Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease.

BACKGROUND: The presence of coronary plaques with high-risk characteristics is strongly associated with adverse cardiac events beyond the identification of coronary stenosis. Testing by coronary computed tomography angiography (CCTA) enables the identification of high-risk plaques (HRP). Referral for CCTA is presently based on pre-test probability estimates including clinical risk factors (CRFs); however, proteomics and/or genetic information could potentially improve patient selection for CCTA and, hence, identification of HRP. We aimed to (1) identify proteomic and genetic features associated with HRP presence and (2) investigate the effect of combining CRFs, proteomics, and genetics to predict HRP presence. METHODS: Consecutive chest pain patients (n = 1462) undergoing CCTA to diagnose obstructive coronary artery disease (CAD) were included. Coronary plaques were assessed using a semi-automatic plaque analysis tool. Measurements of 368 circulating proteins were obtained with targeted Olink panels, and DNA genotyping was performed in all patients. Imputed genetic variants were used to compute a multi-trait multi-ancestry genome-wide polygenic score (GPSMult). HRP presence was defined as plaques with two or more high-risk characteristics (low attenuation, spotty calcification, positive remodeling, and napkin ring sign). Prediction of HRP presence was performed using the glmnet algorithm with repeated fivefold cross-validation, using CRFs, proteomics, and GPSMult as input features. RESULTS: HRPs were detected in 165 (11%) patients, and 15 input features were associated with HRP presence. Prediction of HRP presence based on CRFs yielded a mean area under the receiver operating curve (AUC) ± standard error of 73.2 ± 0.1, versus 69.0 ± 0.1 for proteomics and 60.1 ± 0.1 for GPSMult. Combining CRFs with GPSMult increased prediction accuracy (AUC 74.8 ± 0.1 (P = 0.004)), while the inclusion of proteomics provided no significant improvement to either the CRF (AUC 73.2 ± 0.1, P = 1.00) or the CRF + GPSMult (AUC 74.6 ± 0.1, P = 1.00) models, respectively. CONCLUSIONS: In patients with suspected CAD, incorporating genetic data with either clinical or proteomic data improves the prediction of high-risk plaque presence. TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT02264717 (September 2014).

Exercise electrocardiography for pre-test assessment of the likelihood of coronary artery disease.

OBJECTIVES: To develop a tool including exercise electrocardiography (ExECG) for patient-specific clinical likelihood estimation of patients with suspected obstructive coronary artery disease (CAD). METHODS: An ExECG-weighted clinical likelihood (ExECG-CL) model was developed in a training cohort of patients with suspected obstructive CAD undergoing ExECG. Next, the ExECG-CL model was applied in a CAD validation cohort undergoing ExECG and clinically driven invasive coronary angiography and a prognosis validation cohort and compared with the risk factor-weighted clinical likelihood (RF-CL) model for obstructive CAD discrimination and prognostication, respectively.In the CAD validation cohort, obstructive CAD was defined as >50% diameter stenosis on invasive coronary angiography. For prognosis, the endpoint was non-fatal myocardial infarction and death. RESULTS: The training cohort consisted of 1214 patients (mean age 57 years, 57% males). In the CAD (N=408; mean age 55 years, 53% males) and prognosis validation (N=3283; mean age 57 years, 57% males) cohorts, 11.8% patients had obstructive CAD and 4.4% met the endpoint. In the CAD validation cohort, discrimination of obstructive CAD was similar between the ExECG-CL and RF-CL models: area under the receiver-operating characteristic curves 83.1% (95% CIs 77.5% to 88.7%) versus 80.7% (95% CI 74.6% to 86.8%), p=0.14. In the ExECG-CL model, more patients had very low (≤5%) clinical likelihood of obstructive CAD compared with the RF-CL (42.2% vs 36.0%, p<0.01) where obstructive CAD prevalence and event risk remained low. CONCLUSIONS: ExECG incorporated into a clinical likelihood model improves reclassification of patients to a very low clinical likelihood group with very low prevalence of obstructive CAD and favourable prognosis.

