AIMS: To identify features predictive of hospital coronary artery bypass graft (CABG) surgery in patients with acute coronary syndromes (ACSs). METHODS AND RESULTS: Data from 17,434 patients enrolled in an observational study were analysed. Patients in private hospitals were more likely to undergo CABG than those in public hospitals (10.3% vs. 6.9%, P<0.01); CABG was more frequent in the USA than in Europe (11.9 % vs 3.5%, P<0.01). Clinical features independently predictive of CABG on multivariable analysis included no previous CABG, male sex, history of angina, hypertension, hyperlipidaemia, or diabetes, no history of atrial fibrillation or congestive heart failure, ST depression in multiple territories, and absence of ST elevation. These factors were assigned a score to quantify the likelihood of CABG (c-statistic 0.69). This score was predictive regardless of ACS subgroup (c-statistic 0.65-0.71) and remained predictive across institutions regardless of the frequency with which CABG was performed. The score was of greatest clinical utility among hospitals performing CABG in >10% of their ACS patients. CONCLUSIONS: Identifying ACS patients likely to undergo CABG using clinical features alone remains difficult. In hospitals with higher rates of surgical revascularisation, a subgroup of patients with an approximate 30% likelihood of CABG can be identified. Therapy in these patients can be tailored to minimise bleeding risk without compromising outcomes.
AIMS: To identify features predictive of hospital coronary artery bypass graft (CABG) surgery in patients with acute coronary syndromes (ACSs). METHODS AND RESULTS: Data from 17,434 patients enrolled in an observational study were analysed. Patients in private hospitals were more likely to undergo CABG than those in public hospitals (10.3% vs. 6.9%, P<0.01); CABG was more frequent in the USA than in Europe (11.9 % vs 3.5%, P<0.01). Clinical features independently predictive of CABG on multivariable analysis included no previous CABG, male sex, history of angina, hypertension, hyperlipidaemia, or diabetes, no history of atrial fibrillation or congestive heart failure, ST depression in multiple territories, and absence of ST elevation. These factors were assigned a score to quantify the likelihood of CABG (c-statistic 0.69). This score was predictive regardless of ACS subgroup (c-statistic 0.65-0.71) and remained predictive across institutions regardless of the frequency with which CABG was performed. The score was of greatest clinical utility among hospitals performing CABG in >10% of their ACS patients. CONCLUSIONS: Identifying ACS patients likely to undergo CABG using clinical features alone remains difficult. In hospitals with higher rates of surgical revascularisation, a subgroup of patients with an approximate 30% likelihood of CABG can be identified. Therapy in these patients can be tailored to minimise bleeding risk without compromising outcomes.
Authors: Ali Shafiq; Jae-Sik Jang; Faraz Kureshi; Timothy J Fendler; Kensey Gosch; Phil G Jones; David J Cohen; Richard Bach; John A Spertus Journal: Ann Thorac Surg Date: 2016-06-03 Impact factor: 4.330
Authors: Ali Shafiq; Javier Valle; Jae-Sik Jang; Mohammed Qintar; Kensey Gosch; David J Cohen; Mandeep Singh; Richard Bach; John A Spertus Journal: J Am Heart Assoc Date: 2016-06-10 Impact factor: 5.501