BACKGROUND: Limited data exist on the long-term outcomes among diabetic patients undergoing saphenous vein graft (SVG) interventions. Thus, the baseline clinical factors associated with long-term adverse events in these patients are less known. METHODS: Accordingly, we analyzed 1,160 consecutive patients (37.7% with diabetes) undergoing SVG interventions from the Duke Cardiovascular Disease Database (1990-2003). Cox proportional hazards modeling was used to identify predictors of long-term death in diabetic patients. The most significant model predictors were then used to construct a decision tree providing unadjusted Kaplan-Meier survival estimates at a median follow-up of 4 years. RESULTS: At median follow-up of 4 years, death (33.3% vs 18.1%, P < .0001; unadjusted hazard ratio 1.98, 95% CI 1.64-2.38) and death or myocardial infarction (49.6% vs 32.9%, unadjusted hazard ratio 1.71, 95% CI 1.462.00) were significantly higher in patients with diabetes mellitus compared with those without it. In patients with diabetes undergoing SVG interventions, a simple clinical decision algorithm, based on the most significant model predictors, demonstrated that 88% of patients without heart rate >80 beat/min, congestive heart failure, renal insufficiency, or hypertension survived after SVG intervention at median follow-up of 4 years. In contrast, none of the few patients with all these 4 factors survived at follow-up (100% mortality). CONCLUSIONS: Compared with patients without diabetes, diabetic patients undergoing SVG intervention have significantly worse long-term outcomes with one third dying at median follow-up of 4 years. We provide a simple decision tool that allows stepwise risk-stratification using baseline factors in diabetic patients undergoing SVG interventions and identify 4 risk factors associated with extremely poor long-term survival in this cohort.
BACKGROUND: Limited data exist on the long-term outcomes among diabeticpatients undergoing saphenous vein graft (SVG) interventions. Thus, the baseline clinical factors associated with long-term adverse events in these patients are less known. METHODS: Accordingly, we analyzed 1,160 consecutive patients (37.7% with diabetes) undergoing SVG interventions from the Duke Cardiovascular Disease Database (1990-2003). Cox proportional hazards modeling was used to identify predictors of long-term death in diabeticpatients. The most significant model predictors were then used to construct a decision tree providing unadjusted Kaplan-Meier survival estimates at a median follow-up of 4 years. RESULTS: At median follow-up of 4 years, death (33.3% vs 18.1%, P < .0001; unadjusted hazard ratio 1.98, 95% CI 1.64-2.38) and death or myocardial infarction (49.6% vs 32.9%, unadjusted hazard ratio 1.71, 95% CI 1.462.00) were significantly higher in patients with diabetes mellitus compared with those without it. In patients with diabetes undergoing SVG interventions, a simple clinical decision algorithm, based on the most significant model predictors, demonstrated that 88% of patients without heart rate >80 beat/min, congestive heart failure, renal insufficiency, or hypertension survived after SVG intervention at median follow-up of 4 years. In contrast, none of the few patients with all these 4 factors survived at follow-up (100% mortality). CONCLUSIONS: Compared with patients without diabetes, diabeticpatients undergoing SVG intervention have significantly worse long-term outcomes with one third dying at median follow-up of 4 years. We provide a simple decision tool that allows stepwise risk-stratification using baseline factors in diabeticpatients undergoing SVG interventions and identify 4 risk factors associated with extremely poor long-term survival in this cohort.
Authors: Kirsten Riches; John Huntriss; Claire Keeble; Ian C Wood; David J O'Regan; Neil A Turner; Karen E Porter Journal: Diab Vasc Dis Res Date: 2016-12-21 Impact factor: 3.291
Authors: Theodor Baars; Thomas Konorza; Philipp Kahlert; Stefan Möhlenkamp; Raimund Erbel; Gerd Heusch; Petra Kleinbongard Journal: Cardiovasc Diabetol Date: 2013-01-10 Impact factor: 9.951
Authors: Anna C Roberts; Jai Gohil; Laura Hudson; Kyle Connolly; Philip Warburton; Rakesh Suman; Peter O'Toole; David J O'Regan; Neil A Turner; Kirsten Riches; Karen E Porter Journal: J Diabetes Res Date: 2015-04-08 Impact factor: 4.011