An examination of the dynamic changes in prostate‐specific antigen occurring in a population‐based cohort of men over time
Study Type – Diagnosis (exploratory cohort) Level of Evidence 2b What's known on the subject? and What does the study add? A single serum PSA measurement is commonly used as a screening test to identify men with prostate cancer. A rise in PSA over time may identify men at increased risk of pros...
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| Published in | BJU international Vol. 110; no. 3; pp. 375 - 381 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford, UK
Blackwell Publishing Ltd
01.08.2012
Wiley-Blackwell Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1464-4096 1464-410X 1464-410X |
| DOI | 10.1111/j.1464-410X.2011.10925.x |
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| Abstract | Study Type – Diagnosis (exploratory cohort)
Level of Evidence 2b
What's known on the subject? and What does the study add?
A single serum PSA measurement is commonly used as a screening test to identify men with prostate cancer. A rise in PSA over time may identify men at increased risk of prostate cancer. Dynamic measures of PSA change (ex: PSA velocity, PSA doubling time) are frequently used to justify prostate biopsy in men.
We demonstrate that the current serum PSA is the best predictor of future prostate cancer risk among commonly available clinical variables. We show that dynamic measures of PSA change do not improve upon PSA's ability to predict future prostate cancer. Our study suggests that dynamic measures of PSA change may not be useful in screening for prostate cancer.
OBJECTIVE
•
To determine whether prostate‐specific antigen velocity (PSA‐V), PSA doubling time (PSA‐DT), or PSA percentage change (PSA‐PC) add incremental information to PSA alone for community‐based men undergoing prostate cancer (PCa) screening.
PARTICIPANTS AND METHODS
•
A population‐based cohort of 11 872 men from Olmsted County, MN undergoing PSA screening for PCa from 1993 to 2005 was analysed for PSA, PSA‐DT, PSA‐PC and PSA‐V and subsequent PCa.
•
Receiver‐operating characteristics curves and logistic regression were used to calculate the area under the curve (AUC) and Aikaike's information criterion.
•
Reclassification analysis was performed and the net reclassification improvement and integrated discrimination improvement were measured.
•
The method of Begg and Greenes was used to adjust for verification bias.
RESULTS
•
The single best predictor of future PCa was PSA (AUC = 0.773) with PSA‐V (AUC = 0.729) and PSA‐DT/PSA‐PC (AUC = 0.689) performing worse.
•
After age adjustment, combining PSA with PSA‐V (AUC = 0.773) or PSA‐DT/PSA‐PC (AUC = 0.773) resulted in no better predictions than PSA alone.
•
Reclassification analysis showed that adding PSA‐V or PSA‐DT/PSA‐PC to PSA did not result in a meaningful amount of reclassification.
CONCLUSIONS
•
PSA is a better predictor of future PCa than PSA‐V, PSA‐DT, or PSA‐PC.
•
Adding PSA‐V, PSA‐DT, or PSA‐PC to PSA does not result in clinically relevant improvements in the ability to predict future PCa. |
|---|---|
| AbstractList | Study Type – Diagnosis (exploratory cohort)
Level of Evidence 2b
What's known on the subject? and What does the study add?
A single serum PSA measurement is commonly used as a screening test to identify men with prostate cancer. A rise in PSA over time may identify men at increased risk of prostate cancer. Dynamic measures of PSA change (ex: PSA velocity, PSA doubling time) are frequently used to justify prostate biopsy in men.
We demonstrate that the current serum PSA is the best predictor of future prostate cancer risk among commonly available clinical variables. We show that dynamic measures of PSA change do not improve upon PSA's ability to predict future prostate cancer. Our study suggests that dynamic measures of PSA change may not be useful in screening for prostate cancer.
OBJECTIVE
•
To determine whether prostate‐specific antigen velocity (PSA‐V), PSA doubling time (PSA‐DT), or PSA percentage change (PSA‐PC) add incremental information to PSA alone for community‐based men undergoing prostate cancer (PCa) screening.
