Fernanda Lopes

Comparison of ACUITY and CRUSADE Scores in Predicting Major Bleeding during Acute Coronary Syndrome

Background The ACUITY and CRUSADE scores are validated models for prediction of major bleeding events in acute coronary syndrome (ACS). However, the comparative performances of these scores are not known. Objective To compare the accuracy of ACUITY and CRUSADE in predicting major bleeding events during ACS. Methods This study included 519 patients consecutively admitted for unstable angina, non-ST-elevation or ST-elevation myocardial infarction. The scores were calculated based on admission data. We considered major bleeding events during hospitalization and not related to cardiac surgery, according to the Bleeding Academic Research Consortium (BARC) criteria (type 3 or 5: hemodynamic instability, need for transfusion, drop in hemoglobin ≥ 3 g, and intracranial, intraocular or fatal bleeding). Results Major bleeding was observed in 31 patients (23 caused by femoral puncture, 5 digestive, 3 in other sites), an incidence of 6%. While both scores were associated with bleeding, ACUITY demonstrated better C-statistics (0.73, 95% CI = 0.63 - 0.82) as compared with CRUSADE (0.62, 95% CI = 0.53 - 0.71; p = 0.04). The best performance of ACUITY was also reflected by a net reclassification improvement of + 0.19 (p = 0.02) over CRUSADE’s definition of low or high risk. Exploratory analysis suggested that the presence of the variables ‘age’ and ‘type of ACS’ in ACUITY was the main reason for its superiority. Conclusion The ACUITY Score is a better predictor of major bleeding when compared with the CRUSADE Score in patients hospitalized for ACS.

Inconsistency prevents the valuable synergism of explanatory and pragmatic trails

To assess review articles on pragmatic trials in order to describe how authors define the aim of this type of study, how comprehensive methodological topics are covered, and which topics are most valued by authors.

Accuracy of gestalt perception of acute chest pain in predicting coronary artery disease

AIM To test accuracy and reproducibility of gestalt to predict obstructive coronary artery disease (CAD) in patients with acute chest pain. METHODS We studied individuals who were consecutively admitted to our Chest Pain Unit. At admission, investigators performed a standardized interview and recorded 14 chest pain features. Based on these features, a cardiologist who was blind to other clinical characteristics made unstructured judgment of CAD probability, both numerically and categorically. As the reference standard for testing the accuracy of gestalt, angiography was required to rule-in CAD, while either angiography or non-invasive test could be used to rule-out. In order to assess reproducibility, a second cardiologist did the same procedure. RESULTS In a sample of 330 patients, the prevalence of obstructive CAD was 48%. Gestalt’s numerical probability was associated with CAD, but the area under the curve of 0.61 (95%CI: 0.55-0.67) indicated low level of accuracy. Accordingly, categorical definition of typical chest pain had a sensitivity of 48% (95%CI: 40%-55%) and specificity of 66% (95%CI: 59%-73%), yielding a negligible positive likelihood ratio of 1.4 (95%CI: 0.65-2.0) and negative likelihood ratio of 0.79 (95%CI: 0.62-1.02). Agreement between the two cardiologists was poor in the numerical classification (95% limits of agreement = -71% to 51%) and categorical definition of typical pain (Kappa = 0.29; 95%CI: 0.21-0.37). CONCLUSION Clinical judgment based on a combination of chest pain features is neither accurate nor reproducible in predicting obstructive CAD in the acute setting.

Incremental Prognostic Value of the Incorporation of Clinical Data Into Coronary Anatomy Data in Acute Coronary Syndromes: SYNTAX-GRACE Score

Background When performing coronary angiography in patients with acute coronary syndrome (ACS), the anatomical extent of coronary disease usually prevails in the prognostic reasoning. It has not yet been proven if clinical data should be accounted for in risk stratification together with anatomical data. Objective To test the hypothesis that clinical data increment the prognostic value of anatomical data in patients with ACS. Methods Patients admitted with objective criteria for ACS and who underwent angiography during hospitalization were included. Primary outcome was defined as in-hospital cardiovascular death, and the prognostic value of the SYNTAX Score (anatomical data) was compared to that of the SYNTAX-GRACE Score, which resulted from the incorporation of the GRACE Score into the SYNTAX score. The Integrated Discrimination Improvement (IDI) was calculated to evaluate the SYNTAX-GRACE Score ability to correctly reclassify information from the traditional SYNTAX model. Results This study assessed 365 patients (mean age, 64 ± 14 years; 58% male). In-hospital cardiovascular mortality was 4.4%, and the SYNTAX Score was a predictor of that outcome with a C-statistic of 0.81 (95% CI: 0.70 - 0.92; p < 0.001). The GRACE Score was a predictor of in-hospital cardiac death independently of the SYNTAX Score (p < 0.001, logistic regression). After incorporation into the predictive model, the GRACE Score increased the discrimination capacity of the SYNTAX Score from 0.81 to 0.92 (95% CI: 0.87 - 0.96; p = 0.04). Conclusion In patients with ACS, clinical data complement the prognostic value of coronary anatomy. Risk stratification should be based on the clinical-anatomical paradigm, rather than on angiographic data only.