External validation of novel clinical likelihood models to predict obstructive coronary artery disease and prognosis.

OBJECTIVES: The risk factor-weighted and coronary artery calcium score-weighted clinical likelihood (RF-CL and CACS-CL, respectively) models improve discrimination of patients with suspected obstructive coronary artery disease (CAD). However, external validation is warranted.Compared to the 2019 European Society of Cardiology pretest probability (ESC-PTP) model, the aims were (1) to validate the RF-CL and CACS-CL models for identification of obstructive CAD and revascularisation, and (2) to investigate prognosis by CL thresholds. METHODS: Stable de novo chest pain patients (n=1585) undergoing coronary CT angiography (CTA) were investigated. Obstructive CAD was defined as >70% diameter stenosis in a major epicardial vessel on CTA. Decision of revascularisation within 120 days was based on onsite judgement. The endpoint was non-fatal myocardial infarction or cardiovascular death. The ESC-PTP was calculated based on age, sex and symptom typicality, the RF-CL additionally included number of risk factors, and the CACS-CL incorporated CACS to the RF-CL. RESULTS: Obstructive CAD was present in 386/1585 (24.4%) patients, and 91/1585 (5.7%) patients underwent revascularisation. Both the RF-CL and CACS-CL classified more patients to very-low CL (<5%) of obstructive CAD compared with the ESC-PTP model (41.4% and 52.2% vs 19.2%, p<0.001). In very-low CL patients, obstructive CAD and revascularisation prevalences (≤6% and <1%) remained similar combined with low event risk during 5.0 years follow-up. CONCLUSION: In an external validation cohort, the novel RF-CL and CACS-CL models improve categorisation to a very-low CL group with preserved prevalences of obstructive CAD, revascularisation and favourable prognosis.

Combining Polygenic and Proteomic Risk Scores With Clinical Risk Factors to Improve Performance for Diagnosing Absence of Coronary Artery Disease in Patients With de novo Chest Pain.

BACKGROUND: Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS). METHODS: Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography. Based on individual genotypes, a polygenic risk score for CAD (PRSCAD) was calculated. The prediction was performed using combinations of PRSCAD, proteins, and PMRS as features in models using stability selection and machine learning. RESULTS: Prediction of absence of CAD yielded an area under the curve of PRSCAD-model, 0.64±0.03; proteomic-model, 0.58±0.03; and PMRS model, 0.76±0.02. No significant correlation was found between the genetic and proteomic risk scores (Pearson correlation coefficient, -0.04; P=0.13). Optimal predictive ability was achieved by the full model (PRSCAD+protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone (P<0.001). For reclassification purpose, the full model enabled down-classification of 49% (324 of 661) of the 5% to 15% pretest probability patients and 18% (113 of 611) of >15% pretest probability patients. CONCLUSIONS: For patients with chest pain and low-intermediate CAD risk, incorporating targeted proteomics and polygenic risk scores into the risk assessment substantially improved the ability to predict the absence of CAD. Genetics and proteomics seem to add complementary information to the clinical risk factors and improve risk stratification in this large patient group. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT02264717.

Vibrational characterization of cavitation in left ventricular assist device.

