Immune Checkpoint Inhibitor Myocarditis and Left Ventricular Systolic Dysfunction
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, but ICI myocarditis (ICI-M) remains a potentially fatal complication. The clinical implications and predictors of left ventricular ejection fraction (LVEF) <50% in ICI-M are not well understood. OBJECTIVES: The aim of this study was to identify factors associated with LVEF <50% vs ≥50% at the time of hospitalization for ICI-M. A secondary objective was to evaluate the relationship between LVEF and 30-day all-cause mortality. METHODS: The International ICI-Myocarditis Registry, a retrospective, international, multicenter database, included 757 patients hospitalized with ICI-M. Patients were stratified by LVEF as reduced LVEF (<50%) or preserved LVEF (≥50%) on admission. Cox proportional hazards models were used to assess the associations between LVEF and clinical events, and multivariable logistic regression was conducted to examine factors linked to LVEF. RESULTS: , and were more likely to have received chest radiation (24.2% vs 13.5%; P < 0.001). Multivariable analysis identified predictors of LVEF <50%, including exposure to v-raf murine sarcoma viral oncogene homolog B1/mitogen-activated protein kinase inhibitors, pre-existing heart failure, dyspnea at presentation, and at least 40 days from ICI initiation to ICI-M onset. Conversely, myositis symptoms were associated with LVEF ≥50%. LVEF <50% was marginally associated with 30-day all-cause mortality (unadjusted log-rank P = 0.062; adjusted for age, cancer types, and ICI therapy, HR: 1.50; 95% CI: 1.02-2.20). CONCLUSIONS: Dyspnea, time from ICI initiation, a history of heart failure, and prior cardiotoxic therapy may be predictors of an initial LVEF <50% in patients with ICI-M.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".