Complex cardiac implantable electronic device infections in Alberta, Canada: An epidemiologic cohort study of validated administrative data
Bibliographic record
Abstract
OBJECTIVE: To establish the epidemiology of cardiac implantable electronic device (CIED) infections in Alberta, Canada, using validated administrative data. DESIGN: Retrospective, population-based cohort study. SETTING: Alberta Health Services is a province-wide health system that services all of Alberta, Canada. PARTICIPANTS: Adult patients who underwent first-time CIED implantation or generator replacement in Alberta, Canada, between January 1, 2011, and December 31, 2019. METHODS: CIED implant patients were identified from the Paceart database. Patients who developed an infection within 1 year of the index procedure were identified through validated administrative data (International Classification of Diseases, Tenth Revision in Canada). Demographic characteristics of patients were summarized. Logistic regression models were used to analyze device type, comorbidities, and demographics associated with infection rates and mortality. RESULTS: Among 27,830 CIED implants, there were 205 infections (0.74%). Having 2 or more comorbidities was associated with higher infection risk. Generator replacement procedures (odds ratio [OR], 0.55; 95% confidence interval [CI], 0.34-0.84; P = .008), age increase of every 10 years (OR, 0.73; 95% CI, 0.66-0.82; P ≤ .001), and index procedure after 2014 were associated with decreased risk. Comparing the infected to uninfected groups, the hospitalization rates were 2.63 compared to 0.69, and the mortality rates were 10.73% compared to 3.49%, respectively (P < .001). CONCLUSIONS: There is a slightly lower overall rate of CIED infections Alberta, Canada compared to previously described epidemiology. Implants after 2014, and generator replacements showed a decreased burden of infection. Patients with younger age, and 2 or more comorbidities are at greatest risk of CIED infection. The burden of hospitalization and mortality is substantially higher in infected patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".