BACKGROUND: The left ventricular assist device (LVAD) is a mechanical circulatory support device for patients with severe heart failure. Microbubbles caused by cavitation in the LVAD can potentially lead to physiological and pump-related complications. The aim of this study is to characterize the vibrational patterns in the LVAD during cavitation. METHODS: The LVAD was integrated into an in vitro circuit and mounted with a high-frequency accelerometer. Accelerometry signals were acquired with different relative pump inlet pressures ranging from baseline (+20 mmHg) to -600 mmHg in order to induce cavitation. Microbubbles were monitored with dedicated sensors at the pump inlet and outlet to quantify the degree of cavitation. Acceleration signals were analyzed in the frequency domain to identify changes in the frequency patterns when cavitation occurred. RESULTS: Significant cavitation occurred at the low inlet pressure (-600 mmHg) and was detected in the frequency range between 1800 and 9000 Hz. Minor degrees of cavitation at higher inlet pressures (-300 to -500 mmHg) were detected in the frequency range between 500-700, 1600-1700 Hz, and around 12 000 Hz. The signal power of the dominating frequency ranges was statistically significantly different from baseline signals. CONCLUSION: Vibrational measurements in the LVAD can be used to detect cavitation. A significant degree of cavitation could be detected in a wide frequency range, while minor cavitation activity could only be detected in more narrow frequency ranges. Continuous vibrational LVAD monitoring can potentially be used to detect cavitation and minimize the damaging effect associated with cavitation.

Likelihood reclassification by an acoustic-based score in suspected coronary artery disease.

OBJECTIVE: Validation studies of the 2019 European Society of Cardiology pretest probability model (ESC-PTP) for coronary artery disease (CAD) report that 35%-40% of patients have low pretest probability (ESC-PTP 5% to <15%). Acoustic detection of coronary stenoses could potentially improve clinical likelihood stratification. Aims were to (1) investigate the diagnostic performance of an acoustic-based CAD score and (2) study the reclassification potential of a dual likelihood strategy by the ESC-PTP and a CAD score. METHODS: Consecutive patients (n=1683) with stable angina symptoms referred for coronary CT angiography (CTA) underwent heart sound analyses by an acoustic CAD-score device. All patients with ≥50% luminal stenosis in any coronary segment at coronary CTA were referred to investigation with invasive coronary angiography (ICA) with fractional flow reserve (FFR).A predefined CAD-score cut-off ≤20 was used to rule out obstructive CAD. RESULTS: In total, 439 patients (26%) had ≥50% luminal stenosis on coronary CTA. The subsequent ICA with FFR showed obstructive CAD in 199 patients (11.8%). Using the ≤20 CAD-score cut-off for obstructive CAD rule-out, sensitivity was 85.4% (95% CI 79.7 to 90.0), specificity 40.4% (95% CI 37.9 to 42.9), positive predictive value 16.1% (95% CI 13.9 to 18.5) and negative predictive value 95.4% (95% CI 93.4 to 96.9) in all patients. Applying the cut-off in ESC-PTP 5% to <15% patients, 316 patients (48%) were down-classified to very-low likelihood. The obstructive CAD prevalence was 3.5% in this group. CONCLUSION: In a large contemporary cohort of patients with low CAD likelihood, the additional use of an acoustic rule-out device showed a clear potential to downgrade likelihood and could supplement current strategies for likelihood assessment to avoid unnecessary testing. TRIAL REGISTRATION NUMBER: NCT03481712.

Diagnostic performance of a device for acoustic heart sound analysis in patients with suspected myocardial infarction.

BACKGROUND: As only a small proportion of patients with chest pain suffers from myocardial infarction (MI), safe rule-out of MI is of immense importance. Recently an ultrasensitive microphone performing diastolic heart sound analysis (CADScorSystem) for rule-out of coronary artery disease (CAD) has emerged. In this explorational study, we aimed to evaluate the feasibility of the CADScorSystem for diagnosis of MI in the setting of a large emergency department. METHODS: Patients presenting to the emergency department with suspected MI were included. Acoustic heart sound analysis was performed in all patients and automated CAD-score values were calculated via a device-embedded algorithm, which also requires inclusion of three clinical variables: age, sex and presence of hypertension. Patients additionally received serial high-sensitive troponin T measurement measurements to assess the final diagnosis according to third Universal Definition of Myocardial Infarction applying the European Society of Cardiology 0 hour/3 hours algorithm. Diagnostic parameters for MI, considering different CAD-score cut-offs, were computed. RESULTS: Of 167 patients, CAD-scores were available in 61.1%. A total of eight patients were diagnosed with MI. At a cut-off value of <20, CAD-score had a negative predictive value (NPV) of 90.7 (78.4-96.3). The corresponding positive predictive value (PPV) was 6.8 (2.7-16.2). For the adjusted CAD-score (age, sex, hypertension), at a cut-off value of <20, NPV was 90.0 (59.6-99.5) with a PPV of 10.8 (5.3-20.6). CONCLUSION: In this explorative analysis, a transcutaneous ultrasensitive microphone for heart sound analysis resulted in a high NPV analogous to the findings in rule-out of stable CAD in elective patients yet inferior to serial high-sensitivity cardiac troponin measurements and does not seem feasible for application in an emergency setting for rule-out of MI. TRIAL REGISTRATION NUMBER: NCT02355457.

