Acute Cardiac Ischemia Time-insensitive Predictive Instrument Research Articles

An example of medical device-based projection of clinical trial enrollment: Use of electrocardiographic data to identify candidates for a trial in acute coronary syndromes.

To identify potential participants for clinical trials, electronic health records (EHRs) are searched at potential sites. As an alternative, we investigated using medical devices used for real-time diagnostic decisions for trial enrollment. To project cohorts for a trial in acute coronary syndromes (ACS), we used electrocardiograph-based algorithms that identify ACS or ST elevation myocardial infarction (STEMI) that prompt clinicians to offer patients trial enrollment. We searched six hospitals' electrocardiograph systems for electrocardiograms (ECGs) meeting the planned trial's enrollment criterion: ECGs with STEMI or > 75% probability of ACS by the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI). We revised the ACI-TIPI regression to require only data directly from the electrocardiograph, the e-ACI-TIPI using the same data used for the original ACI-TIPI (development set n = 3,453; test set n = 2,315). We also tested both on data from emergency department electrocardiographs from across the US (n = 8,556). We then used ACI-TIPI and e-ACI-TIPI to identify potential cohorts for the ACS trial and compared performance to cohorts from EHR data at the hospitals. Receiver-operating characteristic (ROC) curve areas on the test set were excellent, 0.89 for ACI-TIPI and 0.84 for the e-ACI-TIPI, as was calibration. On the national electrocardiographic database, ROC areas were 0.78 and 0.69, respectively, and with very good calibration. When tested for detection of patients with > 75% ACS probability, both electrocardiograph-based methods identified eligible patients well, and better than did EHRs. Using data from medical devices such as electrocardiographs may provide accurate projections of available cohorts for clinical trials.

Prognostic utility of the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI)

BackgroundWe sought to evaluate the test characteristics of the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) in relation to 30-day major adverse cardiac events (MACE) among patients who presented to the Emergency Department with symptoms suggestive of an acute coronary syndrome. We then examined the test characteristics of various dichotomous ACI-TIPI cut points.MethodsWe prospectively recruited a cohort of Emergency Department (ED) patients with acute chest pain at two urban university hospitals between June and September 2006. Upon enrollment, baseline demographics and cardiac risk factors were collected. An electrocardiogram (ECG) was performed and analyzed with the built-in ACI-TIPI multiple regression model software. An ACI-TIPI probability score was recorded for each patient. Diagnostic test characteristics of ACI-TIPI for MACE (non-ST elevation myocardial infarction (NSTEMI), percutaneous coronary intervention, coronary artery bypass grafting, and all-cause mortality) within 30 days were determined.ResultsOf 144 patients enrolled (mean age 59.1 ± 14.1 years, 59% men), 19 (13%) patients suffered MACE within 30 days. Receiver-operating characteristics (ROC) for ACI-TIPI yielded a c-statistic of 0.69 (95% CI 0.59-0.80, p < 0.01). An ACI-TIPI score of ≥ 20 had 100% sensitivity (95% CI 82-100), 100% negative predictive value (95% CI 86-100), and 21% specificity (14-31%).ConclusionsThese preliminary results suggest that, while ACI-TIPI has limited discriminatory value for MACE overall, a score of < 20 may have 30-day prognostic utility to allow for safe outpatient management in patients with acute chest pain.

Emergency Medical Service Predictive Instrument–Aided Diagnosis and Treatment of Acute Coronary Syndromes and ST-segment Elevation Myocardial Infarction in the IMMEDIATE Trial