PARTICIPANTS AND METHODS
•
A population‐based cohort of 11 872 men from Olmsted County, MN undergoing PSA screening for PCa from 1993 to 2005 was analysed for PSA, PSA‐DT, PSA‐PC and PSA‐V and subsequent PCa.
•
Receiver‐operating characteristics curves and logistic regression were used to calculate the area under the curve (AUC) and Aikaike's information criterion.
•
Reclassification analysis was performed and the net reclassification improvement and integrated discrimination improvement were measured.
•
The method of Begg and Greenes was used to adjust for verification bias.
RESULTS
•
The single best predictor of future PCa was PSA (AUC = 0.773) with PSA‐V (AUC = 0.729) and PSA‐DT/PSA‐PC (AUC = 0.689) performing worse.
•
After age adjustment, combining PSA with PSA‐V (AUC = 0.773) or PSA‐DT/PSA‐PC (AUC = 0.773) resulted in no better predictions than PSA alone.
•
Reclassification analysis showed that adding PSA‐V or PSA‐DT/PSA‐PC to PSA did not result in a meaningful amount of reclassification.
CONCLUSIONS
•
PSA is a better predictor of future PCa than PSA‐V, PSA‐DT, or PSA‐PC.
•
Adding PSA‐V, PSA‐DT, or PSA‐PC to PSA does not result in clinically relevant improvements in the ability to predict future PCa. • To determine whether prostate-specific antigen velocity (PSA-V), PSA doubling time (PSA-DT), or PSA percentage change (PSA-PC) add incremental information to PSA alone for community-based men undergoing prostate cancer (PCa) screening. • A population-based cohort of 11 872 men from Olmsted County, MN undergoing PSA screening for PCa from 1993 to 2005 was analysed for PSA, PSA-DT, PSA-PC and PSA-V and subsequent PCa. • Receiver-operating characteristics curves and logistic regression were used to calculate the area under the curve (AUC) and Aikaike's information criterion. • Reclassification analysis was performed and the net reclassification improvement and integrated discrimination improvement were measured. • The method of Begg and Greenes was used to adjust for verification bias. • The single best predictor of future PCa was PSA (AUC = 0.773) with PSA-V (AUC = 0.729) and PSA-DT/PSA-PC (AUC = 0.689) performing worse. • After age adjustment, combining PSA with PSA-V (AUC = 0.773) or PSA-DT/PSA-PC (AUC = 0.773) resulted in no better predictions than PSA alone. • Reclassification analysis showed that adding PSA-V or PSA-DT/PSA-PC to PSA did not result in a meaningful amount of reclassification. • PSA is a better predictor of future PCa than PSA-V, PSA-DT, or PSA-PC. • Adding PSA-V, PSA-DT, or PSA-PC to PSA does not result in clinically relevant improvements in the ability to predict future PCa. • To determine whether prostate-specific antigen velocity (PSA-V), PSA doubling time (PSA-DT), or PSA percentage change (PSA-PC) add incremental information to PSA alone for community-based men undergoing prostate cancer (PCa) screening.OBJECTIVE• To determine whether prostate-specific antigen velocity (PSA-V), PSA doubling time (PSA-DT), or PSA percentage change (PSA-PC) add incremental information to PSA alone for community-based men undergoing prostate cancer (PCa) screening.• A population-based cohort of 11 872 men from Olmsted County, MN undergoing PSA screening for PCa from 1993 to 2005 was analysed for PSA, PSA-DT, PSA-PC and PSA-V and subsequent PCa. • Receiver-operating characteristics curves and logistic regression were used to calculate the area under the curve (AUC) and Aikaike's information criterion. • Reclassification analysis was performed and the net reclassification improvement and integrated discrimination improvement were measured. • The method of Begg and Greenes was used to adjust for verification bias.PARTICIPANTS AND METHODS• A population-based cohort of 11 872 men from Olmsted County, MN undergoing PSA screening for PCa from 1993 to 2005 was analysed for PSA, PSA-DT, PSA-PC and PSA-V and subsequent PCa. • Receiver-operating characteristics curves and logistic regression were used to calculate the area under the curve (AUC) and Aikaike's information criterion. • Reclassification analysis was performed and the net reclassification improvement and integrated discrimination improvement were measured. • The method of Begg and Greenes was used to adjust for verification bias.