Acute kidney injury following resection of hepatocellular carcinoma: prognostic value of the acute kidney injury network criteria
Bibliographic record
Abstract
<h3>Background:</h3> Acute kidney injury (AKI) is associated with increased morbidity and mortality after liver resection. Patients with hepatocellular carcinoma (HCC) have a higher risk of AKI owing to the underlying association between hepatic and renal dysfunction. Use of the Acute Kidney Injury Network (AKIN) diagnostic criteria is recommended for patients with cirrhosis, but remains poorly studied following liver resection. We compared the prognostic value of the AKIN creatinine and urine output criteria in terms of postoperative outcomes following liver resection for HCC. <h3>Methods:</h3> All patients who underwent a liver resection for HCC from January 2010 to June 2016 were included. We used AKIN urine output and creatinine criteria to assess for AKI within 48 hours of surgery. <h3>Results:</h3> Eighty liver resections were performed during the study period. Cirrhosis was confirmed in 80%. Median hospital stay was 9 (interquartile range 7–12) days, and 30-day mortality was 2.5%. The incidence of AKI was higher based on the urine output than on the creatinine criterion (53.8% v. 20%), and was associated with prolonged hospitalization and 30-day postoperative mortality when defined by serum creatinine (hospital stay: 11.2 v. 20.1 d, <i>p</i> = 0.01; mortality: 12.5% v. 0%, <i>p</i> < 0.01), but not urine output (hospital stay: 15.6 v. 10 d, <i>p</i> = 0.05; mortality: 2.3% v. 2.7%, <i>p</i> > 0.99). <h3>Conclusion</h3> The urine output criterion resulted in an overestimation of AKI and compromised the prognostic value of AKIN criteria. Revision may be required to account for the exacerbated physiologic postoperative reduction in urine output in patients with HCC.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".