Background. A challenge for emergency medical service (EMS) is accurate identification of acute coronary syndromes (ACS) and ST-segment elevation myocardial infarction (STEMI) for immediate treatment and transport. The electrocardiograph-based acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) and the thrombolytic predictive instrument (TPI) have been shown to improve diagnosis and treatment in emergency departments (EDs), but their use by paramedics in the community has been less studied. Objective. To identify candidates for participation in the Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency Care (IMMEDIATE) Trial, we implemented EMS use of the ACI-TIPI and the TPI in out-of-hospital electrocardiographs and evaluated its impact on paramedic on-site identification of ACS and STEMI as a community-based approach to improving emergency cardiac care. Methods. Ambulances in the study municipalities were outfitted with electrocardiographs with ACI-TIPI and TPI software. Using a before–after quasi-experimental design, in Phase 1, for seven months, paramedics were provided with the ACI-TIPI/TPI continuous 0–100% predictions automatically printed on electrocardiogram (ECG) text headers to supplement their identification of ACS; in Phase 2, for 11 months, paramedics were told to identify ACS based on an ACI-TIPI cutoff probability of ACS ≥ 75% and/or TPI detection of STEMI. In Phase 3, this cutoff approach was used in seven additional municipalities. Confirmed diagnoses of ACS, acute myocardial infarction (AMI), and STEMI were made by blinded physician review for 100% of patients. Results. In Phase 1, paramedics identified 107 patients as having ACS; in Phase 2, 104. In Phase 1, 45.8% (49) of patients so identified had ACS confirmed, which increased to 76.0% (79) in Phase 2 (p < 0.001). Of those with ACS, 59.2% (29) had AMI in Phase 1 versus 84.8% (67) with AMI in Phase 2 (p < 0.01), and STEMI was confirmed in 40.8% (20) versus 68.4% (54), respectively (p < 0.01). In Phase 3, of 226 patients identified by paramedics as having ACS, 74.3% (168) had ACS confirmed, of whom 81.0% (136) had AMI and 65.5% (110) had STEMI. Among patients with ACS, the proportion who received percutaneous coronary intervention (PCI) was 30.6% (15) in Phase 1, increasing to 57.0% (45) in Phase 2 (p < 0.004) and 50.6% (85) in Phase 3, and the proportions of patients with STEMI receiving PCI rose from 75.0% (15) to 83.3% (45) (p < 0.4) and 82.7% (91). Conclusions. In a wide range of EMS systems, use of electrocardiographs with ACI-TIPI and TPI decision support using a 75% ACI-TIPI cutoff improves paramedic diagnostic performance for ACS, AMI, and STEMI and increases the proportions of patients who receive PCI. Key words: acute coronary syndromes; acute myocardial infarction; electrocardiology; emergency medical service; clinical decision support.

Early Cardiac Catheterization Laboratory Activation by Paramedics for Patients with ST-segment Elevation Myocardial Infarction on Prehospital 12-Lead Electrocardiograms

Background. Prompt reperfusion in ST-segment elevation myocardial infarction (STEMI) saves lives. Although studies have shown that paramedics can reliably interpret STEMI on prehospital 12-lead electrocardiograms (p12ECGs), prehospital activation of the cardiac catheterization laboratory by emergency medical services (EMS) has not yet gained widespread acceptance. Objective. To quantify the potential reduction in time to percutaneous coronary intervention (PCI) by early prehospital activation of the cardiac catheterization laboratory in STEMI. Methods. This prospective, observational study enrolled all patients diagnosed with STEMI by paramedics in a mid-sized regional EMS system. Patients were enrolled if: 1) the paramedic interpreted STEMI on the p12ECG, 2) the Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) score was 75%% or greater, and 3) the patient was transported to either of two area PCI centers. Data recorded included the time of initial EMS “STEMI alert” from the scene, time of arrival at the emergency department (ED), and time of actual catheterization laboratory activation by the ED physician, all using synchronized clocks. The primary outcome measure was the time difference between the STEMI alert and the actual activation (i.e., potential time savings). The false-positive rate (patients incorrectly diagnosed with STEMI by paramedics) was also calculated and compared with a locally accepted false-positive rate of 10%%. Results. Twelve patients were enrolled prior to early termination of the study. The mean and median potential time reductions were 15 and 11 minutes, respectively (range 7–29 minutes). There was one false STEMI alert (8.3%% false-positive rate) for a patient with a right bundle branch block who subsequently had a non–ST-segment elevation myocardial infarction. The study was terminated when our cardiologists adopted a prehospital catheterization laboratory activation protocol based on our initial data. Conclusion. Important reductions in time to reperfusion seem possible by activation of the catheterization laboratory by EMS from the scene, with an acceptably low false-positive rate in this small sample. This type of clinical research can inform multidisciplinary policies and bring about meaningful clinical practice changes.

