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Record W2751090440 · doi:10.1111/ajt.14491

Normothermic ex vivo kidney perfusion for graft quality assessment prior to transplantation

2017· article· en· W2751090440 on OpenAlexaff
J. Moritz Kaths, Mátyás Hamar, Juan Echeverri, Ivan Linares, Peter Urbanellis, Jun Yu Cen, Sujani Ganesh, Luke S. Dingwell, Paul S. F. Yip, Rohan John, Darius Bägli, István Mucsi, Anand Ghanekar, David Grant, Lisa A. Robinson, Markus Selzner

Bibliographic record

VenueAmerican Journal of Transplantation · 2017
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity of TorontoToronto General HospitalHospital for Sick ChildrenUniversity Health Network
FundersNovartis
KeywordsMedicineMachine perfusionTransplantationKidneyUrologyPerfusionRenal functionEx vivoCreatinineKidney transplantationCold storageInternal medicineSurgeryIn vivoLiver transplantationBiology

Abstract

fetched live from OpenAlex

Normothermic ex vivo kidney perfusion (NEVKP) represents a novel approach for graft preservation and functional improvement in kidney transplantation. We investigated whether NEVKP also allows graft quality assessment before transplantation. Kidneys from 30-kg pigs were recovered in a model of heart-beating donation (group A) after 30 minutes (group B) or 60 minutes (group C) (n = 5/group) of warm ischemia. After 8 hours of NEVKP, contralateral kidneys were resected, grafts were autotransplanted, and the pigs were followed for 3 days. After transplantation, renal function measured based on peak serum creatinine differed significantly among groups (P < .05). Throughout NEVKP, intrarenal resistance was lowest in group A and highest in group C (P < .05). intrarenal resistance at the initiation of NEVKP correlated with postoperative renal function (P < .001 at NEVKP hour 1). Markers of acid-base homeostasis (pH, HCO3–, base excess) differed among groups (P < .05) and correlated with posttransplantation renal function (P < .001 for pH at NEVKP hour 1). Similarly, lactate and aspartate aminotransferase were lowest in noninjured grafts versus donation after circulatory death kidneys (P < .05) and correlated with posttransplantation kidney function (P < .001 for lactate at NEVKP hour 1). In conclusion, assessment of perfusion characteristics and clinically available perfusate biomarkers during NEVKP allows the prediction of posttransplantation graft function. Thus, NEVKP might allow decision-making regarding whether grafts are suitable for transplantation. Normothermic ex vivo kidney perfusion (NEVKP) represents a novel approach for graft preservation and functional improvement in kidney transplantation. We investigated whether NEVKP also allows graft quality assessment before transplantation. Kidneys from 30-kg pigs were recovered in a model of heart-beating donation (group A) after 30 minutes (group B) or 60 minutes (group C) (n = 5/group) of warm ischemia. After 8 hours of NEVKP, contralateral kidneys were resected, grafts were autotransplanted, and the pigs were followed for 3 days. After transplantation, renal function measured based on peak serum creatinine differed significantly among groups (P < .05). Throughout NEVKP, intrarenal resistance was lowest in group A and highest in group C (P < .05). intrarenal resistance at the initiation of NEVKP correlated with postoperative renal function (P < .001 at NEVKP hour 1). Markers of acid-base homeostasis (pH, HCO3–, base excess) differed among groups (P < .05) and correlated with posttransplantation renal function (P < .001 for pH at NEVKP hour 1). Similarly, lactate and aspartate aminotransferase were lowest in noninjured grafts versus donation after circulatory death kidneys (P < .05) and correlated with posttransplantation kidney function (P < .001 for lactate at NEVKP hour 1). In conclusion, assessment of perfusion characteristics and clinically available perfusate biomarkers during NEVKP allows the prediction of posttransplantation graft function. Thus, NEVKP might allow decision-making regarding whether grafts are suitable for transplantation.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.379
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations76
Published2017
Admission routes1
Has abstractyes

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