Preoperative untargeted SPME-LC/HRMS-based metabolomic profiling in cardiac surgical patients identified prognostic biomarkers for postoperative outcomes
Bibliographic record
Abstract
BACKGROUND: Circulating metabolite profiles may reflect the physiological status before cardiac surgery and could contribute to predicting clinical outcome. Accordingly, metabolite levels may improve risk models as clinical models alone using known, or suspected, risk factors may have limited applicability to subtler organ-specific injury. Hence, the aim of this study was to comprehensively describe perioperative changes in metabolic profiles in cardiac surgical patients requiring cardiopulmonary bypass to find association with outcomes. RESULTS: In this work, a sequential extraction strategy using two SPME devices with different selectivity (C18/PAN- and C8-SCX/PAN-coated fibers) next to dual-mode separation (hydrophilic interaction - reversed-phase) was proposed for improving plasma metabolome coverage and data quality, and consequently facilitating discriminative biomarker discovery. This complementary methodology enhanced metabolome coverage demonstrating that patients undergoing complex open cardiac surgery (n = 22) could be efficiently distinguished from low-risk surgery patients (n = 27) based on their metabolic profiles. Metabolite profiles belonging to the high-risk patients showed higher levels of accumulation of incomplete products of fatty acid (FA) β-oxidation, bile acids, glucuronides, and lipid mediators derived from polyunsaturated FAs, along with perturbations in essential amino acid metabolism that potentially might be linked with multiple poor clinical outcomes (i.e. postoperative seizure, ischemic-thrombotic complications, death). SIGNIFICANCE: The analytical workflow, presented in this study, greatly expanded the capacity for comprehensive metabolite profiling in demanding biological matrices. With more patients undergoing complex cardiac surgery at the advanced age, this work adds to improving perioperative care through implementing metabolic solutions that may streamline recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".