Association of metabolic syndrome and its individual components with outcomes among patients with high-risk non-ST-segment elevation acute coronary syndromes.

BACKGROUND: The relationship of metabolic syndrome and its individual components (obesity, hypertension, glucose intolerance, high triglycerides, and low high-density lipoprotein cholesterol) with 1-year mortality in non-ST-segment elevation acute coronary syndromes (NSTE ACS) patients is not known. METHODS: The association of metabolic syndrome (and its individual components) with all-cause mortality within 1 year was assessed in NSTE ACS patients enrolled in the EARLY ACS trial. Adjusted hazard ratio (HR) and 95% CIs are reported. RESULTS: Of 9,406 patients, 2,596 (27.6%) had metabolic syndrome. Compared with those without metabolic syndrome, patients with this syndrome were younger, were more often female, and had a higher prevalence of comorbid conditions and higher-risk presenting features. Metabolic syndrome was not associated with increased 1-year mortality (HR 1.20, 95% CI 0.97-1.47; P = .09). The risk of 1-year mortality varied across the individual components: high-density lipoprotein <40 mg/dL (men)/<50 mg/dL (women; or dyslipidemia) was associated with higher risk (HR 1.52, 95% CI 1.15-2.02), and triglycerides >150 mg/dL (or dyslipidemia) was associated with lower risk (HR 0.66, 95% CI 0.54-0.81), whereas the other components (ie, body mass index >30 kg/m(2), fasting plasma glucose >100 mg/dL or diabetes, systolic blood pressure >130 mm Hg or diastolic >85 mm Hg [or hypertension]) were associated with neutral risk of this event. CONCLUSIONS: The individual components of metabolic syndrome had varying associations with 1-year mortality, and as an integrated diagnosis, metabolic syndrome was not significantly associated with 1-year mortality. Thus, patient case-mix of the studied NSTE ACS population may influence the observed relationship of metabolic syndrome with subsequent cardiovascular events.

Impact of exercise stress testing on diagnostic gene expression in patients with obstructive and nonobstructive coronary artery disease.

A blood-based gene expression test can diagnose obstructive coronary artery disease (CAD). The test is sensitive to inflammatory and immune processes associated with atherosclerosis. Acute exercise engages short-term inflammatory pathways, and exercise stress testing may affect results of gene expression testing during the same diagnostic workup. The objective of this study was to evaluate the effect of exercise on diagnostic gene expression testing. Ten patients with obstructive CAD (≥50% stenosis) and 10 with no/minimal CAD (≤20% stenosis) were identified by angiography. Blood samples for gene expression were obtained at baseline, peak exercise, 30 to 60 minutes after testing, and 24 to 36 hours after testing. Core-lab gene expression analysis yielded raw gene expression scores (GES) for each time point. Linear models were used to estimate changes in GES, adjusting for CAD status and other covariates. GES increased during peak exercise across both genders, with no significant differences as a function of CAD status. The overall adjusted mean GES increase at peak exercise was 0.29 (95% confidence interval 0.22 to 0.36; p <0.001). GES after exercise were not significantly different from baseline. The change in gene expression levels during peak exercise may reflect a transient inflammatory response to acute exercise that may be independent of patient gender or CAD status. In conclusion, CAD GES increase at peak exercise testing and rapidly return to baseline. Such may reflect a transient inflammatory response to acute exercise independent of gender or extent of CAD.

Methods of creatine kinase-MB analysis to predict mortality in patients with myocardial infarction treated with reperfusion therapy.

