Sex Differences in Computed Tomographic Angiography in Patients with Low and Intermediate Pretest Probability of Coronary Artery Disease

Background: Multi-slice computed tomography (MSCT) coronary angiography has become one of the hot spots in cardiovascular imaging technology. Many of the sex-based research have shown that women have different pathogenesis, clinical presentation and complication related to coronary artery disease (CAD) as compared to the males. The aim of this study investigated the relationship between gender and coronary artery calcium (CAC) in patients with chest discomfort with low and intermediate pretest probability of CAD who underwent Coronary computed tomography angiography (CCTA) and referrals by gender for subsequent invasive coronary angiography and revascularization. Methods: This prospective cohort study included 200 patients suspected to have coronary artery disease, negative or equivocal stress tests, with no prior known coronary artery disease (CAD), intermediate pretest probability for CAD according to the scoring method of (15-65 points), and Low likelihood for CAD (< 15 points). Patients were divided into two groups according to gender and were followed up. All patients underwent Full history taking, full clinical examination, routine laboratory investigation, resting and exercise ECG, echocardiography, CT coronary angiography and invasive Coronary angiography. Results: Patients with mild calcium score level were significantly higher in no CAD group than CAD group (p <0.001) and patients with high calcium score were significantly higher in CAD group than no CAD group (p <0.001). In univariate regression analysis age, typical chest pain, obesity, coronary Ca score, and hyperlipidemia are independent predictors for CAD in females. In multivariate regression analysis, age, typical chest pain, hypertension, and coronary Ca score are predictors for CAD in males. Coronary calcium score is a good predicator for CAD (AUC =0.901, 95% CI =0.851-0.938, p value <0.001). At cut off value > 101, it has 70.97% sensitivity, 90.79% specificity, 92.6% PPV, and 65.7% NPV. Moreover, it is a good predicator for CAD in females (AUC =0.894, 95% CI =0.823 – 0.944, p value <0.001). At cut off value > 101, it has 60.71% sensitivity, 91.67% specificity, 87.2% PPV, and 71.4% NPV. Conclusions: In patients with chest discomfort with low and intermediate pretest probability of CAD who underwent CCTA and subsequent invasive coronary angiography and revascularization, female patients had lower age, hypertension, pretest probability score, calcium score, atypical angina, nonanginal chest pain and obstructive CAD but had higher BMI, typical angina than males’ group. In females, coronary calcium score is a good predicator for CAD. When its level exceeds 100, it has 60.71% sensitivity and 91.67% specificity. In addition, it was found that in females typical chest pain and coronary Ca score are predictors for CAD and in males, age, typical chest pain, hypertension, and coronary Ca score are predictors for CAD.

A pretest model of obstructive coronary artery disease based on machine learning: from the C-Strat study / 中华内科杂志

Objective:To develop a pretest probability model of obstructive coronary artery disease with machine learning based on multi-site Chinese population data.Methods:Chinese regiStry in early deTection and Risk strAtificaTion of coronary plaques (C-Strat) study is a prospective multi-center cohort study, in which consecutive patients with suspected obstructive coronary artery disease and ≥64 detector row coronary computed tomography angioplasty (CCTA) evaluation were included. Data from the patients were randomly split into a training set (70%) and a test set (30%). More than 50% of coronary artery stenosis by CCTA was defined as positive outcome. A boosted ensemble algorithm (XGBoost), 10-fold cross-validation and Bayesian optimization were used to establish a new prediction model-CARDIACS(pretest probability model from Chinese registry in eARly Detection and rIsk stratificAtion of Coronary plaques Study), and a logistic regression was used to establish a model-LOGISTIC in training set. The test set was used for validation and comparison among CARDIACS, LOGISTIC, UDFM (updated Diamond-Forrester Model) and DFCASS(Diamond-Forrester and CASS).Results:The study population included 29 455 patients with age of (57.0±9.7) years and 44.8% women, of whom 19.1% (5 622/29 455) had obstructive coronary artery disease. For CARDIACS, the age, the reason for visit and the body mass index (BMI) were the most important predictive variables. In the independent test set, the area under the curve (AUC) of CARDIACS was 0.72 (95% CI 0.70-0.73), which was significantly superior to that of LOGISTIC (AUC 0.69, 95% CI 0.68-0.71, P=0.015), UDFM (AUC 0.64, 95% CI 0.62-0.65, P<0.001) and DFCASS (AUC 0.66, 95% CI 0.64-0.67, P<0.001), respectively. Conclusion:Based on Chinese population, the study developed a new pretest probability model--CARDIACS, which was superior to the traditional models. CARDIACS is expected to assist in the clinical decision-making for patients with stable chest pain.

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.

A Study to Validate the Pretest Probability of Malignancy in Solitary Pulmonary Nodule / 결핵및호흡기질환

BACKGROUND: Solitary pulmonary nodules (SPN) are encountered incidentally in 0.2% of patients who undergo chest X-ray or chest CT. Although SPN has malignant potential, it cannot be treated surgically by biopsy in all patients. The first stage is to determine if patients with SPN require periodic observation and biopsy or resection. An important early step in the management of patients with SPN is to estimate the clinical pretest probability of a malignancy. In every patient with SPN, it is recommended that clinicians estimate the pretest probability of a malignancy either qualitatively using clinical judgment or quantitatively using a validated model. This study examined whether Bayesian analysis or multiple logistic regression analysis is more predictive of the probability of a malignancy in SPN. METHODS: From January 2005 to December 2008, this study enrolled 63 participants with SPN at the Kangnam Sacred Hospital. The accuracy of Bayesian analysis and Bayesian analysis with a FDG-PET scan, and Multiple logistic regression analysis was compared retrospectively. The accurate probability of a malignancy in a patient was compared by taking the chest CT and pathology of SPN patients with <30 mm at CXR incidentally. RESULTS: From those participated in study, 27 people (42.9%) were classified as having a malignancy, and 36 people were benign. The result of the malignant estimation by Bayesian analysis was 0.779 (95% confidence interval [CI], 0.657 to 0.874). Using Multiple logistic regression analysis, the result was 0.684 (95% CI, 0.555 to 0.796). This suggests that Bayesian analysis provides a more accurate examination than multiple logistic regression analysis. CONCLUSION: Bayesian analysis is better than multiple logistic regression analysis in predicting the probability of a malignancy in solitary pulmonary nodules but the difference was not statistically significant.