Identification of Acute Respiratory Distress Syndrome subphenotypes denovo using routine clinical data: a retrospective analysis of ARDS clinical trials
ABSTRACT Rationale The acute respiratory distress syndrome (ARDS) is a heterogenous condition, and identification of subphenotypes may help in better risk stratification.Objectives Identify ARDS subphenotypes using new simpler methodology and readily available clinical variables.Design Retrospective Cohort Study of ARDS trials.Setting Data from the U.S. ARDSNet trials and from the international ART trial.Participants 3763 patients from ARDSNet datasets and 1010 patients from the ART dataset.Primary and secondary outcome measures The primary outcome was 60-day or 28-day mortality, depending on what was reported in the original trial. K-means cluster analysis was performed to identify subgroups. For feature selection, sets. Sets of candidate variables were tested to assess their ability to produce different probabilities for mortality in each cluster. Clusters were compared to biomarker data, allowing identification of subphenotypes.Results Data from 4,773 patients was analyzed. Two subphenotypes (A and B) resulted in optimal separation in the final model, which included nine routinely collected clinical variables, namely: heart rate, mean arterial pressure, respiratory rate, bilirubin, bicarbonate, creatinine, PaO2, arterial pH, and FiO2. Participants in subphenotype B showed increased levels of pro-inflammatory markers, had consistently higher mortality, lower number of ventilator-free days at day 28, and longer duration of ventilation compared to patients in the subphenotype A.Conclusions Routinely available clinical data can successfully identify two distinct subphenotypes in adult ARDS patients. This work may facilitate implementation of precision therapy in ARDS clinical trials.ARTICLE SUMMARY Strengths and limitations of this study <jats:list list-type="bullet">Largest cohort of patients used to identify subphenotypes of ARDS patients.Subphenotypes were validated in the population of a large international ARDS randomized controlled trial.Subphenotypes were identified by using only routinely collected clinical data.Our use of data exclusively from randomized controlled trials does not prove generalizability to unselected ARDS populations.The clinical utility of the subphenotypes have to be validated in a prospective study..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
bioRxiv.org - (2022) vom: 25. Mai Zur Gesamtaufnahme - year:2022 |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Duggal, Abhijit [VerfasserIn] |
|---|
| Links: |
|---|
| doi: |
10.1101/2021.07.08.21260102 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
XBI032165005 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | XBI032165005 | ||
| 003 | DE-627 | ||
| 005 | 20230429091443.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 210712s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1101/2021.07.08.21260102 |2 doi | |
| 035 | |a (DE-627)XBI032165005 | ||
| 035 | |a (biorXiv)10.1101/2021.07.08.21260102 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 570 |q DE-84 | |
| 100 | 1 | |a Duggal, Abhijit |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Identification of Acute Respiratory Distress Syndrome subphenotypes denovo using routine clinical data: a retrospective analysis of ARDS clinical trials |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a ABSTRACT Rationale The acute respiratory distress syndrome (ARDS) is a heterogenous condition, and identification of subphenotypes may help in better risk stratification.Objectives Identify ARDS subphenotypes using new simpler methodology and readily available clinical variables.Design Retrospective Cohort Study of ARDS trials.Setting Data from the U.S. ARDSNet trials and from the international ART trial.Participants 3763 patients from ARDSNet datasets and 1010 patients from the ART dataset.Primary and secondary outcome measures The primary outcome was 60-day or 28-day mortality, depending on what was reported in the original trial. K-means cluster analysis was performed to identify subgroups. For feature selection, sets. Sets of candidate variables were tested to assess their ability to produce different probabilities for mortality in each cluster. Clusters were compared to biomarker data, allowing identification of subphenotypes.Results Data from 4,773 patients was analyzed. Two subphenotypes (A and B) resulted in optimal separation in the final model, which included nine routinely collected clinical variables, namely: heart rate, mean arterial pressure, respiratory rate, bilirubin, bicarbonate, creatinine, PaO2, arterial pH, and FiO2. Participants in subphenotype B showed increased levels of pro-inflammatory markers, had consistently higher mortality, lower number of ventilator-free days at day 28, and longer duration of ventilation compared to patients in the subphenotype A.Conclusions Routinely available clinical data can successfully identify two distinct subphenotypes in adult ARDS patients. This work may facilitate implementation of precision therapy in ARDS clinical trials.ARTICLE SUMMARY Strengths and limitations of this study <jats:list list-type="bullet">Largest cohort of patients used to identify subphenotypes of ARDS patients.Subphenotypes were validated in the population of a large international ARDS randomized controlled trial.Subphenotypes were identified by using only routinely collected clinical data.Our use of data exclusively from randomized controlled trials does not prove generalizability to unselected ARDS populations.The clinical utility of the subphenotypes have to be validated in a prospective study. | ||
| 700 | 1 | |a Kast, Rachel |e verfasserin |4 aut | |
| 700 | 1 | |a Van Ark, Emily |e verfasserin |4 aut | |
| 700 | 1 | |a Bulgarelli, Lucas |e verfasserin |4 aut | |
| 700 | 1 | |a Siuba, Matthew T. |e verfasserin |4 aut | |
| 700 | 1 | |a Osborn, Jeff |e verfasserin |4 aut | |
| 700 | 1 | |a Rey, Diego |e verfasserin |4 aut | |
| 700 | 1 | |a Zampieri, Fernando G |e verfasserin |4 aut | |
| 700 | 1 | |a Cavalcanti, Alexandre B |e verfasserin |4 aut | |
| 700 | 1 | |a Maia, Israel S |e verfasserin |4 aut | |
| 700 | 1 | |a Paisani, Denise M |e verfasserin |4 aut | |
| 700 | 1 | |a Laranjeira, Ligia N |e verfasserin |4 aut | |
| 700 | 1 | |a Neto, Ary Serpa |e verfasserin |4 aut | |
| 700 | 1 | |a Octávio Deliberato, Rodrigo |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t bioRxiv.org |g (2022) vom: 25. Mai |
| 773 | 1 | 8 | |g year:2022 |g day:25 |g month:05 |
| 856 | 4 | 0 | |u https://doi.org/10.1136/bmjopen-2021-053297 |z lizenzpflichtig |3 Volltext |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1101/2021.07.08.21260102 |z kostenfrei |3 Volltext |
| 912 | |a GBV_XBI | ||
| 912 | |a SSG-OLC-PHA | ||
| 951 | |a AR | ||
| 952 | |j 2022 |b 25 |c 05 | ||
| 953 | |2 045F |a 570 | ||