POST-OPERATIVE LIVER DECOMPENSATION EVENTS FOLLOWING PARTIAL HEPATECTOMY AMONG PATIENTS WITH CIRRHOSIS AND HEPATOCELLULAR CARCINOMA
Bibliographic record
Abstract
Background: Partial hepatectomy, or liver resection, is a potentially-curative therapy for patients diagnosed with hepatocellular carcinoma (HCC). Because the majority of patients with HCC have pre-existing cirrhosis, pre-operative decision-making must consider severity of liver dysfunction to mitigate adverse liver-related post-operative outcomes. Objectives: The goals of this thesis were: 1) to critically appraise currently available prognostic models for predicting the risk of post-operative liver decompensation events (POLDEs), and 2) to identify patient-level, pre-operative predictors of POLDEs among individuals with cirrhosis and HCC undergoing liver resection. Methods: A systematic review of the literature was performed to identify multivariable prognostic models predicting the risk of POLDEs following liver resection. Study details regarding patients, outcomes, predictors, methodology, statistical analyses were abstracted. Studies were qualitatively assessed for risk of bias. A population-based retrospective cohort study was also conducted, of patients with cirrhosis and incident HCC diagnosed between 2007-2017 in the province of Ontario. Cox proportional hazards regression was used to identify independent predictors of POLDE-free survival, and cause-specific hazards for POLDEs and death. Results: In total, 36 multivariable prognostic modelling studies were identified; 25 focused on model development, 3 performed development and external validation, and 8 validated pre- existing models. Commonly used predictors in these models were serum bilirubin, platelet count, and indocyanine green retention rate at 15 minutes (ICGR15). Due to statistical and methodologic concerns, all studies were assessed a high risk of bias. In the population study, the cohort comprised 611 patients with cirrhosis and incident HCC, who subsequently underwent liver resection. Of these, 160 (26.2%) experienced at least 1 POLDE within 2 years of resection and 189 (30.9%) died in the same timeframe. Independent predictors of inferior POLDE-free survival were presence of diabetes, major liver resection, and previous non-malignant decompensation. In contrast, hepatitis B cirrhosis etiology appeared to be protective. In cause-specific analysis, the same risk factors were associated with POLDEs, except for planned extent of liver resection. Age (per year) and history of previous non-malignant decompensation were cause-specific predictors of death. Conclusions: Currently available multivariable prognostic models for predicting the risk of post- operative liver decompensation events following liver resection have limited validity and applicability for routine clinical use. We have identified patient and disease-related factors associated with POLDE-free survival and POLDEs, which can be used for improved patient selection and to develop prognostic tools, with the aim of improving post-operative outcomes among patients with cirrhosis and HCC.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".