Heart Failure Decompensation with Cardiogenic Shock Exhibits Distinct Sequential Inflammatory Profiles
Bibliographic record
Abstract
AIMS: The inflammatory profile of cardiogenic shock (CS) after myocardial infarction affects outcomes; however, little is known about the impact of inflammatory changes in CS caused by acute decompensated heart failure (ADHF-CS). We measured levels of inflammatory cytokines in patients with ADHF-CS admitted to a cardiac intensive care unit (CICU). METHODS: We identified patients admitted to our CICU with ADHF-CS who had consented to having biospecimens stored. We identified two comparator groups of patients with HF seen as outpatients with stored biospecimens: firstly, those who had no history of decompensation and did not develop CS during follow-up after sample acquisition (stable HF), and secondly, a group of patients who developed CS during follow-up (pre-CS). All samples underwent 48-plex cytokine and white blood cell differential testing with the differences between groups analysed by comparing means. RESULTS: Eighty-four ADHF-CS patients were identified who had samples obtained at a median of 2 [inter-quartile range (IQR) 0-7] days after CICU admission. Thirty-six pre-CS outpatients had samples taken 137 (IQR 41-258) days before admission with CS, and 338 stable HF control patients were included. Cytokine profiles differed between ADHF-CS and stable HF. Patients with CS had higher pro-inflammatory cytokine levels [including interleukin-1 (IL-1), interleukin-6 (IL-6) and interleukin-8 (IL-8)] and total white cell counts than stable HF patients. Analysis of the pre-CS outpatient group suggested an intermediate stage in subacute transition to CS. CONCLUSIONS: ADHF-CS is characterized by high levels of pro-inflammatory cytokines and total white count, compared with ambulatory HF. Decompensation from HF has two distinct inflammatory phases that may help identify outpatients at risk of CS.
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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 | 0.001 | 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".