An Exploratory Clustering Study of Rare Adverse Events in Drug Eluting Stents Patients
Bychowiec Bartosz 1, Piskorski Jarosław 2, Stanislawska Katarzyna 1, Dziarmaga Mieczysław 1, Minczykowski Andrzej 1, Wykretowicz Andrzej 1, Wysocki Henryk 1
1Department of Cardiology – Intensive Therapy and Internal Diseases,
Poznań University of Medical Sciences
ul. Przybyszewskiego 49, Poznań, Poland
e-mail: bartek.bychowiec@gmail.com, kasia.stanislawska@onet.eu,
mdziarmaga@poczta.onet.pl, aminczykowski@gmail.com, awykreto@ptkardio.pl, hwysocki@plusnet.pl
2Institute of Physics, University of Zielona Góra,
ul. Szafrana 4a, Zielona Góra, Poland
e-mail: jaropis@zg.home.pl
Received:
Received: 17 January; revised: 16 April 2010; accepted: 23 April; published online: 30 June 2010
DOI: 10.12921/cmst.2010.16.01.05-11
OAI: oai:lib.psnc.pl:708
Abstract:
We present an exploratory study of a group of patients (150) who have undergone precutaneous transluminal coronary angioplasty with the use of drug eluting stents. We concentrate on the most often studied rare adverse event, i.e. death, as well as the still unexplored cancer variable. The aim of the study is to identify possible statistical hypotheses for a subsequent, large sample study. The results of this study may lead to a change in the therapy administered after precutaneous coronary interventions which will reduce the mortality rate. To achieve this, we use clustering techniques such as hierarchical cluster analysis, principal components methods and interactive brushing. We show that death cases cluster in the space defined by the available variables, while the cancer cases do not seem to cluster.
Key words:
computationally intensive statistical analysis, data mining, medical visualization, PTCA
References:
[1] E.D. Grech, ABC of Interventional Cardiology. BMJ Publishing Group, London (2004).
[2] D.S. Lippincott, Grossman’s Cardiac Catheterization. Angiography & Intervention, 7-ed., Baim, Williams and Wilkins, Philadelphia (2006).
[3] P. Kay, Cardiac Catheterization and Percutaneous Interventions I. Taylor and Francis, London (2004).
[4] J. Tu et al., NEJM, 357, 1393-1402 (2007).
[5] A. Agresti, Categorical Data Analysis. 2-ed., Wiley-Interscience, Hoboken, New Jersey (2002).
[6] J.W. Tukey, Exploratory Data Analysis. Addison-Wesley, Reading (1977).
[7] www.rproject.org
[8] www.ggobi.org
[9] W. Hardle, L. Simar, Applied Multivariate Statistical. Analysis 2-ed., Springer, Berlin (2007).
[10] B.S. Everitt, An R and S-PLUS Companion to Multivariate Analysis. Springer, Berlin (2005).
[11] T.N. Nguyen, A Practical Handbook of Advanced Interventional Cardiology. 2-ed. Futura, Blackwell Publishing, Malden (2003).
[12] E.J. Topol et al., Textbook of Cardiovascular Medicine. Lippincott Williams and Wilkins, Philadelphia (2002).
[13] P. Libby, R.O. Bonow, D.P. Zipes, Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine. 8-ed., Sounder Elsevier, Philadelphia (2008).
[14] http://www.if.uz.zgora.pl/~jaropis/recBrush.html
[15] S.B. King et. al., J Am Coll Cardiol. 51, 172-209 (2008).
[16] D. Cook, D.F. Swayne, A. Buja, D.T. Lang, Interactive and Dynamic Graphics for Data Analysis: With R and Ggobi (Use R). Springer, New York (2007).
We present an exploratory study of a group of patients (150) who have undergone precutaneous transluminal coronary angioplasty with the use of drug eluting stents. We concentrate on the most often studied rare adverse event, i.e. death, as well as the still unexplored cancer variable. The aim of the study is to identify possible statistical hypotheses for a subsequent, large sample study. The results of this study may lead to a change in the therapy administered after precutaneous coronary interventions which will reduce the mortality rate. To achieve this, we use clustering techniques such as hierarchical cluster analysis, principal components methods and interactive brushing. We show that death cases cluster in the space defined by the available variables, while the cancer cases do not seem to cluster.
Key words:
computationally intensive statistical analysis, data mining, medical visualization, PTCA
References:
[1] E.D. Grech, ABC of Interventional Cardiology. BMJ Publishing Group, London (2004).
[2] D.S. Lippincott, Grossman’s Cardiac Catheterization. Angiography & Intervention, 7-ed., Baim, Williams and Wilkins, Philadelphia (2006).
[3] P. Kay, Cardiac Catheterization and Percutaneous Interventions I. Taylor and Francis, London (2004).
[4] J. Tu et al., NEJM, 357, 1393-1402 (2007).
[5] A. Agresti, Categorical Data Analysis. 2-ed., Wiley-Interscience, Hoboken, New Jersey (2002).
[6] J.W. Tukey, Exploratory Data Analysis. Addison-Wesley, Reading (1977).
[7] www.rproject.org
[8] www.ggobi.org
[9] W. Hardle, L. Simar, Applied Multivariate Statistical. Analysis 2-ed., Springer, Berlin (2007).
[10] B.S. Everitt, An R and S-PLUS Companion to Multivariate Analysis. Springer, Berlin (2005).
[11] T.N. Nguyen, A Practical Handbook of Advanced Interventional Cardiology. 2-ed. Futura, Blackwell Publishing, Malden (2003).
[12] E.J. Topol et al., Textbook of Cardiovascular Medicine. Lippincott Williams and Wilkins, Philadelphia (2002).
[13] P. Libby, R.O. Bonow, D.P. Zipes, Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine. 8-ed., Sounder Elsevier, Philadelphia (2008).
[14] http://www.if.uz.zgora.pl/~jaropis/recBrush.html
[15] S.B. King et. al., J Am Coll Cardiol. 51, 172-209 (2008).
[16] D. Cook, D.F. Swayne, A. Buja, D.T. Lang, Interactive and Dynamic Graphics for Data Analysis: With R and Ggobi (Use R). Springer, New York (2007).
