Introduction
Methods
Search strategy
Study eligibility criteria
Data extraction
Quality assessment
Results
Literature search and study selection
Study characteristics and quality assessment
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% |
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% |
Biomarkers associated with 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) |
Biomarkers associated with 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 |