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Introduction and Objectives: Obstructive coronary artery disease (CAD) remains the most common etiology of heart failure with reduced ejection fraction (HFrEF). However, there is controversy whether invasive coronary angiography (ICA) should be used initially to exclude CAD in patients presenting with new-onset HFrEF of unknown etiology. Our study aimed to develop a clinical score to quantify the risk of obstructive CAD in these patients. Methods: We performed a cross-sectional observational study of 452 consecutive patients presenting with new-onset HFrEF of unknown etiology undergoing elective ICA in one academic center, between January 2005 and December 2019. Independent predictors for obstructive CAD were identified. A risk score was developed using multivariate logistic regression of designated variables. The accuracy and discriminative power of the predictive model were assessed. Results: A total of 109 patients (24.1%) presented obstructive CAD. Six independent predictors were identified and included in the score: male gender (2 points), diabetes (1 point), dyslipidemia (1 point), smoking (1 point), peripheral arterial disease (1 point), and regional wall motion abnormalities (3 points). Patients with a score ≤3 had less than 15% predicted probability of obstructive CAD. Our score showed good discriminative power (C-statistic 0.872; 95% CI 0.834–0.909: p

Introduction and Objectives: The 2019 ESC guidelines on chronic coronary syndromes updated the method for estimating the pre-test probability (PTP) of obstructive coronary artery disease (CAD). We aimed to compare the performance of the new PTP method against the 2013 prediction model in patients with stable chest pain undergoing coronary computed tomography angiography (CCTA) for suspected CAD. Methods: We conducted a single-center cross-sectional study enrolling 320 consecutive patients undergoing CCTA for suspected CAD. Obstructive CAD was defined as any ≥50% luminal stenosis on CCTA. Whenever invasive coronary angiography was subsequently performed, patients were reclassified accordingly. The two PTP prediction models were assessed for calibration, discrimination and the ability to change the downstream diagnostic pathway. Results: The observed prevalence of obstructive CAD was 16.3% (n=52). The 2013 prediction model significantly overestimated the likelihood of obstructive CAD (relative overestimation of 130%, p=0.005), while the updated 2019 method showed good calibration (relative underestimation of 6.5%, p=0.712). The two approaches showed similar discriminative power, with C-statistics of 0.73 (95% CI: 0.66-0.80) and 0.74 (95% CI: 0.66-0.81) for the 2013 and 2019 methods, respectively (p=0.933). Reclassification of PTP using the new method resulted in a net reclassification improvement of 0.10 (p=0.001). Conclusions: The updated 2019 prediction model provides a more accurate estimation of pre-test probabilities of obstructive CAD than the previous model. Adoption of this new score may improve disease prediction and influence the selection of non-invasive testing. Resumo: Introdução e objetivos: As novas guidelines da ESC de síndromes coronárias crónicas de 2019 atualizaram o método para estimar a probabilidade pré-teste (PPT) de doença arterial coronária (DAC) obstrutiva. O objetivo do nosso trabalho foi comparar o desempenho do novo método de estimativa da PPT com o modelo de 2013 em doentes com angina estável submetidos a angiotomografia das artérias coronárias por suspeita de DAC. Métodos: Foi realizado um estudo transversal de centro único envolvendo 320 doentes consecutivos submetidos a angiotomografia coronária por suspeita de DAC. DAC obstrutiva foi definida como qualquer estenose ≥50% na angiotomografia. Sempre que realizada angiografia coronária invasiva, os doentes foram reclassificados em conformidade. Os dois modelos de previsão da PPT foram avaliados quanto à calibração, discriminação e capacidade para alterar a marcha diagnóstica subsequente. Resultados: A prevalência observada de DAC obstrutiva foi de 16,3% (n=52). O modelo de previsão de 2013 sobrestimou significativamente a probabilidade de DAC obstrutiva (sobrestimação relativa de 130%, valor-p 0,005), enquanto o método de 2019 mostrou uma boa calibração (subestimativa relativa de 6,5%, valor-p 0,712). As duas abordagens mostraram poder discriminativo semelhante, com estatística C de 0,73 (IC 95%: 0,66-0,80) e 0,74 (IC 95%: 0,66-0,81) para os métodos de 2013 e 2019, respetivamente (valor-p 0,933). A reclassificação da PPT usando o novo método resultou numa melhoria de reclassificação líquida de 0,10 (valor-p=0,001). Conclusões: O modelo de previsão atualizado de 2019 permite uma estimativa mais precisa das probabilidades pré-teste de DAC obstrutiva do que o modelo anterior. A adoção deste novo método pode melhorar a previsão de doença e ter impacto na seleção dos testes diagnósticos não invasivos.

