Assessment of Prognostic Value of Aspartate Aminotransferase-to-Platelet Ratio Index in Patients With Hepatocellular Carcinoma: Meta-Analysis of 28 Cohort Studies
Bibliographic record
Abstract
Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors globally; it is valuable to predict its prognosis after treatment. Aspartate aminotransferase-to-platelet index (APRI), a non-invasive biomarker consists of two routine test parameters easily available in all the patients. Our study aimed to investigate whether APRI can serve as an independent prognostic marker in the patients with HCC. Methods: We extensively searched PubMed, Embase, and Web of Science databases on June 20, 2021 to determine all relevant literature. The studies that explored the association between the APRI levels and prognosis of patients with HCC and reported risk estimate data were included. The Newcastle-Ottawa Scale was used to assess the quality of the included studies. Results: A total of 1,097 articles were initially identified, of which 28 studies involving 11,041 patients met the eligibility criteria for the meta-analysis. The pooled hazard ratios (HRs) for overall survival (OS) and disease-free survival (DFS) were 1.77 (95% CI : 1.53–2.05, P < 0.001) and 1.59 (95% CI : 1.47–1.71, P < 0.001), respectively, suggesting a significant correlation between the increased APRI levels and poor prognosis in the patients with HCC. In the subgroup analyses, statistical significance of the correlation disappeared in the Korean and Japanese population and in the patients undergoing transarterial chemoembolization (TACE). Of note, the current results may be overestimated due to publication bias, but the conclusion remained unchanged when the bias was adjusted. Conclusion: High APRI levels are associated with poor OS and DFS in the patients with HCC. In most cases, pretreatment APRI can be used as an independent prognostic factor, but it is necessary to incorporate other predictive prognostic systems to ensure accuracy. Further studies are needed to determine the specific beneficiary population and the optimal cutoff value.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".