Over 6 million patients undergo evaluation for chest pain in the United States each year [
1,
2]. A large subset of these patients will have a diagnosis other than an acute coronary syndrome (ACS), while 1-5% of these patients will be inappropriately discharged with true myocardial infarctions [
3,
4]. Misinterpretation of electrocardiogram findings has been cited as a major contributor to missed myocardial infarctions in the emergency department (ED) [
3]. Patients who are inappropriately discharged have a mortality rate that is nearly twice that of patients who are admitted [
4]. Unfortunately, efforts to identify these high-risk patients by historical data and available risk scores have proven unreliable [
5‐
7]. To maintain both safety and efficiency, recent work had addressed patients at very low risk for ACS who may not require further testing [
8], highly sensitive cardiac biomarkers [
9‐
15], rapid imaging modalities for cardiac risk stratification [
16‐
20], and post-risk stratification scores that incorporate demographic and clinical data [
21]. In this context, a highly sensitive, rapid, and non-invasive risk assessment tool would improve the triage of patients with chest pain, especially in low-risk ED populations. Furthermore, such a tool could prove to be useful in settings where biomarkers and advanced technology are not readily accessible.
The acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) was developed as a means to risk-stratify patients in real time [
22,
23]. Subsequent studies have demonstrated that this tool accurately estimates the risk of acute cardiac ischemia in an undifferentiated patient population, and substantially speeds decision making and triage by novice clinicians [
22,
24‐
26]. When used in a low-risk population of patients with chest pain admitted to an observation unit, a dichotomous ACI-TIPI cut point of 20% or greater was shown to be predictive of nonnegative exercise treadmill tests [
27]. Two systematic reviews of available cardiac risk stratification technologies by the National Heart Attack Alert Program have graded the evidence supporting ACI-TIPI as Class A, one of only three tools to receive this rating [
28].
The ability of an ACI-TIPI cut point to identify patients at very low risk of myocardial infarction (less than 2% at 45 days) has been studied in patients admitted to the hospital or chest pain observation units [
8,
22,
25,
29], but not as a prognostic indicator in an undifferentiated cohort presenting with symptoms concerning for myocardial infarction. Identification of patients with chest pain who are at low-risk for cardiovascular events at 30 days may improve decision-making regarding initial triage and disposition. The primary objective in this prospective multicenter study is to define an optimal ACI-TIPI cut point that can predict 30-day major adverse cardiac events (MACE) in patients with acute chest pain.