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Incomplete testosterone suppression with luteinizing hormone‐releasing hormone agonists: does it happen and does it matter?

2012· article· en· W1554183270 on OpenAlexaffabout
Tom Pickles, Jeremy Hamm, William Morris, William E. Schreiber, Scott Tyldesley

Bibliographic record

VenueBritish Journal of Urology · 2012
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsVancouver General HospitalUniversity of British ColumbiaBC Cancer Agency
FundersSanofiAbbott Laboratories
KeywordsLuteinizing hormoneTestosterone (patch)EndocrinologyGonadotropic cellInternal medicineHormoneMedicine

Abstract

fetched live from OpenAlex

UNLABELLED: What's known on the subject? and What does the study add? Previous reports, with small numbers of patients, have described the problem of incomplete testosterone suppression (>1.1 or 1.7 nmol/L) with LHRH agonists. Various predisposing factors have been suggested: different drug agents and patient factors such as age, pretreatment testosterone levels and weight. Such incomplete testosterone suppression has been shown in one small report to be associated with increased PSA failure rates and in another report in those with metastases, with worse survival. This study used testosterone assays that are more accurate at low levels than those used in most previous reports in a large dataset of 2196 men, and confirmed incomplete testosterone suppression (breakthrough) rates >1.7 nmol/L of 3.4% and >1.1 nmol/L of 6.6%. We showed that younger age was strongly associated with the risk of breakthrough, with a minor effect of increasing body mass index. Repeated breakthroughs were more common (16%) in those who had already had one breakthrough. Interim measures of cancer control (PSA kinetics during LHRH therapy) were inferior in those with a breakthrough, and those with breakthroughs between 1.1 and 1.7 nmol/L had worse long-term biochemical control rates. OBJECTIVES: • To describe breakthrough rates above castrate levels of testosterone, in a population-based series of men undergoing adjuvant luteinizing hormone-releasing hormone (LHRH) agonist therapy with curative radiation therapy. • To explore the predisposing factors for such breakthroughs and their impact on subsequent outcomes. PATIENTS AND METHODS: • All men treated for prostate cancer between 1998 and 2007 with curative radiation in the province of British Columbia, Canada were potentially eligible (n= 11752). Of these, 2196 fulfilled the eligibility criteria. • Serial testosterone measurements were obtained during continuous LHRH therapy. • Breakthrough rates >1.1 nmol/L and >1.7 nmol/L were calculated for each LHRH injection and for each patient course. • Predisposing factors were identified, and early surrogates of oncological outcome (neoadjuvant nadir and post-treatment nadir) were determined. RESULTS: • The risk of a breakthrough >1.1 nmol/L was 6.6%, and >1.7 nmol/L was 3.4% per patient course and 5.4% and 2.2% per LHRH injection (inclusive ranges). • Repeated breakthroughs occurred in 16% of patients. • Younger men were more liable to breakthroughs (P < 0.001). • Early PSA kinetic surrogates of cancer control were inferior in those with breakthroughs. • Neither overall biochemical non-evidence of disease (bNED) nor survival were compromised, although subgroup analysis showed inferior 5-year bNED in those with breakthroughs of 1.1-1.7 nmol/L vs those without (58% vs 73%, respectively; P= 0.048). CONCLUSIONS: • Breakthroughs with LHRH agonists occur occasionally per injection, but occur commonly per patient course of treatment, and adversely affect early surrogate measures of outcome. • The monitoring of testosterone levels during therapy is therefore advised.

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)0.006
metaresearch head score (Gemma)0.020
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 score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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

Citations59
Published2012
Admission routes2
Has abstractyes

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