Jianping Xiang1, Jihnhee Yu2, Kenneth V Snyder3, Elad I Levy3, Adnan H Siddiqui3, Hui Meng1. 1. Toshiba Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, New York, USA Department of Mechanical and Aerospace Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA Department of Neurosurgery, University at Buffalo, State University of New York, Buffalo, New York, USA. 2. Department of Biostatistics, University at Buffalo, State University of New York, Buffalo, New York, USA. 3. Toshiba Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, New York, USA Department of Neurosurgery, University at Buffalo, State University of New York, Buffalo, New York, USA Department of Radiology, University at Buffalo, State University of New York, Buffalo, New York, USA.
Abstract
BACKGROUND: We previously established three logistic regression models for discriminating intracranial aneurysm rupture status based on morphological and hemodynamic analysis of 119 aneurysms. In this study, we tested if these models would remain stable with increasing sample size, and investigated sample sizes required for various confidence levels (CIs). METHODS: We augmented our previous dataset of 119 aneurysms into a new dataset of 204 samples by collecting an additional 85 consecutive aneurysms, on which we performed flow simulation and calculated morphological and hemodynamic parameters, as done previously. We performed univariate significance tests on these parameters, and multivariate logistic regression on significant parameters. The new regression models were compared against the original models. Receiver operating characteristics analysis was applied to compare the performance of regression models. Furthermore, we performed regression analysis based on bootstrapping resampling statistical simulations to explore how many aneurysm cases were required to generate stable models. RESULTS: Univariate tests of the 204 aneurysms generated an identical list of significant morphological and hemodynamic parameters as previously (from the analysis of 119 cases). Furthermore, multivariate regression analysis produced three parsimonious predictive models that were almost identical to the previous ones, with model coefficients that had narrower CIs than the original ones. Bootstrapping showed that 10%, 5%, 2%, and 1% convergence levels of CI required 120, 200, 500, and 900 aneurysms, respectively. CONCLUSIONS: Our original hemodynamic-morphological rupture prediction models are stable and improve with increasing sample size. Results from resampling statistical simulations provide guidance for designing future large multi-population studies. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
BACKGROUND: We previously established three logistic regression models for discriminating intracranial aneurysm rupture status based on morphological and hemodynamic analysis of 119 aneurysms. In this study, we tested if these models would remain stable with increasing sample size, and investigated sample sizes required for various confidence levels (CIs). METHODS: We augmented our previous dataset of 119 aneurysms into a new dataset of 204 samples by collecting an additional 85 consecutive aneurysms, on which we performed flow simulation and calculated morphological and hemodynamic parameters, as done previously. We performed univariate significance tests on these parameters, and multivariate logistic regression on significant parameters. The new regression models were compared against the original models. Receiver operating characteristics analysis was applied to compare the performance of regression models. Furthermore, we performed regression analysis based on bootstrapping resampling statistical simulations to explore how many aneurysm cases were required to generate stable models. RESULTS: Univariate tests of the 204 aneurysms generated an identical list of significant morphological and hemodynamic parameters as previously (from the analysis of 119 cases). Furthermore, multivariate regression analysis produced three parsimonious predictive models that were almost identical to the previous ones, with model coefficients that had narrower CIs than the original ones. Bootstrapping showed that 10%, 5%, 2%, and 1% convergence levels of CI required 120, 200, 500, and 900 aneurysms, respectively. CONCLUSIONS: Our original hemodynamic-morphological rupture prediction models are stable and improve with increasing sample size. Results from resampling statistical simulations provide guidance for designing future large multi-population studies. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
Authors: L Goubergrits; J Schaller; U Kertzscher; N van den Bruck; K Poethkow; Ch Petz; H-Ch Hege; A Spuler Journal: J R Soc Interface Date: 2011-09-28 Impact factor: 4.118
Authors: E Sander Connolly; Alejandro A Rabinstein; J Ricardo Carhuapoma; Colin P Derdeyn; Jacques Dion; Randall T Higashida; Brian L Hoh; Catherine J Kirkness; Andrew M Naidech; Christopher S Ogilvy; Aman B Patel; B Gregory Thompson; Paul Vespa Journal: Stroke Date: 2012-05-03 Impact factor: 7.914
Authors: Felicitas J Detmer; Bong Jae Chung; Fernando Mut; Martin Slawski; Farid Hamzei-Sichani; Christopher Putman; Carlos Jiménez; Juan R Cebral Journal: Int J Comput Assist Radiol Surg Date: 2018-08-09 Impact factor: 2.924
Authors: Jianping Xiang; Nicole Varble; Jason M Davies; Ansaar T Rai; Kenichi Kono; Shin-Ichiro Sugiyama; Mandy J Binning; Rabih G Tawk; Hoon Choi; Andrew J Ringer; Kenneth V Snyder; Elad I Levy; L Nelson Hopkins; Adnan H Siddiqui; Hui Meng Journal: World Neurosurg Date: 2017-09-15 Impact factor: 2.104
Authors: Felicitas J Detmer; Daniel Lückehe; Fernando Mut; Martin Slawski; Sven Hirsch; Philippe Bijlenga; Gabriele von Voigt; Juan R Cebral Journal: Int J Comput Assist Radiol Surg Date: 2019-09-04 Impact factor: 2.924
Authors: H Rajabzadeh-Oghaz; J Wang; N Varble; S-I Sugiyama; A Shimizu; L Jing; J Liu; X Yang; A H Siddiqui; J M Davies; H Meng Journal: AJNR Am J Neuroradiol Date: 2019-10-24 Impact factor: 3.825
Authors: Philipp Berg; Samuel Voß; Gábor Janiga; Sylvia Saalfeld; Aslak W Bergersen; Kristian Valen-Sendstad; Jan Bruening; Leonid Goubergrits; Andreas Spuler; Tin Lok Chiu; Anderson Chun On Tsang; Gabriele Copelli; Benjamin Csippa; György Paál; Gábor Závodszky; Felicitas J Detmer; Bong J Chung; Juan R Cebral; Soichiro Fujimura; Hiroyuki Takao; Christof Karmonik; Saba Elias; Nicole M Cancelliere; Mehdi Najafi; David A Steinman; Vitor M Pereira; Senol Piskin; Ender A Finol; Mariya Pravdivtseva; Prasanth Velvaluri; Hamidreza Rajabzadeh-Oghaz; Nikhil Paliwal; Hui Meng; Santhosh Seshadhri; Sreenivas Venguru; Masaaki Shojima; Sergey Sindeev; Sergey Frolov; Yi Qian; Yu-An Wu; Kent D Carlson; David F Kallmes; Dan Dragomir-Daescu; Oliver Beuing Journal: Int J Comput Assist Radiol Surg Date: 2019-05-03 Impact factor: 2.924