Neutrophil Counts Help Predict Free Wall Rupture Following St-elevation Myocardial Infarction
Bibliographic record
Abstract
PURPOSE: Free wall rupture (FWR) is a lethal complication after acute myocardial infarction; however, the un-derlying mechanisms of FWR are unclear. This study analyzes the relationship between neutrophil counts and FWR following ST-elevation myocardial infarction (STEMI). METHODS: The case group was STEMI patients with FWR and the control group was STEMI patients without FWR (case-control ratio was 1:4). The demographic data, clinical manifestation and laboratory test results were retrospectively collected and analyzed. RESULTS: Of a total of 6,712 consecutive STEMI patients, 78 patients (1.2%) had FWR. Compared with STEMI patients, patients with FWR were older and more likely to be female with an anterior infarct. White blood cell (WBC) counts were significantly higher in the FWR group. Moreover, we found that the elevated neutrophil counts mainly accounted for the elevated WBC counts. There was also a correlation between the age and neu-trophil counts (P=0.0109); as patient age increased, neutrophil counts decreased (P=0.0387). We also found no correlation between neutrophil counts and the time between myocardial infarction attack and FWR; however, when dividing these patients into FWR ≤48 h after admission to hospital for STEMI and FWR >48 h, there was a significant difference in neutrophil counts (P=0.0196). Furthermore, the results of logistic regression analy-sis showed that increased neutrophil was an independent risk factor for FWR (odds ratio: 2.404, confidence interval: 1.055-5.477). CONCLUSION: Elevated neutrophil counts were found to be the main cause of differences in WBC counts be-tween FWR and STEMI. Elevated neutrophil was an independent risk factor for FWR. This study provided clues for further research and development of therapeutics for the prevention of FWR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".