Metabolomics investigations hold promise for the characterization of small molecules, metabolites, which govern the ultimate manifestation of cardiac phenotypes. In this study, we employed a mass spectrometry-based metabolomics approach to identify metabolic marker(s), which dynamically reflect the cardiac performance of heart failure patients amid the implantation of mechanical circulatory support. Using the MRM-based and triple quadrupole technology platform, we have quantified 266 metabolites native to human plasma and collected from thirteen heart failure patients. The temporal profile of these metabolites was sampled from 1 day prior to the implantation of mechanical circulatory support, as well as 1-, 3-, 5-, and 7-day following their surgical interventions. We identified subgroups of these metabolites with coordinated behaviors that are interesting to their diseased phenotypes. In a pair-wise correlation analysis, 36.8% (98 out of 266) of metabolites were significantly correlated. Intriguingly, majority of which (65 out of 98) are representing the functional groups of phosphatidylcholines; several of them are known to have close associations with the pathogenesis of cardiovascular diseases. In addition, there are 33 metabolites contributing to multiple functional groups, including twelve of them belong to sphingomyelines, ten of them in the family of lysophosphatidylcholines, eight amino acids (Gln, Ser, Ala, His, Lys, Gly, Thr, and Arg), as well as three fatty acids (eicosapentaenoic acid, pentadecenoic acid, and heptadecenoic acid). The behaviors of these 266 metabolites have constituted individualized metabolic fingerprints. Delineation of the intrinsic relationships among alterations in distinct metabolite groups and their reflected cardiac function will enable us to identify new metabolic markers aiding stratification and/or prediction on the clinical outcome of each individual patient undergoing the treatment of mechanical circulatory support. This personalized metabolic fingerprint will offer unique prognostic utilities, supporting clinical decision-making process to deliver intervention that is most effective and beneficial to an individual.