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Record W3114625330 · doi:10.21203/rs.2.18172/v1

Incidence, risk factors and outcomes of postoperative acute kidney injury in elderly patients undergoing abdominal surgery

2019· preprint· en· W3114625330 on OpenAlexaff
Jianghua Shen, Simiao Zhao, Denglei Ma, Ming‐Hui Chen, Suying Yan

Bibliographic record

VenueResearch Square (Research Square) · 2019
Typepreprint
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineAcute kidney injuryIncidence (geometry)FurosemideAbdominal surgeryRenal functionRetrospective cohort studySurgeryLogistic regressionRisk factorAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Abstract Objectives To investigate the incidence, risk factors and outcomes of acute kidney injury (AKI) in elderly patients undergoing abdominal surgery. Methods A retrospective study exploring the incidence of AKI in patients older than 75 years within 48 hours after abdominal surgery was conducted. Patients' preoperative characteristics, intraoperative management including medication and outcomes were evaluated for associations with AKI using a logistic regression model.Results During the 2.5-year period, a total of 409 abdominal surgeries were performed. Both pre- and post-operative SCr measurements were available for 329 (80.4%) cases. 26 patients (7.9%) developed AKI, of whom 25 (7.6%) and 1 (0.3%) reached the AKI stages 1 and 2 respectively. Older age (83.0 vs 80.4 years; p=0.002), preoperative liver function damage represented by AST (47.5 vs 21.0 IU/L; p=0.023), intraoperative combined administration of hydroxyethyl starch(HES) and furosemide (15.38% vs 1.65%; p=0.003) were independent risk factors for the development of postoperative AKI. Furthermore, AKI patients had significantly longer ICU stay (3 vs 0 days; p<0.001) and higher in-hospital mortality (23.08% vs 2.31%; p<0.001)Conclusion Intraoperative combined administration of HES and furosemide is an independent factor which can be controlled by anesthesiologists and surgeons for AKI. This provides important recommendations for reducing the incidence of postoperative AKI.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.053
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0110.004
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0020.007
Research integrity0.0020.016
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 head0.064
GPT teacher head0.422
Teacher spread0.358 · 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; both teacher heads agree on what is shown here.

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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