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

Andreas N. Strobl, Andrew J. Vickers, Ben Van Calster, Ewout Steyerberg, Robin J. Leach, Ian M. Thompson, Donna P. Ankerst

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)87-93
Number of pages7
JournalJournal of Biomedical Informatics
Volume56
DOIs
StatePublished - Aug 1 2015

Keywords

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

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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