Acute and chronic complication profiles among patients with chronic kidney disease in Alberta, Canada: a retrospective observational study
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) poses a substantial burden to individuals, caregivers, and healthcare systems. CKD is associated with higher risk for adverse events, including renal failure, cardiovascular disease, and death. This study aims to describe comorbidities and complications in patients with CKD. METHODS: We conducted a retrospective observational study linking administrative health databases in Alberta, Canada. Adults with CKD were identified (April 1, 2010 and March 31, 2019) and indexed on the first diagnostic code or laboratory test date meeting the CKD algorithm criteria. Cardiovascular, renal, diabetic, and other comorbidities were described in the two years before index; complications were described for events after index date. Complications were stratified by CKD stage, atherosclerotic cardiovascular disease (ASCVD), and type 2 diabetes mellitus (T2DM) status at index. RESULTS: The cohort included 588,170 patients. Common chronic comorbidities were hypertension (36.9%) and T2DM (24.1%), while 11.4% and 2.6% had ASCVD and chronic heart failure, respectively. Common acute complications were infection (58.2%) and cardiovascular hospitalization (24.4%), with rates (95% confidence interval [CI]) of 29.4 (29.3-29.5) and 8.37 (8.32-8.42) per 100 person-years, respectively. Common chronic complications were dyslipidemia (17.3%), anemia (14.7%), and hypertension (11.1%), with rates (95% CI) of 11.9 (11.7-12.1), 4.76 (4.69-4.83), and 13.0 (12.8-13.3) per 100 person-years, respectively. Patients with more advanced CKD, ASCVD, and T2DM at index exhibited higher complication rates. CONCLUSIONS: Over two-thirds of patients with CKD experienced complications, with higher rates observed in those with cardio-renal-metabolic comorbidities. Strategies to mitigate risk factors and complications can reduce patient burden.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".