Development of a biomarker mortality risk model in acute respiratory distress syndrome
There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective stratification would optimize participant selection for clinical trial enrollment by focusing on those most likely to benefit from new interventions. Our...
Saved in:
| Published in | Critical care (London, England) Vol. 23; no. 1; pp. 410 - 8 |
|---|---|
| Main Authors | , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
BioMed Central Ltd
16.12.2019
BioMed Central BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1364-8535 1466-609X 1364-8535 1466-609X 1366-609X |
| DOI | 10.1186/s13054-019-2697-x |
Cover
Loading…
| Abstract | There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective stratification would optimize participant selection for clinical trial enrollment by focusing on those most likely to benefit from new interventions. Our objective was to develop a prognostic, biomarker-based model for predicting mortality in adult patients with acute respiratory distress syndrome.
This is a secondary analysis using a cohort of 252 mechanically ventilated subjects with the diagnosis of acute respiratory distress syndrome. Survival to day 7 with both day 0 (first day of presentation) and day 7 sample availability was required. Blood was collected for biomarker measurements at first presentation to the intensive care unit and on the seventh day. Biomarkers included cytokine-chemokines, dual-functioning cytozymes, and vascular injury markers. Logistic regression, latent class analysis, and classification and regression tree analysis were used to identify the plasma biomarkers most predictive of 28-day ARDS mortality.
From eight biologically relevant biomarker candidates, six demonstrated an enhanced capacity to predict mortality at day 0. Latent-class analysis identified two biomarker-based phenotypes. Phenotype A exhibited significantly higher plasma levels of angiopoietin-2, macrophage migration inhibitory factor, interleukin-8, interleukin-1 receptor antagonist, interleukin-6, and extracellular nicotinamide phosphoribosyltransferase (eNAMPT) compared to phenotype B. Mortality at 28 days was significantly higher for phenotype A compared to phenotype B (32% vs 19%, p = 0.04).
An adult biomarker-based risk model reliably identifies ARDS subjects at risk of death within 28 days of hospitalization. |
|---|---|
| AbstractList | There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective stratification would optimize participant selection for clinical trial enrollment by focusing on those most likely to benefit from new interventions. Our objective was to develop a prognostic, biomarker-based model for predicting mortality in adult patients with acute respiratory distress syndrome.BACKGROUNDThere is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective stratification would optimize participant selection for clinical trial enrollment by focusing on those most likely to benefit from new interventions. Our objective was to develop a prognostic, biomarker-based model for predicting mortality in adult patients with acute respiratory distress syndrome.This is a secondary analysis using a cohort of 252 mechanically ventilated subjects with the diagnosis of acute respiratory distress syndrome. Survival to day 7 with both day 0 (first day of presentation) and day 7 sample availability was required. Blood was collected for biomarker measurements at first presentation to the intensive care unit and on the seventh day. Biomarkers included cytokine-chemokines, dual-functioning cytozymes, and vascular injury markers. Logistic regression, latent class analysis, and classification and regression tree analysis were used to identify the plasma biomarkers most predictive of 28-day ARDS mortality.METHODSThis is a secondary analysis using a cohort of 252 mechanically ventilated subjects with the diagnosis of acute respiratory distress syndrome. Survival to day 7 with both day 0 (first day of presentation) and day 7 sample availability was required. Blood was collected for biomarker measurements at first presentation to the intensive care unit and on the seventh day. Biomarkers included cytokine-chemokines, dual-functioning cytozymes, and vascular injury markers. Logistic regression, latent class analysis, and classification and regression tree analysis were used to identify the plasma