RT Journal Article
SR Electronic
T1 Determinants of success of coronary angioplasty in patients with a chronic total occlusion: a multiple logistic regression model to improve selection of patients.
JF British Heart Journal
JO Heart
FD BMJ Publishing Group Ltd and British Cardiovascular Society
SP 126
OP 131
DO 10.1136/hrt.70.2.126
VO 70
IS 2
A1 K H Tan
A1 N Sulke
A1 N A Taub
A1 E Watts
A1 S Karani
A1 E Sowton
YR 1993
UL http://heart.bmj.com/content/70/2/126.abstract
AB OBJECTIVE--To study the determinants of success of coronary angioplasty in patients with chronic total occlusions, and to formulate a multiple logistic regression model to improve selection of patients. DESIGN--A retrospective analysis of clinical and angiographic data on a consecutive series of patients. PATIENTS--312 patients (mean age 55, range 31 to 79 years, 86% men) who underwent coronary angioplasty procedure for a chronic total occlusion between 1981 and 1992. RESULTS--Procedural success was achieved in 191 lesions (61.2%). A major complication occurred in six patients (1.9%). Multiple stepwise logistic regression analysis identified the presence of bridging collaterals (p < 0.001), the absence of a tapered entry configuration (p < 0.001), estimated duration of occlusion of greater than three months (p = 0.001), and a vessel diameter of less than 3 mm (p = 0.003) as independent predictors of procedural failure. The logistic regression model was used to classify patients into groups of high, intermediate, and low probability of procedural success with cut off points of 70% and 30%. The predictive value for procedural success (probability > or = 70%) was 91% (95% confidence intervals (95% CI) 83% to 96%) and predictive value for procedural failure (probability < 30%) was 81% (95% CI 64% to 92%). CONCLUSIONS--Percutaneous transluminal coronary angioplasty of chronic total occlusions is associated with a low risk of acute complication. Procedural success is influenced by easily identifiable clinical and angiographic features and the multiple regression model described may help to improve selection of patients.