Cardiovascular Events and Mortality in Adults with Kidney Failure after Major Noncardiac Surgery
Bibliographic record
Abstract
Background: People with kidney failure have a high incidence of major surgery. Despite this surgical exposure, there is a paucity of literature investigating postoperative CV events and death. We aimed to determine the risk of these outcomes based on surgery type. Methods: This retrospective cohort study used administrative health data from Alberta, Canada from April 2005 to February 2017. Adults (≥18 years) with kidney failure (receipt of chronic dialysis or two outpatient eGFR measures <15 mL/min/1.73m2) admitted to hospital for a surgical procedure were included. Surgery type, categorized using ICD-10 codes from hospitalization data, was examined for association with acute myocardial infarction (AMI) and death within 30 days of surgery using multivariable logistic regression. We adjusted for demographics, comorbidities, preoperative laboratory measures, procedure urgency, and kidney disease specific variables. Results: 3398 people with kidney failure had a major surgery (1905 hemodialysis; 590 peritoneal dialysis; 903 non-dialysis). Most of the cohort was male (61.0%), the median age was 61.5 years (IQR 50.0, 72.7), and over half of the procedures were urgent (56.9%). 198 people (5.8%) had an AMI or died within 30 days of major surgery. Kidney transplantation had the lowest frequency of the outcome and were the reference group. After adjustment, vascular, skin and soft tissue, intraabdominal, musculoskeletal, retroperitoneal, anorectal, and neurosurgical procedures had statistically higher odds of AMI or death compared to kidney transplantation (Figure 1). Conclusions: Major non-transplant surgery in people with kidney failure is associated with a high risk of AMI and death, which has implications for the direction of future perioperative research in this population. Funding: Government Support - Non-U.S.Odds of AMI and Death for people with kidney failure undergoing major non-cardiac surgery stratified by surgery type.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".