Identification and validation of respiratory subphenotypes in patients with COVID-19 acute respiratory distress syndrome undergoing prone position
Abstract Background The heterogeneity of acute respiratory distress syndrome (ARDS) patients is a challenge for the development of effective treatments. This study aimed to identify and characterize novel respiratory subphenotypes of COVID-19 ARDS, with potential implications for targeted patient ma...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-11-01
|
| Series: | Annals of Intensive Care |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13613-024-01414-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585355629428736 |
|---|---|
| author | Mônica R. da Cruz Pedro Azambuja Kátia S. C. Torres Fernanda Lima-Setta André M. Japiassú Denise M. Medeiros |
| author_facet | Mônica R. da Cruz Pedro Azambuja Kátia S. C. Torres Fernanda Lima-Setta André M. Japiassú Denise M. Medeiros |
| author_sort | Mônica R. da Cruz |
| collection | DOAJ |
| description | Abstract Background The heterogeneity of acute respiratory distress syndrome (ARDS) patients is a challenge for the development of effective treatments. This study aimed to identify and characterize novel respiratory subphenotypes of COVID-19 ARDS, with potential implications for targeted patient management. Methods Consecutive ventilated patients with PCR-confirmed COVID-19 infection, in which prone positioning was clinically indicated for moderate or severe ARDS, were included in a prospective cohort. The patients were assigned to development or validation cohorts based on a temporal split. The PaO2/FiO2 ratio, respiratory compliance, and ventilatory ratio were assessed longitudinally throughout the first prone session. The subphenotypes were derived and validated using machine learning techniques. A K-means clustering implementation designed for joint trajectory analysis was utilized for the unsupervised classification of the development cohort. A random forest model was trained on the labeled development cohort and used to validate the subphenotypes in the validation cohort. Results 718 patients were included in a prospective cohort analysis. Of those, 504 were assigned to the development cohort and 214 to the validation cohort. Two distinct subphenotypes, labeled A and B, were identified. Subphenotype B had a lower PaO2/FiO2 response during the prone session, higher ventilatory ratio, and lower compliance than subphenotype A. Subphenotype B had a higher proportion of females (p < 0.001) and lung disease (p = 0.005), higher baseline SAPS III (p = 0.002) and SOFA (p < 0.001) scores, and lower body mass index (p = 0.05). Subphenotype B had also higher levels of the pro-inflammatory biomarker IL-6 (p = 0.017). Subphenotype B was independently associated with an increased risk of 60-day mortality (OR 1.89, 95% CI 1.51–2.36). Additionally, Subphenotype B was associated with a lower number of ventilator-free days on day 28 (p < 0.001) and a lower hospital length of stay (p < 0.001). The subphenotypes were reproducible in the validation cohort. Conclusion Our study successfully identified and validated two distinct subphenotypes of COVID-19 ARDS based on key respiratory parameters. The findings suggest potential implications for better patient stratification, risk assessment, and treatment personalization. Future research is warranted to explore the utility of these novel subphenotypes for guiding targeted therapeutic strategies in COVID-19 ARDS. |
| format | Article |
| id | doaj-art-62b1da2140d74ae7a7ff4e93dc9e64b5 |
| institution | Kabale University |
| issn | 2110-5820 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Annals of Intensive Care |
| spelling | doaj-art-62b1da2140d74ae7a7ff4e93dc9e64b52025-01-26T12:52:26ZengSpringerOpenAnnals of Intensive Care2110-58202024-11-0114111110.1186/s13613-024-01414-yIdentification and validation of respiratory subphenotypes in patients with COVID-19 acute respiratory distress syndrome undergoing prone positionMônica R. da Cruz0Pedro Azambuja1Kátia S. C. Torres2Fernanda Lima-Setta3André M. Japiassú4Denise M. Medeiros5Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation (INI-Fiocruz)Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation (INI-Fiocruz)Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation (INI-Fiocruz)National Institute of Women, Children and Adolescents Health Fernandes Figueira, Oswaldo Cruz Foundation (IFF-Fiocruz)Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation (INI-Fiocruz)Evandro Chagas National Institute of Infectious Diseases, Oswaldo Cruz Foundation (INI-Fiocruz)Abstract Background The heterogeneity of acute respiratory distress syndrome (ARDS) patients is a challenge for the development of effective treatments. This study aimed to identify and characterize novel respiratory subphenotypes of