Cardiac surgery patients experience rapidly evolving hemodynamics in early post-operative period requiring intensive support. Identifying hemodynamic subphenotypes from these data can inform personalized management. Using 24-hour high-resolution physiologic and treatment data from 6,630 MIMIC-IV and 1,963 SICdb patients, we trained a transformer encoder with a reconstruction-contrastive objective to derive patient-level embeddings capturing multivariate temporal dynamics within first 24h of ICU stay and compared them against those generated by dynamic time warping (DTW). Spectral clustering uncovered three reproducible hemodynamic subphenotypes. Compared with subphenotype 1, subphenotype 3 received more IV fluids, vasopressors, inotropes, and exhibited higher in-hospital mortality (OR 5.85, 95 % CI 2.43-14.13), longer ICU stay (7.12 days, 95% CI: 5.52-8.73) and hospitalization (8.86 days, 95% CI: 6.57-11.16). DTW derived subphenotypes had weaker prognostic separation. Thus, contrastive-transformer framework identified more clinically meaningful temporal hemodynamic subphenotypes that may optimize post-operative risk stratification and inform personalized management.

A.S. is a consultant for Roche Diagnostics Corporation. G.N.N. is a founder of Renalytix, Pensieve, Verici, provides consultancy services to AstraZeneca, Reata, Renalytix, Siemens Healthineer and Variant Bio, and serves as a scientific advisory board member for Renalytix and Pensieve. He also has equity in Renalytix, Pensieve and Verici. J.A.K. reports receiving consulting fees from Astute Medical/bioMerieux, Astellas, Alexion, Chugai Pharma, Novartis, Mitsubishi Tenabe and GE Healthcare and is a full time employee of Spectral Medical. All remaining authors have declared no conflicts of interest.

This study was supported by NIH/NIDDK grant 5K08DK131286 (AS). This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this publication was also supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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All datasets used and analyzed in this present study are publicly available. Each dataset can be found in its associated online repository (MIMIC-IV: https://physionet.org/content/mimiciv/2.2/; SICdb: https://physionet.org/content/sicdb/1.0.8/). The SICdb data is deidentified.

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