biomarkers most predictive of 28-day ARDS mortality.From eight biologically relevant biomarker candidates, six demonstrated an enhanced capacity to predict mortality at day 0. Latent-class analysis identified two biomarker-based phenotypes. Phenotype A exhibited significantly higher plasma levels of angiopoietin-2, macrophage migration inhibitory factor, interleukin-8, interleukin-1 receptor antagonist, interleukin-6, and extracellular nicotinamide phosphoribosyltransferase (eNAMPT) compared to phenotype B. Mortality at 28 days was significantly higher for phenotype A compared to phenotype B (32% vs 19%, p = 0.04).RESULTSFrom eight biologically relevant biomarker candidates, six demonstrated an enhanced capacity to predict mortality at day 0. Latent-class analysis identified two biomarker-based phenotypes. Phenotype A exhibited significantly higher plasma levels of angiopoietin-2, macrophage migration inhibitory factor, interleukin-8, interleukin-1 receptor antagonist, interleukin-6, and extracellular nicotinamide phosphoribosyltransferase (eNAMPT) compared to phenotype B. Mortality at 28 days was significantly higher for phenotype A compared to phenotype B (32% vs 19%, p = 0.04).An adult biomarker-based risk model reliably identifies ARDS subjects at risk of death within 28 days of hospitalization.CONCLUSIONSAn adult biomarker-based risk model reliably identifies ARDS subjects at risk of death within 28 days of hospitalization. There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective stratification would optimize participant selection for clinical trial enrollment by focusing on those most likely to benefit from new interventions. Our objective was to develop a prognostic, biomarker-based model for predicting mortality in adult patients with acute respiratory distress syndrome. This is a secondary analysis using a cohort of 252 mechanically ventilated subjects with the diagnosis of acute respiratory distress syndrome. Survival to day 7 with both day 0 (first day of presentation) and day 7 sample availability was required. Blood was collected for biomarker measurements at first presentation to the intensive care unit and on the seventh day. Biomarkers included cytokine-chemokines, dual-functioning cytozymes, and vascular injury markers. Logistic regression, latent class analysis, and classification and regression tree analysis were used to identify the plasma biomarkers most predictive of 28-day ARDS mortality. From eight biologically relevant biomarker candidates, six demonstrated an enhanced capacity to predict mortality at day 0. Latent-class analysis identified two biomarker-based phenotypes. Phenotype A exhibited significantly higher plasma levels of angiopoietin-2, macrophage migration inhibitory factor, interleukin-8, interleukin-1 receptor antagonist, interleukin-6, and extracellular nicotinamide phosphoribosyltransferase (eNAMPT) compared to phenotype B. Mortality at 28 days was significantly higher for phenotype A compared to phenotype B (32% vs 19%, p = 0.04). An adult biomarker-based risk model reliably identifies ARDS subjects at risk of death within 28 days of hospitalization. There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective stratification would optimize participant selection for clinical trial enrollment by focusing on those most likely to benefit from new interventions. Our objective was to develop a prognostic, biomarker-based model for predicting mortality in adult patients with acute respiratory distress syndrome. This is a secondary analysis using a cohort of 252 mechanically ventilated subjects with the diagnosis of acute respiratory distress syndrome. Survival to day 7 with both day 0 (first day of presentation) and day 7 sample availability was required. Blood was collected for biomarker measurements at first presentation to the intensive care unit and on the seventh day. Biomarkers included cytokine-chemokines, dual-functioning cytozymes, and vascular injury markers. Logistic regression, latent class analysis, and classification and regression tree analysis were used to identify the plasma biomarkers most predictive of 28-day ARDS mortality. From eight biologically relevant biomarker candidates, six demonstrated an enhanced capacity to predict mortality at day 0. Latent-class analysis identified two biomarker-based phenotypes. Phenotype A exhibited significantly higher plasma levels of