COVID-19 ARDS, with potential implications for targeted patient management. Methods Consecutive ventilated patients with PCR-confirmed COVID-19 infection, in which prone positioning was clinically indicated for moderate or severe ARDS, were included in a prospective cohort. The patients were assigned to development or validation cohorts based on a temporal split. The PaO2/FiO2 ratio, respiratory compliance, and ventilatory ratio were assessed longitudinally throughout the first prone session. The subphenotypes were derived and validated using machine learning techniques. A K-means clustering implementation designed for joint trajectory analysis was utilized for the unsupervised classification of the development cohort. A random forest model was trained on the labeled development cohort and used to validate the subphenotypes in the validation cohort. Results 718 patients were included in a prospective cohort analysis. Of those, 504 were assigned to the development cohort and 214 to the validation cohort. Two distinct subphenotypes, labeled A and B, were identified. Subphenotype B had a lower PaO2/FiO2 response during the prone session, higher ventilatory ratio, and lower compliance than subphenotype A. Subphenotype B had a higher proportion of females (p < 0.001) and lung disease (p = 0.005), higher baseline SAPS III (p = 0.002) and SOFA (p < 0.001) scores, and lower body mass index (p = 0.05). Subphenotype B had also higher levels of the pro-inflammatory biomarker IL-6 (p = 0.017). Subphenotype B was independently associated with an increased risk of 60-day mortality (OR 1.89, 95% CI 1.51–2.36). Additionally, Subphenotype B was associated with a lower number of ventilator-free days on day 28 (p < 0.001) and a lower hospital length of stay (p < 0.001). The subphenotypes were reproducible in the validation cohort. Conclusion Our study successfully identified and validated two distinct subphenotypes of COVID-19 ARDS based on key respiratory parameters. The findings suggest potential implications for better patient stratification, risk assessment, and treatment personalization. Future research is warranted to explore the utility of these novel subphenotypes for guiding targeted therapeutic strategies in COVID-19 ARDS.https://doi.org/10.1186/s13613-024-01414-yCovid-19Acute respiratory distress syndromeARDSSubphenotypesMechanical ventilationMachine learning |
| spellingShingle | Mônica R. da Cruz Pedro Azambuja Kátia S. C. Torres Fernanda Lima-Setta André M. Japiassú Denise M. Medeiros Identification and validation of respiratory subphenotypes in patients with COVID-19 acute respiratory distress syndrome undergoing prone position Annals of Intensive Care Covid-19 Acute respiratory distress syndrome ARDS Subphenotypes Mechanical ventilation Machine learning |
| title | Identification and validation of respiratory subphenotypes in patients with COVID-19 acute respiratory distress syndrome undergoing prone position |
| title_full | Identification and validation of respiratory subphenotypes in patients with COVID-19 acute respiratory distress syndrome undergoing prone position |
| title_fullStr | Identification and validation of respiratory subphenotypes in patients with COVID-19 acute respiratory distress syndrome undergoing prone position |
| title_full_unstemmed | Identification and validation of respiratory subphenotypes in patients with COVID-19 acute respiratory distress syndrome undergoing prone position |
| title_short | Identification and validation of respiratory subphenotypes in patients with COVID-19 acute respiratory distress syndrome undergoing prone position |
| title_sort | identification and validation of respiratory subphenotypes in patients with covid 19 acute respiratory distress syndrome undergoing prone position |
| topic | Covid-19 Acute respiratory distress syndrome ARDS Subphenotypes Mechanical ventilation Machine learning |
| url | https://doi.org/10.1186/s13613-024-01414-y |
| work_keys_str_mv | AT monicardacruz identificationandvalidationofrespiratorysubphenotypesinpatientswithcovid19acuterespiratorydistresssyndromeundergoingproneposition AT pedroazambuja identificationandvalidationofrespiratorysubphenotypesinpatientswithcovid19acuterespiratorydistresssyndromeundergoingproneposition AT katiasctorres identificationandvalidationofrespiratorysubphenotypesinpatientswithcovid19acuterespiratorydistresssyndromeundergoingproneposition AT fernandalimasetta identificationandvalidationofrespiratorysubphenotypesinpatientswithcovid19acuterespiratorydistresssyndromeundergoingproneposition AT andremjapiassu identificationandvalidationofrespiratorysubphenotypesinpatientswithcovid19acuterespiratorydistresssyndromeundergoingproneposition AT denisemmedeiros identificationandvalidationofrespiratorysubphenotypesinpatientswithcovid19acuterespiratorydistresssyndromeundergoingproneposition |