A general microsimulation toolkit for patient specific predictions, treatment efficiency and life expectancy

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

Abstract

Microsimulation can be used to predict the prognosis of an individual patient based on a virtual patient population of copies of that patient. In this study we compare the outcomes of an existing validated microsimulation program that is designed to study valvular heart disease and a newly developed microsimulation program that is designed to study heart diseases in general. We studied in depth the results of both systems to model the prognosis of a 40 year old male patient undergoing allograft surgery. Furthermore we studied the model results in relation to age and sex to provide a general overview of the most important outcome variables including operative mortality, average survival time, average event free time and average time to reoperation. Our results show a good agreement between the two systems regarding all simulations of allograft surgery. We intend to use the newly developed software to explore other disease/event related prognostic models.

Original languageEnglish
Title of host publicationComputing in Cardiology 2011, CinC 2011
Pages561-564
Number of pages4
Publication statusPublished - 2011
EventComputing in Cardiology 2011, CinC 2011 - Hangzhou, China
Duration: 18 Sept 201121 Sept 2011

Publication series

SeriesComputing in Cardiology
Volume38
ISSN0276-6574

Conference

ConferenceComputing in Cardiology 2011, CinC 2011
Country/TerritoryChina
CityHangzhou
Period18/09/1121/09/11

Bibliographical note

Published by Computing in Cardiology, 2011.
http://www.cinc.org/
Articles in this volume are copyright (C) 2011 by their respective authors,
and are licensed by their authors under the Creative Commons Attribution
License 2.5 (CCAL).
For the full text of the CCAL, please visit:
http://creativecommons.org/licenses/by/2.5/

Fingerprint

Dive into the research topics of 'A general microsimulation toolkit for patient specific predictions, treatment efficiency and life expectancy'. Together they form a unique fingerprint.

Cite this