perform a meta-analysis on multivariate data because of heterogeneity of the included studies, as transformation of raw data, biomarker concentration cut-offs, time until outcome, and the variables used in the multivariate analyses varied widely between studies. This could be an incentive to standardize the presentation of ARDS biomarker research in terms of statistics and outcome for future analyses or to make individual patient data accessible.
ARDS biomarkers are presumed to reflect the pathophysiology of ARDS, characterized by alveolar-capillary membrane injury, high permeability alveolar oedema, and migration of inflammatory cells [3]. Previously, Terpstra et al. [19] proposed that biomarkers for ARDS development were correlated with alveolar tissue injury, whereas biomarkers for ARDS mortality correlated more with inflammation. In this systematic review, we found that the majority of biomarkers tested for both ARDS development and mortality were surrogates for inflammation. However, following qualitative inspection, biomarkers for inflammation were not evidently associated with either ARDS development or mortality. In contrast, markers for alveolar epithelial injury (plasma RAGE and SpD) and endothelial permeability (plasma Ang-2) seem to be associated with ARDS development. Therefore, we should consider how we intend to use (a set of) biomarkers in patients with ARDS.
A biomarker for ARDS development should be specific for ARDS, i.e. a biomarker that reflects alveolar injury or alveolar-capillary injury. Half of plasma biomarkers for ARDS development included in this study reflected inflammation. An increase in inflammatory biomarkers is known to correlate with increased disease severity scores [71, 97, 110]. In turn, the majority of studies in this review found significantly higher disease severity scores in the critically ill patients that eventually developed ARDS. Thus, plasma biomarkers for inflammation rather represented an estimation of disease severity and its associated increased risk for the development of ARDS. In addition, biomarkers for inflammation in plasma lack the specificity to diagnose ARDS, as they are unlikely to differentiate sepsis with ARDS from sepsis without ARDS. In contrast, locally sampled biomarkers for inflammation, for example in the alveolar space, could potentially diagnose ARDS [111]. Biomarkers used for ARDS mortality or for the identification of less heterogeneous ARDS phenotypes do not require to be ARDS specific, provided that they adequately predict or stratify patients with ARDS.
The heterogeneity of ARDS has been recognized as a major contributor to the negative randomized controlled trial results among patients with ARDS [11]. Therefore, it is necessary to identify homogeneous ARDS phenotypes that are more likely to respond to an intervention. This is known as predictive enrichment [112]. Previously, patients with ARDS have been successfully stratified based on clinical parameters, such as ARDS risk factor (pulmonary or extra-pulmonary) or PaO2/FiO2 ratio [113]. ARDS biomarkers could be used to stratify patients with ARDS based on biological or pathophysiological phenotype. For example, trials of novel therapies designed to influence vascular permeability may benefit from preferentially enrolling patients with high Ang-2 concentrations. Recently, clinical parameters have been combined with a set of biomarkers in a retrospective latent class analysis. In three trials, two distinct phenotypes were found: hyperinflammatory and hypoinflammatory ARDS [16, 17]. Patients with the hyperinflammatory phenotype had reduced mortality rate with higher positive end-expiratory pressures and with liberal fluid treatment, whereas the trials themselves found no difference between the entire intervention groups. The next step is to validate the identification of ARDS phenotypes based on latent class analysis in prospective studies. An adequate combination of biomarkers and clinical parameters remains to be established. Until now, there is no list of biomarkers that are associated with ARDS development or mortality independently of clinical parameters. This systematic review may guide the selection of ARDS biomarkers used for predictive enrichment.
This systematic review has limitations. First, the intent of this systematic review was to perform a meta-analysis. However, we decided not to perform a meta-analysis, as the biomarker data handling and outcomes varied widely among studies, and pooling would have resulted in a non-informative estimate [21]. Arguably, this is a positive result, as it refrains us from focusing on the few biomarkers that could be pooled in a meta-analysis and guides us into a direction were multiple biomarkers combined with other parameters are of interest. In a heterogeneous syndrome as ARDS, the one biomarker probably does not exist. Second, the first sampling moment varied between sampling at ICU admission until 72 h following ICU admission. Initially, ARDS is characterized by an exudative phase followed by a second proliferative phase and late fibrotic phase [3]. The moment of sampling likely influences biomarker concentrations, as both alveolar membrane injury and inflammation increase during the exudative phase. This is also seen in six biomarkers that have been measured at separate days, resulting in a significant change in adjusted OR for four biomarkers (Table 4) [61, 98, 104, 105]. Third, the aim of this systematic review was to assess the independent risk effects of biomarkers measured in various bodily fluid compartments. However, the majority of studies assessed biomarkers in plasma. It remains to be answered whether other bodily fluid compartments, for example from the airways and alveolar space themselves, might outperform ARDS biomarkers in plasma, especially for ARDS development. Fourth, all studies found in this systematic review used a clinical definition of ARDS as standard for ARDS diagnosis. Given the poor correlation between a clinical diagnosis and a histopathological diagnosis of ARDS, these studies are diagnosing a very heterogeneous disease syndrome [710]. In order to actually evaluate ARDS development, biomarkers should be compared to a histopathological image of DAD, although acquiring histology poses great challenges by itself. Fifth, as only biomarkers assessed in multivariate analyses were included in this study, new promising biomarkers evaluated in univariate analyses were excluded from this study. Lastly, non-significant biomarkers in multivariate analyses were more likely not to be reported, although some studies report non-significant results nonetheless.

Conclusion

In here, we present a list of biomarkers for ARDS mortality and ARDS development tested in multivariate analyses. In multiple studies that assessed Ang-2 and RAGE, high plasma levels were associated with an increased risk of ARDS development. We did not find a biomarker that independently predicted mortality in all studies that assessed the biomarker. Furthermore, biomarker data reporting and variables used in multivariate analyses differed greatly between studies. Taken together, we should look for a combination of biomarkers and clinical parameters in a structured approach in order to find more homogeneous ARDS phenotypes. This systematic review may guide the selection of ARDS biomarkers for ARDS phenotyping.

Supplementary information

Supplementary information accompanies this paper at https://​doi.​org/​10.​1186/​s13054-020-02913-7.

Acknowledgements

We thank Wan-Jie Gu (abbreviated in the text as WG) for his support in study eligibility evaluation (Nanjing University, China).
We thank Wichor Bramer and Elise Krabbendam (Biomedical Information Specialists Medical Library Erasmus MC) for their support in the literature search.
Not applicable
Not applicable

Competing interests

PZ, WR, PS, and HE have no conflict of interest. DG received speaker’s fee and travel expenses from Dräger, GE Healthcare (medical advisory board 2009–2012), Maquet, and Novalung (medical advisory board).
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Metadaten
Titel
A systematic review of biomarkers multivariately associated with acute respiratory distress syndrome development and mortality
verfasst von
Philip van der Zee
Wim Rietdijk
Peter Somhorst
Henrik Endeman
Diederik Gommers
Publikationsdatum
01.12.2020
Verlag
BioMed Central
Erschienen in
Critical Care / Ausgabe 1/2020
Elektronische ISSN: 1364-8535
DOI
https://doi.org/10.1186/s13054-020-02913-7

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