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A predictive instrument using contrast echocardiography in patients presenting to the emergency department with chest
Kevin Wei1, Dawn Peters, Todd Belcik
1Division of Cardiovascular Medicine and Biostatistics, Oregon Health and Science University, Portland, Oregon 97239, USA.
Insights
A new risk score combining clinical, ECG, and MCE data accurately predicts adverse cardiac events within 48 hours for emergency department patients. This tool can help identify low-risk patients for early discharge.
Area of Science:
- Cardiology
- Emergency Medicine
- Diagnostic Imaging
Background:
- Risk stratification of emergency department (ED) patients with suspected cardiac chest pain (CP) and undifferentiated electrocardiograms (ECGs) is challenging.
- Accurate prediction of adverse cardiac events is crucial for timely intervention and patient management.
Purpose of the Study:
- To develop and validate a risk score using clinical, ECG, and myocardial contrast echocardiography (MCE) variables to predict adverse events within 48 hours.
- To improve the risk stratification of patients presenting with undifferentiated CP in the ED.
Main Methods:
- A risk model was developed in 1166 patients (cohort 1) and validated in 720 patients (cohort 2).
- Patients with CP (≥30 minutes) and non-ST-elevation ECG were assessed using MCE for regional function (RF) and myocardial perfusion (MP).
- Multivariate analysis identified ECG abnormalities, abnormal RF with normal MP, and abnormal RF with abnormal MP as significant predictors.
Main Results:
- The risk score demonstrated high predictive accuracy with a concordance probability of 0.82 (cohort 1) and 0.83 (cohort 2).
- The score stratified patients into 5 distinct risk groups, with event rates ranging from 0.3% to 58%.
- Significant predictors included ECG abnormalities (OR 2.5-2.9) and MCE findings (OR 3.5-9.6).
Conclusions:
- A bedside risk score integrating clinical, ECG, and MCE findings accurately predicts short-term adverse events in patients with suspected cardiac CP and non-diagnostic ECGs.
- This predictive instrument can enhance ED care by enabling risk-based management, potentially allowing low-risk patients (score of 0) to be discharged.
- Further validation in multicenter studies is recommended.
Objective:
Risk stratification of patients presenting to the emergency department (ED) with suspected cardiac chest pain (CP) and an undifferentiated electrocardiogram (ECG) is difficult. We hypothesized that in these patients a risk score incorporating clinical, ECG, and myocardial contrast echocardiography (MCE) variables would accurately predict adverse events occurring within the next 48 hours.
Methods:
Patients with CP lasting for 30 minutes or more who did not have ST-segment elevation on the ECG, were enrolled. Regional function (RF) and myocardial perfusion (MP) were assessed by MCE. A risk model was developed in the initial 1166 patients (cohort 1) and validated in subsequent 720 patients (cohort 2). Any abnormality or ST changes on ECG (odds ratio [OR] 2.5; 95% confidence interval [CI], 1.4-4.5, P = .002, and OR 2.9, 95% CI, 1.7-4.8, P < .001, respectively), abnormal RF with normal MP (OR 3.5, 95% CI, 1.8-6.5, P < .001), and abnormal RF with abnormal MP (OR 9.6, 95% CI, 5.8-16.0, P < .001) were found to be significant multivariate predictors of nonfatal myocardial infarction or cardiac death.
Results:
The estimate of the probability of concordance for the risk model was 0.82 for cohort 1 and 0.83 for cohort 2. The risk score in both cohorts stratified patients into 5 distinct risk groups with event rates ranging from 0.3% to 58%.
Conclusion:
A simple predictive instrument has been developed from clinical, ECG, and MCE findings obtained at the bedside that can accurately predict events occurring within 48 hours in patients presenting to the ED with suspected cardiac CP and an ECG that is not diagnostic for acute ischemic injury. Its application could enhance care of patients with CP in the ED. For instance, patients with a risk score of 0 could be discharged from the ED without further workup. However, this needs to be validated in a multicenter study.
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