Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators
| Autor: | Andreas N. Strobl, Ben Van Calster, Donna P. Ankerst, Andrew J. Vickers, Ewout W. Steyerberg, Robin J. Leach, Ian M. Thompson |
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| Přispěvatelé: | Public Health |
| Rok vydání: | 2015 |
| Předmět: |
Adult
Male Prostate biopsy Databases Factual Revision Biopsy Logistic regression Health Informatics Risk Assessment Article Cohort Studies Prostate cancer Bayes' theorem SDG 3 - Good Health and Well-being Discrimination Statistics Humans Medicine Prostate Cancer Prevention Trial Aged Aged 80 and over medicine.diagnostic_test business.industry Prostatic Neoplasms Reproducibility of Results Bayes Theorem Middle Aged Prostate-Specific Antigen medicine.disease United States 3. Good health Computer Science Applications Random forest Prostate-specific antigen Logistic Models England Austria Calibration Prediction business Risk assessment Algorithms |
| Zdroj: | Journal of Biomedical Informatics, 56, 87-93. Academic Press |
| ISSN: | 1532-0464 |
| DOI: | 10.1016/j.jbi.2015.05.001 |
| Popis: | 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. publisher: Elsevier articletitle: Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators journaltitle: Journal of Biomedical Informatics articlelink: http://dx.doi.org/10.1016/j.jbi.2015.05.001 content_type: article copyright: Copyright © 2015 Elsevier Inc. All rights reserved. ispartof: Journal of Biomedical Informatics vol:56 pages:87-93 ispartof: location:United States status: published |
| Databáze: | OpenAIRE |
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