Hepatorenal syndrome: diagnostic accuracy, clinical features, and outcome in a tertiary care center
Bibliographic record
Abstract
OBJECTIVES: Hepatorenal syndrome (HRS) is a common and life-threatening entity that is associated with advanced liver disease. There are limited data on predisposing or precipitating factors related to the development of this syndrome. The aims of this study were: 1) to determine the accuracy of the diagnosis of HRS in an urban tertiary care center; 2) to compare features of HRS with those of non-HRS renal failure; and 3) to document the precipitating factors for HRS in this setting. METHODS: We conducted a retrospective chart review of 46 patients at the Health Sciences Center in Winnipeg, Manitoba, who were diagnosed with HRS within the last 10 yr. In addition to assessing the accuracy of the diagnosis of HRS (as defined by the International Ascites Club), we also developed a database that included clinical and biochemical features of these patients' liver disease, possible predisposing factors, precipitating events, etiology of cirrhosis, and mortality rates. A cohort of 19 patients with advanced liver disease and renal failure not ascribed to HRS (non-HRS) served as controls. RESULTS: A total of 46 patients were labeled as having HRS during their hospital stay, of whom 27 (59%) fulfilled International Ascites Club criteria. Their mean age was 51.8 +/- 12.1 yr, and 67% were male. Demographic and biochemical findings in the HRS patients were similar to those of the non-HRS patients. Hepatic encephalopathy (p < 0.03) and jaundice (p < 0.01) more commonly preceeded renal failure in the HRS group than in the controls. The most common predisposing factor for HRS was alcohol abuse. Precipitating factors for HRS included infection in 48% patients, GI bleeding in 33%, and over paracentesis in 27%. Drug-induced renal disease was significantly more common in patients with non-HRS renal failure. Contrary to previous reports, nonsteroidal anti-inflammatory drugs were not common precipitating factors of HRS. Alcohol-related liver disease was the most common underlying etiology of liver disease in all patients who developed renal failure but was proportionally higher in the HRS group. Mortality was almost universal in both groups, regardless of the cause of renal failure. CONCLUSIONS: The diagnostic accuracy (or lack thereof) of HRS highlights the importance of ensuring that diagnostic criteria are met in studies describing the natural history and/or the results of therapeutic intervention in HRS. Alcohol abuse is a common predisposing factor for the development of HRS. Infection, GI bleed, and aggressive paracentesis were the most common precipitating factors for HRS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".