37: Utility of Acute Cardiac Ischemia Time-Insensitive Predictive Instrument in the Chest Pain Observation Unit

To evaluate if the Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) is associated with outcomes for emergency department (ED) patients in the chest pain observation unit (CPU).

Triage and Diagnosis of Chest Pain in Rural Hospitals: Implementation of the ACI-TIPI in the High Plains Research Network

The Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) has been shown to improve diagnostic accuracy of acute cardiac ischemia (ACI) and decrease coronary care unit admissions in urban emergency departments. The purpose of this study was to determine the impact of the ACI-TIPI on triage and diagnosis of patients with chest pain in rural hospitals. We undertook a controlled trial of the impact ACI-TIPI use in the High Plains Research Network (HPRN). Main outcome measures were the triage of patients in emergency departments (admission, transfer, or discharge home) and diagnostic accuracy. There were 1,861 patients seen during a 10-month period. Forty-five percent of all patients complaining of chest pain were discharged home from the emergency department. Eight percent were transferred from the emergency department, and another 10% were transferred later after admission. Among patients with acute myocardial infarction or unstable angina, 22.2% were transferred directly from the emergency department and only 3% were discharged home when ACI-TIPI was available, compared with 18.7% transferred and 5.2% discharged home when not available (P = .4). Diagnostic accuracy was high and not statistically different with the addition of the ACI-TIPI score (86.8% ACI-TIPI off vs 89.0% ACI-TIPI on, P = .15), Physicians in the HPRN provided appropriate diagnosis and triage to patients with chest pain. Routine addition of the ACI-TIPI score did not improve diagnostic accuracy or significantly change triage. Further research on ACI-TIPI in rural hospitals is necessary before recommending routine use of the ACI-TIPI.

The Potential of Training to Increase Acceptance and Use of Computerized Decision Support Systems for Medical Diagnosis

The goals of this study were to understand the reasons underlying the limited use of medical decision-support tools and to explore the potential of a computer-based tutorial to mitigate barriers to use. Medical decision-support tools such the Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) have demonstrated statistical validity and clinical impact for patient safety but have seen limited adoption and use. The study developed a brief Web-based "demystifying" ACI-TIPI tutorial employing case-based training and evaluated the effectiveness of that tutorial in changing self-reported attitudes and behaviors. Clinicians using the tutorial reported greater understanding of how to use the ACI-TIPI score appropriately and increased confidence in the score. Case studies in the tutorial that provided examples of how to use the score for actual cases were rated as especially helpful. This study suggests that a primary barrier to the use of statistical decision support tools for patient diagnosis is lack of training or experience in combining a population-based numerical risk score with other diagnostic information about the individual patient's case that is not considered in that score. The results of this study indicate that there is a potential for a relatively brief tutorial to increase acceptance and use of decision support tools for medical diagnosis. These findings have the potential for the identification of methods to help clinicians learn how to use statistical and probabilistic information to better assess risk and to promote integration of decision support tools into medical decision making for improvement of patient safety.

Performance of a Population-based Cardiac Risk Stratification Tool in Asian Patients with Chest Pain

Most contemporary cardiac risk stratification tools have been derived and validated in mixed-race populations. Their validity in single-race populations has not been tested. The authors sought to compare the performance of a risk stratification tool between a mixed-race U.S. patient population and an Asian patient population. This study is an analysis of data from the Internet Tracking Registry for Acute Coronary Syndromes (i(*)trACS) registry of patients with chest pain presenting to the emergency departments of eight U.S. centers and one site in Singapore. The Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) was computed for included patients, and its performance in predicting acute coronary syndrome (ACS) was compared between patients from the United States and Singapore. Of the 11,991 included patients, 1,120 experienced ACS. Although the ACI-TIPI demonstrated similar accuracy among groups (area under the curve, 0.729 [U.S.] vs. 0.719 [Singapore]; p = 0.5611), sensitivity and specificity were different when equal ACI-TIPI thresholds were considered. Recreating the logistic regression models used to create the ACI-TIPI showed similar results between the derived parameters and the parameters estimated for the U.S. group. In contrast, age older than 50 years (log-odds ratio [LOR], 0.107; 95% confidence interval [CI] = 0.518 to 0.713), male gender (LOR, 0.487; 95% CI = 0.149 to 1.122), and chest pain as a primary complaint (LOR, 0.237; 95% CI = 0.139 to 0.613) had little predictive power in patients from Singapore. Differences exist in presentation and factors associated with ACS among patients from the United States and Singapore that may affect the performance of risk stratification tools. These findings suggest that cardiac clinical decision rules need international validation.