angiopoietin-2, macrophage migration inhibitory factor, interleukin-8, interleukin-1 receptor antagonist, interleukin-6, and extracellular nicotinamide phosphoribosyltransferase (eNAMPT) compared to phenotype B. Mortality at 28 days was significantly higher for phenotype A compared to phenotype B (32% vs 19%, p = 0.04). An adult biomarker-based risk model reliably identifies ARDS subjects at risk of death within 28 days of hospitalization. Background There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective stratification would optimize participant selection for clinical trial enrollment by focusing on those most likely to benefit from new interventions. Our objective was to develop a prognostic, biomarker-based model for predicting mortality in adult patients with acute respiratory distress syndrome. Methods This is a secondary analysis using a cohort of 252 mechanically ventilated subjects with the diagnosis of acute respiratory distress syndrome. Survival to day 7 with both day 0 (first day of presentation) and day 7 sample availability was required. Blood was collected for biomarker measurements at first presentation to the intensive care unit and on the seventh day. Biomarkers included cytokine-chemokines, dual-functioning cytozymes, and vascular injury markers. Logistic regression, latent class analysis, and classification and regression tree analysis were used to identify the plasma biomarkers most predictive of 28-day ARDS mortality. Results From eight biologically relevant biomarker candidates, six demonstrated an enhanced capacity to predict mortality at day 0. Latent-class analysis identified two biomarker-based phenotypes. Phenotype A exhibited significantly higher plasma levels of angiopoietin-2, macrophage migration inhibitory factor, interleukin-8, interleukin-1 receptor antagonist, interleukin-6, and extracellular nicotinamide phosphoribosyltransferase (eNAMPT) compared to phenotype B. Mortality at 28 days was significantly higher for phenotype A compared to phenotype B (32% vs 19%, p = 0.04). Conclusions An adult biomarker-based risk model reliably identifies ARDS subjects at risk of death within 28 days of hospitalization. Abstract Background There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective stratification would optimize participant selection for clinical trial enrollment by focusing on those most likely to benefit from new interventions. Our objective was to develop a prognostic, biomarker-based model for predicting mortality in adult patients with acute respiratory distress syndrome. Methods This is a secondary analysis using a cohort of 252 mechanically ventilated subjects with the diagnosis of acute respiratory distress syndrome. Survival to day 7 with both day 0 (first day of presentation) and day 7 sample availability was required. Blood was collected for biomarker measurements at first presentation to the intensive care unit and on the seventh day. Biomarkers included cytokine-chemokines, dual-functioning cytozymes, and vascular injury markers. Logistic regression, latent class analysis, and classification and regression tree analysis were used to identify the plasma biomarkers most predictive of 28-day ARDS mortality. Results From eight biologically relevant biomarker candidates, six demonstrated an enhanced capacity to predict mortality at day 0. Latent-class analysis identified two biomarker-based phenotypes. Phenotype A exhibited significantly higher plasma levels of angiopoietin-2, macrophage migration inhibitory factor, interleukin-8, interleukin-1 receptor antagonist, interleukin-6, and extracellular nicotinamide phosphoribosyltransferase (eNAMPT) compared to phenotype B. Mortality at 28 days was significantly higher for phenotype A compared to phenotype B (32% vs 19%, p = 0.04). Conclusions An adult biomarker-based risk model reliably identifies ARDS subjects at risk of death within 28 days of hospitalization. Background There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective stratification would optimize participant selection for clinical trial enrollment by focusing on those most likely to benefit from new interventions. Our objective was to develop a prognostic, biomarker-based model for predicting mortality in adult patients with acute respiratory distress syndrome. Methods This is a secondary analysis using a cohort of 252 mechanically ventilated subjects with the diagnosis