Percutaneous Coronary Intervention and Drug-Eluting Stent Use Among Patients ≥85 Years of Age in the United States

ObjectivesThis study assessed the comparative effectiveness of drug-eluting stents (DES) versus bare-metal stents (BMS) among patients ≥85 years of age.BackgroundDespite an aging population, little is known about the comparative effectiveness of DES versus BMS among patients age ≥85 years undergoing percutaneous coronary intervention (PCI).MethodsWe examined 471,006 PCI patients age ≥65 years at 947 hospitals in the National Cardiovascular Data Registry between 2004 and 2008 and linked to Medicare claims data. Long-term outcomes (median follow-up 640.8 ± 423.5 days) were compared between users of DES and BMS.ResultsPatients age ≥85 years comprise an increasing proportion of PCIs performed among elderly subjects, yet rates of DES use declined the most in this age group. Compared with BMS, use of DES was associated with lower mortality: age ≥85 years, 29% versus 38% (adjusted hazard ratio [HR]: 0.80 [95% confidence interval (CI): 0.77 to 0.83]); age 75 to 84 years, 17% versus 25% (HR: 0.77 [95% CI: 0.75 to 0.79]); and age 65 to 74 years, 10% versus 16% (HR: 0.73 [95% CI: 0.71 to 0.75]). However, the adjusted mortality difference narrowed with increasing age (pinteraction <0.001). In contrast, the adjusted HR for myocardial infarction rehospitalization associated with DES use was significantly lower with increasing age: age ≥85 years, 9% versus 12% (HR: 0.77 [95% CI: 0.71 to 0.83]); age 75 to 84 years, 7% versus 9% (HR: 0.81 [95% CI: 0.77 to 0.84]); and age 65 to 74 years, 7% versus 8% (HR: 0.84 [95% CI: 0.80 to 0.88]) (pinteraction <0.001).ConclusionsIn this national study of older patients undergoing PCI, declines in DES use were most pronounced among those aged ≥85 years, yet lower adverse-event rates associated with DES versus BMS use were observed

ACC/AHA/SCAI/AMA–Convened PCPI/NCQA 2013 Performance Measures for Adults Undergoing Percutaneous Coronary Intervention A Report of the American College of Cardiology/American Heart Association Task Force on Performance Measures, the Society for Cardiovascular Angiography and Interventions, the American Medical Association–Convened Physician Consortium for Performance Improvement, and the National Committee for Quality Assurance

