Crossing the chasm: caution for use of angiotensin receptor-neprilysin inhibition in patients with cardiogenic shock– a case report
Bibliographic record
Abstract
BACKGROUND: Vasoplegia has been reported in patients receiving angiotensin receptor-neprilysin inhibitors (ARNI) with heart failure with reduced ejection fraction (HFrEF). We present a case of vasoplegic shock after initiation of ARNI in a hospitalized 65-year-old man recovering from cardiogenic shock (CS) and acute kidney injury (AKI). CASE SUMMARY: A 65-year-old man with HFrEF presented to a community hospital with CS with evidence of poor perfusion with a lactate of 5.6 mmol/L and creatinine (Cr) 125 µmol/L. He was treated with intravenous furosemide infusion. Subsequently, his lactate normalized but he developed an AKI with a Cr of 176 µmol/L. He was then started on ARNI and beta blockers. Over the next 24 h, he developed a vasoplegic shock necessitating multiple vasopressors and a transfer to a tertiary academic centre. With supportive therapy, his vasoplegic shock improved and he was discharged home. DISCUSSION: PARADIGM-HF found that the introduction of an ARNI in patients with ambulatory symptomatic HFrEF reduces the risk of death and heart failure hospitalization. Most recently, PIONEER-HF showed that ARNI reduced N-terminal pro-B-type natriuretic peptide levels at 4 and 8 weeks, without significantly different rates of medication-related adverse effects. However, thus far, no clinical trials have examined the role of ARNI in CS. Our case report highlights the risk of vasoplegic shock caused by initiation of ARNI in patients hospitalized with CS especially in whom renal and hepatic impairment is present.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".