of acute respiratory distress syndrome. Survival to day 7 with both day 0 (first day of presentation) and day 7 sample availability was required. Blood was collected for biomarker measurements at first presentation to the intensive care unit and on the seventh day. Biomarkers included cytokine-chemokines, dual-functioning cytozymes, and vascular injury markers. Logistic regression, latent class analysis, and classification and regression tree analysis were used to identify the plasma biomarkers most predictive of 28-day ARDS mortality. Results From eight biologically relevant biomarker candidates, six demonstrated an enhanced capacity to predict mortality at day 0. Latent-class analysis identified two biomarker-based phenotypes. Phenotype A exhibited significantly higher plasma levels of angiopoietin-2, macrophage migration inhibitory factor, interleukin-8, interleukin-1 receptor antagonist, interleukin-6, and extracellular nicotinamide phosphoribosyltransferase (eNAMPT) compared to phenotype B. Mortality at 28 days was significantly higher for phenotype A compared to phenotype B (32% vs 19%, p = 0.04). Conclusions An adult biomarker-based risk model reliably identifies ARDS subjects at risk of death within 28 days of hospitalization. Keywords: ARDS, Predictive analytics, Biomarkers, Mortality |
| ArticleNumber | 410 |
| Audience | Academic |
| Author | Bime, Christian Miller, Edmund J. Casanova, Nancy Lynn, Heather Camp, Sara M. Ndukum, Juliet Lussier, Yves Mekontso-Dessap, Armand Oita, Radu C. Abraham, Ivo Carter, Darrick Garcia, Joe G. N. Downs, Charles A. |
| Author_xml | – sequence: 1 givenname: Christian orcidid: 0000-0003-4787-2685 surname: Bime fullname: Bime, Christian – sequence: 2 givenname: Nancy surname: Casanova fullname: Casanova, Nancy – sequence: 3 givenname: Radu C. surname: Oita fullname: Oita, Radu C. – sequence: 4 givenname: Juliet surname: Ndukum fullname: Ndukum, Juliet – sequence: 5 givenname: Heather surname: Lynn fullname: Lynn, Heather – sequence: 6 givenname: Sara M. surname: Camp fullname: Camp, Sara M. – sequence: 7 givenname: Yves surname: Lussier fullname: Lussier, Yves – sequence: 8 givenname: Ivo surname: Abraham fullname: Abraham, Ivo – sequence: 9 givenname: Darrick surname: Carter fullname: Carter, Darrick – sequence: 10 givenname: Edmund J. surname: Miller fullname: Miller, Edmund J. – sequence: 11 givenname: Armand surname: Mekontso-Dessap fullname: Mekontso-Dessap, Armand – sequence: 12 givenname: Charles A. surname: Downs fullname: Downs, Charles A. – sequence: 13 givenname: Joe G. N. surname: Garcia fullname: Garcia, Joe G. N. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31842964$$D View this record in MEDLINE/PubMed |
| BookMark | eNp1kktv1DAUhSPUij7gB7BBkdiwSRs_4scGqSqvSpXYAFvL8WPwNIkH26k6_54bpoVOBfLCvtffPcmxzkl1MMXJVdUr1J4hJNh5RqTtaNMi2WAmeXP3rDpGhNFGdKQ7eHQ-qk5yXrct4oKR59URQYJiyehx9f29u3VD3IxuKnX0ta77EEedblyqx5iKHkLZ1inkGyitG-ow1drMxdXJ5U1IusS0rW3IBepc5-1kUxzdi-rQ6yG7l_f7afXt44evl5-b6y-fri4vrhvTsbY0mnnGuRVGWOoNloL3vbVSe-c71EuprWWy51gagpHpqGeOQMHBvKXEU3JaXe10bdRrtUkBfn2rog7qdyOmldKpBDM4JQxyutfcWsIp66mQlvbek45TC30NWu92Wpu5H5018CJJD3ui-zdT-KFW8VYxiRjuMAi8vRdI8efsclFjyMYNg55cnLPCBHNJMRIE0DdP0HWc0wRPtVACsxbIv9RKg4Ew-QjfNYuoumCoJQh1jAF19g8KlnVjMJAYH6C_N_D6sdE_Dh9SAQDfASbFnJPzyoSiS4iL7zAo1Kolf2qXPwX5U0v-1B1MoieTD-L_n_kF9G3eZw |
| CitedBy_id | crossref_primary_10_3390_diagnostics11020332 crossref_primary_10_1134_S0022093024020285 crossref_primary_10_1093_pnasnexus_pgae173 crossref_primary_10_1016_j_heliyon_2025_e42146 crossref_primary_10_1097_NNR_0000000000000597 crossref_primary_10_1016_j_jtauto_2022_100181 crossref_primary_10_3390_metabo12070655 crossref_primary_10_1002_jcla_23488 crossref_primary_10_1186_s12890_022_02057_0 crossref_primary_10_3389_fphys_2023_1129413 crossref_primary_10_3390_ijms22010205 crossref_primary_10_3389_fphar_2021_808797 crossref_primary_10_1042_BSR20231934 crossref_primary_10_1016_S2213_2600_20_30124_7 crossref_primary_10_1165_rcmb_2022_0022ED crossref_primary_10_3390_biomedicines12122924 crossref_primary_10_3390_diagnostics13213296 crossref_primary_10_1042_CS20201448 crossref_primary_10_1016_j_jaci_2020_07_009 crossref_primary_10_1016_j_pulmoe_2022_01_016 crossref_primary_10_3389_fphys_2022_916159 crossref_primary_10_1016_j_accpm_2021_100996 crossref_primary_10_1038_s41598_021_02100_w crossref_primary_10_3390_ijms232315205 crossref_primary_10_1097_PCC_0000000000002570 