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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
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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.


Copyright ©2004 by the American College of Physicians


Ann Intern Med. 1997;127(7):538-556. doi:10.7326/0003-4819-127-7-199710010-00006
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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.

Figures

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Appendix Figure. Generation of the database used for constructing and testing predictive instruments. A. Component predictive instrument for 30-day mortality. B. Component predictive instrument for 1-year mortality. C. Component predictive instrument for cardiac arrest. D. Component predictive instrument for thrombolysis-related intracranial hemorrhage. E. Component predictive instrument for thrombolysis-related major bleed.
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Figure 1.
Acute (30-day) mortality. Top.Bottom.n

The thrombolytic predictive instrument's predicted probability of 30-day mortality by patient age. The example is for a man with an initial systolic blood pressure of 130 mm Hg, a heart rate of 70 beats/min, no history of diabetes, one lead with abnormal Q waves, 60% of leads with early changes, and an infarction size of 20 for an anterior acute myocardial infarction (AMI) and 5 for an inferior AMI. TT = thrombolytic therapy. Predicted and actual 30-day mortality rates. Mortality rates as predicted by the 30-day mortality component predictive instrument and the actual mortality rates, in deciles of ascending severity of illness (risk for dying), are depicted on the combined development and test data sets ( = 2369).

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Figure 2.
Long-term (1-year) mortality. Top.Bottom.n

The thrombolytic predictive instrument's predicted probability of 1-year mortality by patient age. The example is for a man with an initial systolic blood pressure of 130 mm Hg, a heart rate of 70 beats/min, no history of diabetes, one lead with abnormal Q waves, 60% of leads with early changes, and an infarction size of 20 for an anterior acute myocardial infarction (AMI) and 5 for an inferior AMI. TT = thrombolytic therapy. Predicted and actual 1-year mortality rates. Mortality rates as predicted by the 1-year mortality component predictive instrument and the actual mortality rates, in deciles of ascending severity of illness (risk for dying), are depicted on the combined development and test data sets ( = 1878).

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Figure 3.
Cardiac arrest. Top.Bottom.

The thrombolytic predictive instrument's predicted probability of cardiac arrest by systolic blood pressure. The example is for a baseline set of characteristics for a 65-year-old patient with 1 hour of chest pain, 8 mm of ST-segment elevation, and an infarction size of 10. TT = thrombolytic therapy. Predicted and actual cardiac arrest rates. Cardiac arrest rates as predicted by the cardiac arrest component predictive instrument and the actual cardiac arrest rates, in quartiles of ascending severity of illness, are depicted. These rates are not the prevalence-adjusted rates but are on a scale of prevalence observed among the patients in the model construction data set (61 patients with a cardiac arrest among 296 total patients [20.6%]).

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Figure 4.
Intracranial hemorrhage. Top.Bottom.

The thrombolytic predictive instrument's predicted probability of intracranial hemorrhage by pulse pressure. The example is for patients with a systolic blood pressure of 140 mm Hg or more. Predicted and actual intracranial hemorrhage rates. Intracranial hemorrhage rates as predicted by the intracranial hemorrhage component predictive instrument and the actual intracranial hemorrhage rates, in quartiles of ascending severity of illness, are depicted. These rates are not the prevalence-adjusted rates but are on a scale of prevalence observed among the patients in the model construction data set (18 patients with an intracranial hemorrhage among 190 patients [9.5%]).

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Figure 5.
Major bleeding. Top.Bottom.

The thrombolytic predictive instrument's predicted probability of major bleeding by systolic blood pressure. The predicted probabilities for a major nonintracranial bleeding episode requiring a transfusion are for a 50-year-old patient with a heart rate of 90 beats/min and no history of hypertension. Predicted and actual rates of major bleeding. Rates of bleeding that require transfusion, as predicted by the major bleed component predictive instrument, and the actual rate of bleeding requiring transfusion, in deciles of ascending severity of illness, are depicted. These rates are not the prevalence-adjusted rates but are on a scale of prevalence observed among the patients in the model construction data set (55 patients with a major systemic bleeding episode among 740 patients [7.4%]).

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Figure 6.
Thrombolytic predictive instrument electrocardiograms. Top.Bottom.

Example of a thrombolytic predictive instrument electrocardiogram for a patient likely to benefit from thrombolytic therapy. Example of a thrombolytic predictive instrument electrocardiogram for a patient less clearly likely to benefit from thrombolytic therapy. MI = myocardial infarction.

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