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Record W4406741560 · doi:10.1053/j.ajkd.2024.11.014

Differences in Postoperative Disposition by Kidney Disease Severity: A Population-Based Cohort Study

2025· article· en· W4406741560 on OpenAlexafffundabout
Tyrone G. Harrison, Tayler Scory, Brenda R. Hemmelgarn, Mary Brindle, Oluwatomilayo Daodu, Michelle M. Graham, Matthew T. James, Ngan N. Lam, Pavel S Roshanov, Khara M. Sauro, Paul E. Ronksley

Bibliographic record

VenueAmerican Journal of Kidney Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsWestern UniversityLibin Cardiovascular Institute of AlbertaUniversity of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health ResearchAgency for Healthcare Research and QualityAlberta HealthKidney Foundation of CanadaUniversity of AlbertaCumming School of Medicine, University of CalgaryBrigham and Women's HospitalUniversity of CalgaryHarvard University
KeywordsMedicineDispositionKidney diseaseCohortPopulationDiseaseCohort studyInternal medicineIntensive care medicineEnvironmental health

Abstract

fetched live from OpenAlex

RATIONALE & OBJECTIVE: People with advanced kidney disease undergo more noncardiac operations compared with the general population, with a higher risk of perioperative cardiac events and death. However, little is known about the associations between severity of preoperative kidney dysfunction with postoperative length of hospitalization and discharge disposition; these were the focus of this study. STUDY DESIGN: Population-based retrospective cohort. SETTING & PARTICIPANTS: Adults from Alberta, Canada, undergoing inpatient major noncardiac surgery between April 2005 and February 2019. EXPOSURE: Categorical preoperative outpatient estimated glomerular filtration rate (eGFR) or kidney failure status. OUTCOME: Length of stay (LOS), days alive at home after surgery within 30 and 90 days, and discharge disposition location. ANALYTICAL APPROACH: Associations were estimated with unadjusted and adjusted generalized estimating equation models. RESULTS: We identified 927,560 inpatient surgeries in 666,770 people (55.9% female; median age, 57.4 years). People receiving dialysis had the longest LOS (11 days [95% CI, 6-29]), 2 times greater than that among people with normal kidney function (adjusted incidence rate ratio [IRR], 2.21 [95% CI, 2.10-2.32]). This group also had the fewest days alive at home within the first 30 days after surgery, with an IRR of 0.69 (95% CI, 0.67-0.70) compared with people with normal eGFR. The majority of people (82.8%) were discharged home without nursing support after surgery, though people receiving dialysis were discharged to a facility with 24-hour nursing care nearly 4 times more often. There were graded increases in risks of these outcomes with lower levels of kidney function. LIMITATIONS: Many people did not have preoperative kidney function assessed, reflecting standard clinical practice in the general population. CONCLUSIONS: After major surgery, people with kidney disease spend more time recovering in hospital and have less independence from postdischarge nursing supports than otherwise similar patients who have normal or near normal kidney function. These differences were more pronounced for those with the most severe stages of kidney disease. PLAIN-LANGUAGE SUMMARY: People with kidney disease have surgery more frequently, with worse outcomes, compared with others in the general population. However, little is known about how long they spend in hospital afterward and whether they will be discharged home or to other facilities. To understand this more, we examined nearly 1 million surgeries performed in Alberta, Canada. Compared with people who have normal kidney function and are undergoing surgery, people with the most advanced kidney disease spent more than 2 times longer in hospital and were more likely to be discharged to long-term care facilities instead of being discharged to their homes. Future research is needed to understand the factors that predict who will experience prolonged hospitalization and to develop interventions to enable earlier discharge for people with kidney disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.291

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.266
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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