Monocyte distribution width enhances the detection of infection in patients after primary percutaneous coronary intervention
Bibliographic record
Abstract
BACKGROUND: Monocyte distribution width (MDW) may serve as an infection marker in acute myocardial infarction (AMI) patients undergoing primary percutaneous coronary intervention (PCI), where infection rates range from 2.4% to 16.6%. We evaluated the association of increased MDW levels with infection occurrence and to assess MDW-based models for predicting infection risk and prolonged hospital length of stay (LOS) ≥ 7 days. METHODS AND RESULTS: This retrospective cohort study included AMI patients undergoing PCI at a Taiwanese tertiary teaching hospital from January 1, 2020, to September 30, 2021. Logistic regression models incorporating MDW, Quick Sequential Organ Failure Assessment (qSOFA) score, age, and C-reactive protein (CRP) levels were compared to the Canada Acute Coronary Syndrome (C-ACS) score and stress hyperglycemia ratio (SHR). Among 252 patients, 12.7% developed infections, a rate that falls within the expected range. Infections were more frequent among patients with LOS ≥ 7 days (42.9% vs. 1.1%). A three-parameter model (qSOFA score ≥2, age ≥ 65, and MDW ≥ 20) demonstrated good performance for new infection (AUC: 0.834) and LOS ≥ 7 days (AUC: 0.714). Adding CRP ≥ 2 mg/dL improved predictions for infection (AUC: 0.909) and LOS ≥ 7 days (AUC: 0.798), outperforming the C-ACS score (AUC: 0.807) and SHR (AUC: 0.784). CONCLUSION: MDW emerges as a promising biomarker for assessing the risks of infection and prolonged hospital LOS in patients with AMI. Its early use may facilitate timely clinical interventions, including earlier initiation of antibiotic therapy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".