PURPOSE: To test whether body mass index (BMI) improves pre- or post-operative biochemical recurrence (BCR) predictions after radical prostatectomy. MATERIALS AND METHODS: Pre- and post-operative data were available in 2416 and 2499 men, respectively. Cox regression models addressed the association between BMI and the rate of BCR after adjusting for pre- and post-operative predictors. Predictive accuracy was quantified using Harrell's concordance index, with and without BMI and subjected to 200 bootstraps to reduce overfit bias. Differences in predictive accuracy were compared using the Mantel-Haenszel test. RESULTS: After adjusting for either pre- or post-operative variables, increasing BMI was a statistically independent risk factor of BCR in both models (both p0.003). Its addition to pre- and post-operative variables respectively increased predictive accuracy measures from 69.6 to 70.2% (+0.6%, p=0.7) and from 78.1 to 78.4% (+0.3%, p=0.8). CONCLUSION: Our data emphasise that despite its significance, inclusion of BMI into models, to predict BCR, does not improve their accuracy.
PURPOSE: To test whether body mass index (BMI) improves pre- or post-operative biochemical recurrence (BCR) predictions after radical prostatectomy. MATERIALS AND METHODS: Pre- and post-operative data were available in 2416 and 2499 men, respectively. Cox regression models addressed the association between BMI and the rate of BCR after adjusting for pre- and post-operative predictors. Predictive accuracy was quantified using Harrell's concordance index, with and without BMI and subjected to 200 bootstraps to reduce overfit bias. Differences in predictive accuracy were compared using the Mantel-Haenszel test. RESULTS: After adjusting for either pre- or post-operative variables, increasing BMI was a statistically independent risk factor of BCR in both models (both p0.003). Its addition to pre- and post-operative variables respectively increased predictive accuracy measures from 69.6 to 70.2% (+0.6%, p=0.7) and from 78.1 to 78.4% (+0.3%, p=0.8). CONCLUSION: Our data emphasise that despite its significance, inclusion of BMI into models, to predict BCR, does not improve their accuracy.
Authors: Jonas Schiffmann; Pierre I Karakiewicz; Michael Rink; L Manka; Georg Salomon; Derya Tilki; Lars Budäus; Raisa Pompe; Sami-Ramzi Leyh-Bannurah; Alexander Haese; P Hammerer; Hartwig Huland; Markus Graefen; Pierre Tennstedt Journal: World J Urol Date: 2018-03-02 Impact factor: 4.226
Authors: Kyo Chul Koo; Young Eun Yoon; Koon Ho Rha; Byung Ha Chung; Seung Choul Yang; Sung Joon Hong Journal: Int Urol Nephrol Date: 2014-05-10 Impact factor: 2.370
Authors: Jing Ma; Haojie Li; Ed Giovannucci; Lorelei Mucci; Weiliang Qiu; Paul L Nguyen; J Michael Gaziano; Michael Pollak; Meir J Stampfer Journal: Lancet Oncol Date: 2008-10-03 Impact factor: 41.316
Authors: Jacqueline M Major; Hillary S Klonoff-Cohen; John P Pierce; Donald J Slymen; Sidney L Saltzstein; Caroline A Macera; Dan Mercola; Michael W Kattan Journal: PLoS One Date: 2011-02-24 Impact factor: 3.240
Authors: Dieuwertje E G Kok; Joep G H van Roermund; Katja K Aben; Moniek W M van de Luijtgaarden; Herbert F M Karthaus; Oncko B van Vierssen Trip; Ellen Kampman; J Alfred Witjes; Lambertus A L M Kiemeney Journal: World J Urol Date: 2010-12-16 Impact factor: 4.226