Positron Emission Tomography and Single-Photon Emission Computed Tomography Imaging in the Diagnosis of Cardiac Implantable Electronic Device Infection
Bibliographic record
Abstract
Background— The use of cardiac implantable electronic devices (CIED) is increasing, and their associated infections result in significant morbidity and mortality. The introduction of better cardiac imaging techniques could be useful for diagnosing this condition and guiding therapy. Our objective was to systematically assess the diagnostic accuracy of Fluor-18-fluorodeoxyglucose positron emission tomography and computed tomography , labeled leukocyte scintigraphy (LS), and Gallium-67 citrate scintigraphy for the diagnosis of CIED infection. Methods and Results— A systematic review of the literature and meta-analysis on the use of all 3 modalities in CIED infection were conducted. Pooled sensitivity, specificity, and summary receiver operating characteristic curves of each imaging modalities were determined. The literature search identified 2493 articles. A total of 13 articles (11 studies for 18 F-FDG PET-CT and 2 for LS), met the inclusion criteria. No studies for 67 Ga citrate scintigraphy met the inclusion criteria. The pooled sensitivity of 18 F-FDG PET-CT for the diagnosis of CIED infection was 87% (95% CI, 82%–91%) and pooled specificity was 94% (95% CI, 88%–98%). The summary receiver operating characteristic curve analysis demonstrated good overall accuracy, with an area under the curve of 0.935. There were insufficient data to do a meta-analysis for LS, but both studies reported sensitivity above 90% and specificity of 100%. Conclusions— Both 18 F-FDG PET-CT and LS yield high sensitivity, specificity, and accuracy, and thus seem to be useful for the diagnosis of CIED infection, based on robust data for 18 F-FDG PET-CT but limited data for LS. When available, 18 F-FDG PET-CT may be preferred.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".