OBJECTIVES: Our objectives were to develop a risk-stratification model addressing the importance of the magnitude and distribution of ST segment depression in predicting long-term outcomes and to validate the model in an analogous patient population. BACKGROUND: Although patients without ST segment elevation presenting with acute coronary syndromes represent an increasingly frequent population admitted to coronary care units, little attention has been paid to quantifying their ST segment abnormalities. METHODS: ST segment depression was categorized into three groups: 1) no ST segment depression; 2) 1-mm ST segment depression in two contiguous leads; and 3) ST segment depression > or =2 mm in two contiguous leads. A logistic regression model was developed using Platelet IIb/IIIa Antagonism for the Reduction of Acute coronary syndrome events in a Global Organization Network (PARAGON-A) data to assess the prognostic value of the extent and distribution of ST segment depression in predicting one-year mortality. The model was validated using the non-ST segment elevation population in Global Use of Strategies To Open occluded arteries in acute coronary syndromes (GUSTO-IIb). RESULTS: ST segment depression was the strongest predictor of one-year mortality, accounting for 35% of the model's predictive power. Patients with ST segment depression > or =2 mm were approximately 6 times (odds ratio [OR] 5.73, 95% confidence interval [CI] 2.8 to 11.6) more likely to die within one year than patients with no ST segment depression. On validation, the model showed good discriminatory power (c-index = 0.75). Patients with ST segment depression > or =2 mm in more than one region were almost 10 times more likely to die within one year than patients with no ST segment depression. CONCLUSIONS: These data provide new evidence supporting the powerful prognostic value of the baseline electrocardiogram and, in particular, the magnitude and distribution of ST segment depression in predicting unfavorable events.
OBJECTIVES: Our objectives were to develop a risk-stratification model addressing the importance of the magnitude and distribution of ST segment depression in predicting long-term outcomes and to validate the model in an analogous patient population. BACKGROUND: Although patients without ST segment elevation presenting with acute coronary syndromes represent an increasingly frequent population admitted to coronary care units, little attention has been paid to quantifying their ST segment abnormalities. METHODS: ST segment depression was categorized into three groups: 1) no ST segment depression; 2) 1-mm ST segment depression in two contiguous leads; and 3) ST segment depression > or =2 mm in two contiguous leads. A logistic regression model was developed using Platelet IIb/IIIa Antagonism for the Reduction of Acute coronary syndrome events in a Global Organization Network (PARAGON-A) data to assess the prognostic value of the extent and distribution of ST segment depression in predicting one-year mortality. The model was validated using the non-ST segment elevation population in Global Use of Strategies To Open occluded arteries in acute coronary syndromes (GUSTO-IIb). RESULTS: ST segment depression was the strongest predictor of one-year mortality, accounting for 35% of the model's predictive power. Patients with ST segment depression > or =2 mm were approximately 6 times (odds ratio [OR] 5.73, 95% confidence interval [CI] 2.8 to 11.6) more likely to die within one year than patients with no ST segment depression. On validation, the model showed good discriminatory power (c-index = 0.75). Patients with ST segment depression > or =2 mm in more than one region were almost 10 times more likely to die within one year than patients with no ST segment depression. CONCLUSIONS: These data provide new evidence supporting the powerful prognostic value of the baseline electrocardiogram and, in particular, the magnitude and distribution of ST segment depression in predicting unfavorable events.
Authors: P Kaul; L K Newby; Y Fu; D B Mark; S G Goodman; G S Wagner; R A Harrington; C B Granger; F Van de Werf; E M Ohman; P W Armstrong Journal: Heart Date: 2005-07 Impact factor: 5.994
Authors: Thao Huynh; James Nasmith; The Minh Luong; Martin Bernier; Chantal Pharand; Zhao Xue-Qiao; Robert P Giugliano; Pierre Theroux Journal: Can J Cardiol Date: 2009-12 Impact factor: 5.223