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Patient-Specific Predictions of Outcomes in Myocardial Infarction for Real-Time Emergency Use: A Thrombolytic Predictive Instrument
- Harry P. Selker, MD, MSPH;
- John L. Griffith, PhD;
- Joni R. Beshansky, RN, MPH;
- Christopher H. Schmid, PhD;
- Robert M. Califf, MD;
- Ralph B. D'Agostino, PhD;
- Michael M. Laks, MD;
- Kerry L. Lee, PhD;
- Charles Maynard, PhD;
- Ronald H. Selvester, MD;
- Galen S. Wagner, MD; and
- W. Douglas Weaver, MD
- From New England Medical Center, Tufts University School of Medicine, and Boston University, Boston, Massachusetts; Duke University Medical Center, Durham, North Carolina; University of Washington School of Medicine, Seattle, Washington; Harbor-UCLA Medical Center and University of Southern California, Los Angeles, California. Acknowledgments: The authors thank the investigators and staff of the studies that formed the basis of the Thrombolytic Predictive Instrument Database: Western Washington Intracoronary Streptokinase Trial, Western Washington Intravenous Streptokinase Trial, Western Washington tPA Study, Myocardial Infarction Triage and Intervention (MITI) Project Registry, Thrombolysis and Angioplasty in Myocardial Infarction (TAMI) Trials, Multicenter Acute Ischemia Heart Disease Predictive Instrument Trial, Boston City Hospital Acute Ischemic Heart Disease Predictive Instrument Trial, and Duke Coronary Care Unit Databank. They also thank Merritt H. Raitt, MD, and John L. Turner, MD, for input on the technical aspects of electrocardiographic and related issues and Bonnie G. Macleod, Teresa E. Pazdral, and Hyla S. Cohen, MS, for their attention to the construction of the Thrombolytic Predictive Instrument Database. Grant Support: In part by grant RO1 HS06208 from the Agency for Health Care Policy and Research. Requests for Reprints: Harry P. Selker, MD, Center for Cardiovascular Health Services Research, Division of Clinical Care Research, New England Medical Center, 750 Washington Street #63, Boston, MA 02111. Current Author Addresses: Drs. Selker, Griffith, and Schmid and Ms. Beshansky: New England Medical Center, 750 Washington Street #63, Boston, MA 02111. Dr. Califf: Duke University Medical Center, 2024 West Main Street, Bay A-108, Durham, NC 27705. Dr. D'Agostino: Boston University, Department of Mathematics, 111 Cummington Street, Boston, MA 02215. Dr. Laks: Harbor-UCLA Medical Center RB-2, 1000 West Carson Street, Torrance, CA 90509. Dr. Lee: Duke University Medical Center, PO Box 3363, Durham, NC 27710. Dr. Maynard: 9833 Belfair Lane, Bellevue, WA 98004. Dr. Selvester: 6298 East Ocean Boulevard, Long Beach, CA 90803. Dr. Wagner: Duke University Medical Center, PO Box 3636, Durham, NC 27710. Dr. Weaver: Division of Cardiology, K-14, Henry Ford Health System, 2799 West Grand Boulevard, Detroit, MI 48202.
Abstract
Background: Thrombolytic therapy can be life-saving in patients with acute myocardial infarction. However, if given too late or insufficiently selectively, it may provide little benefit but still cause serious complications and incur substantial costs.
Objective: To develop a thrombolytic predictive instrument for real-time use in emergency medical service settings that could 1) identify patients likely to benefit from thrombolysis and 2) facilitate the earliest possible use of this therapy.
Design: Creation and validation of logistic regression-based predictive instruments based on secondary analysis of clinical data.
Patients: 4911 patients who had acute myocardial infarction and ST-segment elevation on electrocardiogram; 3483 received thrombolytic therapy.
Measurements: Data were obtained from 13 major clinical trials and registries and directly from medical records, including electrocardiograms obtained at presentation. Input variables include presenting clinical and electrocardiographic features; predictive models generate probabilities for acute (30-day) mortality if and if not treated with thrombolysis, 1-year mortality rates if and if not treated with thrombolysis, cardiac arrest if and if not treated with thrombolysis, thrombolysis-related intracranial hemorrhage, and thrombolysis-related major bleeding episode requiring transfusion. Together, these models constitute the thrombolytic predictive instrument.
Results: The predictive models generated the following mean predictions for patients in the Thrombolytic Predictive Instrument Database: 30-day mortality rate, 7.1%; 1-year mortality rate, 10.9%; rate of cardiac arrest, 3.7%; rate of thrombolysis-related intracranial hemorrhage, 0.6%; and rate of other thrombolysis-related major bleeding episodes, 5.0%. They discriminated well between persons having and those not having the predicted outcome; areas under the receiver-operating characteristic (ROC) curve were between 0.77 and 0.84 for the five outcomes. Calibration between each instrument's predicted and observed rates was excellent. Validation of the predictive instruments for 30-day and 1-year mortality, done on a separate test dataset, yielded areas under the ROC curve of 0.76 for each.
Conclusions: After the basic features of a clinical presentation are entered into a computerized electrocardiograph, the predictions of the thrombolytic predictive instrument can be printed on the electrocardiogram report. This decision aid may facilitate earlier and more appropriate use of thrombolytic therapy in patients with acute myocardial infarction.
- Copyright ©2004 by the American College of Physicians
