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A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study

Identificadores del recurso

de Gonzalo-Calvo D, Molinero M, Benítez ID, Perez-Pons M, García-Mateo N, Ortega A, et al. A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study. Respir Res. 2023 Jun 17;24(1):159.

https://hdl.handle.net/20.500.13003/19054

10.1186/s12931-023-02462-x

1465-993X

37328754

L2023890193

Procedencia

(Docusalut. Repositorio institucional del sistema sanitario público de las Islas Baleares)

Ficha

Título:: A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study
Tema:: Intensive Care Units; Prospective Studies; Biomarkers; MicroRNAs; Critical Illness; Humans; COVID-19; Retrospective Studies; Humanos; Estudios Prospectivos; Biomarcadores; Enfermedad Crítica; Estudios Retrospectivos; MicroARNs; Unidades de Cuidados Intensivos
Descrición:: The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU. This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group. Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR < 0.05). Cox regression analysis revealed that low expression levels of eight miRNAs were associated with a higher risk of death (HR from 1.56 to 2.61). LASSO regression for variable selection was used to construct a miRNA classifier. A 4-blood miRNA signature composed of miR-16-5p, miR-192-5p, miR-323a-3p and miR-451a predicts the risk of all-cause in-ICU mortality (HR 2.5). Kaplan‒Meier analysis confirmed these findings. The miRNA signature provides a significant increase in the prognostic capacity of conventional scores, APACHE-II (C-index 0.71, DeLong test p-value 0.055) and SOFA (C-index 0.67, DeLong test p-value 0.001), and a risk model based on clinical predictors (C-index 0.74, DeLong test-p-value 0.035). For 28-day and 90-day mortality, the classifier also improved the prognostic value of APACHE-II, SOFA and the clinical model. The association between the classifier and mortality persisted even after multivariable adjustment. The functional analysis reported biological pathways involved in SARS-CoV infection and inflammatory, fibrotic and transcriptional pathways. A blood miRNA classifier improves the early prediction of fatal outcomes in critically ill COVID-19 patients.; DdGC has received financial support from Instituto de Salud Carlos III (Miguel Servet 2020: CP20/00041), co-funded by the European Union. MM is the recipient of a predoctoral fellowship (PFIS 2021: FI21/00187) from Instituto de Salud Carlos III and co-funded by the European Union. MCGH is the recipient of a predoctoral fellowship from the University of Lleida. AC is supported by Instituto de Salud Carlos III (Sara Borrell 2021: CD21/00087), co-funded by the European Union. Financial support was provided by the Instituto de Salud Carlos III de Madrid (COV20/00110), co-funded by the European Union and Centro de Investigación Biomedica En Red – Enfermedades Respiratorias (CIBERES). CIBERES is an initiative of the Instituto de Salud Carlos III. Further supported by: Programa de donaciones “estar preparados”; UNESPA (Madrid, Spain); La Fundació La Marató de TV3, projecte amb codi 202108-30/-31.
Idioma:: English
Relación:: CP20/00041; FI21/00187; CD21/00087; COV20/00110; 202108-30/-31; https://doi.org/10.1186/s12931-023-02462-x
Autor/Productor:: de Gonzalo-Calvo, David; Molinero, Marta; Benítez, Iván D; Perez-Pons, Manel; García-Mateo, Nadia; Ortega, Alicia; Postigo, Tamara; García-Hidalgo, María C; Belmonte, Thalia; Rodríguez-Muñoz, Carlos; González, Jessica; Torres, Gerard; Gort-Paniello, Clara; Moncusí-Moix, Anna; Estella, Angel; Tamayo Lomas, Luis; Martínez de la Gándara, Amalia; Socias, Lorenzo; Peñasco, Yhivian; de la Torre, María Del Carmen; Bustamante-Munguira, Elena; Gallego Curto, Elena; Martínez Varela, Ignacio; Martin Delgado, María Cruz; Vidal-Cortes, Pablo; López Messa, Juan; Pérez-García, Felipe; Caballero, Jesús; Añón, José M; Loza-Vázquez, Ana; Carbonell, Nieves; Marin-Corral, Judith; Jorge García, Ruth Noemí; Barberà, Carmen; Ceccato, Adrián; Fernández-Barat, Laia; Ferrer, Ricard; Garcia-Gasulla, Dario; Lorente-Balanza, Jose Ángel; Menéndez, Rosario; Motos, Ana; Peñuelas, Oscar; Riera, Jordi; Bermejo-Martin, Jesús F; Torres, Antoni; Barbé, Ferran
Editor:: BMC
Dereitos:: Atribución 4.0 Internacional; http://creativecommons.org/licenses/by/4.0/; open access
Data:: 2023-06-26T08:21:18Z; 2023-06-17
Tipo de recurso:: research article