Association between socioeconomic position and coronary artery calcium score in patients with symptoms suggestive of obstructive coronary artery disease.

AIM: Low socioeconomic-position (SEP) is associated with increased prevalence of cardiovascular disease. Whether this is caused by earlier development of atherosclerotic calcifications is not well understood. This study aimed to investigate the association between SEP and coronary artery calcium score (CACS) in a population presenting with symptoms suggestive of obstructive coronary artery disease. METHODS: We included 50,561 patients (mean age 57 â€‹± â€‹11, 53% women) from a national registry undergoing coronary computed tomography angiography (CTA) from 2008 to 2019. CACS was used as outcome in categories; 1-399 and â€‹≥ â€‹400 in regression analyses. SEP was obtained from central registries and defined as mean personal income and length of education. RESULTS: The number of risk factors were negatively associated with income and education among both men and women. The adjusted OR of having a CACS≥400 was 1.67(1.50-1.86) among women with <10 years of education compared to >13 years. For men the corresponding OR was 1.03(0.91-1.16). For women with low income the adjusted OR of CACS ≥400 was 2.29(1.96-2.69) using high income as a reference. For men the corresponding OR was 1.13(0.99-1.29). CONCLUSION: In patients referred for coronary CTA we found an increased level of risk factors among men and women with short education and low income. Among women with longer education and a higher income we demonstrated a lower CACS compared to other women and men. Socioeconomic differences seem to affect the development of CACS beyond what can be explained by traditional risk factors. Part of the observed result may be due to referral bias. GOV IDENTIFIER: None.

Accuracy of a Clinical Applicable Method for Prediction of VO2max Using Seismocardiography.

Cardiorespiratory fitness measured as ˙VO2max is considered an important variable in the risk prediction of cardiovascular disease and all-cause mortality. Non-exercise ˙VO2max prediction models are applicable, but lack accuracy. Here a model for the prediction of ˙VO2max using seismocardiography (SCG) was investigated. 97 healthy participants (18-65 yrs., 51 females) underwent measurement of SCG at rest in the supine position combined with demographic data to predict ˙VO2max before performing a graded exercise test (GET) on a cycle ergometer for determination of ˙VO2max using pulmonary gas exchange measurements for comparison. Accuracy assessment revealed no significant difference between SCG and GET ˙VO2max (mean±95% CI; 38.3±1.6 and 39.3±1.6 ml·min-1·kg-1, respectively. P=0.075). Further, a Pearson correlation of r=0.73, a standard error of estimate (SEE) of 5.9 ml·min-1·kg-1, and a coefficient of variation (CV) of 8±1% were found. The SCG ˙VO2max showed higher accuracy, than the non-exercise model based on the FRIENDS study, when this was applied to the present population (bias=-3.7±1.3 ml·min-1·kg-1, p<0.0001. r=0.70. SEE=7.4 ml·min-1·kg-1, and CV=12±2%). The SCG ˙VO2max prediction model is an accurate method for the determination of ˙VO2max in a healthy adult population. However, further investigation on the validity and reliability of the SCG ˙VO2max prediction model in different populations is needed for consideration of clinical applicability.