crossref_primary_10_1183_13993003_02536_2020 crossref_primary_10_1016_j_ccm_2024_08_014 crossref_primary_10_1165_rcmb_2021_0357OC crossref_primary_10_1186_s13063_023_07201_7 crossref_primary_10_1016_j_biopha_2023_116103 crossref_primary_10_31857_S0869813924040029 crossref_primary_10_3389_fmed_2022_1012827 crossref_primary_10_1038_s41598_021_98053_1 crossref_primary_10_1016_j_trsl_2020_06_010 crossref_primary_10_1016_j_trsl_2020_12_008 crossref_primary_10_3390_diagnostics11122259 crossref_primary_10_1038_s41598_021_04444_9 crossref_primary_10_1183_23120541_00121_2024 crossref_primary_10_1096_fj_202201972RR crossref_primary_10_1515_med_2024_1076 crossref_primary_10_1007_s12024_022_00572_4 crossref_primary_10_1055_s_0040_1713422 crossref_primary_10_1016_j_pccm_2024_04_002 crossref_primary_10_1186_s10020_023_00760_0 crossref_primary_10_1097_CCE_0000000000000518 crossref_primary_10_1080_17476348_2023_2230883 crossref_primary_10_1165_rcmb_2019_0164OC crossref_primary_10_1186_s12871_022_01718_1 crossref_primary_10_1016_j_trsl_2021_06_002 crossref_primary_10_1002_phar_2394 crossref_primary_10_1093_qjmed_hcac234 crossref_primary_10_1177_17534666231181262 crossref_primary_10_1017_dmp_2023_176 crossref_primary_10_3389_fimmu_2023_1268756 crossref_primary_10_1186_s40364_023_00519_z |
| Cites_doi | 10.1016/S2213-2600(18)30177-2 10.1152/physrev.00037.2012 10.1602/neurorx.1.2.182 10.1038/nm.2678 10.1186/2110-5820-4-4 10.1164/rccm.200404-563OC 10.1001/jama.2016.0291 10.1378/chest.107.4.1062 10.1097/MCC.0b013e328334b151 10.1038/s41598-018-20994-x 10.1038/nmeth.4370 10.1164/rccm.201603-0645OC 10.1016/S2213-2600(14)70097-9 10.1165/rcmb.2017-0155OC 10.4103/0972-5229.190369 10.1152/ajplung.00378.2001 10.1002/path.1291 10.1155/2016/3501373 10.1164/rccm.201011-1802OC 10.2217/bmm.14.52 10.1002/cpt.136 10.1164/ajrccm.149.3.7509706 10.1056/NEJMoa062200 10.1164/ajrccm/138.3.720 10.1086/675991 10.1186/1471-2105-10-62 10.1165/rcmb.2012-0048OC 10.1097/01.CCM.0000155788.39101.7E 10.1152/ajplung.00010.2013 10.1136/jim-2016-000358 10.1186/s12950-018-0202-y 10.1097/PCC.0000000000001680 10.1186/s40635-018-0181-6 10.1152/ajplung.00277.2003 10.1097/00003246-198510000-00009 10.1097/CCM.0b013e3182257675 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2019 BioMed Central Ltd. 2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s). 2019 |
| Copyright_xml | – notice: COPYRIGHT 2019 BioMed Central Ltd. – notice: 2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s). 2019 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.1186/s13054-019-2697-x |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC ProQuest Central ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1364-8535 1466-609X 1366-609X |
| EndPage | 8 |
| ExternalDocumentID | oai_doaj_org_article_8c1eaba7dd3746b489d4bff3574dabaa PMC6916252 A610311566 31842964 10_1186_s13054_019_2697_x |
| Genre | Journal Article Research Support, N.I.H., Extramural |
| GeographicLocations | United Kingdom Minneapolis Minnesota United States--US |
| GeographicLocations_xml | – name: United Kingdom – name: Minneapolis Minnesota – name: United States--US |
| GrantInformation_xml | – fundername: NHLBI NIH HHS grantid: P01 HL126609 – fundername: NHLBI NIH HHS grantid: K08 HL141623 – fundername: NHLBI NIH HHS grantid: R42 HL145930 – fundername: NHLBI NIH HHS grantid: R41 HL147769 – fundername: NHLBI NIH HHS grantid: P01 HL134610 – fundername: ; grantid: K08 HL141623; P01 HL126609; R41 HL147769; R42 HL145930 |
| GroupedDBID | --- 0R~ 29F 2WC 4.4 53G 5GY 5VS 6J9 6PF 7X7 88E 8FI 8FJ AAFWJ AAJSJ AASML AAWTL AAYXX ABUWG ACGFS ACJQM ADBBV ADUKV AEGXH AENEX AFKRA AFPKN AHBYD AHMBA AHYZX ALIPV ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIAM AOIJS BAPOH BAWUL BCNDV BENPR BFQNJ BMC BPHCQ BVXVI C6C CCPQU CITATION CS3 DIK E3Z EBD EBLON EBS EMOBN F5P FYUFA GROUPED_DOAJ GX1 HMCUK HYE IAO IHR INH INR ITC KQ8 M1P OK1 PHGZM PHGZT PIMPY PQQKQ PROAC PSQYO RBZ ROL RPM RSV SJN SMD SOJ SV3 TR2 U2A UKHRP WOQ YOC CGR CUY CVF ECM EIF NPM PMFND 3V. 7XB 8FK AZQEC DWQXO EJD K9. PJZUB PKEHL PPXIY PQEST PQUKI PRINS 7X8 5PM PUEGO |
| ID | FETCH-LOGICAL-c560t-a6f677d8c8d4fc2987bbdd9afef51b99add69b729c321c54f6e329c7186d43f43 |
| IEDL.DBID | 7X7 |
| ISSN | 1364-8535 1466-609X |