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1. <dc:title>A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study</dc:title>
2. <dc:creator>de Gonzalo-Calvo, David</dc:creator>
3. <dc:creator>Molinero, Marta</dc:creator>
4. <dc:creator>Benítez, Iván D</dc:creator>
5. <dc:creator>Perez-Pons, Manel</dc:creator>
6. <dc:creator>García-Mateo, Nadia</dc:creator>
7. <dc:creator>Ortega, Alicia</dc:creator>
8. <dc:creator>Postigo, Tamara</dc:creator>
9. <dc:creator>García-Hidalgo, María C</dc:creator>
10. <dc:creator>Belmonte, Thalia</dc:creator>
11. <dc:creator>Rodríguez-Muñoz, Carlos</dc:creator>
12. <dc:creator>González, Jessica</dc:creator>
13. <dc:creator>Torres, Gerard</dc:creator>
14. <dc:creator>Gort-Paniello, Clara</dc:creator>
15. <dc:creator>Moncusí-Moix, Anna</dc:creator>
16. <dc:creator>Estella, Angel</dc:creator>
17. <dc:creator>Tamayo Lomas, Luis</dc:creator>
18. <dc:creator>Martínez de la Gándara, Amalia</dc:creator>
19. <dc:creator>Socias, Lorenzo</dc:creator>
20. <dc:creator>Peñasco, Yhivian</dc:creator>
21. <dc:creator>de la Torre, María Del Carmen</dc:creator>
22. <dc:creator>Bustamante-Munguira, Elena</dc:creator>
23. <dc:creator>Gallego Curto, Elena</dc:creator>
24. <dc:creator>Martínez Varela, Ignacio</dc:creator>
25. <dc:creator>Martin Delgado, María Cruz</dc:creator>
26. <dc:creator>Vidal-Cortes, Pablo</dc:creator>
27. <dc:creator>López Messa, Juan</dc:creator>
28. <dc:creator>Pérez-García, Felipe</dc:creator>
29. <dc:creator>Caballero, Jesús</dc:creator>
30. <dc:creator>Añón, José M</dc:creator>
31. <dc:creator>Loza-Vázquez, Ana</dc:creator>
32. <dc:creator>Carbonell, Nieves</dc:creator>
33. <dc:creator>Marin-Corral, Judith</dc:creator>
34. <dc:creator>Jorge García, Ruth Noemí</dc:creator>
35. <dc:creator>Barberà, Carmen</dc:creator>
36. <dc:creator>Ceccato, Adrián</dc:creator>
37. <dc:creator>Fernández-Barat, Laia</dc:creator>
38. <dc:creator>Ferrer, Ricard</dc:creator>
39. <dc:creator>Garcia-Gasulla, Dario</dc:creator>
40. <dc:creator>Lorente-Balanza, Jose Ángel</dc:creator>
41. <dc:creator>Menéndez, Rosario</dc:creator>
42. <dc:creator>Motos, Ana</dc:creator>
43. <dc:creator>Peñuelas, Oscar</dc:creator>
44. <dc:creator>Riera, Jordi</dc:creator>
45. <dc:creator>Bermejo-Martin, Jesús F</dc:creator>
46. <dc:creator>Torres, Antoni</dc:creator>
47. <dc:creator>Barbé, Ferran</dc:creator>
48. <dc:subject>Intensive Care Units</dc:subject>
49. <dc:subject>Prospective Studies</dc:subject>
50. <dc:subject>Biomarkers</dc:subject>
51. <dc:subject>MicroRNAs</dc:subject>
52. <dc:subject>Critical Illness</dc:subject>
53. <dc:subject>Humans</dc:subject>
54. <dc:subject>COVID-19</dc:subject>
55. <dc:subject>Retrospective Studies</dc:subject>
56. <dc:subject>Humanos</dc:subject>
57. <dc:subject>Estudios Prospectivos</dc:subject>
58. <dc:subject>Biomarcadores</dc:subject>
59. <dc:subject>Enfermedad Crítica</dc:subject>
60. <dc:subject>Estudios Retrospectivos</dc:subject>
61. <dc:subject>COVID-19</dc:subject>
62. <dc:subject>MicroARNs</dc:subject>
63. <dc:subject>Unidades de Cuidados Intensivos</dc:subject>
64. <dc:description>The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU. This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group. Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR < 0.05). Cox regression analysis revealed that low expression levels of eight miRNAs were associated with a higher risk of death (HR from 1.56 to 2.61). LASSO regression for variable selection was used to construct a miRNA classifier. A 4-blood miRNA signature composed of miR-16-5p, miR-192-5p, miR-323a-3p and miR-451a predicts the risk of all-cause in-ICU mortality (HR 2.5). Kaplan‒Meier analysis confirmed these findings. The miRNA signature provides a significant increase in the prognostic capacity of conventional scores, APACHE-II (C-index 0.71, DeLong test p-value 0.055) and SOFA (C-index 0.67, DeLong test p-value 0.001), and a risk model based on clinical predictors (C-index 0.74, DeLong test-p-value 0.035). For 28-day and 90-day mortality, the classifier also improved the prognostic value of APACHE-II, SOFA and the clinical model. The association between the classifier and mortality persisted even after multivariable adjustment. The functional analysis reported biological pathways involved in SARS-CoV infection and inflammatory, fibrotic and transcriptional pathways. A blood miRNA classifier improves the early prediction of fatal outcomes in critically ill COVID-19 patients.</dc:description>
