Clinical Likelihood Prediction of Hemodynamically Obstructive Coronary Artery Disease in Patients With Stable Chest Pain.

BACKGROUND: Selection for invasive angiography is recommended to be based on pretest probabilities (PTPs), and physiological measures of hemodynamical impairment by, for example, fractional flow reserve (FFR) should guide revascularization. The risk factor-weighted clinical likelihood (RF-CL) and coronary artery calcium score-weighted clinical likelihood (CACS-CL) models show superior discrimination of patients with suspected obstructive coronary artery disease (CAD), but validation against hemodynamic impairment is warranted. OBJECTIVES: The aim of this study was to validate the RF-CL and CACS-CL models against hemodynamically obstructive CAD. METHODS: Stable de novo chest pain patients (N = 4,371) underwent coronary computed tomography angiography and subsequently invasive coronary angiography with FFR measurements. Hemodynamically obstructive CAD was defined as invasive FFR ≤0.80 or high-grade stenosis by visual assessment (>90% diameter stenosis). For comparison, a guideline-endorsed basic PTP model was calculated based on age, sex, and symptom typicality. The RF-CL model additionally included the number of risk factors, and the CACS-CL model incorporated the coronary artery calcium score into the RF-CL. RESULTS: In total, 447 of 4,371 (10.9%) patients had hemodynamically obstructive CAD. Both the RF-CL and CACS-CL models classified more patients with a very low clinical likelihood (≤5%) of obstructive CAD compared to the basic PTP model (33.0% and 53.7% vs 12.0%; P < 0.001) with a preserved low prevalence of hemodynamically obstructive CAD (<5% for all models). Against hemodynamically obstructive CAD, calibration and discrimination of the RF-CL and CACS-CL models were superior to the basic PTP model. CONCLUSIONS: The RF-CL and CACS-CL models are well calibrated and superior to a currently recommended basic PTP model to predict hemodynamically obstructive CAD. (Danish Study of Non-Invasive Diagnostic Testing in Coronary Artery Disease [Dan-NICAD]; NCT02264717; Danish Study of Non-Invasive Diagnostic Testing in Coronary Artery Disease 2 [Dan-NICAD 2]; NCT03481712, Danish Study of Non-Invasive Diagnostic Testing in Coronary Artery Disease 3 [Dan-NICAD 3]; NCT04707859).

Coronary Artery Calcium Score-Weighted Clinical Likelihood Model Performance in Patients with Stable Chest Pain and Coronary Artery Calcium Scores of Zero.

Background: For individuals with persistent stable chest pain (SCP) and a coronary artery calcium score (CACS) of 0, it might be challenging to establish the best risk assessment method for determining the individuals who will not benefit from further cardiovascular imaging testing (CIT). Thus, we investigated the CACS-weighted clinical likelihood (CACS-CL) model in SCP patients with a CACS of 0. Methods: Thus, to assess SCP, we originally enrolled 14,232 individuals for CACS and coronary computed tomography angiography (CCTA) scans between January 2016 and January 2018. Finally, patients with a CACS of 0 were included and followed up ​until January 2022. According to the established CACS-CL cutoffs of 15% and 5%, the associations between coronary artery disease (CAD) and major adverse cardiovascular events (MACEs) in risk groups were evaluated, alongside the net reclassification improvement (NRI). Results: Of the 6689 patients with a CACS of 0, the prevalence of CAD increased significantly (p < 0.0001) in patients with higher CACS-CL. However, there was no significant difference in the CAD distribution (p = 0.0637) between patients with CACS-CL < 5% and 5-15%. The association between the CACS-CL = 15%-determined risk groups and the occurrence of MACEs was stronger than for a CACS-CL = 5% (adjusted hazard ratio (HR): 7.24 (95% CI: 1.93-16.42) versus 3.68 (95% CI: 1.50-8.26)). Compared with the cutoff for CACS-CL = 5%, the NRI was 10.61% when using a cutoff for CACS-CL = 15%. Conclusions: Among patients with an SCP and CACS of 0, the CACS-CL model provided accurate predictions of CAD and MACEs. Compared to the cutoff for CACS-CL = 5%, the cutoff for CACS-CL = 15% seemed to be more effective and safer for deferring further CIT. Clinical Trial registration: NCT04691037.

Calcium Scoring Improves Clinical Management in Patients With Low Clinical Likelihood of Coronary Artery Disease.

