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) |