65. <dc:description>DdGC has received financial support from Instituto de Salud Carlos III (Miguel Servet 2020: CP20/00041), co-funded by the European Union. MM is the recipient of a predoctoral fellowship (PFIS 2021: FI21/00187) from Instituto de Salud Carlos III and co-funded by the European Union. MCGH is the recipient of a predoctoral fellowship from the University of Lleida. AC is supported by Instituto de Salud Carlos III (Sara Borrell 2021: CD21/00087), co-funded by the European Union. Financial support was provided by the Instituto de Salud Carlos III de Madrid (COV20/00110), co-funded by the European Union and Centro de Investigación Biomedica En Red – Enfermedades Respiratorias (CIBERES). CIBERES is an initiative of the Instituto de Salud Carlos III. Further supported by: Programa de donaciones “estar preparados”; UNESPA (Madrid, Spain); La Fundació La Marató de TV3, projecte amb codi 202108-30/-31.</dc:description>
66. <dc:date>2023-06-26T08:21:18Z</dc:date>
67. <dc:date>2023-06-26T08:21:18Z</dc:date>
68. <dc:date>2023-06-17</dc:date>
69. <dc:type>research article</dc:type>
70. <dc:identifier>de Gonzalo-Calvo D, Molinero M, Benítez ID, Perez-Pons M, García-Mateo N, Ortega A, et al. A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study. Respir Res. 2023 Jun 17;24(1):159.</dc:identifier>
71. <dc:identifier>https://hdl.handle.net/20.500.13003/19054</dc:identifier>
72. <dc:identifier>10.1186/s12931-023-02462-x</dc:identifier>
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80. <dc:relation>COV20/00110</dc:relation>
81. <dc:relation>202108-30/-31</dc:relation>
82. <dc:relation>https://doi.org/10.1186/s12931-023-02462-x</dc:relation>
83. <dc:rights>Atribución 4.0 Internacional</dc:rights>
84. <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
85. <dc:rights>open access</dc:rights>
86. <dc:publisher>BMC</dc:publisher>
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56. <dim:field element="description" lang="en" mdschema="dc" qualifier="abstract">The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU. This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group. Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR < 0.05). Cox regression analysis revealed that low expression levels of eight miRNAs were associated with a higher risk of death (HR from 1.56 to 2.61). LASSO regression for variable selection was used to construct a miRNA classifier. A 4-blood miRNA signature composed of miR-16-5p, miR-192-5p, miR-323a-3p and miR-451a predicts the risk of all-cause in-ICU mortality (HR 2.5). Kaplan‒Meier analysis confirmed these findings. The miRNA signature provides a significant increase in the prognostic capacity of conventional scores, APACHE-II (C-index 0.71, DeLong test p-value 0.055) and SOFA (C-index 0.67, DeLong test p-value 0.001), and a risk model based on clinical predictors (C-index 0.74, DeLong test-p-value 0.035). For 28-day and 90-day mortality, the classifier also improved the prognostic value of APACHE-II, SOFA and the clinical model. The association between the classifier and mortality persisted even after multivariable adjustment. The functional analysis reported biological pathways involved in SARS-CoV infection and inflammatory, fibrotic and transcriptional pathways. A blood miRNA classifier improves the early prediction of fatal outcomes in critically ill COVID-19 patients.</dim:field>
