| Title |
Refined Analysis of Prostate-specific Antigen Kinetics to Predict Prostate Cancer Active Surveillance Outcomes
|
|---|---|
| Published in |
European Urology, February 2018
|
| DOI | 10.1016/j.eururo.2018.01.017 |
| Pubmed ID | |
| Authors |
Matthew R. Cooperberg, James D. Brooks, Anna V. Faino, Lisa F. Newcomb, James T. Kearns, Peter R. Carroll, Atreya Dash, Ruth Etzioni, Michael D. Fabrizio, Martin E. Gleave, Todd M. Morgan, Peter S. Nelson, Ian M. Thompson, Andrew A. Wagner, Daniel W. Lin, Yingye Zheng |
| Abstract |
For men on active surveillance for prostate cancer, utility of prostate-specific antigen (PSA) kinetics (PSAk) in predicting pathologic reclassification remains controversial. To develop prediction methods for utilizing serial PSA and evaluate frequency of collection. Data were collected from men enrolled in the multicenter Canary Prostate Active Surveillance Study, for whom PSA data were measured and biopsies performed on prespecified schedules. We developed a PSAk parameter based on a linear mixed-effect model (LMEM) that accounted for serial PSA levels. The association of diagnostic PSA and/or PSAk with time to reclassification (increase in cancer grade and/or volume) was evaluated using multivariable Cox proportional hazards models. A total of 851 men met the study criteria; 255 (30%) had a reclassification event within 5 yr. Median follow-up was 3.7 yr. After adjusting for prostate size, time since diagnosis, biopsy parameters, and diagnostic PSA, PSAk was a significant predictor of reclassification (hazard ratio for each 0.10 increase in PSAk=1.6 [95% confidence interval 1.2-2.1, p<0.001]). The PSAk model improved stratification of risk prediction for the top and bottom deciles of risk over a model without PSAk. Model performance was essentially identical using PSA data measured every 6 mo to those measured every 3 mo. The major limitation is the reliability of reclassification as an end point, although it drives most treatment decisions. PSAk calculated using an LMEM statistically significantly predicts biopsy reclassification. Models that use repeat PSA measurements outperform a model incorporating only diagnostic PSA. Model performance is similar using PSA assessed every 3 or 6 mo. If validated, these results should inform optimal incorporation of PSA trends into active surveillance protocols and risk calculators. In this report, we looked at whether repeat prostate-specific antigen (PSA) measurements, or PSA kinetics, improve prediction of biopsy outcomes in men using active surveillance to manage localized prostate cancer. We found that in a large multicenter active surveillance cohort, PSA kinetics improves the prediction of surveillance biopsy outcome. |
X Demographics
The data shown below were collected from the profiles of 59 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 12 | 20% |
| Spain | 7 | 12% |
| United Kingdom | 3 | 5% |
| Brazil | 3 | 5% |
| Mexico | 2 | 3% |
| France | 2 | 3% |
| Switzerland | 2 | 3% |
| Poland | 2 | 3% |
| Egypt | 1 | 2% |
| Other | 6 | 10% |
| Unknown | 19 | 32% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 25 | 42% |
| Scientists | 19 | 32% |
| Practitioners (doctors, other healthcare professionals) | 10 | 17% |
| Science communicators (journalists, bloggers, editors) | 5 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 54 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 22% |
| Other | 5 | 9% |
| Professor | 5 | 9% |
| Student > Bachelor | 4 | 7% |
| Student > Ph. D. Student | 3 | 6% |
| Other | 10 | 19% |
| Unknown | 15 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 20 | 37% |
| Biochemistry, Genetics and Molecular Biology | 4 | 7% |
| Agricultural and Biological Sciences | 2 | 4% |
| Mathematics | 1 | 2% |
| Computer Science | 1 | 2% |
| Other | 5 | 9% |
| Unknown | 21 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 34. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 November 2018.
All research outputs
#1,176,537
of 25,382,440 outputs
Outputs from European Urology
#699
of 6,218 outputs
Outputs of similar age
#28,171
of 451,567 outputs
Outputs of similar age from European Urology
#24
of 77 outputs
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