Format

Send to

Choose Destination
J Biomed Inform. 2015 Aug;56:87-93. doi: 10.1016/j.jbi.2015.05.001. Epub 2015 May 16.

Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators.

Author information

1
TU München, Department of Mathematics, Munich, Germany; HelmholtzZentrum München, Institute of Computational Biology, Munich, Germany. Electronic address: a.strobl@tum.de.
2
Memorial Sloan-Kettering Cancer Center, Department of Epidemiology and Biostatistics, New York City, NY, USA.
3
KU Leuven, Department of Development and Regeneration, Leuven, Belgium.
4
Erasmus MC, Department of Public Health, Rotterdam, The Netherlands.
5
University of Texas Health Science Center at San Antonio, Department of Cellular and Structural Biology, San Antonio, TX, USA; University of Texas Health Science Center at San Antonio, Department of Urology, San Antonio, TX, USA.
6
University of Texas Health Science Center at San Antonio, Department of Urology, San Antonio, TX, USA.
7
TU München, Department of Mathematics, Munich, Germany; HelmholtzZentrum München, Institute of Computational Biology, Munich, Germany; University of Texas Health Science Center at San Antonio, Department of Urology, San Antonio, TX, USA; University of Texas Health Science Center at San Antonio, Department of Epidemiology and Biostatistics, San Antonio, TX, USA.

Abstract

Clinical risk calculators are now widely available but have generally been implemented in a static and one-size-fits-all fashion. The objective of this study was to challenge these notions and show via a case study concerning risk-based screening for prostate cancer how calculators can be dynamically and locally tailored to improve on-site patient accuracy. Yearly data from five international prostate biopsy cohorts (3 in the US, 1 in Austria, 1 in England) were used to compare 6 methods for annual risk prediction: static use of the online US-developed Prostate Cancer Prevention Trial Risk Calculator (PCPTRC); recalibration of the PCPTRC; revision of the PCPTRC; building a new model each year using logistic regression, Bayesian prior-to-posterior updating, or random forests. All methods performed similarly with respect to discrimination, except for random forests, which were worse. All methods except for random forests greatly improved calibration over the static PCPTRC in all cohorts except for Austria, where the PCPTRC had the best calibration followed closely by recalibration. The case study shows that a simple annual recalibration of a general online risk tool for prostate cancer can improve its accuracy with respect to the local patient practice at hand.

KEYWORDS:

Calibration; Discrimination; Logistic regression; Prediction; Prostate cancer; Revision

PMID:
25989018
PMCID:
PMC4532612
DOI:
10.1016/j.jbi.2015.05.001
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center