Journal of the American College of Cardiology Ó 2014 by the American College of Cardiology Foundation, American Heart Association, Inc., American Medical Association, and National Committee for Quality Assurance Published by Elsevier Inc. Vol. 63, No. 7, 2014 ISSN 0735-1097/$36.00 http://dx.doi.org/10.1016/j.jacc.2013.12.003 PERFORMANCE MEASURES ACC/AHA/SCAI/AMA–Convened PCPI/NCQA 2013 Performance Measures for Adults Undergoing Percutaneous Coronary Intervention A Report of the American College of Cardiology/American Heart Association Task Force on Performance Measures, the Society for Cardiovascular Angiography and Interventions, the American Medical Association–Convened Physician Consortium for Performance Improvement, and the National Committee for Quality Assurance Developed in Collaboration With the American Association of Cardiovascular and Pulmonary Rehabilitation and Mended Hearts Endorsed by the American Association of Cardiovascular and Pulmonary Rehabilitation and Mended Hearts WRITING COMMITTEE MEMBERS Brahmajee K. Nallamothu, MD, MPH, FACC, FAHA, Co-Chair*; Carl L. Tommaso, MD, FACC, FAHA, FSCAI, Co-Chairy; H. Vernon Anderson, MD, FACC, FAHA, FSCAI*; Jeffrey L. Anderson, MD, FACC, FAHA, MACP*; Joseph C. Cleveland, J R , MDz; R. Adams Dudley, MD, MBA; Peter Louis Duffy, MD, MMM, FACC, FSCAIy; David P. Faxon, MD, FACC, FAHA*; Hitinder S. Gurm, MD, FACC; Lawrence A. Hamilton, Neil C. Jensen, MHA, MBA; Richard A. Josephson, MD, MS, FACC, FAHA, FAACVPRx; David J. Malenka, MD, FACC, FAHA*; Calin V. Maniu, MD, FACC, FAHA, FSCAIy; Kevin W. McCabe, MD; James D. Mortimer, Manesh R. Patel, MD, FACC*; Stephen D. Persell, MD, MPH; John S. Rumsfeld, MD, PhD, FACC, FAHAjj; Kendrick A. Shunk, MD, PhD, FACC, FAHA, FSCAI*; Sidney C. Smith, J R , MD, FACC, FAHA, FACP{; Stephen J. Stanko, MBA, BA, AA#; Brook Watts, MD, MS *ACC/AHA Representative. ySociety of Cardiovascular Angiography and Interventions Representative. zSociety of Thoracic Surgeons Representative. xAmerican Association of Cardiovascular and Pulmonary Rehabilitation Representative. kACC/AHA Task Force on Performance Measures Liaison. {National Heart Lung and Blood Institute Representative. #Mended Hearts Representative. The measure specifications were approved by the American College of Cardiology Board of Trustees, American Heart Association Science Advisory and Coordinating Committee, in January 2013 and the American Medical Association–Physician Consortium for Performance Improvement in February 2013. This document was approved by the American College of Cardiology Board of Trustees and the American Heart Association Science Advisory and Coordinating Committee in October 2013, and the Society of Cardiovascular Angiography and Interventions in December 2013. The American College of Cardiology requests that this document be cited as follows: Nallamothu BK, Tommaso CL, Anderson HV, Anderson JL, Cleveland JC, Dudley RA, Duffy PL, Faxon DP, Gurm HS, Hamilton LA, Jensen NC, Josephson RA, Malenka DJ, Maniu CV, McCabe KW, Mortimer JD, Patel MR, Persell SD, Rumsfeld JS, Shunk KA, Smith SC, Stanko SJ, Watts B. ACC/AHA/SCAI/AMA–Convened PCPI/NCQA 2013 perfor- mance measures for adults undergoing percutaneous coronary intervention: a report of the American College of Cardiology/American Heart Association Task Force on Performance Measures, the Society for Cardiovascular Angiography and Interventions, the American Medical Association–Convened Physician Consortium for Performance Improvement, and the National Committee for Quality Assurance. J Am Coll Cardiol 2014;63:722–45. This article has been copublished in Circulation. Copies: This document is available on the World Wide Web sites of the American College of Cardiology (www.cardiosource.org) and the American Heart Asso- ciation (http://my.americanheart.org). For copies of this document, please contact Elsevier Inc. Reprint Department, fax (212) 633-3820, e-mail [email protected]. Permissions: Multiple copies, modification, alteration, enhancement, and/or distribution of this document are not permitted without the express permission of the American College of Cardiology. Requests may be completed online via the Elsevier site (http://www.elsevier.com/authors/obtaining- permission-to-re-use-elsevier-material). This Physician Performance Measurement Set (PPMS) and related data specifications were developed by the Physician Consortium for Performance Improvement (the Consortium), including the American College of Cardiology (ACC), the American Heart Association (AHA), and the American Medical Association (AMA), to facilitate quality-improvement activities by physicians. The performance measures contained in this PPMS are not clinical guidelines, do not establish a standard of medical care, and have not been tested for all potential applications. Although copyrighted, they can be reproduced and distributed, without modification, for noncommercial purposesdfor example, use by health care pro

SRH and HrQOL: does social position impact differently on their link with health status?

