Evidence map›Paper›PMID 41959819›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Contrastive Transformer-Driven Discovery of Temporal Hemodynamic Subphenotypes in Cardiac Surgery Patients.

Jacob M Desman, Moein Sabounchi, Wonsuk Oh, Gagan Kumar, Ahmed Shaikh, Rohit Gupta, Umesh Gidwani, Anthony Manasia, Robin Varghese, John Oropello and 15 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Jacob M DesmanCharles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID 0000-0002-5411-6637
Moein SabounchiCharles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Wonsuk OhCharles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Gagan KumarDepartment of Pulmonary and Critical Care Medicine, Northeast Georgia Medical Center, Gainesville, GA, USA.
Ahmed ShaikhInstitute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Rohit GuptaInstitute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Umesh GidwaniInstitute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Anthony ManasiaInstitute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Robin VargheseInstitute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
John OropelloInstitute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Gordon SmithDepartment of Epidemiology and Biostatistics, West Virginia University School of Public Health, Morgantown, West Virginia, USA.
Arash KiaDepartment of Anesthesiology, Perioperative, and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Prem TimsinaWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Ben KaplanWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Avniel Shetreat-KleinDepartment of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Benjamin GlicksbergCharles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Matthieu LegrandDepartment of Anesthesia and Perioperative Care, University of California, San Francisco, California, USA.
Ashish K KhannaDepartment of Anesthesiology, Division of Critical Care Medicine, Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, North Carolina, USA.
John A KellumDepartment of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Patricia KovatchThe Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Roopa Kohli-SethInstitute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Alexander W CharneyCharles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
David ReichWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Girish N NadkarniCharles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Ankit SakhujaCharles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Funding

Conduits: Mount Sinai Health System Translational Science HubUL1TR004419 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Rosalind J Wright · 2022 to 2026
$46.4M
COVID and Translational Science supercomputer (CATS)S10OD030463 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2021 to 2021
$2.0M
Big Omics Data Engine 2 SupercomputerS10OD026880 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2019 to 2019
$2.0M
Using Novel Machine Learning Methods to Personalize Strategies for Prevention of Persistent AKI after Cardiac SurgeryK08DK131286 · NIDDK · WEST VIRGINIA UNIVERSITY · PI Ankit Sakhuja · 2022 to 2026
$774k
NCATS NIH HHS UL1 TR004419NIDDK NIH HHS K08 DK131286NIH HHS S10 OD026880NIH HHS S10 OD030463
6 · The paper itself

Abstract

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.

Identifiers

PMID41959819
PMCPMC13060399

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.