Clinical and technical evidence

A literature search was carried out for this briefing in accordance with the interim process and methods statement. This briefing includes the most relevant or best available published evidence relating to the clinical effectiveness of the technology. Further information about how the evidence for this briefing was selected is available on request by contacting

Published evidence

Two prospective studies involving a total of 1,900 people with symptoms of coronary artery disease (CAD) who were referred to CT coronary angiography (CTCA) or invasive coronary angiography (ICA), are summarised in this briefing.

Table 1 summarises the clinical evidence as well as its strengths and limitations.

Overall assessment of the evidence

A study by Winther et al. (2016) of 225 people with symptoms suggestive of stable angina, compared the diagnostic accuracy of the technology with that of the Diamond-Forrester score, coronary artery calcium score (CACS), and their combinations (using quantitative ICA as the reference standard). This study showed that CAD-score version 2 (V2; the acoustic component only) had a diagnostic accuracy of 72%. This was similar to the Diamond-Forrester score (79%) but lower than the CACS (86%). Combining the CAD-score V2 and the Diamond-Forrester score increased accuracy to 82%. Nearly a third of patients (31%) were re-classified as having very low risk using the Diamond-Forrester score and CAD-score combined, suggesting a potential for this technology in risk stratification.

The Dan-NICAD study (Winther et al. 2018) involved 1,675 patients with symptoms, who had a low to intermediate risk of CAD. This study looked at 2 CAD-score algorithms: the previously used version, CAD-score V2; and the newer algorithm, CAD-score V3, which combined the acoustic algorithm with clinical risk factors. At a cut-off CAD-score of more than 20 the newer algorithm had a sensitivity, specificity, positive predictive value and negative predictive value of 81%, 53%, 16% and 96%, respectively, for detecting haemodynamically important CAD (using invasive fractional flow reserve as a reference standard).

The evidence base for the CADScor system is limited in quantity, with only 1 of the studies evaluating the latest algorithm, which includes both the acoustic and clinical parameters. All of the available data come from centres in Denmark and may not be generalisable to NHS practice because of differences in the initial management and stratification of patients before further diagnostic investigations (CTCA and ICA). Also, most of the comparative data for the technology is against the Diamond-Forrester clinical risk score, which is no longer recommended by NICE as a method for risk stratification before CTCA. This is because the model is known to overestimate the probability of CAD relative to its true prevalence. In the largest multicentre study (Winther et al. 2018), most people (99%) were of European family origin. Further studies in a more heterogeneous population might clarify whether diagnostic accuracy is linked to family origin.

Table 1 Summary of selected studies

Winther et al. (2016)

Study size, design and location

Prospective observational study involving 255 people referred to either CTCA or ICA because of symptoms suggestive of CAD.

Intervention and comparator(s)

Intervention: CADScor.

Comparator: CACS and DF score.

Key outcomes

Diagnostic accuracy (ROC AUC) was 72% for the CAD-score and 79% for DF score (p=0.12), and 86% for CACS (p<0.01). Combining the CAD-score and DF score increased diagnostic accuracy to 82%, which was statistically significantly higher than the standalone CAD-score (p<0.01) and DF score (p<0.05), and comparable to CACS alone (p=0.28). There was a limited benefit in combining CAD-score or DF score with CACS or combining all 3 together. The optimal CAD-score threshold was 24.2. At this value the sensitivity, specificity, PPV and NPV were 76%, 59%, 42% and 87%, respectively. When using CAD-score combined with DF, 31% of people were re-classified as having very low risk of CAD compared with 13% of people with DF score alone. Results from this study suggest that combining clinical risk factors with the use of the CADScor system may optimise patient selection for diagnostic investigation.

Strengths and limitations

Patients were recruited consecutively. Single-centre study that was part-funded by the company. Patients with diastolic murmurs were not included in the study. Data from the study population were used in the development and validation of the CAD-score, implying a risk of overfitting the algorithm to this population.

Winther et al. (2018)

Study size, design and location

Prospective observational study involving 1,675 people with low to intermediate likelihood of CAD who had been referred for CTCA because of symptoms suggestive of obstructive CAD.

Intervention and comparator(s)

Intervention: CADScor system

QCA and FFR were used as reference standards.

Key outcomes

In this study, an updated CAD-score algorithm that included both acoustic features and clinical risk factors was developed and validated. Low risk was indicated by a CAD-score value of equal to or more than 20. CAD-Score correlated moderately with coronary artery calcium score (CACS; r=0.38, p<0.001), with a statistically significant decrease in CAD-score between the CACS groups. 50% of people in the study had an updated CAD-score value ≤20. At this cut-off, sensitivity was 81%, specificity 53%, PPV 16% and NPV 96% for diagnosing haemodynamically important CAD (using QCA as a reference standard). The diagnostic accuracy of CAD-score in detecting anatomically obstructive stenosis (using FFR as a reference standard) was comparable at the same cut-off. Compared with DF, CAD-score had a significantly better diagnostic accuracy in detecting anatomically obstructive CAD (ROC AUC: 66% compared with 72%, p<0.01). CAD-Score was not related to the location of the stenosis.

Strengths and limitations

Involved a large number of consecutively enrolled patients. The cohort was almost entirely Caucasian (99%) with low to intermediate prevalence of CAD, generalisation of results to other populations and healthcare environments may not be possible. Current research is part-funded by the company.

Abbreviations: AUC, area under the curve; CACS, coronary artery calcium score; CTCA, CT coronary angiography; DF, Diamond-Forrester score; FFR, fractional flow reserve; ICA, invasive coronary angiography; NPV, negative predictive value; PPV, positive predictive value; QCA, quantitative coronary analysis; ROC, receiver operating characteristic.

Recent and ongoing studies

Two ongoing studies were identified: