Comment on: HPi: A Novel Parameter to Predict Graft-related Outcome in Adult Living Donor Liver Transplant. What Have We Missed?
Bibliographic record
Abstract
We read the article by Singh et al1 titled “HPi: A Novel Parameter to Predict Graft-related Outcome in Adult Living Donor Liver Transplant” with great interest. We sincerely appreciate the authors for their work. However, we would like to make a few important observations. The authors have defined, Hydraulic power density=ΔP × QGW Where ΔP = pressure gradient across liver during the blood flow, Q = blood flow through the portal vein into liver, and GW = graft weight. They assume that blood flow through the portal vein into liver (Q) to be directly proportional to splenic volume (SV) and substitute Q with SV as an indirect measure of portal flow with some more assumptions. Subsequently, they come up with a novel index called hyperperfusion index (HPi) and enumerate it as, HPi=ΔP post × SVGW, ΔPpost = portal pressure gradient measured 1 h after reperfusion (ΔPpost). However, in substituting Q with SV, authors erroneously missed to consider the proportionality constant in the equation, that is, If, QmL/min ∝ SVmL, then, Q = K × SV (where K is a proportionality constant which has the dimension of time and is unknown). It is not possible to substitute flow with volume, unless a constant K is placed. One cannot replace mL/min with gram or mL only. For example, a patient with 500 g of spleen might have portal flow of 1 L/min. Hence, the equation of HPi, should be rewritten as, HPi=ΔP post × SVGWX k, and thus HPi=ΔP post GRSVRX k, should be the correct equation (GRSVR = graft volume splenic volume ratio). Furthermore, instead of taking direct measure of portal flow, an indirect measure as SV was chosen with some unrealistic assumptions. This was probably done to make preoperative predictions for the need of portal inflow modulation and its advance planning based on the data obtained from the historical cohorts. This idea seems far-fetched as the mandated target ΔPpost is of little value as the actual planning depends on the HPi obtained after implantation. In addition to this, HPi has poor positive predictive value of 56%, meaning, beyond the given cutoff, only 56% will have early allograft dysfunction. This is a serious flaw, as this would mean doing unnecessary portal inflow modulation in at least 44% of the patients! In view of the above observations, we would like to suggest modifying HPi equation as it is by no means the measure of impact force of portal flow and in fact mere ratio of portal pressure gradient post perfusion and graft volume splenic volume ratio. It is possible that individually the ΔPpost and graft volume splenic volume ratio are not able to classify early allograft dysfunction versus no early allograft dysfunction, but the ratio of the 2 does that efficiently as shown by the authors albeit with poor positive predictive value. Also, the authors should have examined the degree of correlation between SV and Q, as this would strengthen their viewpoint. Moreover, they can substitute actual portal flow measured intraoperatively by ultrasonography or by transit time flow meter for Q to calculate actual HPi.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.030 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".