<p>Abstract</p> <p>Background</p> <p>Self-rated Health (SRH) and health-related quality of life (HRQoL) are used to evaluate health disparities. Like all subjective measures of health, they are dependent on health expectations that are associated with socioeconomic characteristics. It is thus needed to analyse the influence played by socioeconomic position (SEP) on the relationship between these two indicators and health conditions if we aim to use them to study health disparities. Our objective is to assess the influence of SEP on the relationship between physical health status and subjective health status, measured by SRH and HRQoL using the SF-36 scale.</p> <p>Methods</p> <p>We used data from the French National Health Survey. SEP was assessed by years of education and household annual income. Physical health status was measured by functional limitations and chronic low back pain.</p> <p>Results</p> <p>Regardless of their health status, people with lower SEP were more likely than their more socially advantaged counterparts to report poor SRH and poorer HRQoL, using any of the indicators of SEP. The negative impact of chronic low back pain on SRH was relatively greater in people with a high SEP than in those with a low SEP. In contrast, chronic low back pain and functional limitations had less impact on physical and mental component scores of quality of life for socially advantaged men and women.</p> <p>Conclusions</p> <p>Both SRH and HRQoL were lower among those reporting functional limitations or chronic low back pain. However, the change varied according SEP and the measure. In relative term, the negative impact of a given health condition seems to be greater on SRH and lower on HRQoL for people with higher SEP in comparison with people with low SEP. Using SRH could thus decrease socioeconomic differences. In contrast using HRQoL could increase these differences, suggesting being cautious when using these indicators for analyzing health disparities.</p

Health-related quality of life after fast-track treatment results from a randomized controlled clinical equivalence trial

Purpose This randomized clinical equivalence trial was designed to evaluate health-related quality of life (HRQoL) after fast-track treatment for low-risk coronary artery bypass (CABG) patients. Methods Four hundred and ten CABG patients were randomly assigned to undergo either short-stay intensive care treatment (SSIC, 8 h of intensive care stay) or control treatment (care as usual, overnight intensive care stay). HRQoL was measured at baseline and 1 month, and one year after surgery using the multidimensional index of life quality (MILQ), the EQ-5D, the Beck Depression Inventory and the State-Trait Anxiety Inventory. Results At one month after surgery, no statistically significant difference in overall HRQoL was found (MILQ-score P-value = .508, overall MILQ-index P-value = .543, EQ-5D VAS P-value = .593). The scores on the MILQ-domains, physical, and social functioning were significantly higher at one month postoperatively in the SSIC group compared to the control group (P-value = .049; 95% CI: 0.01-2.50 and P-value =.014, 95% CI:0.24-2.06, respectively). However, these differences were no longer observed at long-term follow-up. Conclusions According to our definition of clinical equivalence, the HRQoL of SSIC patients is similar to patients receiving care as usual. Since safety and the financial benefits of this intervention were demonstrated in a previously reported analysis, SSIC can be considered as an adequate fast-track intensive care treatment option for low-risk CABG patients

Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best

<p>Abstract</p> <p>Background</p> <p>Associations between measures of subjective health and mortality risk have previously been shown. We assessed the impact and comparative predictive performance of a multi-biomarker panel on this association.</p> <p>Methods</p> <p>Data from 4,261 individuals aged 20-79 years recruited for the population-based Study of Health in Pomerania was used. During an average 9.7 year follow-up, 456 deaths (10.7%) occurred. Subjective health was assessed by SF-12 derived physical (PCS-12) and mental component summaries (MCS-12), and a single-item self-rated health (SRH) question. We implemented Cox proportional-hazards regression models to investigate the association of subjective health with mortality and to assess the impact of a combination of 10 biomarkers on this association. Variable selection procedures were used to identify a parsimonious set of subjective health measures and biomarkers, whose predictive ability was compared using receiver operating characteristic (ROC) curves, C-statistics, and reclassification methods.</p> <p>Results</p> <p>In age- and gender-adjusted Cox models, poor SRH (hazard ratio (HR), 2.07; 95% CI, 1.34-3.20) and low PCS-12 scores (lowest vs. highest quartile: HR, 1.75; 95% CI, 1.31-2.33) were significantly associated with increased risk of all-cause mortality; an association independent of various covariates and biomarkers. Furthermore, selected subjective health measures yielded a significantly higher C-statistic (0.883) compared to the selected biomarker panel (0.872), whereas a combined assessment showed the highest C-statistic (0.887) with a highly significant integrated discrimination improvement of 1.5% (p < 0.01).</p> <p>Conclusion</p> <p>Adding biomarker information did not affect the association of subjective health measures with mortality, but significantly improved risk stratification. Thus, a combined assessment of self-reported subjective health and measured biomarkers may be useful to identify high-risk individuals for intensified monitoring.</p