Quantitative data analysis for exploring outcomes in cardiac surgery.
The article focuses on possibilities of statistical knowledge exploration to predict outcomes of surgical treatments. The outcomes were defined in relation to the measured peri- and intraoperative data, as well as follow-up patient questionnaire. Clinical consequences are expected in terms of a smaller data set with a better ability to predict the surgery outcomes and a better cost performance. The important questions that could discriminate quality of life (QoL) were: Relief from surgery?, Has cardiac surgery effected earlier symptoms? Work capacity? Consultations after the surgery? The performed data analysis proved to be efficient in the complex data set that was collected. Pain relief was identified to be significant, while relations between measured blood laboratory profile and later QoL were weak.
Kircher
A
A
Department of Biomed. Engineering, Linköping University, Sweden.
Antonsson
J
J
Babic
A
A
Casimir-Ahn
H
H
D000368:Aged; D001185:Artificial Intelligence; D001026:Coronary Artery Bypass / Q000706:statistics & numerical data*; D003327:Coronary Disease / Q000601:surgery*; D003627:Data Interpretation, Statistical; D005103:Expert Systems; D005260:Female; D006801:Humans; D008297:Male; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data; D008875:Middle Aged; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D011379:Prognosis; D011788:Quality of Life