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Meinarovich P, Pautova A, Zuev E, Sorokina E, Chernevskaya E, Beloborodova N. An Integrated Approach Based on Clinical Data Combined with Metabolites and Biomarkers for the Assessment of Post-Operative Complications after Cardiac Surgery. J Clin Med 2024; 13:5054. [PMID: 39274267 PMCID: PMC11395730 DOI: 10.3390/jcm13175054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Revised: 08/20/2024] [Accepted: 08/23/2024] [Indexed: 09/16/2024] Open
Abstract
Background: Early diagnosis of post-operative complications is an urgent task, allowing timely prescribing of appropriate therapy and reducing the cost of patient treatment. The purpose of this study was to determine whether an integrated approach based on clinical data, along with metabolites and biomarkers, had greater predictive value than the models built on fewer data in the early diagnosis of post-operative complications after cardiac surgery. Methods: The study included patients (n = 62) admitted for planned cardiac surgery (coronary artery bypass grafting with cardiopulmonary bypass) with (n = 26) or without (n = 36) post-operative complications. Clinical and laboratory data on the first day after surgery were analyzed. Additionally, patients' blood samples were collected before and on the first day after surgery to determine biomarkers and metabolites. Results: Multivariate PLS-DA models, predicting the presence or absence of post-operative complications, were built using clinical data, concentrations of metabolites and biomarkers, and the entire data set (ROC-AUC = 0.80, 0.71, and 0.85, respectively). For comparison, we built univariate models using the EuroScore2 and SOFA scales, concentrations of lactate, the dynamic changes of 4-hydroxyphenyllactic acid, and the sum of three sepsis-associated metabolites (ROC-AUC = 0.54, 0.79, 0.62, 0.58, and 0.70, respectively). Conclusions: The proposed complex model using the entire dataset had the best characteristics, which confirms the expediency of searching for new predictive models based on a variety of factors.
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Affiliation(s)
- Peter Meinarovich
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 25-2 Petrovka Str., 107031 Moscow, Russia
| | - Alisa Pautova
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 25-2 Petrovka Str., 107031 Moscow, Russia
| | - Evgenii Zuev
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 25-2 Petrovka Str., 107031 Moscow, Russia
| | - Ekaterina Sorokina
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 25-2 Petrovka Str., 107031 Moscow, Russia
| | - Ekaterina Chernevskaya
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 25-2 Petrovka Str., 107031 Moscow, Russia
| | - Natalia Beloborodova
- Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 25-2 Petrovka Str., 107031 Moscow, Russia
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Vasunilashorn SM, Dillon ST, Marcantonio ER, Libermann TA. Application of Multiple Omics to Understand Postoperative Delirium Pathophysiology in Humans. Gerontology 2023; 69:1369-1384. [PMID: 37722373 PMCID: PMC10711777 DOI: 10.1159/000533789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 08/23/2023] [Indexed: 09/20/2023] Open
Abstract
Delirium, an acute change in cognition, is common, morbid, and costly, particularly among hospitalized older adults. Despite growing knowledge of its epidemiology, far less is known about delirium pathophysiology. Initial work understanding delirium pathogenesis has focused on assaying single or a limited subset of molecules or genetic loci. Recent technological advances at the forefront of biomarker and drug target discovery have facilitated application of multiple "omics" approaches aimed to provide a more complete understanding of complex disease processes such as delirium. At its basic level, "omics" involves comparison of genes (genomics, epigenomics), transcripts (transcriptomics), proteins (proteomics), metabolites (metabolomics), or lipids (lipidomics) in biological fluids or tissues obtained from patients who have a certain condition (i.e., delirium) and those who do not. Multi-omics analyses of these various types of molecules combined with machine learning and systems biology enable the discovery of biomarkers, biological pathways, and predictors of delirium, thus elucidating its pathophysiology. This review provides an overview of the most recent omics techniques, their current impact on identifying delirium biomarkers, and future potential in enhancing our understanding of delirium pathogenesis. We summarize challenges in identification of specific biomarkers of delirium and, more importantly, in discovering the mechanisms underlying delirium pathophysiology. Based on mounting evidence, we highlight a heightened inflammatory response as one common pathway in delirium risk and progression, and we suggest other promising biological mechanisms that have recently emerged. Advanced multiple omics approaches coupled with bioinformatics methodologies have great promise to yield important discoveries that will advance delirium research.
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Affiliation(s)
- Sarinnapha M. Vasunilashorn
- Division of General Medicine, Department of Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Simon T. Dillon
- Harvard Medical School, Boston, MA, USA
- Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, BIDMC, Boston, MA, USA
- Genomics, Proteomics, Bioinformatics and Systems Biology Center, BIDMC, Boston, MA, USA
| | - Edward R. Marcantonio
- Division of General Medicine, Department of Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Division of Gerontology, Department of Medicine, BIDMC, Boston, MA, USA
| | - Towia A. Libermann
- Harvard Medical School, Boston, MA, USA
- Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, BIDMC, Boston, MA, USA
- Genomics, Proteomics, Bioinformatics and Systems Biology Center, BIDMC, Boston, MA, USA
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