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Limitations of risk score models in patients with acute chest pain
Alex F Manini1, Nina Dannemann2, David F Brown3
1Harvard Affiliated Emergency Medicine Residency, Boston, MA, USA.
Insights
Existing risk scores poorly identify acute coronary syndrome (ACS) in chest pain patients. Cardiac multidetector computed tomography (CMCT) may be needed for accurate risk stratification.
Area of Science:
- Cardiology
- Diagnostic Imaging
- Emergency Medicine
Background:
- Cardiac multidetector computed tomography (CMCT) shows promise for screening acute chest pain patients.
- Established risk models (Goldman, TIMI, Sanchis) exist for risk stratification.
- The utility of these models in conjunction with CMCT candidates is under investigation.
Purpose of the Study:
- To assess the sensitivity of existing cardiovascular risk models in predicting in-hospital acute coronary syndrome (ACS).
- To evaluate if these models can justify the use of CMCT in patients presenting with acute chest pain.
- To test the hypothesis that no model achieves ≥90% sensitivity.
Main Methods:
- 148 patients with chest pain, non-diagnostic ECG, and negative biomarkers were analyzed.
- TIMI, Goldman, and Sanchis risk scores were applied and categorized.
- Agreement between risk scores was assessed using weighted kappa statistics.
Main Results:
- 11% of patients (17/148) were diagnosed with ACS.
- All risk models demonstrated poor sensitivity for ACS detection (35%-53%).
- Risk score agreement was poor to moderate; "low risk" patients had significant ACS rates (8%-9%).
Conclusions:
- Current cardiovascular risk scores exhibit low sensitivity for identifying ACS in acute chest pain patients.
- Findings suggest a need for further investigation in larger cohorts to confirm these results.
- The poor performance of existing scores may support the role of CMCT in this patient population.
Objectives:
Cardiac multidetector computed tomography (CMCT) has potential to be used as a screening test for patients with acute chest pain, but several tools are already used to risk-stratify this population. Risk models exist that stratify need for intensive care (Goldman), short-term prognosis (Thrombolysis in Myocardial Infarction, TIMI), and 1-year events (Sanchis). We applied these cardiovascular risk models to candidates for CMCT and assessed sensitivity for prediction of in-hospital acute coronary syndrome (ACS). We hypothesized that none of the models would achieve a sensitivity of 90% or greater, thereby justifying use of CMCT in patients with acute chest pain.
Methods:
We analyzed TIMI, Goldman, and Sanchis in 148 consecutive patients with chest pain, nondiagnostic electrocardiogram, and negative initial cardiac biomarkers who previously met inclusion and exclusion criteria for the Rule-Out Myocardial Infarction Using Coronary Artery Tomography Study. ACS was adjudicated, and risk scores were categorized based on established criteria. Risk score agreement was assessed with weighted kappa statistics.
Results:
Overall, 17 (11%) of 148 patients had ACS. For all risk models, sensitivity was poor (range, 35%-53%), and 95% confidence intervals did not cross above 77%. Agreement to risk-classify patients was poor to moderate (weighted kappa range, 0.18-0.43). Patients categorized as "low risk" had nonzero rates of ACS using all 3 scoring models (range, 8%-9%).
Conclusions:
Available risk scores had poor sensitivity to detect ACS in patients with acute chest pain. Because of the small number of patients in this data set, these findings require confirmation in larger studies.
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