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MP38-12 PREHABILITATION BODY MASS INDEX (BMI) TARGET FOR COMPLICATION RISK REDUCTION FOLLOWING RADICAL CYSTECTOMY

2021· article· en· W3192231024 on OpenAlexaboutno aff
Louise C. McLoughlin, Wassim Kassouf, Rodney H. Breau, Adrian Fairey, Agnihotram V. Ramanakumar, Afsar Salimi, Eric Hyndman, Darrel Drachenberg, Jonathan I. Izawa, Bobby Shayegan, Jean‐Baptiste Lattouf, Michele Lodde, Ricardo Rendon, Robert Siemens, Claudio Jeldres, Peter M. Black, Girish S. Kulkarni

Bibliographic record

VenueThe Journal of Urology · 2021
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCystectomyPrehabilitationIndex (typography)Body mass indexBladder cancerCancerInternal medicinePhysiology

Abstract

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You have accessJournal of UrologyBladder Cancer: Epidemiology & Evaluation I (MP38)1 Sep 2021MP38-12 PREHABILITATION BODY MASS INDEX (BMI) TARGET FOR COMPLICATION RISK REDUCTION FOLLOWING RADICAL CYSTECTOMY Louise C. McLoughlin, Wassim Kassouf, Rodney H. Breau, Adrian Fairey, Agnihotram V. Ramanakumar, Afsar Salimi, Eric Hyndman, Darrel E. Drachenberg, Jonathan Izawa, Bobby Shayegan, Jean-Baptiste Lattouf, Michele Lodde, Ricardo Rendon, Robert Siemens, Claudio Jeldres, Peter Black, and Girish S. Kulkarni Louise C. McLoughlinLouise C. McLoughlin More articles by this author , Wassim KassoufWassim Kassouf More articles by this author , Rodney H. BreauRodney H. Breau More articles by this author , Adrian FaireyAdrian Fairey More articles by this author , Agnihotram V. RamanakumarAgnihotram V. Ramanakumar More articles by this author , Afsar SalimiAfsar Salimi More articles by this author , Eric HyndmanEric Hyndman More articles by this author , Darrel E. DrachenbergDarrel E. Drachenberg More articles by this author , Jonathan IzawaJonathan Izawa More articles by this author , Bobby ShayeganBobby Shayegan More articles by this author , Jean-Baptiste LattoufJean-Baptiste Lattouf More articles by this author , Michele LoddeMichele Lodde More articles by this author , Ricardo RendonRicardo Rendon More articles by this author , Robert SiemensRobert Siemens More articles by this author , Claudio JeldresClaudio Jeldres More articles by this author , Peter BlackPeter Black More articles by this author , and Girish S. KulkarniGirish S. Kulkarni More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002053.12AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Surgical morbidity after radical cystectomy (RC) is significant, particularly in patients with an above-normal body mass index (BMI). Prehabilitation is a multi-modal, preoperative optimization program with nutritional optimization and weight loss as core principles, reported to reduce complications after major cancer surgery. Weight-loss-focused prehabilitation may apply to patients with an-above normal BMI with a sufficient preoperative intervention window, such as a period of neoadjuvant chemotherapy. Our study objectives are to compare complication rates between BMI categories and identify an optimum BMI target for patients with an above-normal BMI to aim for during prehabilitation to modify their risk of perioperative morbidity. METHODS: Data were extracted from the Canadian Bladder Cancer Information System (CBCIS), a prospective Canadian registry across 13 academic centers. A retrospective analysis was performed on 589 patients who underwent RC. Peri-operative (≤90 days) complications were classified by type and severity according to Clavien-Dindo classification (CDC), where available. Unconditional Logistic regression analysis was performed to determine the association between BMI and complication risk. RESULTS: The median BMI of the cohort was 27 (IQR 7), with 29% classified as having normal BMI (<25 kg/m2), 38% overweight (25-29), 21% Class I (30-35), and 12% ≥ Class II (>35) obesity. The overall complication rate was 40%. Complication detection was highest in the overweight group. Intra-operative blood loss and length of hospital stay were significantly increased in all above-normal BMI groups. Ileus, wound infection and urine leak were the most commonly detected postoperative complications. On multivariable analysis, overweight BMI was independently associated with any complications (OR 1.96, p=0.004) and urine leak (OR 5.22, p=0.034), ≥ Class II obesity BMI was associated with any complications (OR 3.49, p<0.0001), wound infection (OR 6.31, p<0.0001), fascial dehiscence (OR 5.57, p=0.027) and urine leak (OR 5.69, p=0.044). The Class 1 obesity group was not significantly associated with complications (OR 1.38, p=0.259). CONCLUSIONS: This study demonstrates the feasibility of utilizing a national bladder cancer database to record and evaluate RC complications. Overweight and Class II obesity BMI groups were significantly associated with perioperative morbidity. These groups may benefit from a targeted weight-loss intervention during prehabilitation, where feasible, to reduce risk of perioperative morbidity following RC. Source of Funding: This project has been supported by the CBCIS Collaborative and by Bladder Cancer Canada. CBCIS has received unrestricted grants and/or in-kind support from: Bladder Cancer Canada, Merck, Roche, Astra Zeneca, Pfizer/EMD Serono, and Bristol-Myers Squibb. There is no direct role or influence from this funding on this work © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e698-e699 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Louise C. McLoughlin More articles by this author Wassim Kassouf More articles by this author Rodney H. Breau More articles by this author Adrian Fairey More articles by this author Agnihotram V. Ramanakumar More articles by this author Afsar Salimi More articles by this author Eric Hyndman More articles by this author Darrel E. Drachenberg More articles by this author Jonathan Izawa More articles by this author Bobby Shayegan More articles by this author Jean-Baptiste Lattouf More articles by this author Michele Lodde More articles by this author Ricardo Rendon More articles by this author Robert Siemens More articles by this author Claudio Jeldres More articles by this author Peter Black More articles by this author Girish S. Kulkarni 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.083
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0830.017

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 head0.015
GPT teacher head0.296
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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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Published2021
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