ABC stenosis morphology classification and outcome of coronary angioplasty: reassessment with computing techniques.
BACKGROUND
The American College of Cardiology/American Heart Association (ACC/AHA) stenosis morphology classification (MC) stratifies coronary lesions for probability of success and complications after coronary angioplasty (PTCA). Modern computing techniques were used to evaluate the individual predictive value of MC in random PTCA cases.


RESULTS
MC was attributed to the target lesions by consensus of 2 observers. The predictive value regarding procedural success (PS) and major adverse cardiac events (MACE) of MC was analyzed by conventional logistic regression analyses and by inductive machine learning models. The study was adequately powered for the methods applied with 325 target lesions of 250 cases. Overall, PS decreased and MACE increased from type A to type C lesions. Regression analysis identified no single factor as predictive. Logistic regression showed an error rate of 42%. Machine learning techniques achieved an individual predictive error of only 10%, which could be further reduced to 2% by addition of parameters. For PS, MC parameters showed a high ranking for building the model. For MACE, variables of the medical history showed more impact.


CONCLUSIONS
MC per se cannot individually predict PS or MACE. However, when all MC parameters are integrated together with additional lesion-specific and history variables, a high individual predictive value can be achieved. This technique may be clinically helpful for risk stratification in the catheterization laboratory and improvement of classification systems in interventional cardiology.
Maier
W
W
Swiss Cardiovascular Center, University Hospital, Bern, Switzerland.
Mini
O
O
Antoni
J
J
Wischnewsky
M B
MB
Meier
B
B
D000465:Algorithms; D015906:Angioplasty, Balloon, Coronary / Q000009:adverse effects / Q000592:standards / Q000706:statistics & numerical data; D003327:Coronary Disease / Q000628:therapy*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D011237:Predictive Value of Tests; D012984:Software; D016896:Treatment Outcome