| IngestDate | Wed Aug 27 01:30:53 EDT 2025 Thu Aug 21 13:22:41 EDT 2025 Fri Jul 11 05:16:26 EDT 2025 Sat Jul 26 01:26:35 EDT 2025 Tue Jun 17 21:46:33 EDT 2025 Tue Jun 10 20:50:02 EDT 2025 Thu Apr 03 07:04:24 EDT 2025 Tue Jul 01 03:54:54 EDT 2025 Thu Apr 24 22:58:26 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Biomarkers Predictive analytics Mortality ARDS |
| Language | English |
| License | Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c560t-a6f677d8c8d4fc2987bbdd9afef51b99add69b729c321c54f6e329c7186d43f43 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-4787-2685 |
| OpenAccessLink | https://www.proquest.com/docview/2328260794?pq-origsite=%requestingapplication% |
| PMID | 31842964 |
| PQID | 2328260794 |
| PQPubID | 44362 |
| PageCount | 8 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_8c1eaba7dd3746b489d4bff3574dabaa pubmedcentral_primary_oai_pubmedcentral_nih_gov_6916252 proquest_miscellaneous_2327942183 proquest_journals_2328260794 gale_infotracmisc_A610311566 gale_infotracacademiconefile_A610311566 pubmed_primary_31842964 crossref_citationtrail_10_1186_s13054_019_2697_x crossref_primary_10_1186_s13054_019_2697_x |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2019-12-16 |
| PublicationDateYYYYMMDD | 2019-12-16 |
| PublicationDate_xml | – month: 12 year: 2019 text: 2019-12-16 day: 16 |
| PublicationDecade | 2010 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: London |
| PublicationTitle | Critical care (London, England) |
| PublicationTitleAlternate | Crit Care |
| PublicationYear | 2019 |
| Publisher | BioMed Central Ltd BioMed Central BMC |
| Publisher_xml | – name: BioMed Central Ltd – name: BioMed Central – name: BMC |
| References | D Swaroopa (2697_CR2) 2016; 20 G Bellani (2697_CR1) 2016; 315 JF Murray (2697_CR6) 1988; 138 X Sun (2697_CR21) 2013; 305 AD Barker (2697_CR34) 2014; 8 LB Ware (2697_CR8) 2005; 33 M Krzywinski (2697_CR27) 2017; 14 CS Calfee (2697_CR9) 2014; 2 X Sun (2697_CR26) 2012; 47 JL Goldman (2697_CR11) 2014; 4 M Schivo (2697_CR35) 2017; 65 SE Wenzel (2697_CR36) 2012; 18 R Mayeux (2697_CR33) 2004; 1 HP Wiedemann (2697_CR23) 2006; 354 CS Calfee (2697_CR10) 2018; 6 S Amur (2697_CR32) 2015; 98 WA Knaus (2697_CR5) 1985; 13 N Hernandez-Pacheco (2697_CR17) 2018; 6 G Bernard (2697_CR24) 1994; 149 A Krupa (2697_CR13) 2004; 286 GU Meduri (2697_CR15) 1995; 107 KN Iskander (2697_CR37) 2013; 93 2697_CR29 C Calfee (2697_CR40) 2011; 183 M Jabaudon (2697_CR18) 2018; 8 MS Zinter (2697_CR22) 2016; 310 KN Kangelaris (2697_CR7) 2014; 4 KR Famous (2697_CR39) 2017; 195 R Blondonnet (2697_CR30) 2016; 2016 KN Lai (2697_CR16) 2003; 199 W Lutz (2697_CR19) 2004; 17 AK Kurdowska (2697_CR12) 2002; 282 RC Oita (2697_CR25) 2018; 59 VM Ranieri (2697_CR3) 2012; 307 MK Dahmer (2697_CR14) 2018; 19 S Spadaro (2697_CR31) 2019; 16 AJ Frank (2697_CR4) 2010; 16 SQ Ye (2697_CR20) 2005; 171 V Pihur (2697_CR28) 2009; 10 HR Wong (2697_CR38) 2011; 39 |
| References_xml | – volume: 6 start-page: 691 issue: 9 year: 2018 ident: 2697_CR10 publication-title: Lancet Respir Med doi: 10.1016/S2213-2600(18)30177-2 – volume: 310 start-page: L224 issue: 3 year: 2016 ident: 2697_CR22 publication-title: Am J Phys Lung Cell Mol Phys – volume: 93 start-page: 1247 issue: 3 year: 2013 ident: 2697_CR37 publication-title: Physiol Rev doi: 10.1152/physrev.00037.2012 – volume: 1 start-page: 182 issue: 2 year: 2004 ident: 2697_CR33 publication-title: NeuroRx doi: 10.1602/neurorx.1.2.182 – volume: 18 start-page: 716 issue: 5 year: 2012 ident: 2697_CR36 publication-title: Nat Med doi: 10.1038/nm.2678 – volume: 4 start-page: 4 issue: 1 year: 2014 ident: 2697_CR7 publication-title: Ann Intensive Care doi: 10.1186/2110-5820-4-4 – volume: 171 start-page: 361 issue: 4 year: 2005 ident: 2697_CR20 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.200404-563OC – volume: 315 start-page: 788 issue: 8 year: 2016 ident: 2697_CR1 publication-title: JAMA. doi: 10.1001/jama.2016.0291 – volume: 107 start-page: 1062 issue: 4 year: 1995 ident: 2697_CR15 publication-title: Chest. doi: 10.1378/chest.107.4.1062 – volume: 16 start-page: 62 issue: 1 year: 2010 ident: 2697_CR4 publication-title: Curr Opin Crit Care doi: 10.1097/MCC.0b013e328334b151 – volume: 8 start-page: 2603 issue: 1 year: 2018 ident: 2697_CR18 publication-title: Sci Rep doi: 10.1038/s41598-018-20994-x – volume: 17 start-page: 245 issue: 2 year: 2004 ident: 2697_CR19 publication-title: Int J Occup Med Environ Health – volume: 307 start-page: 2526 issue: 23 year: 2012 ident: 2697_CR3 publication-title: JAMA. – volume: 14 start-page: 757 year: 2017 