Identifying biomarker-driven subphenotypes of cardiogenic shock: analysis of prospective cohorts and randomized controlled trialsResearch in context
Summary: Background: Cardiogenic shock (CS) is a heterogeneous clinical syndrome, making it challenging to predict patient trajectory and response to treatment. This study aims to identify biological/molecular CS subphenotypes, evaluate their association with outcome, and explore their impact on he...
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Elsevier
2025-01-01
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| author | Sabri Soussi Tuukka Tarvasmäki Antoine Kimmoun Mojtaba Ahmadiankalati Feriel Azibani Claudia C. dos Santos Kevin Duarte Etienne Gayat Jacob C. Jentzer Veli-Pekka Harjola Benjamin Hibbert Christian Jung Lassus Johan Bruno Levy Zihang Lu Patrick R. Lawler John C. Marshall Janine Pöss Malha Sadoune Alexis Nguyen Alexandre Raynor Katell Peoc'h Holger Thiele Rebecca Mathew Alexandre Mebazaa |
| author_facet | Sabri Soussi Tuukka Tarvasmäki Antoine Kimmoun Mojtaba Ahmadiankalati Feriel Azibani Claudia C. dos Santos Kevin Duarte Etienne Gayat Jacob C. Jentzer Veli-Pekka Harjola Benjamin Hibbert Christian Jung Lassus Johan Bruno Levy Zihang Lu Patrick R. Lawler John C. Marshall Janine Pöss Malha Sadoune Alexis Nguyen Alexandre Raynor Katell Peoc'h Holger Thiele Rebecca Mathew Alexandre Mebazaa |
| author_sort | Sabri Soussi |
| collection | DOAJ |
| description | Summary: Background: Cardiogenic shock (CS) is a heterogeneous clinical syndrome, making it challenging to predict patient trajectory and response to treatment. This study aims to identify biological/molecular CS subphenotypes, evaluate their association with outcome, and explore their impact on heterogeneity of treatment effect (ShockCO-OP, NCT06376318). Methods: We used unsupervised clustering to integrate plasma biomarker data from two prospective cohorts of CS patients: CardShock (N = 205 [2010–2012, NCT01374867]) and the French and European Outcome reGistry in Intensive Care Units (FROG-ICU) (N = 228 [2011–2013, NCT01367093]) to determine the optimal number of classes. Thereafter, a simplified classifier (Euclidean distances) was used to assign the identified CS subphenotypes in three completed randomized controlled trials (RCTs) (OptimaCC, N = 57 [2011–2016, NCT01367743]; DOREMI, N = 192 [2017–2020, NCT03207165]; and CULPRIT-SHOCK, N = 434 [2013–2017, NCT01927549]) and explore heterogeneity of treatment effect with respect to 28-day mortality (primary outcome). Findings: Four biomarker-driven CS subphenotypes (‘adaptive’, ‘non-inflammatory’, ‘cardiopathic’, and ‘inflammatory’) were identified separately in the two cohorts. Patients in the inflammatory and cardiopathic subphenotypes had the highest 28-day mortality (p (log-rank test) = 0.0099 and 0.0055 in the CardShock and FROG-ICU cohorts, respectively). Subphenotype membership significantly improved risk stratification when added to traditional risk factors including the Society for Cardiovascular Angiography and Interventions (SCAI) shock stages (increase in Harrell's C-index by 4% (p = 0.033) and 6% (p = 0.0068) respectively in the CardShock and the FROG-ICU cohorts). The simplified classifier identified CS subphenotypes with similar biological/molecular and outcome characteristics in the three independent RCTs. No significant interaction was observed between treatment effect and subphenotypes. Interpretation: Subphenotypes with the highest concentration of biomarkers of endothelial dysfunction and inflammation (inflammatory) or myocardial injury/fibrosis (cardiopathic) were associated with mortality independently from the SCAI shock stages. Funding: Dr Sabri Soussi was awarded the Canadian Institutes of Health Research (CIHR) Doctoral Foreign Study Award (DFSA) and the Merit Awards Program (Department of Anesthesiology and Pain Medicine, University of Toronto, Canada) for the current study. |
| format | Article |
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| institution | Kabale University |
| issn | 2589-5370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EClinicalMedicine |
