Empirical use of causal inference methods to evaluate survival differences in a real-world registry vs those found in randomized clinical trials.

With heighted interest in causal inference based on real-world evidence, this empirical study sought to understand differences between the results of observational analyses and long-term randomized clinical trials. We hypothesized that patients deemed "eligible" for clinical trials would follow a different survival trajectory from those deemed "ineligible" and that this factor could partially explain results. In a large observational registry dataset, we estimated separate survival trajectories for hypothetically trial-eligible vs ineligible patients under both coronary artery bypass surgery (CABG) and percutaneous coronary intervention (PCI). We also explored whether results would depend on the causal inference method (inverse probability of treatment weighting vs optimal full propensity matching) or the approach to combine propensity scores from multiple imputations (the "across" vs "within" approaches). We found that, in this registry population of PCI/CABG multivessel patients, 32.5% would have been eligible for contemporaneous RCTs, suggesting that RCTs enroll selected populations. Additionally, we found treatment selection bias with different distributions of propensity scores between PCI and CABG patients. The different methodological approaches did not result in different conclusions. Overall, trial-eligible patients appeared to demonstrate at least marginally better survival than ineligible patients. Treatment comparisons by eligibility depended on disease severity. Among trial-eligible three-vessel diseased and trial-ineligible two-vessel diseased patients, CABG appeared to have at least a slight advantage with no treatment difference otherwise. In conclusion, our analyses suggest that RCTs enroll highly selected populations, and our findings are generally consistent with RCTs but less pronounced than major registry findings.

An evaluation of constrained randomization for the design and analysis of group-randomized trials with binary outcomes.

Group-randomized trials are randomized studies that allocate intact groups of individuals to different comparison arms. A frequent practical limitation to adopting such research designs is that only a limited number of groups may be available, and therefore, simple randomization is unable to adequately balance multiple group-level covariates between arms. Therefore, covariate-based constrained randomization was proposed as an allocation technique to achieve balance. Constrained randomization involves generating a large number of possible allocation schemes, calculating a balance score that assesses covariate imbalance, limiting the randomization space to a prespecified percentage of candidate allocations, and randomly selecting one scheme to implement. When the outcome is binary, a number of statistical issues arise regarding the potential advantages of such designs in making inference. In particular, properties found for continuous outcomes may not directly apply, and additional variations on statistical tests are available. Motivated by two recent trials, we conduct a series of Monte Carlo simulations to evaluate the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs, with varying degrees of analysis-based covariate adjustment. Our results indicate that constrained randomization improves the power of the linearization F-test, the KC-corrected GEE t-test (Kauermann and Carroll, 2001, Journal of the American Statistical Association 96, 1387-1396), and two permutation tests when the prognostic group-level variables are controlled for in the analysis and the size of randomization space is reasonably small. We also demonstrate that constrained randomization reduces power loss from redundant analysis-based adjustment for non-prognostic covariates. Design considerations such as the choice of the balance metric and the size of randomization space are discussed.

An evaluation of constrained randomization for the design and analysis of group-randomized trials.

In group-randomized trials, a frequent practical limitation to adopting rigorous research designs is that only a small number of groups may be available, and therefore, simple randomization cannot be relied upon to balance key group-level prognostic factors across the comparison arms. Constrained randomization is an allocation technique proposed for ensuring balance and can be used together with a permutation test for randomization-based inference. However, several statistical issues have not been thoroughly studied when constrained randomization is considered. Therefore, we used simulations to evaluate key issues including the following: the impact of the choice of the candidate set size and the balance metric used to guide randomization; the choice of adjusted versus unadjusted analysis; and the use of model-based versus randomization-based tests. We conducted a simulation study to compare the type I error and power of the F-test and the permutation test in the presence of group-level potential confounders. Our results indicate that the adjusted F-test and the permutation test perform similarly and slightly better for constrained randomization relative to simple randomization in terms of power, and the candidate set size does not substantially affect their power. Under constrained randomization, however, the unadjusted F-test is conservative, while the unadjusted permutation test carries the desired type I error rate as long as the candidate set size is not too small; the unadjusted permutation test is consistently more powerful than the unadjusted F-test and gains power as candidate set size changes. Finally, we caution against the inappropriate specification of permutation distribution under constrained randomization. An ongoing group-randomized trial is used as an illustrative example for the constrained randomization design.

Prediction of long-term mortality after percutaneous coronary intervention in older adults: results from the National Cardiovascular Data Registry.

BACKGROUND: The purpose of this study was to develop a long-term model to predict mortality after percutaneous coronary intervention in both patients with ST-segment elevation myocardial infarction and those with more stable coronary disease. METHODS AND RESULTS: The American College of Cardiology Foundation CathPCI Registry data were linked to the Centers for Medicare and Medicaid Services 100% denominator file by probabilistic matching. Preprocedure demographic and clinical variables from the CathPCI Registry were used to predict the probability of death over 3 years as recorded in the Centers for Medicare and Medicaid Services database. Between 2004 and 2007, 343 466 patients (66%) of 518 195 patients aged ≥65 years undergoing first percutaneous coronary intervention in the CathPCI Registry were successfully linked to Centers for Medicare and Medicaid Services data. This study population was randomly divided into 60% derivation and 40% validation cohorts. Median follow-up was 15 months, with mortality of 3.0% at 30 days and 8.7%, 13.4%, and 18.7% at 1, 2, and 3 years, respectively. Twenty-four characteristics related to demographics, clinical comorbidity, prior history of disease, and indices of disease severity and acuity were identified as being associated with mortality. The C indices in the validation cohorts for patients with and without ST-segment elevation myocardial infarction were 0.79 and 0.78. The model calibrated well across a wide range of predicted probabilities. CONCLUSIONS: On the basis of the large and nationally representative CathPCI Registry, we have developed a model that has excellent discrimination, calibration, and validation to predict survival up to 3 years after percutaneous coronary intervention.

