Spectroscopic diagnosis of myocardial infarction and heart failure by Fourier transform infrared spectroscopy in serum samples.
Cardiovascular disease is the leading cause of death in Western civilization. In this pilot study we evaluated a new method for the diagnosis of myocardial infarction and heart failure by determining the typical fingerprint in the infrared (IR) spectrum of 1 microL of a dried patient serum sample by Fourier transform IR spectroscopy. For classification, cluster analysis and artificial neural networks (ANN) were applied. In this study 567 subjects were enrolled, comprising 225 controls (Co) and 342 patients with myocardial infarction (MI) (n = 157) and heart failure (HF) (n = 185). By applying artificial neural network algorithms, the following sensitivities and specificities of the same spectra were determined: MI versus Co (98%, 97%), HF versus Co (98%, 100%), MI versus HF (100%, 100%), and MI plus HF versus Co (100%, 100%). Based on our data, mid-IR spectroscopy appears to be a promising new method to diagnose heart diseases from serum samples. Artificial neural network algorithms proved to be superior to cluster analysis for correct prediction.
Haas
Stephan L
SL
Department of Medicine II, Mannheim Medical Faculty of the University Heidelberg, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany.
Müller
Ralf
R
Fernandes
Amilcar
A
Dzeyk-Boycheva
Kristina
K
Würl
Susanne
S
Hohmann
Jens
J
Hemberger
Sabrina
S
Elmas
Elif
E
Brückmann
Martina
M
Bugert
Peter
P
Backhaus
Jürgen
J
D000465:Algorithms; D016000:Cluster Analysis; D006333:Heart Failure / Q000097:blood / Q000175:diagnosis*; D006801:Humans; D009203:Myocardial Infarction / Q000097:blood / Q000175:diagnosis*; D016571:Neural Networks, Computer; D010865:Pilot Projects; D015203:Reproducibility of Results; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*