Abdominal aortic calcification quantified by the Morphological Atherosclerotic Calcification Distribution (MACD) index is associated with features of the metabolic syndrome

<p>Abstract</p> <p>Background</p> <p>Abdominal aortic calcifications (AAC) predict cardiovascular mortality. A new scoring model for AAC, the Morphological Atherosclerotic Calcification Distribution (MACD) index may contribute with additional information to the commonly used Aortic Calcification Severity (AC24) score, when predicting death from cardiovascular disease (CVD). In this study we investigated associations of MACD and AC24 with traditional metabolic-syndrome associated risk factors at baseline and after 8.3 years follow-up, to identify biological parameters that may account for the differential performance of these indices.</p> <p>Methods</p> <p>Three hundred and eight healthy women aged 48 to 76 years, were followed for 8.3 ± 0.3 years. AAC was quantified using lumbar radiographs. Baseline data included age, weight, blood pressure, blood lipids, and glucose levels. Pearson correlation coefficients were used to test for relationships.</p> <p>Results</p> <p>At baseline and across all patients, MACD correlated with blood glucose (r²= 0.1, P< 0.001) and to a lesser, but significant extent with traditional risk factors (p < 0.01) of CVD. In the longitudinal analysis of correlations between baseline biological parameters and the follow-up calcification assessment using radiographs we found LDL-cholesterol, HDL/LDL, and the ApoB/ApoA ratio significantly associated with the MACD (P< 0.01). In a subset of patients presenting with calcification at both baseline and at follow-up, all cholesterol levels were significantly associated with the MACD (P< 0.01) index. AC24 index was not correlated with blood parameters.</p> <p>Conclusion</p> <p>Patterns of calcification identified by the MACD, but not the AC24 index, appear to contain useful biological information perhaps explaining part of the improved identification of risk of cardiovascular death of the MACD index. Correlations of MACD but not the AC24 with glucose levels at baseline suggest that hyperglycemia may contribute to unique patterns of calcification indicated by the MACD.</p

Comparing the probability of stroke by the Framingham risk score in hypertensive Korean patients visiting private clinics and tertiary hospitals

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to investigate the pattern of distribution of risk factors for stroke and the 10-year probability of stroke by the Framingham risk score in hypertensive patients visiting private clinics vs. tertiary hospitals.</p> <p>Methods</p> <p>A total of 2,490 hypertensive patients who attended 61 private clinics (1088 patients) and 37 tertiary hospitals (1402 patients) were enrolled. The risk factors for stroke were evaluated using a series of laboratory tests and physical examinations, and the 10-year probability of stroke was determined by applying the Framingham stroke risk equation.</p> <p>Results</p> <p>The proportion of patients who had uncontrolled hypertension despite the use of antihypertensive agents was 49% (66 and 36% of patients cared for at private clinics and tertiary hospitals, respectively; p < 0.001). The average 10-year probability of stroke by the Framingham risk score in hypertensive patients was 21% (approximately 2.2 times higher than of the risk of stroke in the Korean Cancer Prevention Study [KCPS] cohort) and was higher in patients attending tertiary hospitals compared to private clinics (16 and 24% of patients attending private clinics and tertiary hospitals, respectively; p < 0.001).</p> <p>Conclusions</p> <p>Since the 10-year probability of stroke by the Framingham risk score in hypertensive patients attending tertiary hospitals was higher than the risk for patients attending private clinics. We suggest that the more aggressive interventions are needed to prevent and early detect an attack of stroke in hypertensive patients attending tertiary hospitals.</p

Non-contrast cardiac computed tomography can accurately detect chronic myocardial infarction: Validation study

BackgroundThis study evaluates whether non-contrast cardiac computed tomography (CCT) can detect chronic myocardial infarction (MI) in patients with irreversible perfusion defects on nuclear myocardial perfusion imaging (MPI).MethodsOne hundred twenty-two symptomatic patients with irreversible perfusion defect (N = 62) or normal MPI (N = 60) underwent coronary artery calcium (CAC) scanning. MI on these non-contrast CCTs was visually detected based on the hypo-attenuation areas (dark) in the myocardium and corresponding Hounsfield units (HU) were measured.ResultsNon-contrast CCT accurately detected MI in 57 patients with irreversible perfusion defect on MPI, yielding a sensitivity of 92%, specificity of 72%, negative predictive value (NPV) of 90%, and a positive predictive value (PPV) of 77%. On a per myocardial region analysis, non-contrast CT showed a sensitivity of 70%, specificity of 85%, NPV of 91%, and a PPV of 57%. The ROC curve showed that the optimal cutoff value of LV myocardium HU to predict MI on non-contrast CCT was 21.7 with a sensitivity of 97.4% and specificity of 99.7%.ConclusionNon-contrast CCT has an excellent agreement with MPI in detecting chronic MI. This study highlights a novel clinical utility of non-contrast CCT in addition to assessment of overall burden of atherosclerosis measured by CAC

