Can restenosis after coronary angioplasty be predicted from clinical variables?

AbstractObjectives. The purpose of this study was to determine whether variables shown to correlate with restenosis in one group (learning group) could be shown to predict recurrent stenosis in a second group (validation group).Background. Restenosis remains a critical limitation after percutaneous transluminal coronary angioplasty. Although several clinical variables have been shown to correlate with restenosis, there are few data concerning attempts to predict recurrent stenosis.Methods. The source of data was the clinical data bese at Emory University. Patients who had had previous coronary surgery and patients who underwent coronary angioplasty in the setting of acute myocardial Infarction were excluded. A total of 4,006 patients with angiographic restudy after successful angioplisty were identified. They were classified into a learning group of 2,500 patients and a validation group of 1,506 patients. The correlates of restenosis in the learning group were determined by stepwise logistic regression, and a model was developed to predict the probability of restenosis and was tested in the validation group. By using various cut points for the predicted probability of restenosis, a receiver operating characteristic curve was created. Goodness of fit of the model was evaluated by comparing average predicted probabilities with average observed probabilities within subgroups on the basis of risk level determined by linear regression analysis.Results. In the learning group 1,145 patients had restenosis and 1,355 did not. Correlates of restenosis were severe angina, severe diameter stenosis before angioplasty, left anterior descending coronary artery dilation, diabetes, greater diameter stenosis after angioplasty, hypertension, absence of an intimal tear, eccentric morphology and older patient age. The model derived from the learing group was used to predict restenosis in the validation group. By varying the cut point for the predicted probability of restenosis above which restenosis is diagnosed and below which it is not, a receiver operating characteristic curve was created. The curve was close to the line of identity, reflecting a poor predictive ability. However, the model was shown to fit well with the predicted probability of restenosis correlating well with the observed probability (r = 0.98, p = 0.0001).Conclusions. Clinical variables provide limited ability to predict definitively whether a particular patient will have restenosis. However, the current model may be used to predict the probability of restenosis, with some uncertainty, at least in well characterized patients who have already had angioplasy

Radio source counts at 30 GHz

We observe 1,377 NVSS point sources with the Green Bank Telescope (GBT) and Caltech Continuum Backend (CCB) at 31.25 GHz. We find the GBT+CCB provides 0.500 mJy rms sensitivity and a 28 arcsec FWHM beam in a 3.5 GHz band centered on 31.25 GHz. We combine these data with observations of 2,225 point sources by the Owens Valley Radio Observatory 40 m (rms sensitivity 2.5 mJy) to provide the most accurate present determination of the contribution of point sources to 1 cm wavelength cosmic microwave background measurements at small scales (< 5 arcmin). Point sources are the most significant contaminant of this signal, which is expected to be dominated by the Sunyaev-Zel’dovich effect from large galaxy clusters, and has been thus far hinted at, but not yet confirmed by, the Cosmic Background Imager

What is the Best Measure of Daytime Sleepiness in Adults With Heart Failure?

Purpose To identify the best screening measure of daytime sleepiness in adults with heart failure (HF). Data sources A total of 280 adults with HF completed the Epworth Sleepiness Scale, the Stanford Sleepiness Scale, and a single Likert item measuring daytime sleepiness. The sensitivity and specificity of these self-report measures were assessed in relation to a measure of daytime dysfunction from poor sleep quality. Conclusions Only 16% of the sample reported significant daytime dysfunction because of poor sleep quality. Those reporting daytime dysfunction were likely to be younger (p \u3c .001), to be unmarried (p = .002), to have New York Heart Association (NYHA) functional class IV HF (p = .015), and to report low income (p = .006) and fewer hours of sleep (p = .015). The measure of daytime sleepiness that was most sensitive to daytime dysfunction was a single Likert item measured on a 10-point (1–10) scale. Patients with a score ≥4 were 2.4 times more likely to have daytime dysfunction than those with a score \u3c4. Implications for practice Complaints of daytime dysfunction because of poor sleep are not common in adults with HF. Routine use of a single question about daytime sleepiness can help nurse practitioners to identify those HF patients with significant sleep issues that may require further screening

