936 THE IMPACT OF AGE AND COMORBIDITIES ON SURVIVAL AFTER RADICAL PROSTATECTOMY IN HIGH-RISK PROSTATE CANCER PATIENTS: A COMPETING-RISKS ANALYSIS
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Advanced III1 Apr 2012936 THE IMPACT OF AGE AND COMORBIDITIES ON SURVIVAL AFTER RADICAL PROSTATECTOMY IN HIGH-RISK PROSTATE CANCER PATIENTS: A COMPETING-RISKS ANALYSIS Alberto Briganti, Steven Joniau, Martin Spahn, Jeffrey Karnes, Marco Bianchi, Maxine Sun, Burkhard Kneitz, Detlef Frohneberg, Pia Bader, Giansilvio Marchioro, Carlo Terrone, Francesco Montorsi, Hein Van Poppel, and Paolo Gontero Alberto BrigantiAlberto Briganti Milan, Italy More articles by this author , Steven JoniauSteven Joniau Leuven, Belgium More articles by this author , Martin SpahnMartin Spahn Würzburg, Germany More articles by this author , Jeffrey KarnesJeffrey Karnes Rochester, MN More articles by this author , Marco BianchiMarco Bianchi Milan, Italy More articles by this author , Maxine SunMaxine Sun Montreal, Canada More articles by this author , Burkhard KneitzBurkhard Kneitz Würzburg, Germany More articles by this author , Detlef FrohnebergDetlef Frohneberg Karlsruhe, Germany More articles by this author , Pia BaderPia Bader Karlsruhe, Germany More articles by this author , Giansilvio MarchioroGiansilvio Marchioro Novara, Italy More articles by this author , Carlo TerroneCarlo Terrone Novara, Italy More articles by this author , Francesco MontorsiFrancesco Montorsi Milan, Italy More articles by this author , Hein Van PoppelHein Van Poppel Leuven, Belgium More articles by this author , and Paolo GonteroPaolo Gontero Turin, Italy More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.1034AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES In the context of high-risk prostate cancer (PCa), the influence of competing causes of death following radical prostatectomy (RP) has been poorly described. We assessed and compared the 10-year other-cause mortality (OCM) rates after RP, according to age at surgery and comorbidities in patients diagnosed with high-risk PCa. METHODS Within a large multi-institutional cohort, 1377 patients treated with RP and pelvic lymph node dissection (PLND) for high-risk PCa (defined as PSA >20 ng/ml or a biopsy Gleason 8-10 or a cT3 disease) were identified. Patients were categorized into three age (<60 vs. 61–70 vs. >71 years) and two comorbidity groups (Charlson Comorbidity Index 0 vs. >1). This combination resulted in six age and comorbidity categories. Competing-risks Poisson regression analyses were performed to assess the 10-year cancer-specific mortality (CSM) and OCM rates after RP. RESULTS Overall, 225 deaths occurred. Of those, 78 (35%), and 147 (65%) patients died of CSM and OCM, respectively. The 10-year CSM and OCM rates after RP according to age and comorbidity profiles are illustrated in Figure 1. Three important findings were noteworthy. First, OCM increased directly with age, regardless of comorbidity profile: from 6 to 16% in patients without comorbidity, and from 4 to 29% in patients with >1 comorbidity. Second, OCM increased with increasing comorbidity (0 vs. >1) from 17 to 22% in patients aged 61–70 years, and from 16 to 29% in patients aged >71 years. Interestingly, if approximately 1 out of 2 deaths could be imputed to PCa in patients without comorbidities aged 71 years or more, in presence of a CCI > 1 only approximately 1 out of 5 deaths were due to PCa within the same age category. Finally, patients in the youngest age category (<60 years), without any comorbidity, were the most at risk of CSM (13%). CONCLUSIONS While PCa represented a competing risk of mortality in younger patients as well as and in older men with high risk disease and a good comorbidity profile, non PCa-related death was the major determinant of worse survival in older patients with a poor comorbidity status. These findings may be useful to better identify surgical candidates amongst high-risk PCa patients. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e381 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Alberto Briganti Milan, Italy More articles by this author Steven Joniau Leuven, Belgium More articles by this author Martin Spahn Würzburg, Germany More articles by this author Jeffrey Karnes Rochester, MN More articles by this author Marco Bianchi Milan, Italy More articles by this author Maxine Sun Montreal, Canada More articles by this author Burkhard Kneitz Würzburg, Germany More articles by this author Detlef Frohneberg Karlsruhe, Germany More articles by this author Pia Bader Karlsruhe, Germany More articles by this author Giansilvio Marchioro Novara, Italy More articles by this author Carlo Terrone Novara, Italy More articles by this author Francesco Montorsi Milan, Italy More articles by this author Hein Van Poppel Leuven, Belgium More articles by this author Paolo Gontero Turin, Italy More articles by this author Expand All Advertisement 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".