• The single best predictor of future PCa was PSA (AUC = 0.773) with PSA-V (AUC = 0.729) and PSA-DT/PSA-PC (AUC = 0.689) performing worse. • After age adjustment, combining PSA with PSA-V (AUC = 0.773) or PSA-DT/PSA-PC (AUC = 0.773) resulted in no better predictions than PSA alone. • Reclassification analysis showed that adding PSA-V or PSA-DT/PSA-PC to PSA did not result in a meaningful amount of reclassification.RESULTS• The single best predictor of future PCa was PSA (AUC = 0.773) with PSA-V (AUC = 0.729) and PSA-DT/PSA-PC (AUC = 0.689) performing worse. • After age adjustment, combining PSA with PSA-V (AUC = 0.773) or PSA-DT/PSA-PC (AUC = 0.773) resulted in no better predictions than PSA alone. • Reclassification analysis showed that adding PSA-V or PSA-DT/PSA-PC to PSA did not result in a meaningful amount of reclassification.• PSA is a better predictor of future PCa than PSA-V, PSA-DT, or PSA-PC. • Adding PSA-V, PSA-DT, or PSA-PC to PSA does not result in clinically relevant improvements in the ability to predict future PCa.CONCLUSIONS• PSA is a better predictor of future PCa than PSA-V, PSA-DT, or PSA-PC. • Adding PSA-V, PSA-DT, or PSA-PC to PSA does not result in clinically relevant improvements in the ability to predict future PCa. Study Type - Diagnosis (exploratory cohort) Level of Evidence2b What's known on the subject? and What does the study add? A single serum PSA measurement is commonly used as a screening test to identify men with prostate cancer. A rise in PSA over time may identify men at increased risk of prostate cancer. Dynamic measures of PSA change (ex: PSA velocity, PSA doubling time) are frequently used to justify prostate biopsy in men. We demonstrate that the current serum PSA is the best predictor of future prostate cancer risk among commonly available clinical variables. We show that dynamic measures of PSA change do not improve upon PSA's ability to predict future prostate cancer. Our study suggests that dynamic measures of PSA change may not be useful in screening for prostate cancer. OBJECTIVE * To determine whether prostate-specific antigen velocity (PSA-V), PSA doubling time (PSA-DT), or PSA percentage change (PSA-PC) add incremental information to PSA alone for community-based men undergoing prostate cancer (PCa) screening. PARTICIPANTS AND METHODS * A population-based cohort of 11 872 men from Olmsted County, MN undergoing PSA screening for PCa from 1993 to 2005 was analysed for PSA, PSA-DT, PSA-PC and PSA-V and subsequent PCa. * Receiver-operating characteristics curves and logistic regression were used to calculate the area under the curve (AUC) and Aikaike's information criterion. * Reclassification analysis was performed and the net reclassification improvement and integrated discrimination improvement were measured. * The method of Begg and Greenes was used to adjust for verification bias. RESULTS * The single best predictor of future PCa was PSA (AUC = 0.773) with PSA-V (AUC = 0.729) and PSA-DT/PSA-PC (AUC = 0.689) performing worse. * After age adjustment, combining PSA with PSA-V (AUC = 0.773) or PSA-DT/PSA-PC (AUC = 0.773) resulted in no better predictions than PSA alone. * Reclassification analysis showed that adding PSA-V or PSA-DT/PSA-PC to PSA did not result in a meaningful amount of reclassification. CONCLUSIONS * PSA is a better predictor of future PCa than PSA-V, PSA-DT, or PSA-PC. * Adding PSA-V, PSA-DT, or PSA-PC to PSA does not result in clinically relevant improvements in the ability to predict future PCa. [PUBLICATION ABSTRACT] |
