1998
DOI: 10.7326/0003-4819-129-11_part_1-199812010-00002
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Use of the Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) To Assist with Triage of Patients with Chest Pain or Other Symptoms Suggestive of Acute Cardiac Ischemia: A Multicenter, Controlled Clinical Trial

Abstract: Use of ACI-TIPI was associated with reduced hospitalization among emergency department patients without acute cardiac ischemia. This result varied as expected according to the CCU and cardiac telemetry unit capacities and physician supervision at individual hospitals. Appropriate admission for unstable angina or acute infarction was not affected. If ACI-TIPI is used widely in the United States, its potential incremental impact may be more than 200000 fewer unnecessary hospitalizations and more than 100000 fewe… Show more

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Cited by 314 publications
(204 citation statements)
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“…[2][3][4][5][6][7][8][9][10][11][12][13] Although clinical algorithms can successfully risk stratify patients, they have not typically been considered useful in identifying a group of patients with a 30-day 1% risk for an adverse event who can safely be discharged from the ED. [2][3][4][5][6][7][8][9][10][11][12][13][14] Coronary computerized tomographic angiography (CTA) has been shown to have excellent diagnostic accuracy when compared to cardiac catheterization [15][16][17][18][19][20][21] and appears to perform as well as myocardial perfusion imaging in identifying patients at low risk for cardiovascular events. [22][23][24][25][26] Observational studies of coronary CTA have found that patients with normal coronary CTA results are at low risk for adverse events over 1-2 years; however, these studies either were small or involved patients who had other standard assessments to aid in clinical management.…”
mentioning
confidence: 99%
“…[2][3][4][5][6][7][8][9][10][11][12][13] Although clinical algorithms can successfully risk stratify patients, they have not typically been considered useful in identifying a group of patients with a 30-day 1% risk for an adverse event who can safely be discharged from the ED. [2][3][4][5][6][7][8][9][10][11][12][13][14] Coronary computerized tomographic angiography (CTA) has been shown to have excellent diagnostic accuracy when compared to cardiac catheterization [15][16][17][18][19][20][21] and appears to perform as well as myocardial perfusion imaging in identifying patients at low risk for cardiovascular events. [22][23][24][25][26] Observational studies of coronary CTA have found that patients with normal coronary CTA results are at low risk for adverse events over 1-2 years; however, these studies either were small or involved patients who had other standard assessments to aid in clinical management.…”
mentioning
confidence: 99%
“…4,29,30 The challenge is to safely identify the subset of low-risk patients who do not require such lengthy and costly rule-out evaluations. 1,31,32 The VCP Rule shows some promise in identifying this group. However, the rule is still limited by the requirement for sampling of cardiac enzymes at 2 hours post initial evaluation, which practically means that the patient needs to be observed in the ED for a minimum of a 3-to 4-hour period.…”
Section: Discussionmentioning
confidence: 99%
“…In practice, a small, but significant, proportion of patients is sent home inappropriately [14] leading to potentially serious clinical errors and litigation. On the other hand, many relatively low-risk patients are inappropriately admitted to telemetry and high dependency units to rule out acute cardiac ischaemia [15]. In the centres used for that study, around 2% of patients were inappropriately discharged from emergency departments, while about 30% of patients presenting with acute chest pain were admitted with possible acute coronary syndrome but ultimately had the diagnosis ruled out.…”
Section: Clinical Motivationmentioning
confidence: 99%
“…Various statistical and computer-based methods have been used to analyze clinical and ECG data from chest pain patients with a view to improving identification of high-risk patients at presentation. These methods include logistic regression [15,9] classification trees [18,19], and artificial neural networks (ANNs) [11,20]. Each of these methods has advantages and disadvantages although, suitably optimized, they can all provide accurate classification of low-and high-risk patients from data available at presentation.…”
Section: Clinical Motivationmentioning
confidence: 99%
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