Relationship between blood pressure measurements recorded on patients' charts in family physicians' offices and subsequent 24 hour ambulatory blood pressure monitoring

BACKGROUND: In most western countries 20% of adults have hypertension. Reports in the literature suggest that from 31 to 86% of treated patients are not at recommended target levels. However it is important to consider how we are determining whether targets are unmet and the degree to which they are unmet. Our underlying hypothesis is that white coat effect is partially responsible for the reported low rates of control of hypertension by primary care practitioners. METHODS: The study population consists of 1142 patients who are being assessed for enrolment in two community-based randomized controlled trials. Patients must have essential hypertension, be on antihypertensive medication, and must not have met their blood pressure targets. We are reporting on the proportion of patients who have not achieved target, and the degree to which they have not achieved their target. We also report on the mean daytime blood pressures on 24 hour ABPM and compare these to mean blood pressures found on the patients' charts. RESULTS: We identified 3284 patient charts of patients with hypertension. Of these, 1142 were determined to be "out of control" (did not achieve target) and 436 agreed to undergo 24 hour ABPM for final determination of eligibility. Overwhelmingly (95.8% of the time) it was the systolic blood pressure that was not under control. However, most of the patients who had not achieved target according to our criteria were within 10 mmHg of the recommended targets. Isolated systolic blood pressure was the best predictor of elevated mean daytime blood pressure on 24 hour ABPM. CONCLUSIONS: At least 35% of patients had not achieved target blood pressure levels and this is primarily due to lack of control of systolic blood pressure. The best predictor of continuing hypertension on 24 hour ABPM was the mean systolic blood pressure on the patients chart. However, only 69% of patients who were uncontrolled according blood pressures recorded in the chart were uncontrolled according to 24 hour ABPM criteria. This suggests that the white coat effect makes blood pressure measurements in the doctor's offices, at least as currently done, not sufficiently accurate for determining treatment endpoint

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%

Nut consumption and risk of atrial fibrillation in the Physicians' Health Study

<p>Abstract</p> <p>Background</p> <p>Atrial Fibrillation is highly prevalent in clinical practice affecting approximately 2.3 million people in USA and 4.5 million people in European Union. The aim of the study was to examine the association between nut consumption and incident atrial fibrillation.</p> <p>Methods</p> <p>Prospective cohort of 21,054 male participants of Physicians' Health Study I. Nut consumption was estimated using food frequency questionnaire and incident atrial fibrillation was ascertained through yearly follow-up questionnaires. Cox regression was used to estimate relative risks of atrial fibrillation.</p> <p>Results</p> <p>The average age was 54.6 ± 9.5 years (40.7-87.1). During a mean follow up of 20 years (median 24 years), 3,317 cases of atrial fibrillation occurred. The crude incidence rate was 7.6, 7.4, 8.2, 7.9, and 6.8 cases/1000 person-years for people reporting nut consumption of rarely/never, 1-3/month, 1/per week, 2-6/week, and ≥ 7/week, respectively. Multivariable adjusted hazard ratios (95% CI) for incident atrial fibrillation were 1.00 (ref), 1.00 (0.90-1.11), 1.09 (0.97-1.21), 1.07 (0.95-1.21), and 0.91 (0.70-1.17) for nut consumption from the lowest to the highest category of nut consumption (p for trend 0.26). No statistically significant association between nut consumption and atrial fibrillation was found when stratified by body mass index (BMI < 25 vs ≥ 25 kg/m²) or age (< 65 vs. ≥ 65 years).</p> <p>Conclusions</p> <p>Our data did not show an association between nut consumption and incident atrial fibrillation among US male physicians.</p