Citations

Background— The use of transcatheter aortic valve replacement (TAVR) to treat aortic stenosis in the United States is growing, yet little is known about the variation in procedural outcomes in community practice. We developed a TAVR in-hospital mortality risk model and used it to quantify variation in mortality rates across United States (US) TAVR centers. Methods and Results— We analyzed data from 22 248 TAVR procedures performed at 318 sites participating in the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy Registry (November 2011 to October 2014). A Bayesian hierarchical model was developed to estimate hospital-specific risk-adjusted mortality rates adjusting for 40 patient baseline factors. A total of 1130 in-hospital deaths (5.1%) were observed. Reliability-adjusted risk-adjusted mortality rate estimates ranged from 3.4% to 7.7% with an interquartile range of 4.8% to 5.4%. A patient’s predicted odds of dying was 80% higher if treated by a hospital 1 standard deviation above the mean compared with a hospital 1 standard deviation below the mean (odds ratio =1.8; 95% credible interval, 1.4%–2.2%). Conclusions— Risk modeling of TAVR in-hospital mortality revealed variation in risk-adjusted mortality rates during the US early commercial experience. Transcatheter Valve Therapy Registry analyses using this model will support research, feedback reporting, and the identification of factors associated with quality.

Background Assessing hospital quality in the performance of carotid endarterectomy ( CEA ) requires appropriate risk adjustment across hospitals with varying case mixes. The aim of this study was to develop and validate a prediction model to assess the risk of in‐hospital stroke or death after CEA that could aid in the assessment of hospital quality. Methods and Results Patients from National Cardiovascular Data Registry (NCDR)'s Carotid Artery Revascularization and Endarterectomy (CARE) Registry undergoing CEA without acute evolving stroke from 2005 to 2013 were included. In‐hospital stroke or death was modeled using hierarchical logistic regression with 20 candidate variables and accounting for hospital‐level clustering. Internal validation was achieved with bootstrapping; model discrimination and calibration were assessed. A total of 213 (1.7%) primary end point events occurred during 12 889 procedures. Independent predictors of stroke or death included age, prior peripheral artery disease, diabetes mellitus, prior coronary artery disease, having a symptomatic carotid lesion, having a contralateral carotid occlusion, or having New York Heart Association Class III or IV heart failure. The model was well calibrated and demonstrated moderate discriminative ability (c‐statistic 0.65). The NCDR CEA score was then developed to support simple, prospective risk quantification in the clinical setting. Conclusions The NCDR CEA score, comprising 7 clinical variables, predicts in‐hospital stroke or death after CEA . This model can be used to estimate hospital risk‐adjusted outcomes for CEA and to assist with the assessment of hospital quality.

Aims: The NCDR CathPCI Registry collects detailed clinical, process-of-care and outcomes data for patients undergoing coronary angiography and percutaneous coronary intervention (PCI) in the USA. The registry contributes to quality of care by providing data feedback on a wide range of performance metrics to participating centres and by facilitating local and national quality improvement efforts. Interventions: No treatments are mandated, participating centres receive routine quality-of-care and outcomes performance feedback reports and access to a quality dashboard for personalized performance reports. Population: Patients undergoing cardiac catheterization and PCI are retrospectively identified. No informed consent is required, as data are anonymised. From inception in 1998, more than 12 million records have been submitted from 1577 participating US centres. Baseline data: Approximately 250 fields encompassing patient demographics, medical history and risk factors, hospital presentation, initial cardiac status, procedural details, medications, laboratory values, and in-hospital outcomes. Linkages with outside sources of data have permitted longitudinal outcomes assessment in some cases. Centre personnel enter the data into the registry, in some cases facilitated by software vendors. There are non-financial incentives for centre participation. Data completeness is noteworthy with most fields missing at rates less than 5%. A comprehensive data quality program is employed to enhance data validity. Endpoints: Main outcome measures include quality process metrics and in-hospital patient outcomes. Data are available for research by application to: http://www.ncdr.com

Background— Measurement of hospital quality has traditionally focused on processes of care and postprocedure outcomes. Appropriateness measures for percutaneous coronary intervention (PCI) assess quality as it relates to patient selection and the decision to perform PCI. The association between patient selection for PCI and processes of care and postprocedural outcomes is unknown. Methods and Results— We included 203 531 patients undergoing nonacute (elective) PCI from 779 hospitals participating in the National Cardiovascular Data Registry (NCDR) CathPCI Registry between July 2009 and April 2011. We examined the association between a hospital's proportion of nonacute PCIs categorized as inappropriate by the 2009 Appropriate Use Criteria (AUC) for Coronary Revascularization and in-hospital mortality, bleeding complications, and use of optimal guideline-directed medical therapy at discharge (ie, aspirin, thienopyridines, and statins). When categorized as hospital tertiles, the range of inappropriate PCI was 0.0% to 8.1% in the lowest tertile, 8.1% to 15.2% in the middle tertile, and 15.2% to 58.6% in the highest tertile. Compared with lowest-tertile hospitals, mortality was not significantly different at middle-tertile (adjusted odds ratio [OR], 0.93; 95% confidence interval [CI], 0.73–1.19) or highest-tertile hospitals (OR, 1.12; 95% CI, 0.88–1.43; P =0.35 for differences between tertiles). Similarly, risk-adjusted bleeding did not vary significantly (middle-tertile OR, 1.13; 95% CI, 1.02–1.16; highest-tertile OR, 1.02; 95% CI, 0.91–1.16; P =0.07 for differences between tertiles) nor did use of optimal medical therapy at discharge (85.3% versus 85.7% versus 85.2%; P =0.58). Conclusions— In a national cohort of nonacute PCIs, a hospital's proportion of inappropriate PCIs was not associated with in-hospital mortality, bleeding, or medical therapy at discharge. This suggests PCI appropriateness measures aspects of hospital PCI quality that are independent of how well the procedure is performed. Therefore, PCI appropriateness and postprocedural outcomes are both important metrics to inform PCI quality.

