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Record W4402967400 · doi:10.34067/kid.0000000589

Association of Hepatorenal Syndrome-Acute Kidney Injury with Mortality in Patients with Cirrhosis Requiring Renal Replacement Therapy

2024· article· en· W4402967400 on OpenAlexaff
Augusto Cama-Olivares, Tianqi Ouyang, Tomonori Takeuchi, Shelsea A. St. Hillien, Jevon E. Robinson, Raymond T. Chung, Giuseppe Cullaro, Constantine Karvellas, Josh Levitsky, Eric S. Orman, Kavish R. Patidar, Kevin R. Regner, Danielle L. Saly, Deirdre Sawinski, Pratima Sharma, J. Pedro Teixeira, Nneka N. Ufere, Juan Carlos Q. Velez, Hani M. Wadei, Nabeel Wahid, Andrew S. Allegretti, Javier A. Neyra, Justin M. Belcher

Bibliographic record

VenueKidney360 · 2024
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Alberta
FundersNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsMedicineRenal replacement therapyHepatorenal syndromeAcute kidney injuryInternal medicineHemodialysisCohortCirrhosisEtiologyHazard ratioIntensive care medicine

Abstract

fetched live from OpenAlex

Key Points In patients with cirrhosis and AKI requiring renal replacement therapy (RRT), hepatorenal syndrome-AKI was not associated with an increased 90-day mortality when compared with other AKI etiologies. Etiology of AKI may not be a critical factor regarding decisions to trial RRT in acutely ill patients with cirrhosis and AKI. Although elevated, mortality rates in this study are comparable with those reported in general hospitalized patients with AKI requiring RRT. Background While AKI requiring renal replacement therapy (AKI-RRT) is associated with increased mortality in heterogeneous inpatient populations, the epidemiology of AKI-RRT in hospitalized patients with cirrhosis is not fully known. Herein, we evaluated the association of etiology of AKI with mortality in hospitalized patients with cirrhosis and AKI-RRT in a multicentric contemporary cohort. Methods This is a multicenter retrospective cohort study using data from the HRS-HARMONY consortium, which included 11 US hospital network systems. Consecutive adult patients admitted in 2019 with cirrhosis and AKI-RRT were included. The primary outcome was 90-day mortality, and the main independent variable was AKI etiology, classified as hepatorenal syndrome (HRS-AKI) versus other (non–HRS-AKI). AKI etiology was determined by at least two independent adjudicators. We performed Fine and Gray subdistribution hazard analyses adjusting for relevant clinical variables. Results Of 2063 hospitalized patients with cirrhosis and AKI, 374 (18.1%) had AKI-RRT. Among them, 65 (17.4%) had HRS-AKI and 309 (82.6%) had non–HRS-AKI, which included acute tubular necrosis in most cases (62.6%). Continuous renal replacement therapy was used as the initial modality in 264 (71%) of patients, while intermittent hemodialysis was used in 108 (29%). The HRS-AKI (versus non–HRS-AKI) group received more vasoconstrictors for HRS management (81.5% versus 67.9%), whereas the non–HRS-AKI group received more mechanical ventilation (64.3% versus 50.8%) and more continuous renal replacement therapy (versus intermittent hemodialysis) as the initial RRT modality (73.9% versus 56.9%). In the adjusted model, HRS-AKI (versus non–HRS-AKI) was not independently associated with increased 90-day mortality (subdistribution hazard ratio, 1.36; 95% confidence interval, 0.95 to 1.94). Conclusions In this multicenter contemporary cohort of hospitalized adult patients with cirrhosis and AKI-RRT, HRS-AKI was not independently associated with an increased risk of 90-day mortality when compared with other AKI etiologies. The etiology of AKI appears less relevant than previously considered when evaluating the prognosis of hospitalized adult patients with cirrhosis and AKI requiring RRT.

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)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.251
Teacher spread0.243 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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