Patient-Specific Predictions of Outcomes in Myocardial Infarction for Real-Time Emergency Use: A Thrombolytic Predictive Instrument

Thrombolytic therapy for acute myocardial infarction has great potential for reducing this most common cause of death in the United States. However, the benefits of this therapy depend on treating appropriate candidates as early as possible. Beyond the early hours and in patients with very small or low-risk infarctions, thrombolytic treatment offers little likelihood of benefit yet still incurs the risk for complications and costs. Many patients who would benefit from thrombolysis are not being treated because they are not recognized as suitable candidates and because of the fear of complications. To broadly maximize the benefit of thrombolytic therapy, a real-time method is needed in the emergency clinical setting that 1) identifies patients most likely to benefit from thrombolytic therapy and 2) facilitates the earliest possible administration of this therapy. Predictive instruments for acute cardiac ischemia have been developed to assist emergency department physicians in making admission decisions for the hospital and coronary care unit [1-4]. These logistic regression predictive models use a patient's clinical and electrocardiographic characteristics at presentation to compute the probability of acute ischemia. Incorporated into a computerized electrocardiograph, the probability can be printed automatically on the electrocardiogram [3-5]. Clinical trials have shown that this decision aid speeds [6] and improves the accuracy of [7] emergency department triage of patients presenting with chest pain or other symptoms that suggest cardiac ischemia. Thus, printing the probabilities of predictive instruments on electrocardiograms may be an effective way to provide decision support to physicians. An electrocardiograph-based instrument that could provide emergency clinicians with real-time predictions of outcomes of thrombolytic therapy for acute infarction might also improve care. Unfortunately, the currently available predictive instrument for death from acute infarction [8] does not take into account the use of thrombolytic therapy, nor does it predict outcomes other than acute mortality. The major purpose of our study was to create a thrombolytic predictive instrument that would predict, for an individual patient, the effect of thrombolysis on the likelihood of key clinical outcomes: acute mortality, long-term mortality, cardiac arrest, and serious complications of thrombolytic therapy [hemorrhagic stroke and major bleeding]. Incorporating this model into a computerized electrocardiograph so that its predictions are automatically printed on the electrocardiogram could help physicians 1) identify the patients most likely to benefit from thrombolytic therapy and 2) facilitate their earliest possible treatment. A secondary purpose was to produce a time-insensitive predictive instrument [4, 8-10] that could provide risk-adjusted outcome predictions for retrospectively judging quality of care. Methods Creation of the thrombolytic predictive instrument required component predictive instruments that would predict the probabilities of five outcomes for a given patient with ST-segment elevation of at least 1 mm [0.1 mV] in two or more leads on the electrocardiogram. These were 1) acute [30-day] mortality from acute infarction if given and if not given thrombolytic therapy; 2) long-term [1-year] mortality if given and if not given thrombolytic therapy; 3) cardiac arrest within 48 hours after the first electrocardiogram if given and if not given thrombolytic therapy; 4) intracranial hemorrhage as a complication, if given thrombolytic therapy; and 5) bleeding that necessitates transfusion, if given thrombolytic therapy. These models were constructed and tested in three phases. Phase One: Creation of the Thrombolytic Predictive Instrument Database The Thrombolytic Predictive Instrument Database and its derivative development and validation data sets included the original data on patients with myocardial infarction from 13 clinical trials and registries [1, 2, 11-22]. Individual studies contributed between 57 and 1387 patients each; together, they included 107 hospitals of all types throughout the United States and 4911 patients treated between 1976 and 1989 (91% were treated after 1980). Because the individual trials and registries used different inclusion and exclusion criteria, we created uniform criteria for inclusion in the Thrombolytic Predictive Instrument Database. All trials of thrombolytic therapy used similar enrollment criteria, but registries and the acute ischemia predictive instrument trials used broader criteria. The database was restricted to persons who would have been prospectively included in the thrombolytic trials: Persons who were 75 years of age or younger, had onset of chest pain or other ischemic symptoms of acute ischemia within 9 hours of presentation, had systolic blood pressure of 190 mm Hg or less, had ST-segment elevation