57. <dim:field element="description" lang="en" mdschema="dc" qualifier="fundingtext">DdGC has received financial support from Instituto de Salud Carlos III (Miguel Servet 2020: CP20/00041), co-funded by the European Union. MM is the recipient of a predoctoral fellowship (PFIS 2021: FI21/00187) from Instituto de Salud Carlos III and co-funded by the European Union. MCGH is the recipient of a predoctoral fellowship from the University of Lleida. AC is supported by Instituto de Salud Carlos III (Sara Borrell 2021: CD21/00087), co-funded by the European Union. Financial support was provided by the Instituto de Salud Carlos III de Madrid (COV20/00110), co-funded by the European Union and Centro de Investigación Biomedica En Red – Enfermedades Respiratorias (CIBERES). CIBERES is an initiative of the Instituto de Salud Carlos III. Further supported by: Programa de donaciones “estar preparados”; UNESPA (Madrid, Spain); La Fundació La Marató de TV3, projecte amb codi 202108-30/-31.</dim:field>
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  1. <mods:namePart>González, Jessica</mods:namePart>
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  1. <mods:namePart>Torres, Gerard</mods:namePart>
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13. <mods:name>
  1. <mods:namePart>Gort-Paniello, Clara</mods:namePart>
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  1. <mods:namePart>Moncusí-Moix, Anna</mods:namePart>
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  1. <mods:namePart>Martínez de la Gándara, Amalia</mods:namePart>
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  1. <mods:namePart>Socias, Lorenzo</mods:namePart>
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  1. <mods:namePart>Peñasco, Yhivian</mods:namePart>
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20. <mods:name>
  1. <mods:namePart>de la Torre, María Del Carmen</mods:namePart>
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  1. <mods:namePart>Bustamante-Munguira, Elena</mods:namePart>
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  1. <mods:namePart>Gallego Curto, Elena</mods:namePart>
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23. <mods:name>
  1. <mods:namePart>Martínez Varela, Ignacio</mods:namePart>
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24. <mods:name>
  1. <mods:namePart>Martin Delgado, María Cruz</mods:namePart>
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25. <mods:name>
  1. <mods:namePart>Vidal-Cortes, Pablo</mods:namePart>
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26. <mods:name>
  1. <mods:namePart>López Messa, Juan</mods:namePart>
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  1. <mods:namePart>Pérez-García, Felipe</mods:namePart>
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  1. <mods:namePart>Caballero, Jesús</mods:namePart>
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  1. <mods:namePart>Añón, José M</mods:namePart>
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  1. <mods:namePart>Carbonell, Nieves</mods:namePart>
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32. <mods:name>
  1. <mods:namePart>Marin-Corral, Judith</mods:namePart>
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  1. <mods:namePart>Jorge García, Ruth Noemí</mods:namePart>
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34. <mods:name>
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35. <mods:name>
  1. <mods:namePart>Ceccato, Adrián</mods:namePart>
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36. <mods:name>
  1. <mods:namePart>Fernández-Barat, Laia</mods:namePart>
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  1. <mods:namePart>Ferrer, Ricard</mods:namePart>
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  1. <mods:namePart>Lorente-Balanza, Jose Ángel</mods:namePart>
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  1. <mods:namePart>Motos, Ana</mods:namePart>
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  1. <mods:namePart>Bermejo-Martin, Jesús F</mods:namePart>
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  1. <mods:namePart>Torres, Antoni</mods:namePart>
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  1. <mods:namePart>Barbé, Ferran</mods:namePart>
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  1. <mods:dateAvailable encoding="iso8601">2023-06-26T08:21:18Z</mods:dateAvailable>
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  1. <mods:dateIssued encoding="iso8601">2023-06-17</mods:dateIssued>
