Impact of bariatric surgery on patients with chronic kidney disease and severe obesity: multiple-linked population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is prevalent among patients with obesity, contributing to increased morbidity and mortality. Metabolic bariatric surgery (MBS) may improve kidney outcomes, but its long-term effects remain unclear. The aim of this study was to examine the association between bariatric surgery and mortality and major adverse kidney events (MAKE) in patients with a diagnosis of CKD and severe obesity. METHODS: Patients with a diagnosis of CKD (stage ≥3 and estimated glomerular filtration rate (eGFR) ≤60 ml per min per 1.73 m2) and BMI ≥35 kg/m2 or who underwent bariatric surgery from 2010 to 2016 in Ontario, Canada, were included. Non-surgical controls were identified from a primary care database. Multiple-linked administrative databases were used to define confounders, including age, BMI, sex, co-morbidities, socio-economic status, psychiatric history, healthcare utilization, substance misuse, and cancer screening. The primary outcomes were all-cause mortality and MAKE (a composite outcome of mortality, 50% decline in eGFR, dialysis initiation, and hospital admission for heart failure, myocardial infarction, and acute kidney injury). A multivariable Cox proportional hazards model was used for analysis. RESULTS: Among 1538 patients (563 surgical and 975 non-surgical) followed for a median of 7.7 years, there were 285 deaths (207 (21.2%) non-surgical and 78 (13.9%) surgical). MBS was associated with 52% lower hazards of mortality (HR 0.48 (95% c.i. 0.34 to 0.67)) and 53% lower hazards of MAKE (HR 0.47 (95% c.i. 0.39 to 0.57)). Benefits were greater in females, patients aged >55 years, and those with a BMI >40 kg/m2. CONCLUSION: MBS was associated with reduced mortality and improved kidney outcomes in CKD patients, particularly in older individuals, females, and those with a higher BMI, highlighting its potential role in high-risk patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".