BACKGROUND: Coronary artery calcium scoring (CACS) improves management of chest pain patients. However, it is unknown whether the benefit of CACS is dependent on the clinical likelihood (CL). OBJECTIVES: This study aims to investigate for which patients CACS has the greatest benefit when added to a CL model. METHODS: Based on data from a clinical database, the CL of obstructive coronary artery disease (CAD) was calculated for 39,837 patients referred for cardiac imaging due to symptoms suggestive of obstructive CAD. Patients were categorized according to the risk factor-weighted (RF-CL) model (very low, ≤5%; low, >5 to ≤15%; moderate >15 to ≤50%; high, >50%). CL was then recalculated incorporating the CACS result (CACS-CL). Reclassification rates and the number needed to test with CACS to reclassify patients were calculated and validated in 3 independent cohorts (n = 9,635). RESULTS: In total, 15,358 (39%) patients were down- or upclassified after including CACS. Reclassification rates were 8%, 75%, 53%, and 30% in the very low, low, moderate, and high RF-CL categories, respectively. Reclassification to very low CACS-CL occurred in 48% of reclassified patients. The number needed to test to reclassify 1 patient from low RF-CL to very low CACS-CL was 2.1 with consistency across age, sex, and cohorts. CACS-CL correlated better to obstructive CAD prevalence than RF-CL. CONCLUSIONS: Added to an RF-CL model for obstructive CAD, CACS identifies more patients unlikely to benefit from further testing. The number needed to test with CACS to reclassify patients depends on the pretest RF-CL and is lowest in patients with low (>5% to ≤15%) likelihood of CAD.

Diagnostic performance of clinical likelihood models of obstructive coronary artery disease to predict myocardial perfusion defects.

AIMS: Clinical likelihood (CL) models are designed based on a reference of coronary stenosis in patients with suspected obstructive coronary artery disease. However, a reference standard for myocardial perfusion defects (MPDs) could be more appropriate. We aimed to investigate the ability of the 2019 European Society of Cardiology pre-test probability (ESC-PTP), the risk-factor-weighted (RF-CL) model, and coronary artery calcium score-weighted (CACS-CL) model to diagnose MPDs. METHODS AND RESULTS: Symptomatic stable de novo chest pain patients (n = 3374) underwent coronary computed tomography angiography and subsequent myocardial perfusion imaging by single-photon emission computed tomography, positron emission tomography, or cardiac magnetic resonance. For all modalities, MPD was defined as coronary computed tomography angiography with suspected stenosis and stress-perfusion abnormality in ≥2 segments. The ESC-PTP was calculated based on age, sex, and symptom typicality, and the RF-CL and CACS-CL additionally included a number of risk factors and CACS. In total, 219/3374 (6.5%) patients had an MPD. Both the RF-CL and the CACS-CL classified substantially more patients to low CL (<5%) of obstructive coronary artery disease compared with the ESC-PTP (32.5 and 54.1 vs. 12.0%, P < 0.001) with preserved low prevalences of MPD (<2% for all models). Compared with the ESC-PTP [area under the receiver-operating characteristic curve (AUC) 0.74 (0.71-0.78)], the discrimination of having an MPD was higher for the CACS-CL model [AUC 0.88 (0.86-0.91), P < 0.001], while it was similar for the RF-CL model [AUC 0.73 (0.70-0.76), P = 0.32]. CONCLUSION: Compared with basic CL models, the RF-CL and CACS-CL models improve down classification of patients to a very low-risk group with a low prevalence of MPD.

The PROMISE Minimal Risk Score Improves Risk Classification of Symptomatic Patients With Suspected CAD.

BACKGROUND: Guidelines for evaluating patients with suspected coronary artery disease (CAD) recommend pretest probability (PTP) estimation but provide no clear recommendations regarding diagnostic testing in patients with >5% to 15% risk of obstructive CAD. The diagnostic and prognostic value of PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS) calculation in this patient group is unknown. OBJECTIVES: This work aims to improve the evaluation of stable patients with suspected CAD by using the PMRS, which identifies patients at minimal risk of CAD and events in patients with >5% to 15% PTP of obstructive CAD. METHODS: Greater than 5% to 15% PTP patients from 2 large clinical trials were used for subcohort derivation: PROMISE (N = 10,003) and Dan-NICAD (Danish study of Non-Invasive Testing in Coronary Artery Disease) (N = 3,252). First, the PMRS cutoff associated with a prevalence of obstructive CAD ≤5% was determined in the >5% to 15% PTP PROMISE core lab computed tomographic angiography patients (discovery cohort: n = 2,191). This cutoff was validated for obstructive CAD in >5% to 15% PTP Dan-NICAD patients (CAD validation cohort: n = 1,386) and for prognostic impact on death and myocardial infarction in >5% to 15% PTP PROMISE non-core lab computed tomographic angiography patients (prognosis validation cohort: n = 2,753). RESULTS: In the discovery cohort, a CAD prevalence of ≤5% was found at a PMRS of ≥34%. In the CAD validation cohort, this cutoff down-classified 442 (31.9%) of >5% to 15% PTP patients into the low PTP group (CAD ≤5%); the prevalence of obstructive CAD in down-classified patients was 3.2% compared to 7.1% in non-down-classified patients. A PMRS ≥34% was nonsignificantly associated with a lower risk of myocardial infarction and death in the prognosis validation cohort (HR: 0.58 [95% CI: 0.29-1.18]; P = 0.13). CONCLUSIONS: For evaluating patients with suspected CAD, a combined use of traditional PTP and the PMRS correctly down-classified one-third of >5% to 15% PTP patients into a group with very low prevalence of obstructive CAD and adverse events. The proposed strategy may improve risk stratification and help reduce unneeded diagnostic testing.