Socioeconomic variations determine the clinical presentation, aetiology, and outcome of infective endocarditis: a prospective cohort study from the ESC-EORP EURO-ENDO (European Infective Endocarditis) registry
Bibliographic record
Abstract
AIMS: Infective endocarditis (IE) is a life-threatening disease associated with high mortality and morbidity worldwide. We sought to determine how socioeconomic factors might influence its epidemiology, clinical presentation, investigation and management, and outcome, in a large international multicentre registry. METHODS AND RESULTS: The EurObservational Programme (EORP) of the European Society of Cardiology EURO-ENDO (European Infective Endocarditis) registry comprises a prospective cohort of 3113 adult patients admitted for IE in 156 hospitals in 40 countries between January 2016 and March 2018. Patients were separated in three groups, according to World Bank economic stratification [group 1: high income (75.6%); group 2: upper-middle income (15.4%); group 3: lower-middle income (9.1%)]. Group 3 patients were younger [median age (interquartile range, IQR): group 1, 66 (53-75) years; group 2, 57 (41-68) years; group 3, 33 (26-43) years; P < 0.001] with a higher frequency of smokers, intravenous drug use, and human immunodeficiency virus infection (all P < 0.001) and presented later [median (IQR) days since symptom onset: group 1, 12 (3-35); group 2, 19 (6-54); group 3, 31 (12-62); P < 0.001] with a higher likelihood of developing congestive heart failure (13.6%, 11.1%, and 22.6%, respectively; P < 0.001) and persistent fever (9.8%, 14.2%, and 27.9%, respectively; P < 0.001). Among 2157 (69.3%) patients with theoretical indication for cardiac surgery, surgery was performed less frequently in group 3 patients (75.5%, 76.8%, and 51.3%, respectively; P < 0.001), who also demonstrated the highest mortality (15.0%, 23.0%, and 23.7%, respectively; P < 0.001). CONCLUSION: Socioeconomic factors influence the clinical profile of patients presenting with IE across the world. Despite younger age, patients from the poorest countries presented with more frequent complications and higher mortality associated with delayed diagnosis and lower use of surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".