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50. <mods:identifier type="citation">de Gonzalo-Calvo D, Molinero M, Benítez ID, Perez-Pons M, García-Mateo N, Ortega A, et al. A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study. Respir Res. 2023 Jun 17;24(1):159.</mods:identifier>
51. <mods:identifier type="uri">https://hdl.handle.net/20.500.13003/19054</mods:identifier>
52. <mods:identifier type="doi">10.1186/s12931-023-02462-x</mods:identifier>
53. <mods:identifier type="essn">1465-993X</mods:identifier>
54. <mods:identifier type="pmid">37328754</mods:identifier>
55. <mods:identifier type="pui">L2023890193</mods:identifier>
56. <mods:abstract>The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU. This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group. Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR < 0.05). Cox regression analysis revealed that low expression levels of eight miRNAs were associated with a higher risk of death (HR from 1.56 to 2.61). LASSO regression for variable selection was used to construct a miRNA classifier. A 4-blood miRNA signature composed of miR-16-5p, miR-192-5p, miR-323a-3p and miR-451a predicts the risk of all-cause in-ICU mortality (HR 2.5). Kaplan‒Meier analysis confirmed these findings. The miRNA signature provides a significant increase in the prognostic capacity of conventional scores, APACHE-II (C-index 0.71, DeLong test p-value 0.055) and SOFA (C-index 0.67, DeLong test p-value 0.001), and a risk model based on clinical predictors (C-index 0.74, DeLong test-p-value 0.035). For 28-day and 90-day mortality, the classifier also improved the prognostic value of APACHE-II, SOFA and the clinical model. The association between the classifier and mortality persisted even after multivariable adjustment. The functional analysis reported biological pathways involved in SARS-CoV infection and inflammatory, fibrotic and transcriptional pathways. A blood miRNA classifier improves the early prediction of fatal outcomes in critically ill COVID-19 patients.</mods:abstract>
57. <mods:language>
  1. <mods:languageTerm>eng</mods:languageTerm>
  </mods:language>
58. <mods:accessCondition type="useAndReproduction">http://creativecommons.org/licenses/by/4.0/</mods:accessCondition>
59. <mods:accessCondition type="useAndReproduction">open access</mods:accessCondition>
60. <mods:accessCondition type="useAndReproduction">Atribución 4.0 Internacional</mods:accessCondition>
61. <mods:titleInfo>
  1. <mods:title>A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study</mods:title>
  </mods:titleInfo>
62. <mods:genre>research article</mods:genre>
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48. <dcterms:abstract>The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU. This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group. Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR < 0.05). Cox regression analysis revealed that low expression levels of eight miRNAs were associated with a higher risk of death (HR from 1.56 to 2.61). LASSO regression for variable selection was used to construct a miRNA classifier. A 4-blood miRNA signature composed of miR-16-5p, miR-192-5p, miR-323a-3p and miR-451a predicts the risk of all-cause in-ICU mortality (HR 2.5). Kaplan‒Meier analysis confirmed these findings. The miRNA signature provides a significant increase in the prognostic capacity of conventional scores, APACHE-II (C-index 0.71, DeLong test p-value 0.055) and SOFA (C-index 0.67, DeLong test p-value 0.001), and a risk model based on clinical predictors (C-index 0.74, DeLong test-p-value 0.035). For 28-day and 90-day mortality, the classifier also improved the prognostic value of APACHE-II, SOFA and the clinical model. The association between the classifier and mortality persisted even after multivariable adjustment. The functional analysis reported biological pathways involved in SARS-CoV infection and inflammatory, fibrotic and transcriptional pathways. A blood miRNA classifier improves the early prediction of fatal outcomes in critically ill COVID-19 patients.</dcterms:abstract>
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xoai

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