Performance of the American Heart Association/American College of Cardiology Guideline-Recommended Pretest Probability Model for the Diagnosis of Obstructive Coronary Artery Disease.

Background Substantial differences exist between different guideline-recommended pretest probability (PTP) models for the detection of obstructive coronary artery disease (CAD). This study was performed to study the performance of the 2021 American Heart Association/American College of Cardiology (AHA/ACC) guideline-recommended PTP (AHA/ACC-PTP) model in assessing the likelihood of obstructive CAD compared with previously proposed models. Methods and Results Symptomatic patients (N=50 561) referred for coronary computed tomography angiography were included. The reference standard was invasive coronary angiography with optional fractional flow reserve measurements. The AHA/ACC-PTP values based on sex and age were calculated and compared with the 2019 European Society of Cardiology guideline PTP values based on sex, age, and symptoms as well as the risk factor-weighted clinical likelihood values based on sex, age, symptoms, and risk factors. The AHA/ACC-PTP maximum values overestimated by a factor of 2.6 the actual prevalence of CAD. Compared with the AHA/ACC-PTP model (area under the receiver-operating curve, 71.5 [95% CI, 70.7-72.2]), inclusion of typicality of symptoms in the European Society of Cardiology guideline PTP improved discrimination of CAD (area under the receiver-operating curve, 75.5 [95% CI, 74.7-76.3]). Inclusion of both symptoms and risk factors in the risk factor-weighted clinical likelihood model further improved discrimination (area under the receiver-operating curve, 77.7 [95% CI, 77.0-78.5]). The proportion of patients classified as very low PTP was lower using the AHA/ACC-PTP (5%) compared with the European Society of Cardiology guideline PTP (19%) and the risk factor-weighted clinical likelihood (49%) models. Conclusions The new AHA/ACC-PTP model overestimates the prevalence of obstructive CAD substantially if type of symptoms and risk factors are not taken into account. Inclusion of both symptoms and risk factors improves model performance and identifies more patients with very low likelihood of CAD in whom further testing can be deferred.

Seismocardiography as a tool for assessment of bi-ventricular pacing.

Objective.Conduction-induced heart failure in patients with left bundle branch block (LBBB) can benefit from cardiac resynchronization therapy (CRT). However, some patients are non-responders to the therapy with one contributing factor being poor optimization of the atrioventricular (AV) pacing delay. In this study, we have investigated the pacing-induced changes in the seismocardiogram (SCG).Approach.14 patients with heart failure, LBBB, and CRT were included. SCG was recorded with pacing turned on and off. Based on a mean SCG heartbeat from each patient, fiducial points were annotated, and cardiac timing intervals (CTI) and amplitudes were derived. These were compared between the CRT group and a group of healthy normal subjects (n= 14). Echocardiography was also used to derive CTI. Intervals derived from the SCG and echocardiogram were correlated.Main results.The isovolumetric contraction time (IVCT) derived from SCG was significantly shorter in the CRT group when the pacemaker was turned on (63.2-52.6 ms,p= 0.027). The first peak-to-peak amplitude in the systolic complex was significantly larger with the pacemaker turned on (p= 0.002), as well as the ∣max-min∣ amplitude in the systolic complex (p= 0.003). Isovolumetric relaxation time and left ventricular ejection time (LVET) were not significantly different between pacemaker settings. Compared to normal subjects, IVCT was significantly prolonged with the pacemaker turned off. All amplitudes were significantly larger in the healthy subject group. IVCT and LVET derived from SCG were significantly correlated to the echocardiogram.Significance.IVCT shortened and SCG amplitudes increased in response to CRT, indicating a more efficient ventricular contraction. This demonstrates the possibility to detect cardio-mechanic changes in response to treatment with the SCG. However, for the patients the systolic part of the SCG was abnormal and difficult to characterize, raising concerns about the correct interpretation of the SCG.

Coronary Artery Disease Detected by Low Frequency Heart Sounds.

