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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".