Prediction of temporary epicardial pacing wire use in cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Placement of temporary epicardial pacing wires (TEPW) at the end of open heart surgery cases is routine but can be associated with complications. Identification of patients who are high risk for requiring pacing would be beneficial on guiding selective TEPW placement. The purpose of this study was to identify predictors of temporary pacing immediately post cardiac surgery. METHODS: A retrospective analysis of patients undergoing cardiac surgery from 2005 to 2016 at the Maritime Heart Center was conducted. Analysis was performed of patients who require pacing on arrival to the cardiovascular intensive care unit (CVICU) compared with those who were not paced. Multivariable logistic regression was used to determine each variable's risk adjusted likelihood of pacing for the entire cohort. Subgroup analysis was performed in the isolated procedures. RESULTS: A total of 11 752 patient underwent surgery from the year 2005 to 2016. Two thousand and fifty-one (17.5%) required pacing on arrival to CVICU. Older age, female sex, preoperative renal failure, lower ejection fraction (EF), preoperative arrhythmia, preoperative use of calcium channel blockers, and longer cross-clamp times were risk factors for pacing. In the isolated coronary artery bypass grafting and aortic valve replacement groups, findings were similar to the overall cohort. Only age, obesity, and chronic obstructive pulmonary disease were risk factors for pacing in the isolated mitral valve (MV) repair group and only preoperative arrhythmia in the isolated MV replacement group. CONCLUSION: We have identified risk factors for TEPW use following cardiac surgery and in isolated procedure subgroups. These risk factors may help guide selective TEPW placement.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".