Risk calculator to predict 30-day mortality in left-sided infective endocarditis. The EURO-ENDO score
Bibliographic record
Abstract
Abstract Background/Introduction Infective endocarditis (IE) is associated with high in-hospital mortality, despite improvements in therapeutic strategies. Nonetheless, there is no prospective risk model to estimate IE mortality. Purpose We sought to develop and validate a calculator to predict 30-day mortality risk regarding to perform surgery or medical treatment alone in left-sided IE. Methods This is a prospective, multicenter registry that included patients between January 2016 and March 2018 with a diagnosis of IE based on ESC 2015 diagnostic criteria. Patients with possible or definite left-sided IE were included in the analyses. Clinical, biological, microbiological and imaging data were collected. The primary end point was 30-day mortality in patients with left-sided IE. The risk calculator was based on multivariable Cox regression models. The accuracy of the logistic regression models was assessed by discrimination and calibration using C-statistic and Hosmer-Lemeshow test. Results Among 3116 patients included, 2171 patients presented left-sided IE and 257 patients (11.8%) died during the first 30 days of IE diagnosis. After multivariable Logistic regression analysis, eleven variables were associated with 30-days mortality and were included in the calculator: previous cardiac surgery, previous stroke/TIA, creatinine >2 mg/dL, S. aureus infection, embolic events on admission, heart failure or cardiogenic shock, vegetation size >14 mm, presence of abscess, severe regurgitation, double left-sided IE and no left valve surgery. There was an excellent correlation between the predicted 30-days mortality in both models with or without performing left valve surgery (area under the receiver operator curve: 0.798 and 0.758, respectively). Moreover, calibration by Hosmer-Lemeshow were 0.085 and 0.09, respectively). Conclusion(s) Our risk score in patients with left-sided IE provides an accurate individualized estimation of 30-day mortality according to perform or not perform left-valve surgery. It allows medical professionals to determine whether submitting patients to surgery or not, and thus improve their prognosis. Funding Acknowledgement Type of funding sources: None.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".