Risk factors for Gram-negative bacterial infection of cardiovascular implantable electronic devices: multicentre observational study (CarDINe Study)
Bibliographic record
Abstract
BACKGROUND: Infections of cardiovascular implantable electronic devices (CIED) are mainly due to Gram-positive bacteria (GPB). Data about Gram-negative bacteria CIED (GNB-CIED) infections are limited. This study aimed to investigate risk factors, clinical and diagnostic characteristics, and outcome of patients with GNB-CIED. METHODS: A multicentre, international, retrospective, case-control-control study was performed on patients undergoing CIED implantation from 2015 to 2019 in 17 centres across Europe. For each patient diagnosed with GNB-CIED, one matching control with GPB-CIED infection and two matching controls without infection were selected. RESULTS: A total of 236 patients were enrolled: 59 with GNB-CIED infection, 59 with GPB-CIED infection and 118 without infection. No between-group differences were found regarding clinical presentation, diagnostic and therapeutic management. A trend toward a higher rate of fluorodeoxyglucose positron emission computed tomography (FDG PET/CT) positivity was observed among patients with GNB than in those with GPB-CIED infection (85.7% vs. 66.7%; P = 0.208). Risk factors for GNB-CIED infection were Charlson Comorbidity Index Score (relative risk reduction, RRR = 1.211; P = 0.011), obesity (RRR = 5.122; P = 0.008), ventricular-pacing ventricular-sensing inhibited-response pacemaker implantation (RRR = 3.027; P = 0.006) and right subclavian vein site of implantation (RRR = 5.014; P = 0.004). At 180-day survival analysis, GNB-CIED infection was associated with increased mortality risk (HR = 1.842; P = 0.067). CONCLUSIONS: Obesity, high number of comorbidities and right subclavian vein implantation site were associated with increased risk of GNB-CIED infection. A prompt therapeutic intervention that may be guided using FDG PET/CT is suggested in patients with GNB-CIED infection, considering the poorer outcome observed in this group.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".