PD56-06 TRANSFUSION RATES AFTER 800 AQUABLATION PROCEDURES USING VARIOUS HEMOSTASIS METHODS
Bibliographic record
Abstract
You have accessJournal of UrologyBenign Prostatic Hyperplasia: Surgical Therapy & New Technology III (PD56)1 Apr 2020PD56-06 TRANSFUSION RATES AFTER 800 AQUABLATION PROCEDURES USING VARIOUS HEMOSTASIS METHODS Dean Elterman*, Thorsten Bach, Enrique Rijo, Vincent Misrai, Paul Anderson, Kevin C. Zorn, Naeem Bhojani, Albert El Hajj, Bilal Chughtai, and Mihir Desai Dean Elterman*Dean Elterman* More articles by this author , Thorsten BachThorsten Bach More articles by this author , Enrique RijoEnrique Rijo More articles by this author , Vincent MisraiVincent Misrai More articles by this author , Paul AndersonPaul Anderson More articles by this author , Kevin C. ZornKevin C. Zorn More articles by this author , Naeem BhojaniNaeem Bhojani More articles by this author , Albert El HajjAlbert El Hajj More articles by this author , Bilal ChughtaiBilal Chughtai More articles by this author , and Mihir DesaiMihir Desai More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000966.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate tissue resection for patients with lower urinary tract symptoms (LUTS) remains the most effective means to provide symptomatic improvement. Many studies have evaluated the bleeding complication profile post-operatively for TURP and report a range up to 7% of patients requiring a blood transfusion, but with typical limitations in treating prostates up to 80mL in size. For larger prostates (>80mL), open prostatectomy and Holmium laser enucleation of the prostate (HOLEP) are the global reference standard surgical options with reported transfusion rates up to 24% and 4%, respectively. The Aquablation procedure is a new, impressively fast, resective BPH surgical alternative harnessing image guidance, high velocity waterjet, and robotic standardized execution, with a rapid learning curve. While several techniques for hemostasis following Aquablation have been utilized, the optimal strategy has not been fully vetted across different prostate sizes. METHODS: The current commercial AQUABEAM robot that performs Aquablation Therapy was first used in 2014. Since then numerous clinical studies have been conducted in various countries; Australia, Canada, Germany, India, Lebanon, Spain, New Zealand, United Kingdom, and the United States. All of the clinical trial data since 2014 have been pooled with the early commercial procedures from France, Germany, and Spain. The objective is to determine if athermal methods are as effective in preventing blood transfusions as the use of cautery across various prostate volume sizes. RESULTS: 801 patients were treated with Aquablation Therapy from 2014 to early 2019. The average prostate volume was 67mL ± 33mL (range 20-280mL) where 31 (3.9%) transfusions were reported. The largest contributing factor to transfusion risk was prostate size and method of traction. There was an increasing risk of transfusions in larger prostates when robust traction using a catheter tensioning device without cautery ranging from 0.8% to 7.8% in prostates ranging from 20mL to 280mL. However, when standard traction (taping the catheter to the leg, gauze knot synched up to the meatus, or no traction at all) was used and where the surgeon performed bladder neck cautery only when necessary, the risk of transfusion 1.4% to 2.5% in prostates ranging from 20mL to 280mL. CONCLUSIONS: While the athermal subgroup with robust traction with a catheter tension device had comparable transfusion rates for smaller prostates, the risk increased significantly as prostate volume increased. With standard traction methods and selective bladder neck cautery, the risk of transfusion is reduced to a 1.9% across all prostate sizes. Source of Funding: PROCEPT BioRobotics funded all clinical trials. © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1189-e1190 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Dean Elterman* More articles by this author Thorsten Bach More articles by this author Enrique Rijo More articles by this author Vincent Misrai More articles by this author Paul Anderson More articles by this author Kevin C. Zorn More articles by this author Naeem Bhojani More articles by this author Albert El Hajj More articles by this author Bilal Chughtai More articles by this author Mihir Desai More articles by this author Expand All Advertisement PDF downloadLoading ...
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".