ident: 2697_CR27 publication-title: Nat Methods doi: 10.1038/nmeth.4370 – volume: 195 start-page: 331 issue: 3 year: 2017 ident: 2697_CR39 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201603-0645OC – volume: 2 start-page: 611 issue: 8 year: 2014 ident: 2697_CR9 publication-title: Lancet Respir Med doi: 10.1016/S2213-2600(14)70097-9 – volume: 59 start-page: 36 issue: 1 year: 2018 ident: 2697_CR25 publication-title: Am J Respir Cell Mol Biol doi: 10.1165/rcmb.2017-0155OC – volume: 20 start-page: 518 issue: 9 year: 2016 ident: 2697_CR2 publication-title: Indian J Crit Care Med doi: 10.4103/0972-5229.190369 – volume: 282 start-page: L1092 issue: 5 year: 2002 ident: 2697_CR12 publication-title: Am J Physiol Lung Cell Mol Physiol doi: 10.1152/ajplung.00378.2001 – volume: 199 start-page: 496 issue: 4 year: 2003 ident: 2697_CR16 publication-title: J Pathol doi: 10.1002/path.1291 – volume: 2016 start-page: 3501373 year: 2016 ident: 2697_CR30 publication-title: Dis Markers doi: 10.1155/2016/3501373 – volume: 183 start-page: 1660 year: 2011 ident: 2697_CR40 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201011-1802OC – volume: 8 start-page: 873 issue: 6 year: 2014 ident: 2697_CR34 publication-title: Biomark Med doi: 10.2217/bmm.14.52 – volume: 98 start-page: 34 issue: 1 year: 2015 ident: 2697_CR32 publication-title: Clin Pharmacol Ther doi: 10.1002/cpt.136 – volume: 149 start-page: 818 year: 1994 ident: 2697_CR24 publication-title: Am J Respir Crit Care Med doi: 10.1164/ajrccm.149.3.7509706 – volume: 354 start-page: 2564 issue: 24 year: 2006 ident: 2697_CR23 publication-title: N Engl J Med doi: 10.1056/NEJMoa062200 – volume: 138 start-page: 720 issue: 3 year: 1988 ident: 2697_CR6 publication-title: Am Rev Respir Dis doi: 10.1164/ajrccm/138.3.720 – volume: 4 start-page: 280 issue: 2 year: 2014 ident: 2697_CR11 publication-title: Pulm Circ doi: 10.1086/675991 – volume: 10 start-page: 62 issue: 1 year: 2009 ident: 2697_CR28 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-10-62 – volume: 47 start-page: 628 issue: 5 year: 2012 ident: 2697_CR26 publication-title: Am J Respir Cell Mol Biol doi: 10.1165/rcmb.2012-0048OC – volume: 33 start-page: S217 issue: 3 Suppl year: 2005 ident: 2697_CR8 publication-title: Crit Care Med doi: 10.1097/01.CCM.0000155788.39101.7E – volume: 305 start-page: L467 issue: 7 year: 2013 ident: 2697_CR21 publication-title: Am J Physiol Lung Cell Mol Physiol doi: 10.1152/ajplung.00010.2013 – volume: 65 start-page: 953 issue: 6 year: 2017 ident: 2697_CR35 publication-title: J Invest Med doi: 10.1136/jim-2016-000358 – volume: 16 start-page: 1 issue: 1 year: 2019 ident: 2697_CR31 publication-title: J Inflamm doi: 10.1186/s12950-018-0202-y – volume: 19 start-page: 930 issue: 10 year: 2018 ident: 2697_CR14 publication-title: Pediatr Crit Care Med doi: 10.1097/PCC.0000000000001680 – volume: 6 start-page: 16 issue: 1 year: 2018 ident: 2697_CR17 publication-title: Intensive Care Med Exp doi: 10.1186/s40635-018-0181-6 – volume: 286 start-page: L1105 issue: 6 year: 2004 ident: 2697_CR13 publication-title: Am J Physiol Lung cell Mol Physiol doi: 10.1152/ajplung.00277.2003 – volume: 13 start-page: 818 issue: 10 year: 1985 ident: 2697_CR5 publication-title: Crit Care Med doi: 10.1097/00003246-198510000-00009 – ident: 2697_CR29 – volume: 39 start-page: 2511 issue: 11 year: 2011 ident: 2697_CR38 publication-title: Crit Care Med doi: 10.1097/CCM.0b013e3182257675 |
| SSID | ssj0017863 |
| Score | 2.5307832 |
| Snippet | There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective... Background There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome. Effective... Abstract Background There is a compelling unmet medical need for biomarker-based models to risk-stratify patients with acute respiratory distress syndrome.... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 410 |
