Association of Left Ventricular Ejection Fraction with Mortality and Hospitalizations
Bibliographic record
Abstract
BACKGROUND: Although echocardiography is widely used to measure left ventricular ejection fraction (LVEF), its prognostic value has not been demonstrated in a broad range of patients including those acutely hospitalized for cardiac or noncardiac causes. We determined whether greater degrees of left ventricular systolic dysfunction were associated with progressively increasing risks of death or cardiovascular hospitalizations among patients in hospital or outpatient settings. METHODS: A total of 27,323 patients with LVEF measured and 19,445 matched controls were followed for 223,034 person-years. Outcomes of total mortality, cardiovascular death, cardiovascular hospitalizations, and heart failure hospitalizations were examined using cause-specific hazard competing-risks analysis. RESULTS: In the study cohort (median age, 68 [interquartile range, 58-77], 14,828 women [31.7%]), the hazard ratios (95% CI) for all-cause death were 1.67 (1.57-1.77), 1.30 (1.24-1.36), and 1.17 (1.11-1.23) when LVEF was <25%, 25%-35%, or 36%-45% compared with LVEF 46%-55% (all P < .001). Rates of cardiovascular death were similarly higher with lower LVEF. The hazard ratios for cardiovascular hospitalization were 1.35 (1.27-1.42), 1.21 (1.16-1.27), and 1.13 (1.07-1.18) for LVEFs <25%, 25%-35%, and 36%-45%, respectively (all P < .001). The rate of heart failure hospitalizations was amplified, with hazard ratios of 1.71 (1.59-1.85), 1.39 (1.31-1.48), and 1.21 (1.13-1.29) for LVEFs <25%, 25%-35%, or 36%-45% (all P < .001). The rate of mortality and hospitalizations increased comparably with greater reductions in LVEF during both inpatient cardiac or noncardiac admissions (P < .001). CONCLUSIONS: Quantitative echocardiographic LVEF stratified the risk of death and hospitalization in a wide range of clinical settings, including during noncardiac admissions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".