Characteristics of EGRET Blazars in the VLBA Imaging and Polarimetry Survey (VIPS)

We examine the radio properties of EGRET-detected blazars observed as part of the VLBA Imaging and Polarimetry Survey (VIPS). VIPS has a flux limit roughly an order of magnitude below the MOJAVE survey and most other samples that have been used to study the properties of EGRET blazars. At lower flux levels, radio flux density does not directly correlate with gamma-ray flux density. We do find that the EGRET-detected blazars tend to have higher brightness temperatures, greater core fractions, and possibly larger than average jet opening angles. A weak correlation is also found with jet length and with polarization. All of the well-established trends can be explained by systematically larger Doppler factors in the gamma-ray loud blazars, consistent with the measurements of higher apparent velocities found in monitoring programs carried out at radio frequencies above 10 GHz.Comment: 20 pages, 7 figures, accepted to Ap

Impact of Scottish smoke-free legislation on smoking quit attempts and prevalence

&lt;p&gt;&lt;b&gt;Objectives:&lt;/b&gt; In Scotland, legislation was implemented in March 2006 prohibiting smoking in all wholly or partially enclosed public spaces. We investigated the impact on attempts to quit smoking and smoking prevalence.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Methods:&lt;/b&gt; We performed time series models using Box-Jenkins autoregressive integrated moving averages (ARIMA) on monthly data on the gross ingredient cost of all nicotine replacement therapy (NRT) prescribed in Scotland in 2003–2009, and quarterly data on self-reported smoking prevalence between January 1999 and September 2010 from the Scottish Household Survey.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Results:&lt;/b&gt; NRT prescription costs were significantly higher than expected over the three months prior to implementation of the legislation. Prescription costs peaked at £1.3 million in March 2006; £292,005.9 (95% CI £260,402.3, £323,609, p&#60;0.001) higher than the monthly norm. Following implementation of the legislation, costs fell exponentially by around 26% per month (95% CI 17%, 35%, p&#60;0.001). Twelve months following implementation, the costs were not significantly different to monthly norms. Smoking prevalence fell by 8.0% overall, from 31.3% in January 1999 to 23.7% in July–September 2010. In the quarter prior to implementation of the legislation, smoking prevalence fell by 1.7% (95% CI 2.4%, 1.0%, p&#60;0.001) more than expected from the underlying trend.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Conclusions:&lt;/b&gt; Quit attempts increased in the three months leading up to Scotland's smoke-free legislation, resulting in a fall in smoking prevalence. However, neither has been sustained suggesting the need for additional tobacco control measures and ongoing support.&lt;/p&gt

Predictors of Objectively Measured Medication Nonadherence in Adults With Heart Failure

Background—Medication nonadherence rates are high. The factors predicting nonadherence in heart failure remain unclear. Methods and Results—A sample of 202 adults with heart failure was enrolled from the northeastern United States and followed for 6 months. Specific aims were to describe the types of objectively measured medication adherence (eg, taking, timing, dosing, drug holidays) and to identify contributors to nonadherence 6 months after enrollment. Latent growth mixture modeling was used to identify distinct trajectories of adherence. Indicators of the 5 World Health Organization dimensions of adherence (socioeconomic, condition, therapy, patient, and healthcare system) were tested to identify contributors to nonadherence. Two distinct trajectories were identified and labeled persistent adherence (77.8%) and steep decline (22.3%). Three contributors to the steep decline in adherence were identified. Participants with lapses in attention (adjusted OR, 2.65; P=0.023), those with excessive daytime sleepiness (OR, 2.51; P=0.037), and those with ≥2 medication dosings per day (OR, 2.59; P=0.016) were more likely to have a steep decline in adherence over time than to have persistent adherence. Conclusions—Two distinct patterns of adherence were identified. Three potentially modifiable contributors to nonadherence have been identified