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Record W4393376182 · doi:10.1097/ccm.0000000000006284

Quantifying the Impact of Alternative Definitions of Sepsis-Associated Acute Kidney Injury on its Incidence and Outcomes: A Systematic Review and Meta-Analysis

2024· review· en· W4393376182 on OpenAlexaff
Lachlan Donaldson, Ruan Vlok, Ken Sakurai, Morgan Burrows, Gabrielle McDonald, Karthik Venkatesh, Sean M. Bagshaw, Rinaldo Bellomo, Anthony Delaney, John Myburgh, Naomi Hammond, Balasubramanian Venkatesh

Bibliographic record

VenueCritical Care Medicine · 2024
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of AlbertaAlberta Health Services
Fundersnot available
KeywordsMedicineAcute kidney injurySepsisIncidence (geometry)Intensive care medicineMeta-analysisEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To derive a pooled estimate of the incidence and outcomes of sepsis-associated acute kidney injury (SA-AKI) in ICU patients and to explore the impact of differing definitions of SA-AKI on these estimates. DATA SOURCES: Medline, Medline Epub, EMBASE, and Cochrane CENTRAL between 1990 and 2023. STUDY SELECTION: Randomized clinical trials and prospective cohort studies of adults admitted to the ICU with either sepsis and/or SA-AKI. DATA EXTRACTION: Data were extracted in duplicate. Risk of bias was assessed using adapted standard tools. Data were pooled using a random-effects model. Heterogeneity was assessed by using a single covariate logistic regression model. The primary outcome was the proportion of participants in ICU with sepsis who developed AKI. DATA SYNTHESIS: A total of 189 studies met inclusion criteria. One hundred fifty-four reported an incidence of SA-AKI, including 150,978 participants. The pooled proportion of patients who developed SA-AKI across all definitions was 0.40 (95% CI, 0.37-0.42) and 0.52 (95% CI, 0.48-0.56) when only the Risk Injury Failure Loss End-Stage, Acute Kidney Injury Network, and Improving Global Outcomes definitions were used to define SA-AKI. There was significant variation in the incidence of SA-AKI depending on the definition of AKI used and whether AKI defined by urine output criteria was included; the incidence was lowest when receipt of renal replacement therapy was used to define AKI (0.26; 95% CI, 0.24-0.28), and highest when the Acute Kidney Injury Network score was used (0.57; 95% CI, 0.45-0.69; p < 0.01). Sixty-seven studies including 29,455 participants reported at least one SA-AKI outcome. At final follow-up, the proportion of patients with SA-AKI who had died was 0.48 (95% CI, 0.43-0.53), and the proportion of surviving patients who remained on dialysis was 0.10 (95% CI, 0.04-0.17). CONCLUSIONS: SA-AKI is common in ICU patients with sepsis and carries a high risk of death and persisting kidney impairment. The incidence and outcomes of SA-AKI vary significantly depending on the definition of AKI used.

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)0.041
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.086
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0340.061
Bibliometrics0.0140.013
Science and technology studies0.0010.001
Scholarly communication0.0060.003
Open science0.0040.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.359
GPT teacher head0.548
Teacher spread0.188 · 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.

Study designMeta-analysis
DomainMethods
GenreReview

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

Citations13
Published2024
Admission routes1
Has abstractyes

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