| spelling | doaj-art-8afea6143f2f4eb1b6c5c0d5a592bde62025-01-22T05:43:41ZengElsevierEClinicalMedicine2589-53702025-01-0179103013Identifying biomarker-driven subphenotypes of cardiogenic shock: analysis of prospective cohorts and randomized controlled trialsResearch in contextSabri Soussi0Tuukka Tarvasmäki1Antoine Kimmoun2Mojtaba Ahmadiankalati3Feriel Azibani4Claudia C. dos Santos5Kevin Duarte6Etienne Gayat7Jacob C. Jentzer8Veli-Pekka Harjola9Benjamin Hibbert10Christian Jung11Lassus Johan12Bruno Levy13Zihang Lu14Patrick R. Lawler15John C. Marshall16Janine Pöss17Malha Sadoune18Alexis Nguyen19Alexandre Raynor20Katell Peoc'h21Holger Thiele22Rebecca Mathew23Alexandre Mebazaa24Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, Ontario, Canada; University of Paris Cité, Inserm UMR-S 942, Cardiovascular Markers in Stress Conditions (MASCOT), Paris, France; Corresponding author. Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, Ontario, Canada.Heart and Lung Center, Cardiology, Helsinki University Hospital and University of Helsinki, FinlandCHRU de Nancy, 26920, Service de Réanimation Médicale Brabois, Nancy, Grand Est, France; INSERM, 27102, U 1433 CIC-P, Paris, Île-de-France, France; Université de Lorraine, 137665, CHRU de Nancy, Nancy, Grand Est, FranceDepartment of Public Health Sciences, Queen's University, Kingston, ON, CanadaUniversity of Paris Cité, Inserm UMR-S 942, Cardiovascular Markers in Stress Conditions (MASCOT), Paris, FranceInterdepartmental Division of Critical Care, St Michael's Hospital, Keenan Research Centre for Biomedical Science and Institute of Medical Sciences, Faculty of Medicine, University of Toronto, Toronto, ON, CanadaUniversité de Lorraine, Centre D’Investigations Cliniques Plurithématique, Institut National de la Santé et de la Recherche Médicale U1116, Nancy, FranceUniversity of Paris Cité, Inserm UMR-S 942, Cardiovascular Markers in Stress Conditions (MASCOT), Paris, France; Department of Anaesthesiology, Critical Care, Lariboisière - Saint-Louis Hospitals, DMU Parabol, Assistance Publique-Hôpitaux de Paris Nord, University of Paris Cité, FranceDepartment of Cardiovascular Medicine, Mayo Clinic Rochester, Rochester, MN, 55905, USADepartment of Emergency Medicine, Helsinki University Hospital, University of Helsinki, Helsinki, FinlandDepartment of Cardiovascular Medicine, Mayo Clinic Rochester, Rochester, MN, 55905, USA; CAPITAL Research Group, Division of Cardiology, Department of Medicine, University of Ottawa, Heart Institute, Ottawa, CanadaDepartment of Cardiology, Pulmonology and Angiology, University Hospital, Düsseldorf, GermanyHeart and Lung Center, Cardiology, Helsinki University Hospital and University of Helsinki, FinlandCHRU de Nancy, 26920, Service de Réanimation Médicale Brabois, Nancy, Grand Est, France; INSERM, 27102, U 1433 CIC-P, Paris, Île-de-France, France; Université de Lorraine, 137665, CHRU de Nancy, Nancy, Grand Est, FranceDepartment of Public Health Sciences, Queen's University, Kingston, ON, CanadaMcGill University Health Centre, Montreal, QC, Canada; Peter Munk, University of Toronto, Toronto, ON, CanadaInterdepartmental Division of Critical Care, St Michael's Hospital, Keenan Research Centre for Biomedical Science and Institute of Medical Sciences, Faculty of Medicine, University of Toronto, Toronto, ON, CanadaHeart Center Leipzig at University of Leipzig and Leipzig Heart Science, Leipzig, GermanyUniversity of Paris Cité, Inserm UMR-S 942, Cardiovascular Markers in Stress Conditions (MASCOT), Paris, FranceUniversity of Paris Cité, Inserm UMR-S 942, Cardiovascular Markers in Stress Conditions (MASCOT), Paris, FranceClinical Biochemistry Laboratory, Bichat Hospital, APHP, Paris, FranceClinical Biochemistry Laboratory, Bichat Hospital, APHP, Paris, France; Université Paris Cité, CRI, UMR 1149, Inserm, F-75018, Paris, FranceHeart Center Leipzig at University of Leipzig and Leipzig Heart Science, Leipzig, GermanyCAPITAL Research Group, Division of Cardiology, Department of Medicine, University of Ottawa, Heart Institute, Ottawa, CanadaUniversity of Paris Cité, Inserm UMR-S 942, Cardiovascular Markers in Stress Conditions (MASCOT), Paris, France; Department of Anaesthesiology, Critical Care, Lariboisière - Saint-Louis Hospitals, DMU Parabol, Assistance Publique-Hôpitaux de Paris Nord, University of Paris Cité, FranceSummary: Background: Cardiogenic shock (CS) is a heterogeneous clinical syndrome, making it challenging to predict patient trajectory and response to treatment. This study aims to identify biological/molecular CS subphenotypes, evaluate