OBJECTIVES: Previous studies have observed an increase in low frequency diastolic heart sounds in patients with coronary artery disease (CAD). The aim was to develop and validate a diagnostic, computerized acoustic CAD-score based on heart sounds for the non-invasive detection of CAD. METHODS: Prospective study enrolling 463 patients referred for elective coronary angiography. Pre-procedure non-invasive recordings of heart sounds were obtained using a novel acoustic sensor. A CAD-score was defined as the power ratio between the 10-90 Hz frequency spectrum and the 90-300 Hz frequency spectrum of the mid-diastolic heart sound. Quantitative coronary angiography analysis was performed by a blinded core laboratory and patients grouped according to the results: obstructive CAD defined by the presence of at least one ≥ 50% stenosis, non-obstructive CAD as patients with a maximal stenosis in the 25-50% interval and non-CAD as no coronary lesions exceeding 25%. We excluded patients with potential confounders or incomplete data (n = 245). To avoid over-fitting the final cohort of 218 patients was randomly divided into to a training group for development (n = 127) and a validation group (n = 91). RESULTS: In both the training and the validation group the CAD-score was significantly increased in CAD patients compared to non-CAD patients (p < 0.0001). In the validation group the area under the receiver-operating curve was 77% (95% CI 63-91%). Sensitivity was 71% (95% CI 59-82%) and specificity 64% (95% CI 45-83%). CONCLUSION: The acoustic CAD-score is a new, inexpensive, non-invasive method to detect CAD, which may supplement clinical risk stratification and reduce the need for subsequent non-invasive and invasive testing.

Improved pre-test likelihood estimation of coronary artery disease using phonocardiography.

Aims: Current early risk stratification of coronary artery disease (CAD) consists of pre-test probability scoring such as the 2019 ESC guidelines on chronic coronary syndromes (ESC2019), which has low specificity and thus rule-out capacity. A newer clinical risk factor model (risk factor-weighted clinical likelihood, RF-CL) showed significantly improved rule-out capacity over the ESC2019 model. The aim of the current study was to investigate if the addition of acoustic features to the RF-CL model could improve the rule-out potential of the best performing clinical risk factor models. Methods and results: Four studies with heart sound recordings from 2222 patients were pooled and distributed into two data sets: training and test. From a feature bank of 40 acoustic features, a forward-selection technique was used to select three features that were added to the RF-CL model. Using a cutoff of 5% predicted risk of CAD, the developed acoustic-weighted clinical likelihood (A-CL) model showed significantly (P < 0.05) higher specificity of 48.6% than the RF-CL model (specificity of 41.5%) and ESC 2019 model (specificity of 6.9%) while having the same sensitivity of 84.9% as the RF-CL model. Area under the curve of the receiver operating characteristic for the three models was 72.5% for ESC2019, 76.7% for RF-CL, and 79.5% for A-CL. Conclusion: The proposed A-CL model offers significantly improved rule-out capacity over the ESC2019 model and showed better overall performance than the RF-CL model. The addition of acoustic features to the RF-CL model was shown to significantly improve early risk stratification of symptomatic patients suspected of having stable CAD.

Spectral analysis of heart sounds associated with coronary artery disease.

Objective. The aim of this study was to find spectral differences of diagnostic interest in heart sound recordings of patients with coronary artery disease (CAD) and healthy subjects.Approach. Heart sound recordings from three studies were pooled, and patients with clear diagnostic outcomes (positive: CAD and negative: Non-CAD) were selected for further analysis. Recordings from 1146 patients (191 CAD and 955 Non-CAD) were analyzed for spectral differences between the two groups using Welch's spectral density estimate. Frequency spectra were estimated for systole and diastole segments, and time-frequency spectra were estimated for first (S1) and second (S2) heart sound segments. An ANCOVA model with terms for diagnosis, age, gender, and body mass index was used to evaluate statistical significance of the diagnosis term for each time-frequency component.Main results. Diastole and systole segments of CAD patients showed increased energy at frequencies 20-120 Hz; furthermore, this difference was statistically significant for the diastole. CAD patients showed decreased energy for the mid-S1 and mid-S2 segments and conversely increased energy before and after the valve sounds. Both S1 and S2 segments showed regions of statistically significant difference in the time-frequency spectra.Significance. Results from analysis of the diastole support findings of increased low-frequency energy from previous studies. Time-frequency components of S1 and S2 sounds showed that these two segments likely contain heretofore untapped information for risk assessment of CAD using phonocardiography; this should be considered in future works. Further development of features that build on these findings could lead to improved acoustic detection of CAD.

