OBJECTIVE: To develop a simple risk model as a basis for evaluating care of patients admitted with acute myocardial infarction. METHODS: From coronary care registers, biochemistry records and hospital management systems, 2153 consecutive patients with confirmed acute myocardial infarction were identified. With 30 day all cause mortality as the end point, a multivariable logistic regression model of risk was constructed and validated in independent patient cohorts. The areas under receiver operating characteristic curves were calculated as an assessment of sensitivity and specificity. The model was reapplied to a number of commonly studied subgroups for further assessment of robustness. RESULTS: A three variable model was developed based on age, heart rate, and systolic blood pressure on admission. This produced an individual probability of death by 30 days (P(30)) where P(30) = 1/(1 + exp(-L(30))) and L(30) = -5.624 + (0.085 x age) + (0.014 x heart rate) - (0.022 x systolic blood pressure). The areas under the receiver operating characteristic curves for the reference and test cohorts were 0.79 (95% CI 0.76 to 0.82) and 0.76 (95% CI 0.72 to 0.79), respectively. To aid application of the model to routine clinical audit, a normogram relating observed mortality and sample size to the likelihood of a significant deviation from the expected 30 day mortality rate was constructed. CONCLUSIONS: This risk model is simple, reproducible, and permits quality of care of acute myocardial infarction patients to be reliably evaluated both within and between centres.
OBJECTIVE: To develop a simple risk model as a basis for evaluating care of patients admitted with acute myocardial infarction. METHODS: From coronary care registers, biochemistry records and hospital management systems, 2153 consecutive patients with confirmed acute myocardial infarction were identified. With 30 day all cause mortality as the end point, a multivariable logistic regression model of risk was constructed and validated in independent patient cohorts. The areas under receiver operating characteristic curves were calculated as an assessment of sensitivity and specificity. The model was reapplied to a number of commonly studied subgroups for further assessment of robustness. RESULTS: A three variable model was developed based on age, heart rate, and systolic blood pressure on admission. This produced an individual probability of death by 30 days (P(30)) where P(30) = 1/(1 + exp(-L(30))) and L(30) = -5.624 + (0.085 x age) + (0.014 x heart rate) - (0.022 x systolic blood pressure). The areas under the receiver operating characteristic curves for the reference and test cohorts were 0.79 (95% CI 0.76 to 0.82) and 0.76 (95% CI 0.72 to 0.79), respectively. To aid application of the model to routine clinical audit, a normogram relating observed mortality and sample size to the likelihood of a significant deviation from the expected 30 day mortality rate was constructed. CONCLUSIONS: This risk model is simple, reproducible, and permits quality of care of acute myocardial infarctionpatients to be reliably evaluated both within and between centres.
Authors: J Collinson; M D Flather; K A Fox; I Findlay; E Rodrigues; P Dooley; P Ludman; J Adgey; T J Bowker; R Mattu Journal: Eur Heart J Date: 2000-09 Impact factor: 29.983
Authors: K L Lee; L H Woodlief; E J Topol; W D Weaver; A Betriu; J Col; M Simoons; P Aylward; F Van de Werf; R M Califf Journal: Circulation Date: 1995-03-15 Impact factor: 29.690
Authors: Richard A Brogan; Christopher J Malkin; Phillip D Batin; Alexander D Simms; James M McLenachan; Christopher P Gale Journal: World J Cardiol Date: 2014-08-26
Authors: Gaurav Gulati; Jenica Upshaw; Benjamin S Wessler; Riley J Brazil; Jason Nelson; David van Klaveren; Christine M Lundquist; Jinny G Park; Hannah McGinnes; Ewout W Steyerberg; Ben Van Calster; David M Kent Journal: Circ Cardiovasc Qual Outcomes Date: 2022-03-31