Zero Calcium Score as a Filter for Further Testing in Patients Admitted to the Coronary Care Unit with Chest Pain

Background The accuracy of zero coronary calcium score as a filter in patients with chest pain has been demonstrated at the emergency room and outpatient clinics, populations with low prevalence of coronary artery disease (CAD). Objective To test the gatekeeping role of zero calcium score in patients with chest pain admitted to the coronary care unit (CCU), where the pretest probability of CAD is higher than that of other populations. Methods Patients underwent computed tomography for calcium scoring, and obstructive CAD was defined by a minimum 70% stenosis on invasive angiography. Results In 146 patients studied, the prevalence of CAD was 41%. A zero calcium score was present in 35% of the patients. The sensitivity and specificity of zero calcium score yielded a negative likelihood ratio of 0.16. After logistic regression adjustment for pretest probability, zero calcium score was independently associated with lower odds of CAD (OR = 0.12, 95%CI = 0.04-0.36), increasing the area under the ROC curve of the clinical model from 0.76 to 0.82 (p = 0.006). Zero calcium score provided a net reclassification improvement of 0.20 (p = 0.0018) over the clinical model when using a pretest probability threshold of 10% for discharging without further testing. In patients with pretest probability < 50%, zero calcium score had a negative predictive value of 95% (95%CI = 83%-99%), with a number needed to test of 2.1 for obtaining one additional discharge. Conclusion Zero calcium score substantially reduces the pretest probability of obstructive CAD in patients admitted to the CCU with acute chest pain. (Arq Bras Cardiol. 2017; [online].ahead print, PP.0-0)

A Multivariate Model for Prediction of Obstructive Coronary Disease in Patients with Acute Chest Pain: Development and Validation

Background Currently, there is no validated multivariate model to predict probability of obstructive coronary disease in patients with acute chest pain. Objective To develop and validate a multivariate model to predict coronary artery disease (CAD) based on variables assessed at admission to the coronary care unit (CCU) due to acute chest pain. Methods A total of 470 patients were studied, 370 utilized as the derivation sample and the subsequent 100 patients as the validation sample. As the reference standard, angiography was required to rule in CAD (stenosis ≥ 70%), while either angiography or a negative noninvasive test could be used to rule it out. As predictors, 13 baseline variables related to medical history, 14 characteristics of chest discomfort, and eight variables from physical examination or laboratory tests were tested. Results The prevalence of CAD was 48%. By logistic regression, six variables remained independent predictors of CAD: age, male gender, relief with nitrate, signs of heart failure, positive electrocardiogram, and troponin. The area under the curve (AUC) of this final model was 0.80 (95% confidence interval [95%CI] = 0.75 - 0.84) in the derivation sample and 0.86 (95%CI = 0.79 - 0.93) in the validation sample. Hosmer-Lemeshows test indicated good calibration in both samples (p = 0.98 and p = 0.23, respectively). Compared with a basic model containing electrocardiogram and troponin, the full model provided an AUC increment of 0.07 in both derivation (p = 0.0002) and validation (p = 0.039) samples. Integrated discrimination improvement was 0.09 in both derivation (p < 0.001) and validation (p < 0.0015) samples. Conclusion A multivariate model was derived and validated as an accurate tool for estimating the pretest probability of CAD in patients with acute chest pain.

DETERMINANTS AND APPROPRIATENESS OF DECISION FOR INVASIVE STRATEGY IN NON-ST-SEGMENT ELEVATION ACUTE CORONARY SYNDROMES

Risk stratification properly based on prognostic models should guide decision for invasive or selective strategy in non-ST-elevation acute coronary syndromes (NST-ACS). We aimed to (1) identify clinical determinants of invasive strategy in an environment that medical staff regularly uses the GRACE