Test-retest reliability of hepatic venous pressure gradient and impact on trial design: a study in 289 patients from the control groups of 20 randomized trials
Bibliographic record
Abstract
Abstract Background and Aims: Portal hypertension (PH) is a major driver for cirrhosis complications. Portal pressure is estimated in practice by the hepatic venous pressure gradient (HVPG). The assessment of HVPG changes has been used for drug development in PH. This study aimed at quantifying the test-retest reliability and consistency of HVPG in the specific context of RCTs for the treatment of PH in cirrhosis and its impact on power calculations for trial design. Method: We conducted a search of published RCTs in patients with cirrhosis reporting individual patient-level data of HVPG at baseline and after an intervention, and that included a placebo or untreated control arm. Baseline and follow-up HVPG in the control groups were extracted after digitizing the plots. We assessed different reliability parameters and the potential impact of study characteristics. Results: We retrieved a total of 289 before-after HVPG measurements in the placebo/untreated groups from 20 RCTs. Time range between the two HVPGs measurements was 20 min to 730 days. Test-retest reliability was higher in studies including only compensated patients and, therefore, modelled sample size calculations for trials in compensated cirrhosis were lower than for decompensated cirrhosis. Higher proportion of alcohol-related cirrhosis and unicentric trials were associated with lower differences between baseline and follow-up measurements. Smallest detectable difference in an individual was 24% and 32% in compensated and decompensated patients respectively Conclusion: The test-retest reliability of HVPG is overall excellent, but higher in studies limited to compensated cirrhosis. These findings should be taking into account when powering trials based in the effects on HVPG or when consider HVPG as a tool to guide therapy of portal hypertension
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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 | 0.253 | 0.428 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".