Predicting obstructive coronary artery disease in heart failure with reduced ejection fraction: A practical clinical score.

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.

The updated pre-test probability model of the 2019 ESC guidelines improves prediction of obstructive coronary artery disease.

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.

Clinical judgment to estimate pretest probability in the diagnosis of Cushing's syndrome under a Bayesian perspective / Contribuição do julgamento clínico na estimativa da probabilidade pré-teste do diagnóstico da síndrome de Cushing sob a perspectiva Bayesiana

OBJECTIVE: To estimate the pretest probability of Cushing's syndrome (CS) diagnosis by a Bayesian approach using intuitive clinical judgment. MATERIALS AND METHODS: Physicians were requested, in seven endocrinology meetings, to answer three questions: "Based on your personal expertise, after obtaining clinical history and physical examination, without using laboratorial tests, what is your probability of diagnosing Cushing's Syndrome?"; "For how long have you been practicing Endocrinology?"; and "Where do you work?". A Bayesian beta regression, using the WinBugs software was employed. RESULTS: We obtained 294 questionnaires. The mean pretest probability of CS diagnosis was 51.6% (95%CI: 48.7-54.3). The probability was directly related to experience in endocrinology, but not with the place of work. CONCLUSION: Pretest probability of CS diagnosis was estimated using a Bayesian methodology. Although pretest likelihood can be context-dependent, experience based on years of practice may help the practitioner to diagnosis CS. Arq Bras Endocrinol Metab. 2012;56(9):633-7.

OBJETIVO: Estimar a probabilidade pré-teste do diagnóstico de síndrome de Cushing (SC) por meio de julgamento clínico utilizando abordagem Bayesiana. MATERIAIS E MÉTODOS: Médicos res­ponderam a três perguntas, em sete congressos de endocrinologia. Após obtenção da história clínica/exame físico, sem exames laboratoriais, apenas com base em sua experiência pessoal, qual a probabilidade de diagnosticar SC?; Há quanto tempo você pratica endocrinologia?; Onde você trabalha? Uma regressão beta Bayesiana, utilizando o software WinBugs, foi empregada. RESULTADOS: Foram obtidos 294 questionários. A estimativa Bayesiana da probabilidade média de diagnosticar SC foi 51,6% (IC 95%: 48,7-54,3). Houve relação direta entre probabilidade de diagnosticar SC e experiência da prática endócrina, porém não com o local de trabalho. CONCLUSÃO: A probabilidade pré-teste do diagnóstico de SC foi estimada utilizando uma metodologia Bayesiana. Embora a probabilidade pré-teste possa ser dependente do contexto, a experiência de anos de prática pode auxiliar no diagnóstico intuitivo da CS. Arq Bras Endocrinol Metab. 2012;56(9):633-7.