Risk of Kidney Failure, Death, and Cardiovascular Events After Lower Limb Complications in Patients With CKD
Bibliographic record
Abstract
OBJECTIVE: Lower limb complications are major adverse events in patients with peripheral artery disease (PAD) and chronic kidney disease (CKD). These complications can lead to morbidity, disability, reduced quality of life, and higher health care costs. We sought to determine how interim lower limb complications modify the subsequent risk of progression to kidney failure, all-cause mortality before kidney failure, and cardiovascular (CV) events in a cohort of patients with CKD stages G3 to G5. METHODS: We performed a retrospective cohort study using patient-level data obtained by linking several administrative databases from Manitoba, Canada. We used Fine and Gray regression models for the primary outcomes of (1) kidney failure adjusted for the competing risk of all-cause mortality, (2) death before kidney failure, and (3) cardiovascular-related hospitalization with the competing risk of non-CV death. RESULTS: A total of 92,618 patients were included in the final cohort, with a median follow-up time of 2.56 years. Compared with patients who did not experience an interim lower limb complication, there was a higher risk of kidney failure (adjusted hazard ratio [HR] 2.51, 95% confidence interval [CI] 2.10-3.00), all-cause mortality before kidney failure (adjusted HR 2.73, 95% CI 2.55-2.92), and CV events (adjusted HR 2.12, 95% CI 1.90-2.38). CONCLUSIONS: Interim lower limb complications are associated with an increased risk of kidney failure, all-cause mortality before kidney failure, and cardiovascular-related hospitalization. Clinical trials of screening and treatment strategies for patients with CKD at risk for lower limb complications may help determine optimal strategies to manage this risk.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".