Polygenic Risk Score-Enhanced Risk Stratification of Coronary Artery Disease in Patients With Stable Chest Pain.

BACKGROUND: Polygenic risk scores (PRSs) are associated with coronary artery disease (CAD), but the clinical potential of using PRSs at the single-patient level for risk stratification has yet to be established. We investigated whether adding a PRS to clinical risk factors (CRFs) improves risk stratification in patients referred to coronary computed tomography angiography on a suspicion of obstructive CAD. METHODS: In this prespecified diagnostic substudy of the Dan-NICAD trial (Danish study of Non-Invasive testing in Coronary Artery Disease), we included 1617 consecutive patients with stable chest symptoms and no history of CAD referred for coronary computed tomography angiography. CRFs used for risk stratification were age, sex, symptoms, prior or active smoking, antihypertensive treatment, lipid-lowering treatment, and diabetes. In addition, patients were genotyped, and their PRSs were calculated. All patients underwent coronary computed tomography angiography. Patients with a suspected ≥50% stenosis also underwent invasive coronary angiography with fractional flow reserve. A combined end point of obstructive CAD was defined as a visual invasive coronary angiography stenosis >90%, fractional flow reserve <0.80, or a quantitative coronary analysis stenosis >50% if fractional flow reserve measurements were not feasible. RESULTS: The PRS was associated with obstructive CAD independent of CRFs (adjusted odds ratio, 1.8 [95% CI, 1.5-2.2] per SD). The PRS had an area under the curve of 0.63 (0.59-0.68), which was similar to that for age and sex. Combining the PRS with CRFs led to a CRF+PRS model with area under the curve of 0.75 (0.71-0.79), which was 0.04 more than the CRF model (P=0.0029). By using pretest probability (pretest probability) cutoffs at 5% and 15%, a net reclassification improvement of 15.8% (P=3.1×10-4) was obtained, with a down-classification of risk in 24% of patients (211 of 862) in whom the pretest probability was 5% to 15% based on CRFs alone. CONCLUSIONS: Adding a PRS improved risk stratification of obstructive CAD beyond CRFs, suggesting a modest clinical potential of using PRSs to guide diagnostic testing in the contemporary clinical setting. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02264717.

Validation of the European Society of Cardiology pre-test probability model for obstructive coronary artery disease.

AIMS: Estimation of pre-test probability (PTP) of disease in patients with suspected coronary artery disease (CAD) is a common challenge. Due to decreasing prevalence of obstructive CAD in patients referred for diagnostic testing, the European Society of Cardiology suggested a new PTP (2019-ESC-PTP) model. The aim of this study was to validate that model. METHODS AND RESULTS: Symptomatic patients referred for coronary computed tomography angiography (CTA) due to suspected CAD in a geographical uptake area of 3.3 million inhabitants were included. The reference standard was a combined endpoint of CTA and invasive coronary angiography (ICA) with obstructive CAD defined at ICA as a ≥50% diameter stenosis or fractional flow reserve ≤0.80 when performed. The 2019-ESC-PTP, 2013-ESC-PTP, and CAD Consortium basic PTP scores were calculated based on age, sex, and symptoms. Of the 42 328 identified patients, coronary stenosis was detected in 8.8% using the combined endpoint. The 2019-ESC-PTP and CAD Consortium basic scores classified substantially more patients into the low PTP groups (PTP < 15%) than did the 2013-ESC-PTP (64% and 65% vs. 16%, P < 0.001). Using the combined endpoint as reference, calibration of the 2019-ESC-PTP model was superior to the 2013-ESC-PTP and CAD Consortium basic score. CONCLUSION: The new 2019-ESC-PTP model is well calibrated and superior to the previously recommended models in predicting obstructive stenosis detected by a combined endpoint of CTA and ICA.

