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  • Title: A simplified lesion classification for predicting success and complications of coronary angioplasty. Registry Committee of the Society for Cardiac Angiography and Intervention.
    Author: Krone RJ, Laskey WK, Johnson C, Kimmel SE, Klein LW, Weiner BH, Cosentino JJ, Johnson SA, Babb JD.
    Journal: Am J Cardiol; 2000 May 15; 85(10):1179-84. PubMed ID: 10801997.
    Abstract:
    In 1988, the American College of Cardiology/American Heart Association (ACC/AHA) Task Force on Assessment of Diagnostic and Therapeutic Cardiovascular Procedures presented a classification of coronary lesions utilizing 26 lesion features to predict the success and complications of balloon angioplasty. Using data from the Registry of the Society for Cardiac Angiography and Interventions (SCAI) we evaluated the ability of this classification to predict success and complications. Lesion success, death in hospital, emergency cardiac bypass surgery, and major adverse events were evaluated in 41,071 patients who underwent single-vessel angioplasty from January 1993 to June 1996. Logistic models using the ACC/AHA lesion classification, vessel patency, or both, were compared. A new classification based on the interaction of the ACC/AHA classification plus lesion patency was compared with the existing ACC/AHA classification. Vessel patency, added to the ACC/AHA classification, improved prediction of lesion success (p </=0.0001). Class A and patent B lesions had similar success and complication rates, so a simplified classification (SCAI) using only 7 lesion characteristics could be created. This system (I: non-C patent, II: C patent, III: non-C occluded, and IV: C occluded) improved prediction of lesion success compared with the ACC/AHA classification (Bayesian Information Criterion statistic: ACC/AHA 16539, SCAI 15956; and area under the receiver- operating characteristics curve 0.659, 0.693, respectively). The SCAI classification was preferred for predicting major complications and in-hospital death and was similar to the ACC/AHA classification for predicting emergency bypass surgery.
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