The Hepatorenal Syndrome Patient Pathway: Retrospective Analysis of Electronic Health Records
Bibliographic record
Abstract
Hepatorenal syndrome (HRS) is among the leading causes of hospitalization and mortality in patients with chronic liver disease. To assess the HRS patient journey from preadmission to postdischarge to understand patient characteristics, disease progression, treatment patterns, and outcomes. We conducted a retrospective study using real-world data from a nationwide electronic health record database (Cerner Health Facts, Kansas City, Missouri). We used ICD-9/10 diagnosis codes to identify patients hospitalized with HRS between January 1, 2009, and January 31, 2018. We assessed patient characteristics and history, clinical presentation, treatment, and outcomes. Regression analysis was conducted to assess the association between patient characteristics and survival while adjusting for demographic and clinical covariates. The study included 3563 patients (62% men). Precipitants of HRS included gastrointestinal bleeding (18%), diuretics and infections (30%), and paracentesis (26%). Although 21% of patients had liver injury exclusively associated with alcohol use, 20% had hepatitis C, 8% had nonalcoholic steatohepatitis, and the etiology of the remainder (51%) was either some combination of conditions or unknown. A total of 42% of patients received vasopressors, including octreotide and midodrine (10%), other combinations of vasopressors (11%), or another single vasopressor (21%). In-hospital mortality was 34%, and 14% of patients were discharged to hospice. Regression analysis showed patients with acute-on-chronic liver failure had higher mortality in acute-on-chronic liver failure grades 1 (odds ratio = 1.59), 2 (odds ratio = 2.49), and 3 (odds ratio = 4.53) versus no acute-on-chronic liver failure. Among survivor patients, 38% were readmitted within 90 days of discharge; 23% of readmissions were HRS-related. The HRS patient journey presented in this study highlights inconsistencies in, and provides insight into, associated hospital-based treatment strategies. A mortality rate of 34% along with a readmission rate of 23% associated with HRS-related complications warrant more disease awareness and effective treatment. Further research is needed to examine the interactions between the etiology of cirrhosis, precipitants, treatment, and outcomes. (Curr Ther Res Clin Exp. 2022; 82:XXX–XXX)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".