Advanced heart sound analysis as a new prognostic marker in stable coronary artery disease.

Aims: Recent technological advances enable diagnosing of obstructive coronary artery disease (CAD) from heart sound analysis with a high negative predictive value. However, the prognostic impact of this approach remains unknown. To investigate the prognostic value of heart sound analysis as two scores, the Acoustic-score and the CAD-score, in patients with suspected CAD which is treated according to standard of care. Methods and results: Consecutive patients with angina symptoms referred for coronary computed tomography angiography (CTA) were enrolled. The Acoustic-score was developed from eight acoustic CAD-related features. This score was combined with risk factors to generate the CAD-score. A cut-off score >20 was pre-specified for both scores to indicate disease. If coronary CTA raised suspicion of obstructive CAD, patients were referred to invasive angiography and revascularized when indicated. Of 1675 enrolled patients, 1464 (87.4%) were included in this substudy. The combined primary endpoint was all-cause mortality and myocardial infarction (n = 26). Follow-up was 3.1 (2.7-3.4) years. Of patients with primary endpoints, the Acoustic-score was >20 in 25 (96%); the CAD-score was >20 in 22 (85%). In an unadjusted Cox analysis of the primary endpoints, the hazard ratio for scores >20 under current standard clinical care was 12.6 (1.7-93.2) for the Acoustic-score and 5.4 (1.9-15.7) for the CAD-score. The CAD-score contained prognostic information even after adjusting for lipid-lowering therapy initiation, stenosis at CTA, and early revascularization. Conclusion: Heart sound analysis seems to carry prognostic information and may improve initial risk stratification of patients with suspected CAD. Clinicaltrialsorg ID: NCT02264717.

Incorporating Coronary Calcification Into Pre-Test Assessment of the Likelihood of Coronary Artery Disease.

BACKGROUND: The prevalence of obstructive coronary artery disease (CAD) in symptomatic patients referred for diagnostic testing has declined, warranting optimization of individualized diagnostic strategies. OBJECTIVES: This study sought to present a simple, clinically applicable tool enabling estimation of the likelihood of obstructive CAD by combining a pre-test probability (PTP) model (Diamond-Forrester approach using sex, age, and symptoms) with clinical risk factors and coronary artery calcium score (CACS). METHODS: The new tool was developed in a cohort of symptomatic patients (n = 41,177) referred for diagnostic testing. The risk factor-weighted clinical likelihood (RF-CL) was calculated through PTP and risk factors, while the CACS-weighted clinical likelihood (CACS-CL) added CACS. The 2 calculation models were validated in European and North American cohorts (n = 15,411) and compared with a recently updated PTP table. RESULTS: The RF-CL and CACS-CL models predicted the prevalence of obstructive CAD more accurately in the validation cohorts than the PTP model, and markedly increased the area under the receiver-operating characteristic curves of obstructive CAD: for the PTP model, 72 (95% confidence intervals [CI]: 71 to 74); for the RF-CL model, 75 (95% CI: 74 to 76); and for the CACS-CL model, 85 (95% CI: 84 to 86). In total, 38% of the patients in the RF-CL group and 54% in the CACS-CL group were categorized as having a low clinical likelihood of CAD, as compared with 11% with the PTP model. CONCLUSIONS: A simple risk factor and CACS-CL tool enables improved prediction and discrimination of patients with suspected obstructive CAD. The tool empowers reclassification of patients to low likelihood of CAD, who need no further testing.