Introduction and Objectives: Obstructive coronary artery disease (CAD) remains the most common etiology of heart failure with reduced ejection fraction (HFrEF). However, there is controversy whether invasive coronary angiography (ICA) should be used initially to exclude CAD in patients presenting with new-onset HFrEF of unknown etiology. Our study aimed to develop a clinical score to quantify the risk of obstructive CAD in these patients., Methods: We performed a cross-sectional observational study of 452 consecutive patients presenting with new-onset HFrEF of unknown etiology undergoing elective ICA in one academic center, between January 2005 and December 2019. Independent predictors for obstructive CAD were identified. A risk score was developed using multivariate logistic regression of designated variables. The accuracy and discriminative power of the predictive model were assessed., Results: A total of 109 patients (24.1%) presented obstructive CAD. Six independent predictors were identified and included in the score: male gender (2 points), diabetes (1 point), dyslipidemia (1 point), smoking (1 point), peripheral arterial disease (1 point), and regional wall motion abnormalities (3 points). Patients with a score ≤3 had less than 15% predicted probability of obstructive CAD. Our score showed good discriminative power (C-statistic 0.872; 95% CI 0.834-0.909: p<0.001) and calibration (p=0.333 from the goodness-of-fit test)., Conclusions: A simple clinical score showed the ability to predict the risk of obstructive CAD in patients presenting with new-onset HFrEF of unknown etiology and may guide the clinician in selecting the most appropriate diagnostic modality for the assessment of obstructive CAD., (Copyright © 2022 Sociedade Portuguesa de Cardiologia. Publicado por Elsevier España, S.L.U. All rights reserved.)

Introduction and Objectives: The 2019 ESC guidelines on chronic coronary syndromes updated the method for estimating the pre-test probability (PTP) of obstructive coronary artery disease (CAD). We aimed to compare the performance of the new PTP method against the 2013 prediction model in patients with stable chest pain undergoing coronary computed tomography angiography (CCTA) for suspected CAD., Methods: We conducted a single-center cross-sectional study enrolling 320 consecutive patients undergoing CCTA for suspected CAD. Obstructive CAD was defined as any ≥50% luminal stenosis on CCTA. Whenever invasive coronary angiography was subsequently performed, patients were reclassified accordingly. The two PTP prediction models were assessed for calibration, discrimination and the ability to change the downstream diagnostic pathway., Results: The observed prevalence of obstructive CAD was 16.3% (n=52). The 2013 prediction model significantly overestimated the likelihood of obstructive CAD (relative overestimation of 130%, p=0.005), while the updated 2019 method showed good calibration (relative underestimation of 6.5%, p=0.712). The two approaches showed similar discriminative power, with C-statistics of 0.73 (95% CI: 0.66-0.80) and 0.74 (95% CI: 0.66-0.81) for the 2013 and 2019 methods, respectively (p=0.933). Reclassification of PTP using the new method resulted in a net reclassification improvement of 0.10 (p=0.001)., Conclusions: The updated 2019 prediction model provides a more accurate estimation of pre-test probabilities of obstructive CAD than the previous model. Adoption of this new score may improve disease prediction and influence the selection of non-invasive testing., (Copyright © 2022 Sociedade Portuguesa de Cardiologia. Publicado por Elsevier España, S.L.U. All rights reserved.)