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Impact of angiotensin inhibitors on pathologic complete response with neoadjuvant chemotherapy (NAC) for muscle-invasive bladder cancer (MIBC).

2021· article· en· W3133812191 on OpenAlexaff
Jonathan Thomas, Gregory R. Pond, Catherine Curran, Dory Freeman, Praful Ravi, Matthew Mossanen, Mark A. Preston, Graeme S. Steele, Charlene Mantia, Bradley A. McGregor, Guru Sonpavde

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineBladder cancerInternal medicineGemcitabineUrologyOncologyCystectomyCancerGastroenterology

Abstract

fetched live from OpenAlex

432 Background: The renin-angiotensin system (RAS) is involved in regulation of angiogenesis, cell proliferation, desmoplasia and immunosuppression. Angiotensin converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARB) may have antitumor effects partly by inhibiting transforming growth factor (TGF)-β, a major resistance mechanism in bladder cancer. Methods: Patients (pts) with muscle invasive bladder cancer (MIBC) treated or not treated with ACEi/ARB while receiving preceding radical cystectomy (RC) were assessed for pathologic complete response (pCR) defined as pT0N0 and overall survival (OS). Pathologic features, performance status, clinical stage, type and number of cycles of NAC, and presence of grade ≥3 toxicities were collected retrospectively. The Kaplan-Meier method was used to estimate overall survival (OS). Logistic and Cox regression was used to explore factors potentially prognostic for pCR and OS respectively. Results: 187 patients received NAC followed by RC. The mean age at the time of NAC was 65. 71% were male and 29% were female. Of the 187 patients, 61% received Cisplatin/Gemcitabine and 28.3% received dose dense MVAC. Of patients receiving NAC, 53 (28%) had a pCR. The 5-year OS was 64%. There were 41 (21.9%) patients taking an ACEi and 24 (12.8%) patients taking an ARB at the start of NAC. Of the 41 patients who took an ACEi, 17 (41.5%) had a pCR; of the 146 patients who did not take an ACEi, 36 (24.7%) had a pCR. ACEi intake during NAC was the only factor associated with pCR on multivariable analysis (odds ratio of 2.17 [95% CI 1.05-4.48] p = 0.037). pCR was the only factor shown to be associated with significantly improved OS (Hazard Ratio 0.18 [95% CI 0.07-0.45] p = < 0.001). After adjusting for pCR, ACEi was not significantly prognostic of OS (HR = 1.12, 95% CI = 0.60 to 2.09, p = 0.72). ARB intake while receiving NAC was not associated with pCR or OS. Conclusions: ACEi intake was associated with significantly increased pCR in patients with MIBC receiving NAC, and pCR was the only significant factor associated with OS. We hypothesize that ACEi may augment the activity of NAC and increase pCR, which translates to improved OS. ACEi intake was not associated with improvement in OS potentially due to competing causes of mortality in patients requiring ACEi. Our data requires validation.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.130
GPT teacher head0.473
Teacher spread0.342 · 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 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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