Clinical implications in early diagnosis of infective endocarditis
Bibliographic record
Abstract
Abstract Introduction The diagnostic delay of infective endocarditis (IE) is common due to its low clinical suspicion and often insidious course. An early diagnosis is essential for prompt treatment initiation and for preventing adverse outcomes. Purpose This study aims to analyze the time from the onset of symptoms to a confirmed diagnosis via echocardiography, considering the APORTEI score and microbiological identification, as well as implications for complications and mortality. Methods This was a retrospective observational study conducted on patients diagnosed with IE between 2016 and 2023. The comparison of the mean duration from symptom onset to diagnosis across different groups was performed using Student's t-test (for 2 groups) and ANOVA (for >2 groups). Post-hoc analyses were conducted when statistically significant differences were identified. Results Among a total of 188 patients (72.5% male; median age: 69 years, interquartile range [IQR]: 68), the median duration from the first symptom to diagnosis was 17 days (IQR: 4–92). Significant new-onset valvular dysfunction (including stenosis, insufficiency, or mixed lesions) was diagnosed in 65.6% of patients; however, no statistically significant differences were observed between the groups. Regarding the incidence of cerebral embolisms, systemic embolisms, and septic shock, there were no statistically significant differences. Differences in the time to diagnosis were observed between infections caused by different microorganisms (F[2,28] = 6, p = 0.04), particularly between S. bovis and S. aureus (p = 0.014), with earlier diagnosis noted in cases involving S. aureus. Significant differences were also found when stratifying by APORTEI risk score (low, moderate, high, and very high risk) (F[3,14] = 3, p = 0.027), with significance between low risk compared to high and very high risk (p = 0.03 and p = 0.027, respectively). Over a follow-up period of 28.4 months, 33.3% of patients died due to cardiovascular causes. These patients were diagnosed earlier, with a mean time to diagnosis of 15.2 days, whereas those who survived experienced a delay in the diagnosis, with a mean of 30.9 days (p = 0.02). Conclusions Patients with a poorer prognosis were those diagnosed earlier, probably due to a more aggressive disease course, which also coincided with more virulent microorganisms (E. coli, S. aureus) and higher risk stratifications according to the APORTEI score.Mean days and microorganismsMortality and mean days
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".