| SubjectTerms | Adult Adult respiratory distress syndrome Algorithms APACHE ARDS Biological markers Biomarkers Biomarkers - analysis Biomarkers - blood Classification Clinical trials Critical care Cytokines Cytokines - analysis Cytokines - blood EDTA Female Humans Interleukin 1 Receptor Antagonist Protein - analysis Interleukin 1 Receptor Antagonist Protein - blood Interleukin-1beta - analysis Interleukin-1beta - blood Interleukin-6 - analysis Interleukin-6 - blood Interleukin-8 - analysis Interleukin-8 - blood Interleukins Intramolecular Oxidoreductases - analysis Intramolecular Oxidoreductases - blood Latent Class Analysis Logistic Models Macrophage Migration-Inhibitory Factors - analysis Macrophage Migration-Inhibitory Factors - blood Macrophages Male Medical prognosis Medical research Middle Aged Mortality Niacinamide Nicotinamide Phosphoribosyltransferase - analysis Nicotinamide Phosphoribosyltransferase - blood Patients Peptide Fragments - analysis Peptide Fragments - blood Phenotypes Predictive analytics Prognosis Respiratory distress syndrome Respiratory Distress Syndrome - blood Respiratory Distress Syndrome - epidemiology Respiratory Distress Syndrome - mortality Risk assessment Risk Assessment - methods Risk Assessment - standards Sepsis Sphingosine-1-Phosphate Receptors - analysis Sphingosine-1-Phosphate Receptors - blood United Kingdom Vesicular Transport Proteins - analysis Vesicular Transport Proteins - blood |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Li9RAEC5kD-JFXJ_RVVoQBCHsJOlUdx93xWUR9OTK3pp-4oBmZHYG9udb1cmMEwS9eEy6E9KVenxNVX8F8AadMT0F2rql6FHLZFzttUGy-BBUkkqSX-Zqi894eSU_XvfXB62-uCZspAceBXeqQ5OcdyrGTkn0Upsofc5dr2Sk-wUaUczbbaam_IHS2E05zEbj6Q156p6rLUzdolH17SwKFbL-P13yQUya10seBKCLB3B_Qo7ibPziY7iThodw99OUG38EXw_qf8QqCyf4aD1X36zFjwKyCXALLiUXpf2NWA7Che0mifXvdLuIy_H0iNhRGTyGq4sPX95f1lPXhDoQetnUDjMqFXXQUebQGq28j9G4nHLfeGPIoaHxhKlD1zahlxlTRxcUozDKLsvuCRwNqyE9A4HB5IROkz9nGjl6Y79IyXBrjiZklSpY7KRow0Qpzp0tvtuytdBoR8FbErxlwdvbCt7tH_k58mn8bfI5_5r9RKbCLjdIQeykIPZfClLBW_6xlg2WPi646dwBLZGpr-wZcqcL3sZWcDKbSYYW5sM71bCTod9YAqS0QVuQV6vg9X6Yn-TitSGttmUOjTMWreDpqEn7JZFLlZz5rkDNdGy25vnIsPxWaMCRkH3bt8__h5BewL2WrYPstcETONqst-kloa2Nf1UM6xfSPyrC priority: 102 providerName: Directory of Open Access Journals |
| Title | Development of a biomarker mortality risk model in acute respiratory distress syndrome |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/31842964 https://www.proquest.com/docview/2328260794 https://www.proquest.com/docview/2327942183 https://pubmed.ncbi.nlm.nih.gov/PMC6916252 https://doaj.org/article/8c1eaba7dd3746b489d4bff3574dabaa |
| Volume | 23 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Za9wwEBZtAqUvoXfdposKhULBZG3LOp5KtiSEQkMJTVn6ImQd6UJrJ3tAfn5nZK2zppAXgy3J6JhLmtE3hHzgRqkaFG1egvbImVcmb6TiwPHWCs8EA7mM0Rbn_OySfZ3X83TgtkphlVuZGAW16yyekR-B5gdLeArk8_n6JsesUehdTSk0HpJ9hC7DkC4xHzZchZAxk1pRQV9ALdXJq1lIfrQC2V1j_IXKS65EfjvSSxG-_38hvaOlxhGUOyrp9Ak5SLYkPe4X_yl54Ntn5NG35C1_Tn7uRATRLlBD8bI9xuMs6d9odoMJTjG4nMaEOHTRUmM3a0-Xdw546hb9fRK6BTd4QS5PT358OctTHoXcgj2zzg0PXAgnrXQs2FJJ0TTOKRN8qItGKRBxXDVgZduqLGzNAvcVvIDW4o5VgVUvyV7btf41odyq4LmRIOERWA7-WE-9V5iso7BB-IxMt7OobQIZx1wXf3TcbEiu-4nXMPEaJ17fZuTT0OS6R9i4r_IMl2aoiODY8UO3vNKJ17S0hTeNEc5VgvGGSeVYE0JVC-bgu8nIR1xYjSwMnbMm3USAISIYlj7mmPsCN7YZORzVBNaz4-ItaejE-it9R6gZeT8UY0sMZ2t9t4l1oByt04y86ilpGBIIWYa-8IyIEY2NxjwuaRe_IzA4B1u_rMs393frLXlcIt0Dbxb8kOytlxv_DiyrdTOJ7DMh-7OT8-8Xk3g-Ac-L2a9_qGwlzg |
| linkProvider | ProQuest |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9QwDLfGTQJeEN8UBgQJhIRU7dqmSfOA0AabNradENrQ3kKaDzgJeuM-xPin-Bux-3G7Cmlve7wmqRrH9s8-OzbAS2GUyhFo4xTRI-ZembgslECJt1Z6LjnqZcq2GIm9E_7xND9dg7_dXRhKq-x0Yq2o3cTSf-SbiPxoCQ-Rfd6d_YqpaxRFV7sWGg1bHPg_v9Flm73d_4Dn-ypNd3eO3-_FbVeB2CK6z2MjgpDSFbZwPNgUfe6ydE6Z4EOelEqhwAtVos1pszSxOQ_CZ_gDdbhwPAs8w_deg3WeoSszgPXtndGnz8u4hSzq3m1JhrtHIMzbOCqu3ZwhWuSU8aHiVCgZn_eQsG4Y8D8srOBiP2dzBQR3b8Ot1nplWw273YE1X92F60dtfP4efFnJQWKTwAyj6_2UATRlP2tDH41-RunsrG7Bw8YVM3Yx92x6EfJnbtzcYGFdOYX7cHIlNH4Ag2pS-UfAhFXBC1MgplApO3xjPvReUXuQxAbpIxh2VNS2LWtO3TV-6Nq9KYRuCK-R8JoIr88jeLNcctbU9Lhs8jYdzXIileOuH0ym33Qr3bqwiTelkc5lkouSF8rxMoQsl9zhcxPBazpYTUoDP86a9u4DbpHKb-ktQd02yJWOYKM3E4Xd9oc71tCtspnpC9GI4MVymFZSAl3lJ4t6Do6TPRzBw4aTlltCtc4p-h6B7PFYb8_9kWr8vS5FLtC7SPP08eWf9Rxu7B0fHerD_dHBE7iZkgygZkjEBgzm04V_inbdvHzWChODr1ctv_8A0_FhrQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Development+of+a+biomarker+mortality+risk+model+in+acute+respiratory+distress+syndrome&rft.jtitle=Critical+care+%28London%2C+England%29&rft.au=Bime%2C+Christian&rft.au=Casanova%2C+Nancy&rft.au=Oita%2C+Radu+C.&rft.au=Ndukum%2C+Juliet&rft.date=2019-12-16&rft.issn=1364-8535&rft.eissn=1364-8535&rft.volume=23&rft.issue=1&rft_id=info:doi/10.1186%2Fs13054-019-2697-x&rft.externalDBID=n%2Fa&rft.externalDocID=10_1186_s13054_019_2697_x |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1364-8535&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1364-8535&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1364-8535&client=summon |