| Author | Denton, Brian T. Inman, Brant A. Zhang, Jingyu Shah, Nilay D. |
| AuthorAffiliation | Philips Research North America, Briarcliff Manor, NY Division of Health Care Policy and Research, Mayo Clinic College of Medicine, Rochester, MN Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, Raleigh, NC, USA Division of Urology, Duke University Medical Center, Durham, NC |
| AuthorAffiliation_xml | – name: Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, Raleigh, NC, USA – name: Philips Research North America, Briarcliff Manor, NY – name: Division of Health Care Policy and Research, Mayo Clinic College of Medicine, Rochester, MN – name: Division of Urology, Duke University Medical Center, Durham, NC |
| Author_xml | – sequence: 1 givenname: Brant A. surname: Inman fullname: Inman, Brant A. – sequence: 2 givenname: Jingyu surname: Zhang fullname: Zhang, Jingyu – sequence: 3 givenname: Nilay D. surname: Shah fullname: Shah, Nilay D. – sequence: 4 givenname: Brian T. surname: Denton fullname: Denton, Brian T. |
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| Keywords | Human PSA percentage change Nephrology Urinary system disease Prostate disease Doubling time Male Tumoral marker Malignant tumor PSA velocity Velocity Urology Prostate specific antigen Dynamics Cohort study PSA doubling time Adult Population Kinetics Male genital diseases Prostate cancer Cancer prostate-specific antigen |
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| References | 2004; 63 2010 2006; 98 2007; 121 2009 1996; 71 2006; 176 2011; 77 2007; 52 1983; 39 2008; 101 2007; 99 2007; 109 2008; 71 2009; 27 2009; 56 2003; 349 2006; 49 2008; 27 2008; 28 2005; 51 2008; 26 2009; 101 1999; 130 2011; 26 1998; 7 1999; 91 e_1_2_8_27_2 e_1_2_8_28_2 e_1_2_8_29_2 e_1_2_8_23_2 e_1_2_8_24_2 e_1_2_8_25_2 e_1_2_8_26_2 e_1_2_8_9_2 e_1_2_8_2_2 e_1_2_8_4_2 e_1_2_8_3_2 e_1_2_8_6_2 e_1_2_8_5_2 e_1_2_8_8_2 e_1_2_8_7_2 e_1_2_8_20_2 e_1_2_8_21_2 e_1_2_8_22_2 e_1_2_8_16_2 e_1_2_8_17_2 e_1_2_8_18_2 e_1_2_8_19_2 e_1_2_8_12_2 e_1_2_8_13_2 e_1_2_8_34_2 e_1_2_8_14_2 e_1_2_8_15_2 e_1_2_8_31_2 e_1_2_8_30_2 e_1_2_8_10_2 e_1_2_8_33_2 e_1_2_8_11_2 e_1_2_8_32_2 22313542 - BJU Int. 2012 Aug;110(3):381-2 |
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| Snippet | Study Type – Diagnosis (exploratory cohort)
Level of Evidence 2b
What's known on the subject? and What does the study add?
A single serum PSA measurement is... • To determine whether prostate-specific antigen velocity (PSA-V), PSA doubling time (PSA-DT), or PSA percentage change (PSA-PC) add incremental information to... Study Type - Diagnosis (exploratory cohort) Level of Evidence2b What's known on the subject? and What does the study add? A single serum PSA measurement is... |
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| SubjectTerms | Adult Age Factors Aged Aged, 80 and over Biological and medical sciences Cohort Studies Early Detection of Cancer Gynecology. Andrology. Obstetrics Humans Male Male genital diseases Medical sciences Men Middle Aged Nephrology. Urinary tract diseases Prostate cancer Prostate-Specific Antigen - metabolism prostate‐specific antigen Prostatic Neoplasms - diagnosis PSA doubling time PSA percentage change PSA velocity ROC Curve Sensitivity and Specificity Tumors Tumors of the urinary system Urinary tract. Prostate gland |
| Title | An examination of the dynamic changes in prostate‐specific antigen occurring in a population‐based cohort of men over time |
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