FP346ANY INFECTION IS A RISK FACTOR FOR FUTURE CARDIOVASCULAR EVENTS IN CHRONIC KIDNEY DISEASE
Bibliographic record
Abstract
Introduction and Aims: Chronic Kidney Disease (CKD) affects 10% of adults and is associated with increased morbidity and mortality. Infection (Infct) has been described in CKD populations as impacting hospitalizations, morbidity and mortality. Inflammation and infection has been linked to cardiovascular disease. We hypothesized that infections in CKD patients may be linked to developing CV events (CVE) in CKD patients. Methods: CanPREDDICT is an observational pan-Canadian cohort of prospectively followed up CKD patients with eGFR 15-45ml/min/1.73m2; followed q6 monthly for 3 years then annually for 2 additional years. Infct were defined by use of antibiotics, categorized by anatomical location and counted for each six monthly interval. Outcomes of interest were any CVE. SPSS used for analysis and time to CVE used in a time-dependent covariate analysis. Results: 2294 of 2544 patients had sufficient data for inclusion in the analysis (median follow up 2.86 years). Median age was 70.4 years, males (62.4%) and Caucasians (88.9%). A CVE occurred in 281 patients (12.1%); predominately ischemic (7.2%), then CHF (5.6%) and other CVE (1.0%). There were 187 deaths (8.2%), 1.6% were CV deaths. Patients developing a CVE were older (74.1 vs 69.7 years, p= 0.001) were more likely to have a background of diabetes (62.3% vs 45.2%, p< 0.001), CV Disease (68.0% vs 40.2%, p< 0.001) and a lower baseline eGFR (25.8,vs 27.7 ml/min/1.73m2 p= 0.008) and lower hemoglobin (120 vs 123 g/l, p<0.001). An Infct occurred in 480 patients (20.9%). Patients with an infection were more likely to develop a CVE (28.5% vs 19.9%, p= 0.001). The hazard ratio for CVE following an Infct is 2.90 (95% confidence interval 2.22- 3.78, p< 0.001). We stratified patients into 4 groups by history of previous CV disease and developing Infct. Using patients with no CV disease prior to enrollment and who did not develop an Infct as the reference group we determined that Infct exposure carries similar risk for future CVE compared to patients with a previous history of CV disease [FIGURE]. The highest risk remains in those with a background of CV disease who subsequently developed an Infct. Multivariate analysis taking into account other risk factors for CV events confirms Infct is an independent risk factor for cardiovascular events in CKD (p< 0.001).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".