of 1 mm (0.1 mV) or more in at least two contiguous leads on electrocardiogram, and had no known contraindications to thrombolytic therapy. The steps involved in generating specific data sets for each component predictive instrument are shown in the Appendix Figure. Appendix Figure. For all patients in the database, we obtained electrocardiograms from the time of first presentation and follow-up and uniformly coded them lead-by-lead by using standardized measurement criteria [23]. Important missing data were obtained from original study and hospital records. Missing data on 30-day and 1-year mortality were obtained from the National Death Index. Original hospital records were reviewed for the data needed to confirm the intracranial hemorrhage and cardiac arrest component predictive instruments [11]. For the data needed to create the intracranial hemorrhage, detailed medical record reviews were done for all 59 thrombolysis-treated patients who were designated by their original studies as having had a stroke and for 216 control patients, matched by hospital, who are described elsewhere [24]. For the data needed to construct the cardiac arrest, medical records were reviewed for 428 patients recorded as having had a cardiac arrest and for 428 control patients matched by hospital. Medical records were available for 85% of these patients; data obtained from the medical records included presenting electrolyte concentrations; electrocardiogram details, including corrected QT intervals [25]; details of any invasive procedures; and details of the possible cardiac arrest. Finally, data from the participating studies were compared for consistency of known major effects on outcomes [11]. One subset, the Duke Coronary Care Unit Database from the prethrombolytic era, had substantially sicker patients who were much more likely to have previously had infarctions; thus, this subset was not included in the final model development or in test sets. Before the mortality models were constructed, the database was randomly divided into a development data set, which consisted of two thirds of all patients (n = 3263), and a test data set, which consisted of the remaining one third (n = 1648). Patients were randomly assigned to one data set or the other; stratification was done on study of origin to ensure that similar proportions of patients from the contributing studies were assigned to the development and test sets. For the cardiac arrest, intracranial hemorrhage, and major bleeding models, all patients were used for development. Phase Two: Construction of the Thrombolytic Predictive Instrument A separate predictive instrument, each designed to be a component of the thrombolytic predictive instrument, was developed for each of the five outcomes by using logistic regression. For each component predictive instrument, we selected a subset of clinically important and statistically significant variables for inclusion in preliminary regression models. We then investigated alternative forms and combinations of these variables, reformulating models to optimize performance while keeping the models as parsimonious as possible. In this process, we created two special electrocardiogram-based variables to reflect two key determinants of the effect of thrombolytic therapy on outcome: a measure of acute infarction size based on ST segments and an indicator of earliness in the course of the acute infarction, based on T-wave changes (Appendices A and B). The logistic model for the 30-day mortality instrument was constructed on 1568 patients, 1224 of whom received thrombolytic therapy (Appendix Figure, panel A). The 1-year mortality instrument was based on the 1237 patients (894 of whom received thrombolytic therapy) for whom 1-year follow-up information was available (Appendix Figure, panel B). Patients who died during their index hospitalization were classified as dead at 30 days, even if their hospital stay was longer than 30 days. The model for the cardiac arrest component predictive instrument was based on 296 patients, 61 of whom had confirmed primary cardiac arrests (defined as sudden loss of consciousness associated with ventricular tachycardia or fibrillation requiring cardioversion or cardiopulmonary resuscitation within 48 hours of admission) and 221 of whom received thrombolytic therapy (Appendix Figure, panel C). Patients originally considered to have possibly had a cardiac arrest were excluded if the cardiac arrest was thought to have occurred before admission, after coronary artery bypass surgery, after progressive hemodynamic deterioration of more than 1 hour or presentation while in cardiogenic shock (Killip class IV), or during catheter manipulations associated with cardiac catheterization or if the cardiac arrest was not confirmed upon record review. Control patients were excluded because of missing data or failure to satisfy inclusion criteria. The thrombolysis-related intracranial hemorrhage component predictive instrument was based on 190 patients (18 with intracranial hemorrhage) [24] (Append