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.
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.
Although life expectancy estimation is vital to decision making for localized prostate cancer, there are few, if any, valid and usable tools.
Our goal was to create and validate a prediction model for other cause mortality in localized prostate cancer patients that could aid clinician’s initial treatment decisions at the point of care.
These men had sufficient follow-up for our endpoints of 10- and 15-year mortality and also had self-reported comorbidity data.Risk models may optimize PCa detection and classification with regard to improved PCa risk assessment and avoidance of unnecessary prostate biopsies.Clinical risk calculators are now widely available but have generally been implemented in a static and one-size-fits-all fashion.We previously reported a systematic review in which we found a dearth of accurate life expectancy tools that could be incorporated into clinical workflow .Tools gave estimates of life expectancy that could not be integrated with cancer risk estimates, used invalid or unsubstantiated methods to adjust life expectancy for comorbidity, and provided estimates that appear implausible or could not be easily used in routine clinical practice.Life expectancy for prostate cancer patients were close to that of a typical US man who was 3 years younger.