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

Interventional cardiology : Cost-effectiveness of PCI guided by fractional flow reserve

Coronary revascularization strategies have been evaluated in numerous clinical trials. As coronary revascularization has become more common, concerns over financial costs have increased

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

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

Double jeopardy: the influence of excessive daytime sleepiness and impaired cognition on health‐related quality of life in adults with heart failure

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106066/1/ejhfhfs054.pd