The decision curve is a graphical summary recently proposed for assessing the potential clinical impact of risk prediction biomarkers or risk models for recommending treatment or intervention. It was applied recently in an article in Journal of Clinical Oncology to measure the impact of using a genomic risk model for deciding on adjuvant radiation therapy for prostate cancer treated with radical prostatectomy. We illustrate the use of decision curves for evaluating clinical- and biomarker-based models for predicting a man’s risk of prostate cancer, which could be used to guide the decision to biopsy. Decision curves are grounded in a decision-theoretical framework that accounts for both the benefits of intervention and the costs of intervention to a patient who cannot benefit. Decision curves are thus an improvement over purely mathematical measures of performance such as the area under the receiver operating characteristic curve. However, there are challenges in using and interpreting decision curves appropriately. We caution that decision curves cannot be used to identify the optimal risk threshold for recommending intervention. We discuss the use of decision curves for miscalibrated risk models. Finally, we emphasize that a decision curve shows the performance of a risk model in a population in which every patient has the same expected benefit and cost of intervention. If every patient has a personal benefit and cost, then the curves are not useful. If subpopulations have different benefits and costs, subpopulation-specific decision curves should be used. As a companion to this article, we released an R software package called DecisionCurve for making decision curves and related graphics.

No abstract

The etiology and treatment of hypertrophic scar remain puzzles even after decades of research. A significant reason is the lack of an accepted animal model of the process. The female, red Duroc pig model was described long ago. Since the skin of the pig is similar to that of humans, we are attempting to validate this model and found it to be encouraging. In this project we quantified myofibroblasts, mast cells and collagen nodules in the thick scar of the Duroc pig and compared these to the values for human hypertrophic scar. We found the results to be quite similar and so further validated the model. In addition, we observed that soon after wounding an inflammatory cell layer forms. The thickness of the inflammatory layer approaches the thickness of the skin removed as if the remaining dermis "knows" how much dermis is gone. In deep wounds this inflammatory layer thickens and this thickness is predictive of the thickness of the ultimate scar.