P975Incidence and risk factors for all-cause hospitalizations in patients with atrial fibrillation: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Atrial fibrillation (AF) and associated co-morbidities consume substantial health care resources. The incidence of all-cause hospital admissions in this large patient population, however, is poorly characterized. We therefore performed a comprehensive systematic review and meta-analysis to assess the incidence of all-cause hospitalizations in patients with AF. Methods: We searched PubMed, Embase and the Cochrane Library up to June, 2017, to identify all studies that provided information on the incidence of all-cause hospitalizations in AF patients. Studies that reported cause-specific admissions only were excluded. Pooled incidence rates were calculated using DerSimonian-Liard random-effects models. Meta-regression models were constructed to identify characteristics of AF patients associated with all-cause hospitalizations. Results: We identified 44 studies which reported all-cause hospitalizations from 285,837 AF patients with a cumulative follow up time of 376'584 person-years (py). The cumulative incidence of all-cause hospitalization was 40 (95% confidence interval [CI], 35–45) per 100 py (Figure). In 25 reported studies, incidence of cardiovascular (CV) hospitalization was 24 (95% CI, 20–28) per 100 py and 15 (95% CI, 12–18) per 100 py for non-cardiovascular (non-CV) hospitalization, respectively. The incidence estimates were highly heterogeneous (I2=99.9%) and ranged from 8 to 88 per 100 py. Patient characteristics associated with an increased rate of CV hospitalizations were a higher prevalence of heart failure (β=0.27; 95% CI, 0.06–0.49, p=0.015) and chronic pulmonary disease (β=1.23; 95% CI, 0.04–2.43, p=0.04). Associated characteristics for higher rates of non-CV hospitalizations were longer follow up time (β=3.2; 95% CI, 0.36–6, p=0.03), peripheral artery disease (β=0.7; 95% CI, 0.21–1.21, p=0.01), presence of malignancies (β=0.7; 95% CI, 0.6–0.77, p=0.001), and chronic pulmonary disease (β=0.9; 95% CI, 0.4–1.4, p=0.002). Conclusions: The average incidence of hospitalizations among patients with AF is high, which constitute a huge burden for health care systems. Prominent co-morbidities at least partly explained the detected heterogeneity. More detailed information on underlying causes and mechanisms are urgently needed.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.033 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".