Diagnostic accuracy of Computer-Assisted Database-Derived pretest probability assessment for acute coronary syndrome

Study objectives: We determine the diagnostic accuracy of a noninvasive clinical screening instrument for the detection of acute coronary syndrome (ACS) in emergency department (ED) patients. The screening instrument is composed of clinical data input into a computer to estimate the probability of ACS. Methods: We aim to enroll 1,000 patients with complete data who were observed in an ED chest pain evaluation unit at Carolinas Medical Center, Duke University, and the University of California–Davis, and prospectively collect data to determine the pretest probability (PTP) of ACS for each patient using 3 methods of PTP assessment: (1) a computer-assisted, database-derived method; (2) the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) method; and (3) physician estimate to the nearest 1%. This information will be used to compare which method of PTP assessment categorizes the highest percentage of patients as being very low risk for ACS (ie, PTP Results: We completed enrollment on August 1, 2004, and follow-up and adjudication of outcomes continues. We enrolled a total of 1,154 patients and expect to obtain valid follow-up in 85% of subjects. Complete pretest probability are data available for 723 patients. Only 12 (1.6%) patients with a positive outcome were identified. The mean (median) pretest probability estimates by the 3 methods were 5.2% (4.0%) for physician estimate, 6.4% (4.3%) for attribute matching, and 13.6% (9.9%) for ACI-TIPI. The percentage of all patients estimated to have a pretest probability of ACS of less than 2% was 19% (95% confidence interval [CI] 16% to 22%) for clinicians, 34% (95% CI 31% to 38%) for attribute-matching, and 9% (95% CI 7% to 12%) for ACI-TIPI. No patient with a pretest probability of less than 2% estimated by any of the 3 methods developed ACS. Conclusion: At interim analysis, the rate of positive outcomes for ACS was unexpectedly low in this sample of patients evaluated in ED chest pain units. Attribute matching categorized significantly more patients as having a very low pretest probability compared with clinician estimate or ACI-TIPI.

Test of the Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) for Prehospital Use

Study objectives: To test diagnostic performance for acute cardiac ischemia (ACI) in a manually calculated and in a computerized, ECG-calculated ACI time-insensitive predictive instrument (ACI-TIPI) in prehospital chest pain patients. Methods: We carried out prospective inclusion and data acquisition with retrospective analysis. Over a 6-month period, 439 adult emergency medical services patients with chest pain underwent prehospital electrocardiography. Because of incomplete data, 77 cases were excluded, leaving a study sample of 362 patients. Excluded patients did not differ significantly with respect to age, sex, final diagnosis, or history of myocardial infarction, heart surgery, diabetes, or stroke. ACI-TIPI probabilities of ACI were computed on the basis of the prehospital ECGs as interpreted retrospectively and independently by two study investigators blinded to patient outcome, with a specially programmed electrocardiograph, and with a computer algorithm further modified by logistic-regression analysis. Results: Diagnostic performance on the basis of receiver operating characteristic (ROC) curve areas of the ACI-TIPI was scored, by the two physician readers, .73 and .74; and by ECG, .75. Patients with low ACI-TIPI probability (0% to 9%) had no acute myocardial infarctions, a 2.3% incidence of angina, and no prehospital life-threatening events. Conclusion: ACI-TIPI probabilities of ACI as generated by a specially programmed electrocardiograph are comparable to those based on physician ECG interpretations and may be useful in the prehospital evaluation of chest pain. [Aufderheide TP, Rowlandson I, Lawrence SW, Kuhn EM, Selker HP: Test of the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) for prehospital use. Ann Emerg Med February 1996;27:193-198.]