their association with outcome, and explore their impact on heterogeneity of treatment effect (ShockCO-OP, NCT06376318). Methods: We used unsupervised clustering to integrate plasma biomarker data from two prospective cohorts of CS patients: CardShock (N = 205 [2010–2012, NCT01374867]) and the French and European Outcome reGistry in Intensive Care Units (FROG-ICU) (N = 228 [2011–2013, NCT01367093]) to determine the optimal number of classes. Thereafter, a simplified classifier (Euclidean distances) was used to assign the identified CS subphenotypes in three completed randomized controlled trials (RCTs) (OptimaCC, N = 57 [2011–2016, NCT01367743]; DOREMI, N = 192 [2017–2020, NCT03207165]; and CULPRIT-SHOCK, N = 434 [2013–2017, NCT01927549]) and explore heterogeneity of treatment effect with respect to 28-day mortality (primary outcome). Findings: Four biomarker-driven CS subphenotypes (‘adaptive’, ‘non-inflammatory’, ‘cardiopathic’, and ‘inflammatory’) were identified separately in the two cohorts. Patients in the inflammatory and cardiopathic subphenotypes had the highest 28-day mortality (p (log-rank test) = 0.0099 and 0.0055 in the CardShock and FROG-ICU cohorts, respectively). Subphenotype membership significantly improved risk stratification when added to traditional risk factors including the Society for Cardiovascular Angiography and Interventions (SCAI) shock stages (increase in Harrell's C-index by 4% (p = 0.033) and 6% (p = 0.0068) respectively in the CardShock and the FROG-ICU cohorts). The simplified classifier identified CS subphenotypes with similar biological/molecular and outcome characteristics in the three independent RCTs. No significant interaction was observed between treatment effect and subphenotypes. Interpretation: Subphenotypes with the highest concentration of biomarkers of endothelial dysfunction and inflammation (inflammatory) or myocardial injury/fibrosis (cardiopathic) were associated with mortality independently from the SCAI shock stages. Funding: Dr Sabri Soussi was awarded the Canadian Institutes of Health Research (CIHR) Doctoral Foreign Study Award (DFSA) and the Merit Awards Program (Department of Anesthesiology and Pain Medicine, University of Toronto, Canada) for the current study.http://www.sciencedirect.com/science/article/pii/S2589537024005923Cardiogenic shockUnsupervised learningCritical careHeterogeneity of treatment effectPrecision medicine |
| spellingShingle | Sabri Soussi Tuukka Tarvasmäki Antoine Kimmoun Mojtaba Ahmadiankalati Feriel Azibani Claudia C. dos Santos Kevin Duarte Etienne Gayat Jacob C. Jentzer Veli-Pekka Harjola Benjamin Hibbert Christian Jung Lassus Johan Bruno Levy Zihang Lu Patrick R. Lawler John C. Marshall Janine Pöss Malha Sadoune Alexis Nguyen Alexandre Raynor Katell Peoc'h Holger Thiele Rebecca Mathew Alexandre Mebazaa Identifying biomarker-driven subphenotypes of cardiogenic shock: analysis of prospective cohorts and randomized controlled trialsResearch in context EClinicalMedicine Cardiogenic shock Unsupervised learning Critical care Heterogeneity of treatment effect Precision medicine |
| title | Identifying biomarker-driven subphenotypes of cardiogenic shock: analysis of prospective cohorts and randomized controlled trialsResearch in context |
| title_full | Identifying biomarker-driven subphenotypes of cardiogenic shock: analysis of prospective cohorts and randomized controlled trialsResearch in context |
| title_fullStr | Identifying biomarker-driven subphenotypes of cardiogenic shock: analysis of prospective cohorts and randomized controlled trialsResearch in context |
| title_full_unstemmed | Identifying biomarker-driven subphenotypes of cardiogenic shock: analysis of prospective cohorts and randomized controlled trialsResearch in context |
| title_short | Identifying biomarker-driven subphenotypes of cardiogenic shock: analysis of prospective cohorts and randomized controlled trialsResearch in context |
| title_sort | identifying biomarker driven subphenotypes of cardiogenic shock analysis of prospective cohorts and randomized controlled trialsresearch in context |
| topic | Cardiogenic shock Unsupervised learning Critical care Heterogeneity of treatment effect Precision medicine |
| url | http://www.sciencedirect.com/science/article/pii/S2589537024005923 |
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