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Ray M, Ruthazer R, Beshansky JR, et al. A predictive model to identify patients with suspected acute coronary syndromes at high risk of cardiac arrest or in-hospital mortality: An IMMEDIATE Trial sub-study. Int J Cardiol Heart Vasc. 2015;9:37-42doi: 10.1016/j.ijcha.2015.07.001.
Ray, M., Ruthazer, R., Beshansky, J. R., Kent, D. M., Mukherjee, J. T., Alkofide, H., & Selker, H. P. (2015). A predictive model to identify patients with suspected acute coronary syndromes at high risk of cardiac arrest or in-hospital mortality: An IMMEDIATE Trial sub-study. International journal of cardiology. Heart & vasculature, 937-42. https://doi.org/10.1016/j.ijcha.2015.07.001
Ray, Madhab, et al. "A predictive model to identify patients with suspected acute coronary syndromes at high risk of cardiac arrest or in-hospital mortality: An IMMEDIATE Trial sub-study." International journal of cardiology. Heart & vasculature vol. 9 (2015): 37-42. doi: https://doi.org/10.1016/j.ijcha.2015.07.001
Ray M, Ruthazer R, Beshansky JR, Kent DM, Mukherjee JT, Alkofide H, Selker HP. A predictive model to identify patients with suspected acute coronary syndromes at high risk of cardiac arrest or in-hospital mortality: An IMMEDIATE Trial sub-study. Int J Cardiol Heart Vasc. 2015 Dec 01;9:37-42. doi: 10.1016/j.ijcha.2015.07.001. Epub 2015 Aug 27. PMID: 26913292; PMCID: PMC4762054.
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Int J Cardiol Heart Vasc. 2015 Dec 01;9:37-42. doi: 10.1016/j.ijcha.2015.07.001. Epub 2015 Aug 27.

A predictive model to identify patients with suspected acute coronary syndromes at high risk of cardiac arrest or in-hospital mortality: An IMMEDIATE Trial sub-study.

International journal of cardiology. Heart & vasculature

Madhab Ray, Robin Ruthazer, Joni R Beshansky, David M Kent, Jayanta T Mukherjee, Hadeel Alkofide, Harry P Selker

Affiliations

  1. Lahey Hospital and Medical Center, Burlington, MA, United States; Tufts Clinical and Translational Science Institute, Tufts University, Boston, MA, United States; Sackler School of Graduate Biomedical Sciences, Tufts University, Boston, MA, United States.
  2. Center for Cardiovascular Health Services Research, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, United States.
  3. Center for Cardiovascular Health Services Research, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, United States; Regis College, Regulatory and Clinical Research Management, Weston, MA, United States.
  4. Tufts Clinical and Translational Science Institute, Tufts University, Boston, MA, United States; Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, United States; Sackler School of Graduate Biomedical Sciences, Tufts University, Boston, MA, United States.
  5. Tufts Clinical and Translational Science Institute, Tufts University, Boston, MA, United States; Sackler School of Graduate Biomedical Sciences, Tufts University, Boston, MA, United States.
  6. Center for Cardiovascular Health Services Research, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, United States; Tufts Clinical and Translational Science Institute, Tufts University, Boston, MA, United States.

PMID: 26913292 PMCID: PMC4762054 DOI: 10.1016/j.ijcha.2015.07.001

Abstract

BACKGROUND: The IMMEDIATE Trial of emergency medical service use of intravenous glucose-insulin-potassium (GIK) very early in acute coronary syndromes (ACS) showed benefit for the composite outcome of cardiac arrest or in-hospital mortality.

OBJECTIVES: This analysis of IMMEDIATE Trial data sought to develop a predictive model to help clinicians identify patients at highest risk for this outcome and most likely to benefit from GIK.

METHODS: Multivariable logistic regression was used to develop a predictive model for the composite endpoint cardiac arrest or in-hospital mortality using the 460 participants in the placebo arm of the IMMEDIATE Trial.

RESULTS: The final model had four variables: advanced age, low systolic blood pressure, ST elevation in the presenting electrocardiogram, and duration of time since ischemic symptom onset. Predictive performance was good, with a C statistic of 0.75, as was its calibration. Stratifying patients into three risk categories based on the model's predictions, there was an absolute risk reduction of 8.6% with GIK in the high-risk tertile, corresponding to 12 patients needed to treat to prevent one bad outcome. The corresponding values for the low-risk tertile were 0.8% and 125, respectively.

CONCLUSIONS: The multivariable predictive model developed identified patients with very early ACS at high risk of cardiac arrest or death. Using this model could assist treating those with greatest potential benefit from GIK.

Keywords: Acute coronary syndrome; Cardiac arrest; Emergency medical service; Glucose–insulin–potassium (GIK); Mortality; Predictive model

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