Phenotyping Refractory Cardiogenic Shock Patients Receiving Venous-arterial Extracorporeal Membrane Oxygenation with Machine Learning Algorithms
medRxiv (Cold Spring Harbor Laboratory)(2023)
Abstract
Abstract Background: Refractory cardiogenic shock (CS) patients receiving venous-arterial extracorporeal membrane oxygenation (VA-ECMO) have a wide range of mortality, machine algorithm methods may explain the potential heterogeneity of these patients. Methods: Between January 2018 and May 2021, 210 patients with CS who were receiving VA-ECMO support were enrolled and analyzed retrospectively. The k-means consensus agnostic algorithm was used. Patients were divided into three clusters based on covariates, such as platelet count (PLT), aspartic acid transaminase (AST), Interleukin-6 (IL-6), prothrombin time (PT), and serum lactate level 24 hours after ECMO initiation. The clinical and laboratory profiles were analyzed. Results: Among 210 CS with CS receiving ECMO, 148 (70.5%) were men, with a median age of 62 years (interquartile range (IQR): 53-67). Overall, 104 (49.5%) patients survived to discharge with 142 (67.6%) survived on ECMO. The patients were phenotyped into three clusters: (1) "platelet preserved (I)" Phenotype (36 [17.1%] patients), characterized by a preserved platelet count; (2) "hyperinflammatory (II)" phenotype (72 [34.3%] patients), characterized by a significant inflammatory response with higher Interleukin-6 (IL-6), and Interleukin-10 (IL-10) levels; and (3) "hepatic-renal (III)" phenotype (102 [48.6%] patients), characterized by unfavorable conditions in creatinine, aspartic acid transaminase, alanine aminotransferase, direct bilirubin, and prothrombin time. The in-hospital mortality rates were 25.0%, 52.8%, and 55.9% for phenotypes I, II, and III, respectively (P = 0.005). Conclusion: The consensus k-means algorithm analysis identified three phenotypes in refractory patients with CS receiving VA-ECMO: "platelet preserved," "hyperinflammatory," and "hepatic-renal." The phenotypes are associated with the clinical profile and mortality, allowing treatment strategies for subsets of patients with CS receiving ECMO to be developed.
### Competing Interest Statement
The authors have declared no competing interest.
### Funding Statement
National Natural Science Foundation of China (No. 82170400 to Xiaotong Hou): The mechanism of erythroid precursors regulate negatively monocytes immune function through Galectin-9-TIM3 pathway. National Natural Science Foundation of China (No. 81870305 to Xiaotong Hou): The negative regulation mechanism of co-inhibitory molecule TIGIT on the function of inflammatory monocytes in patients supported with ECMO. Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support, Code: ZYLX202111. Beijing Hospitals Authority "Ascent Plan", Code: FDL20190601. Beijing Key Specialist Project for Major Epidemic Prevention and Control Supported by the National Key Research and Development Program of China (No. 2021YFC2701703) Beijing Nova Program (No. 2022064) Social Development Science and Technology Project (CYSF2215) from Chaoyang District Bureau of Science and Technology and Information Technology
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The study was approved by the institutional ethics committee/review board of the Beijing Anzhen Hospital. Informed consent for demographic, physiological and hospital-outcome data analyses was not obtained because this observational study did not modify existing diagnostic or therapeutic strategies. However, patients and/or relatives were informed about the anonymous data collection and that they could decline inclusion.
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Key words
refractory cardiogenic shock patients,machine learning algorithms,machine learning,venous-arterial
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