A clinical scoring system in undifferentiated chest pain predicting undetectable troponin concentration

Abstract

a b s t r a c tBackground: Chest pain is the most common reason for emergency admission to hospital, but the majority of these are due to non-cardiac pain. We sought to determine which combination of clinical features is more likely to predict an undetectable troponin level in patients presenting with chest pain. Methods: We collected data over a two-month period on consecutive patients presenting acutely to hospital with chest pain and who had a troponin I measured. We recorded basic demographics, risk factors, pain distribution, associated symptoms, physical findings and ECG changes. The parameters significantly associated with troponin positivity were entered into a stepwise logistic regression analysis and the resulting model's coefficients were used to construct a simple clinical score to categorise patients into low, medium or high probability of having a positive troponin. Results: 26 of 157 (16.6%) patients had a positive troponin. The variables retained in the regression model were: age >65, heart rate >80, previous myocardial infarction, diabetes and pain radiating to either arm. The model showed good discrimination (area under ROC curve 0.869, 95% CI 0.806 e 0.917). Using the regression model's coefficients, patients were grouped into low, intermediate or high probability groups. Being in the low probability group had a negative predictive value of 97.8% and being in the high probability group had a positive predictive value of 65.2%. The majority (73.9%) of patients could be categorised as either low or high probability. Discussion: This simple scoring system, if prospectively validated, may be useful in identifying low risk patients with chest pain who are unlikely to have elevation of serum troponin concentration.

Validating a Pragmatic Approach to Cognitive Screening in Stroke Prevention Clinics Using the Montreal Cognitive Assessment

“…20,21 Statistical approaches to 3-class classification problems have been developed 22,23 and expanded beyond to multiple class classification problems. 24 This approach has been used to support clinical decision making, for example, in screening patients presenting with chest pain, 25 or in differentiating normal aging from CI and dementia. 26 We seek to validate a pragmatic, clinically useful method using ROC and area under the curve analysis of the MoCA to identify patients at low, intermediate and high likelihood of having CI.…”

“…The same concept applies to the methodology of classifying patients when using 2 cut points as with that of a single, optimized cut point. 23,25,26 When a single cut point was chosen, those who scored below the single cut point generated for both high specificity and specificity formed the high-risk group, and those who scored above the cut point formed the low-risk group. When 2 cut points were chosen, those who scored below the cut point generated for high specificity formed the high-risk group and those who scored above the cut point generated for high sensitivity formed the low-risk group.…”