Background— Temporal trends and contemporary data characterizing the impact of patient age on in-hospital outcomes of percutaneous coronary interventions are lacking. We sought to determine the importance of age by assessing the in-hospital mortality of stratified age groups in the National Cardiovascular Data Registry. Methods and Results— In-hospital mortality after percutaneous coronary intervention on 1 410 069 patients was age stratified into 4 groups—group 1 (age <40, n=25 679), group 2 (40 to 59, n=496 204), group 3 (60 to 79, n=732 574), and group 4 (≥80, n=155 612)—admitted from January 1, 2001, to December 31, 2006. Overall in-hospital mortality was 1.22%; in-hospital mortality was 0.60%, 0.59%, 1.26%, and 3.16% in groups 1 to 4, respectively, P <0.0001. Overall temporal improvement per calendar year in the adjusted in-hospital mortality after percutaneous coronary intervention was noted in most groups; however, this finding was significant only in the 2 older age groups, group 3 (odds ratio, 0.94; 95% CI, 0.92 to 0.96) and group 4 (odds ratio, 0.95; 95% CI, 0.92 to 0.97). The absolute mortality reduction was greatest in the most elderly group, those over the age of 80 years. Conclusions— In-hospital mortality after percutaneous coronary intervention has fallen for all age groups over the past 6 years. However, the largest absolute reduction was seen among patients 80 years of age or older.

Background— We sought to validate the recently developed Mayo Clinic Risk Score model for in-hospital mortality after percutaneous coronary intervention using an independent data set. The Mayo Clinic Risk Score has 7 simple clinical and noninvasive variables, available before coronary angiography, for prediction of in-hospital mortality. External validation using an independent data set would support broader applicability of the model. Methods and Results— In-hospital mortality after percutaneous coronary intervention on 309 351 patients from the National Cardiovascular Data Registry admitted from January 1, 2004, to March, 30, 2006, was studied. Using the Mayo Clinic Risk Score equation, we assigned predicted probabilities of death to each patient. The area under the receiver-operating characteristics curve was 0.884, indicating excellent discrimination overall as well as among subgroups, including gender, diabetes mellitus, renal failure, low ejection fraction, different age groups, and multivessel disease. Ninety-seven percent of patients undergoing percutaneous coronary intervention had a Mayo Clinic Risk Score <10, indicating low to intermediate risk. The Mayo Clinic Risk Score model initially slightly underpredicted event rates when applied in National Cardiovascular Data Registry data (observed 1.23% versus predicted 1.10%), but this underprediction was corrected after recalibration. The recalibrated risk score discriminated (c index=0.885) and calibrated well in an National Cardiovascular Data Registry validation data set consisting of procedures performed between April 1, 2006, and March 30, 2007. Conclusions— Seven variables can be combined into a convenient risk scoring system before coronary angiography is performed to predict in-hospital mortality after percutaneous coronary intervention. This model may be useful for providing patients with individualized, evidence-based estimates of procedural risk as part of the informed consent process before percutaneous coronary intervention.

Background— Current risk models predict in-hospital mortality after either coronary artery bypass graft surgery or percutaneous coronary interventions separately, yet the overlap suggests that the same variables can define the risks of alternative coronary reperfusion therapies. Our goal was to seek a preprocedure risk model that can predict in-hospital mortality after either percutaneous coronary intervention or coronary artery bypass graft surgery. Methods and Results— We tested the ability of the recently validated, integer-based Mayo Clinic Risk Score (MCRS) for percutaneous coronary intervention, which is based solely on preprocedure variables (age, creatinine, ejection fraction, myocardial infarction ≤24 hours, shock, congestive heart failure, and peripheral vascular disease), to predict in-hospital mortality among 370 793 patients in the Society of Thoracic Surgeons database undergoing isolated coronary artery bypass graft surgery from 2004 to 2006. For the Society of Thoracic Surgeons coronary artery bypass graft surgery population studied, the median age was 66 years (quartiles 1 to 3, 57 to 74 years), with 37.2% of patients ≥70 years old. A high prevalence of comorbid conditions, including diabetes mellitus (37.1%), hypertension (80.5%), peripheral vascular disease (15.3%), and renal disease (creatinine ≥1.4 mg/dL; 11.8%), was present. A strong association existed between the MCRS and the observed mortality in the Society of Thoracic Surgeons database. The in-hospital mortality ranged between 0.3% (95% confidence interval 0.3% to 0.4%) with a score of 0 on the MCRS and 33.8% (95% confidence interval 27.3% to 40.3%) with an MCRS score of 20 to 24. The discriminatory ability of the MCRS was moderate, as measured by the area under the receiver operating characteristic curve (C-statistic=0.715 to 0.784 among various subgroups); performance was inferior to the Society of Thoracic Surgeons model for most categories tested. Conclusions— This model, which is based on 7 preprocedure risk variables, may be useful for providing patients with individualized, evidence-based estimates of procedural risk as part of the informed consent process before percutaneous or surgical revascularization.