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Urology. 2017 Jun;104:137-142. doi: 10.1016/j.urology.2017.01.039. Epub 2017 Feb 22.

Evaluation of Prostate Cancer Risk Calculators for Shared Decision Making Across Diverse Urology Practices in Michigan.

Author information

1
Department of Urology, University of Michigan, Ann Arbor, MI. Electronic address: gauffen@med.umich.edu.
2
Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI.
3
Department of Urology, University of Michigan, Ann Arbor, MI.
4
Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI; Division of Nephrology, Department of Medicine, University of Michigan, Ann Arbor, MI.
5
Lakeside Urology PC, St. Joseph, MI.
6
Department of Urology, University of Michigan, Ann Arbor, MI; Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI.

Abstract

OBJECTIVE:

To compare the predictive performance of a logistic regression model developed with contemporary data from a diverse group of urology practices to that of the Prostate Cancer Prevention Trial (PCPT) Risk Calculator version 2.0.

MATERIALS AND METHODS:

With data from all first-time prostate biopsies performed between January 2012 and March 2015 across the Michigan Urological Surgery Improvement Collaborative (MUSIC), we developed a multinomial logistic regression model to predict the likelihood of finding high-grade cancer (Gleason score ≥7), low-grade cancer (Gleason score ≤6), or no cancer on prostate biopsy. The performance of the MUSIC model was evaluated in out-of-sample data using 10-fold cross-validation. Discrimination and calibration statistics were used to compare the performance of the MUSIC model to that of the PCPT risk calculator in the MUSIC cohort.

RESULTS:

Of the 11,809 biopsies included, 4289 (36.3%) revealed high-grade cancer; 2027 (17.2%) revealed low-grade cancer; and the remaining 5493 (46.5%) were negative. In the MUSIC model, prostate-specific antigen level, rectal examination findings, age, race, and family history of prostate cancer were significant predictors of finding high-grade cancer on biopsy. The 2 models, based on similar predictors, had comparable discrimination (multiclass area under the curve = 0.63 for the MUSIC model and 0.62 for the PCPT calculator). Calibration analyses demonstrated that the MUSIC model more accurately predicted observed outcomes, whereas the PCPT risk calculator substantively overestimated the likelihood of finding no cancer while underestimating the risk of high-grade cancer in this population.

CONCLUSION:

The PCPT risk calculator may not be a good predictor of individual biopsy outcomes for patients seen in contemporary urology practices.

Comment in

PMID:
28237530
DOI:
10.1016/j.urology.2017.01.039
[Indexed for MEDLINE]

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