Key result
Historical prediction models (Duke Clinical Score and Diamond-Forrester) significantly overestimated CAD prevalence (51% and 38% predicted vs 21% actual; P<0.001) with modest predictive ability.
Why the study?
An optimal investigation strategy for patients with suspected angina pectoris remains elusive, and the relative value of guideline-recommended historical models versus pain characteristics alone to predict coronary artery disease needed evaluation.
Do historical prediction models (DCS and DF) accurately predict the presence of CAD in patients with suspected angina pectoris compared to pain characteristics alone?
Observational (n=1,376)
Do historical prediction models (DCS and DF) accurately predict the presence of CAD in patients with suspected angina pectoris compared to pain characteristics alone?
Effect estimate: AUC 0.695 (DCS) and 0.693 (DF)
p-value: p=<0.001
Historical prediction models like the Duke Clinical Score and Diamond-Forrester model significantly overestimate CAD prevalence and offer modest predictive ability that is not clinically superior to symptom characteristics alone in contemporary chest pain clinic patients.
Historical models may overestimate CAD prevalence without outperforming symptoms alone; leaves open need for contemporary validation before routine use.
BACKGROUND: An optimal investigation strategy for patients with suspected angina pectoris (AP) remains elusive. Present guidelines use the Duke Clinical Score (DCS) or the Diamond-Forrester (DF) model to compute the likelihood of coronary artery disease (CAD). This prospective study of patients referred to a chest pain clinic compares the relative values of these two historical models and of pain characteristics only to predict the presence of CAD. PATIENTS AND METHODS: Overall, 1376 patients reviewed in a chest pain clinic were assigned to five CAD likelihood groups (<10, 10-29, 30-60, 61-90 and >90%) using DCS and to three CAD likelihood groups (<15, 15-85 and >85%) using the DF model. Patients were diagnosed with CAD when they had either obstructive (>70%) coronary stenoses or a positive functional test. RESULTS: In all, 652 (47%) patients had nonanginal CP, 412 (30%) patients had atypical AP and 312 (23%) had typical AP. Four hundred seventeen (30%) patients were not investigated for CAD because of nonanginal symptoms and/or low CAD probability. The actual CAD prevalence was 21% versus a DCS predicted one of 51% and a DF model predicted one of 38% (P<0.001). Both models had modest predictive abilities with areas under the curve of of 0.695 and 0.693 and did not show useful clinical superiority over a prediction model using pain characteristics only (area under the curve: 0.65). CONCLUSION: CAD prevalence in patients referred for suspected AP is significantly lower than expected by using historical prediction models. The use of risk factors profile and demographics in addition to symptoms characteristics does not improve diagnostic accuracy.
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Thiru et al. (2019) conducted an observational in Suspected angina pectoris (n=1,376). Duke Clinical Score (DCS) and Diamond-Forrester (DF) model vs. Pain characteristics only was evaluated on Presence of coronary artery disease (obstructive >70% coronary stenoses or positive functional test) (AUC 0.695 (DCS) and 0.693 (DF), p=<0.001). Historical prediction models (Duke Clinical Score and Diamond-Forrester) significantly overestimated CAD prevalence (51% and 38% predicted vs 21% actual; P<0.001) with modest predictive ability.
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