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• W4318966480 abstract "BACKGROUND: Precision medicine aims to change treatment from a “one-size-fits-all” approach to customized therapies based on the individual patient. Applying a precision medicine approach to a heterogeneous condition, such as the cardiopulmonary bypass (CPB)–induced inflammatory response, first requires identification of homogeneous subgroups that correlate with biological markers and postoperative outcomes. As a first step, we derived clinical phenotypes of the CPB-induced inflammatory response by identifying patterns in perioperative clinical variables using machine learning and simulation tools. We then evaluated whether these phenotypes were associated with biological response variables and clinical outcomes. METHODS: This single-center, retrospective cohort study used Cleveland Clinic registry data from patients undergoing cardiac surgery with CPB from January 2010 to March 2020. Biomarker data from a subgroup of patients enrolled in a clinical trial were also included. Patients undergoing emergent surgery, off-pump surgery, transplantation, descending thoracoabdominal aortic surgery, and planned ventricular assist device placement were excluded. Preoperative and intraoperative variables of patient baseline characteristics (demographics, comorbidities, and laboratory data) and perioperative data (procedural data, CPB duration, and hemodynamics) were analyzed to derive clinical phenotypes using K-means–based consensus clustering analysis. Proportion of ambiguously clustered was used to assess cluster size and optimal cluster numbers. After clusters were formed, we summarized perioperative profiles, inflammatory biomarkers (eg, interleukin [IL]-6 and IL-8), kidney biomarkers (eg, urine neutrophil gelatinase–associated lipocalin [NGAL] and IL-18), and clinical outcomes (eg, mortality and hospital length of stay). Pairwise standardized difference was reported for all summarized variables. RESULTS: Of 36,865 eligible cardiac surgery cases, 25,613 met inclusion criteria. Cluster analysis derived 3 clinical phenotypes: α, β, and γ. Phenotype α (n = 6157 [24%]) included older patients with more comorbidities, including heart and kidney failure. Phenotype β (n = 10,572 [41%]) patients were younger and mostly male. Phenotype γ (n = 8884 [35%]) patients were 58% female and had lower body mass index (BMI). Phenotype α patients had worse outcomes, including longer hospital length of stay (mean = 9 days for α versus 6 for both β [absolute standardized difference {ASD} = 1.15] and γ [ASD = 1.08]), more kidney failure, and higher mortality. Inflammatory biomarkers (IL-6 and IL-8) and kidney injury biomarkers (urine NGAL and IL-18) were higher with the α phenotype compared to β and γ immediately after surgery. CONCLUSIONS: Deriving clinical phenotypes that correlate with response biomarkers and outcomes represents an initial step toward a precision medicine approach for the management of CPB-induced inflammatory response and lays the groundwork for future investigation, including an evaluation of the heterogeneity of treatment effect." @default.
• W4318966480 created "2023-02-03" @default.
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• W4318966480 date "2022-11-04" @default.
• W4318966480 modified "2023-09-24" @default.
• W4318966480 title "Derivation and Validation of Clinical Phenotypes of the Cardiopulmonary Bypass–Induced Inflammatory Response" @default.
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• W4318966480 doi "https://doi.org/10.1213/ane.0000000000006247" @default.
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