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LBA-15 RENAL HYPOTHERMIA DURING PARTIAL NEPHRECTOMY: A RANDOMIZED CONTROLLED TRIAL

2019· article· en· W2942043726 on OpenAlexaboutno aff
Rodney H. Breau, Dean Fergusson, Kristen McAlpine, Greg Knoll, Christopher Morash, Luke T. Lavallée, Sonya Cnossen, Ranjeeta Mallick, Antonio Finelli, Michael Jewett, Bradley C. Leibovich, Anil Kapoor, Frédéric Pouliot, Jonathan I. Izawa, Ricardo Rendon, Ilias Cagiannos

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNephrectomyHypothermiaRandomized controlled trialIntensive care medicineInternal medicineKidney

Abstract

fetched live from OpenAlex

You have accessJournal of UrologySunday Next Frontier (LBA)1 Apr 2019LBA-15 RENAL HYPOTHERMIA DURING PARTIAL NEPHRECTOMY: A RANDOMIZED CONTROLLED TRIAL Rodney Breau, Dean Fergusson, Kristen McAlpine, Greg Knoll, Chris Morash, Luke Lavallee, Sonya Cnossen, Ranjeeta Mallick, Antonio Finelli, Michael Jewett, Bradley Leibovich, Anil Kapoor, Frederic Pouliot, Jonathan Izawa, Ricardo Rendon, and Ilias Cagiannos* Rodney BreauRodney Breau More articles by this author , Dean FergussonDean Fergusson More articles by this author , Kristen McAlpineKristen McAlpine More articles by this author , Greg KnollGreg Knoll More articles by this author , Chris MorashChris Morash More articles by this author , Luke LavalleeLuke Lavallee More articles by this author , Sonya CnossenSonya Cnossen More articles by this author , Ranjeeta MallickRanjeeta Mallick More articles by this author , Antonio FinelliAntonio Finelli More articles by this author , Michael JewettMichael Jewett More articles by this author , Bradley LeibovichBradley Leibovich More articles by this author , Anil KapoorAnil Kapoor More articles by this author , Frederic PouliotFrederic Pouliot More articles by this author , Jonathan IzawaJonathan Izawa More articles by this author , Ricardo RendonRicardo Rendon More articles by this author , and Ilias Cagiannos*Ilias Cagiannos* More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000557507.23335.eeAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: To preserve renal function, partial nephrectomy is the preferred treatment for patients with renal tumours. Renal hypothermia during partial nephrectomy has been advocated and performed by surgeons for decades to reduce ischemic damage. The purpose of this trial was to determine the effectiveness and safety of renal hypothermia during partial nephrectomy. METHODS: Patients at six academic tertiary care hospitals were randomized to renal hypothermia or no renal hypothermia during open partial nephrectomy. Broad inclusion criteria were used and patients were only excluded if they were <18 years old, were pregnant, had surgery planned for the contralateral kidney within 12 months, or had cold agglutinins. Glomerular filtration rate (GFR) was assessed by plasma clearance of 99mTc-DTPA and differential renal function by renal scintigraphy. The primary outcome was GFR at 1-year post-surgery. Secondary outcomes included kidney specific renal function (GFR x % kidney function) at 1-year post-surgery, peri-operative adverse events, and patient reported quality of life. The trial was registered with clinicaltrials.gov (NCT01529658) and was designed with 90% power to detect a difference of 10ml/min/1.73m2 at a 5% significance level. RESULTS: A total of 184 patients were randomized and 161 (79 hypothermia, 82 no hypothermia) were alive with primary outcome information. Hypothermia and no-hypothermia patients had similar 1-year GFR (89.0 vs 80.5 mL/min/1.73m2; mean difference 8.5, 95% CI -0.4 to 17.4). The overall decrease in GFR was -9.0 ml/min/1.73m2 in the hypothermia group compared to -8.8 mL/min/1.73m2 in the no-hypothermia group (mean difference 0.2 mL/min/1.73m2, 95% CI -5.4 to 5.7). Kidney specific decrease in GFR was also similar between groups (-7.0 vs -6.8 mL/min/1.73m2; mean difference 0.2 mL/min/1.73m2, 95% CI -3.6 to 4.0). No difference in renal function was observed when patients were stratified by median age, baseline renal function (>45 vs. <45 mL/min/1.73m2) or by duration of ischemia (<20 vs. >20 minutes, <30 vs. >30 minutes). There was no difference in surgical complications or quality of life between the groups. CONCLUSIONS: Renal hypothermia does not improve renal function 1-year post-partial nephrectomy. These findings suggest that intraoperative hypothermia should be abandoned as a method for long-term renal function preservation. These data indirectly support the use of laparoscopic/robotic assisted partial nephrectomy where renal hypothermia is difficult to perform. Source of Funding: Canadian Institute of Health Research Ottawa, Canada; Toronto, Canada; Rochester, MN; Hamilton, Canada; Quebec City, Canada; London, Canada; Halifax, Canada; Ottawa, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e998-e999 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Rodney Breau More articles by this author Dean Fergusson More articles by this author Kristen McAlpine More articles by this author Greg Knoll More articles by this author Chris Morash More articles by this author Luke Lavallee More articles by this author Sonya Cnossen More articles by this author Ranjeeta Mallick More articles by this author Antonio Finelli More articles by this author Michael Jewett More articles by this author Bradley Leibovich More articles by this author Anil Kapoor More articles by this author Frederic Pouliot More articles by this author Jonathan Izawa More articles by this author Ricardo Rendon More articles by this author Ilias Cagiannos* More articles by this author Expand All Advertisement PDF downloadLoading ...

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.279
Teacher spread0.256 · 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 designRandomized trial
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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Published2019
Admission routes1
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