Temporal Trends of the Burden of CKD Among Hospitalized Aortic Stenosis Patients in the Province of Quebec, Canada
Bibliographic record
Abstract
Background: Aortic stenosis (AS) is associated with valvular calcifications which are highly prevalent in chronic kidney disease (CKD). The aim of this study was to describe the temporal trends of comorbid CKD status in patients hospitalized for AS and evaluate the impact of these two conditions on 1-year mortality in the province of Quebec, between 2000 and 2017. Methods: Using the Quebec Integrated Chronic Disease Surveillance System, we identified patients ≥ 20 years with incident AS using ICD-9 and ICD-10 codes, in the hospital discharge database. We then combined hospital discharges and physician billing claims databases to identify patients with comorbid CKD status in the two years prior to the AS diagnosis. Three subgroups of CKD status were considered: 1) non-CKD, 2) pre-dialysis and 3) dialysis. To allow comparison over time, direct adjustment using age distribution of the 2016-2017 AS population was used for proportion, 1-year all-cause and cardiovascular mortality. Results: We included 108,780 patients with incident AS (Women: 51.8%; mean age (±SD): 76.4 ±11.7; non-CKD: 74.2% (n=80,768); pre-dialysis: 24.6% (n=26,809); dialysis: 1.1% (n=1,203). During the study period, the age-adjusted proportion of AS patients with non-CKD comorbid status decreased by 14% (80.7% [95% CI 77.6-84.0] to 69.6% [95% CI 67.2-71.9]). Inversely, the age-adjusted proportion of AS patients with pre-dialysis and dialysis comorbid status increased by 58% (18.5% [95% CI 16.9-20.2] to 29.3% [95% CI 27.8-30.9]) and 46% (0.76% [95% CI 0.5-1.1] to 1.1% [95% CI 0.8-1.4]), respectively. Age-adjusted 1-year all-cause and cardiovascular mortality decreased over time but remained higher in patients with comorbid CKD. In 2015-2016, age-adjusted relative risk (RR) of 1-year all-cause mortality was significantly higher in pre-dialysis (RR=1.56 [95% CI 1.44, 1.69]) and dialysis (RR=2.04 [95% CI 1.62-2.61]) compared to non-CKD patients. Age-adjusted RR of 1-year cardiovascular mortality was also significantly higher in pre-dialysis (RR=1.83 [95% CI 1.66-2.03]) and dialysis (RR=2.28 [95% CI 1.68-3.09]) compared to non-CKD patients. Conclusions: Proportion of patients with incident AS and comorbid CKD increased from 2000 to 2017. One-year all-cause and cardiovascular mortality improved over time but remained higher in AS patients with comorbid CKD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".