Biomarkers in the Diagnosis, Management, and Prognostication of Perioperative Right Ventricular Failure in Cardiac Surgery—Are We There Yet?
Bibliographic record
Abstract
Right ventricular failure (RVF) is a major risk factor for end organ morbidity and mortality following cardiac surgery. Perioperative RVF is difficult to predict and detect, and to date, no convenient, accurate, or reproducible measure of right ventricular (RV) function is available. Few studies have examined the use of biomarkers in RVF, and even fewer have examined their utility in the perioperative setting of patients undergoing cardiac surgery. Of the available classes of biomarkers, this review focuses on biomarkers of (1) inflammation and (2) myocyte injury/stress, due to their superior potential in perioperative RV assessment, including Galectin 3, ST2/sST2, CRP, cTN/hs-cTn, and BNP/NT-proBNP. This review was performed to help highlight the importance of perioperative RV function in patients undergoing cardiac surgery, to review the current modalities of RV assessment, and to provide a review of RV specific biomarkers and their potential utilization in the clinical and perioperative setting in cardiac surgery. Based on current evidence, we suggest the potential utility of ST2, sST2, Gal-3, CRP, hs-cTn, and NT-proBNP in predicting and detecting RVF in cardiac surgery patients, as they encompass the multifaceted nature of perioperative RVF and warrant further investigation to establish their clinical utility.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".