BACKGROUND: Larger infarct size measured by creatine kinase (CK)-MB release is associated with higher mortality and has been used as an important surrogate endpoint in the evaluation of new treatments for ST-segment elevation myocardial infarction (STEMI). Traditional approaches to quantify infarct size include the observed CK-MB peak and calculated CK-MB area under the curve (AUC). We evaluated alternative approaches to quantifying infarct size using CK-MB values, and the relationship between infarct size and clinical outcomes. METHODS: Of 1,850 STEMI patients treated with reperfusion therapy in the COMplement inhibition in Myocardial infarction treated with Angioplasty (COMMA) (percutaneous coronary intervention (PCI)-treated) and the COMPlement inhibition in myocardial infarction treated with thromboLYtics (COMPLY) (fibrinolytic-treated) trials, 1,718 (92.9%) (COMMA, n = 868; COMPLY, n = 850) had at least five of nine protocol-required CK-MB measures. In addition to traditional methods, curve-fitting techniques were used to determine CK-MB AUC and estimated peak CK-MB. Cox proportional hazards modeling assessed the univariable associations between infarct size and mortality, and the composite of death, heart failure, shock and stroke at 90 days. RESULTS: In COMPLY, CK-MB measures by all methods were significantly associated with higher mortality (hazard ratio range per 1,000 units increase: 1.09 to 1.13; hazard ratio range per 1 standard deviation increase: 1.41 to 1.62; P <0.01 for all analyses). In COMMA, the associations were similar but did not reach statistical significance. For the composite outcome of 90-day death, heart failure, shock and stroke, the associations with all CK-MB measures were statistically significant in both the COMMA and COMPLY trials. CONCLUSIONS: Sophisticated curve modeling is an alternative to infarct-size quantification in STEMI patients, but it provides information similar to that of more traditional methods. Future studies will determine whether the same conclusion applies in circumstances other than STEMI, or to studies with different frequencies and patterns of CK-MB data collection.

Impact of Society of Cardiovascular Patient Care accreditation on quality: an ACTION Registry®-Get With The Guidelines™ analysis.

BACKGROUND: The Society of Cardiovascular Patient Care (SCPC) accredits hospital acute coronary syndrome management. The influence of accreditation on the subset of patients diagnosed with acute myocardial infarction (AMI) is unknown. Our purpose was to describe the association between SCPC accreditation and hospital quality metric performance among AMI patients enrolled in ACTION Registry-GWTG (ACTION-GWTG). This program is a voluntary registry that receives self-reported hospital AMI quality metrics data and provides quarterly feedback to 487 US hospitals. METHODS: Using urban nonacademic hospital registry data from January 1, 2007, to June 30, 2010, we performed a 1 to 2 matched pairs analysis, selecting 14 of 733 (1.9%) SCPC accredited and 28 of 309 (9.1%) nonaccredited registry facilities to compare changes in quality metrics between the year before and after SCPC accreditation. RESULTS: All hospitals improved quality metric compliance during the study period. Nonaccredited hospitals started with slightly lower rates of AMI composite score 1 year before accreditation. Although improvement compared with baseline was greater for nonaccredited hospitals (odds ratio = 1.27; 95% confidence interval: 1.20, 1.35) than accredited hospitals (odds ratio = 1.15; 95% confidence interval: 1.07, 1.23) (P = 0.022), the group ended with similar compliance scores (92.1% vs. 92.2%, respectively). Improvements in evaluating left ventricular function (P = 0.0001), adult smoking cessation advice (P = 0.0063), and cardiac rehab referral (P = 0.0020) were greater among nonaccredited hospitals, whereas accredited hospitals had greater improvement in discharge angiotensin-converting-enzyme inhibitor or angiotensin II receptor blocker use for left ventricular systolic dysfunction (P = 0.0238). CONCLUSIONS: All hospitals had high rates of quality metric compliance and finished with similar overall AMI performance composite scores after 1 year.

Can prospective usability evaluation predict data errors?

Increasing amounts of clinical research data are collected by manual data entry into electronic source systems and directly from research subjects. For this manual entered source data, common methods of data cleaning such as post-entry identification and resolution of discrepancies and double data entry are not feasible. However data accuracy rates achieved without these mechanisms may be higher than desired for a particular research use. We evaluated a heuristic usability method for utility as a tool to independently and prospectively identify data collection form questions associated with data errors. The method evaluated had a promising sensitivity of 64% and a specificity of 67%. The method was used as described in the literature for usability with no further adaptations or specialization for predicting data errors. We conclude that usability evaluation methodology should be further investigated for use in data quality assurance.