Predictors of long-term survival after coronary artery bypass grafting surgery: results from the Society of Thoracic Surgeons Adult Cardiac Surgery Database (the ASCERT study).

BACKGROUND: Most survival prediction models for coronary artery bypass grafting surgery are limited to in-hospital or 30-day end points. We estimate a long-term survival model using data from the Society of Thoracic Surgeons Adult Cardiac Surgery Database and Centers for Medicare and Medicaid Services. METHODS AND RESULTS: The final study cohort included 348 341 isolated coronary artery bypass grafting patients aged ≥65 years, discharged between January 1, 2002, and December 31, 2007, from 917 Society of Thoracic Surgeons-participating hospitals, randomly divided into training (n=174 506) and validation (n=173 835) samples. Through linkage with Centers for Medicare and Medicaid Services claims data, we ascertained vital status from date of surgery through December 31, 2008 (1- to 6-year follow-up). Because the proportional hazards assumption was violated, we fit 4 Cox regression models conditional on being alive at the beginning of the following intervals: 0 to 30 days, 31 to 180 days, 181 days to 2 years, and >2 years. Kaplan-Meier-estimated mortality was 3.2% at 30 days, 6.4% at 180 days, 8.1% at 1 year, and 23.3% at 3 years of follow-up. Harrell's C statistic for predicting overall survival time was 0.732. Some risk factors (eg, emergency status, shock, reoperation) were strong predictors of short-term outcome but, for early survivors, became nonsignificant within 2 years. The adverse impact of some other risk factors (eg, dialysis-dependent renal failure, insulin-dependent diabetes mellitus) continued to increase. CONCLUSIONS: Using clinical registry data and longitudinal claims data, we developed a long-term survival prediction model for isolated coronary artery bypass grafting. This provides valuable information for shared decision making, comparative effectiveness research, quality improvement, and provider profiling.

Secondary prevention after coronary artery bypass graft surgery: findings of a national randomized controlled trial and sustained society-led incorporation into practice.

BACKGROUND: Despite evidence supporting the use of aspirin, ß-blockers, angiotensin-converting enzyme inhibitors, and lipid-lowering therapies in eligible patients, adoption of these secondary prevention measures after coronary artery bypass grafting has been inconsistent. We sought to rigorously test on a national scale whether low-intensity continuous quality improvement interventions can be used to speed secondary prevention adherence after coronary artery bypass grafting. METHODS AND RESULTS: A total of 458 hospitals participating in the Society of Thoracic Surgeons National Cardiac Database and treating 361 328 patients undergoing isolated coronary artery bypass grafting were randomized to either a control or an intervention group. The intervention group received continuous quality improvement materials designed to influence the prescription of the secondary prevention medications at discharge. The primary outcome measure was discharge prescription rates of the targeted secondary prevention medications at intervention versus control sites, assessed by measuring preintervention and postintervention site differences. Prerandomization treatment patterns and baseline data were similar in the control (n=234) and treatment (n=224) groups. Individual medication use and composite adherence increased over 24 months in both groups, with a markedly more rapid rate of adherence uptake among the intervention hospitals and a statistically significant therapy hazard ratio in the intervention versus control group for all 4 secondary prevention medications. CONCLUSIONS: Provider-led, low-intensity continuous quality improvement efforts can improve the adoption of care processes into national practice within the context of a medical specialty society infrastructure. The findings of the present trial have led to the incorporation of study outcome metrics into a medical society rating system for ongoing quality improvement.

Delay from symptom onset to hospital presentation for patients with non-ST-segment elevation myocardial infarction.

BACKGROUND: Secular trends and factors associated with delay time from symptom onset to hospital presentation are known for patients with ST-segment elevation myocardial infarction (STEMI) but are less well-described for non-STEMI. METHODS: We studied 104 622 patients with non-STEMI enrolled at 568 hospitals participating in the Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes With Early Implementation of the American College of Cardiology/American Heart Association Guidelines (CRUSADE) National Quality Improvement Initiative from January 1, 2001, to December 31, 2006. We examined secular trends and factors associated with delay time and the association of delay time with in-hospital mortality. RESULTS: Median delay time from symptom onset to hospital presentation was 2.6 hours (interquartile range, 1.3-6.0) and has been stable from 2001 to 2006 (P value for trend, .16). After multivariable adjustment, factors associated with longer delay time included older age, female sex, nonwhite race, diabetes, and current smoking. In addition, compared with those who presented during weekday daytime (>8 am to 4 pm), patients who presented during weekday and weekend nights (>12 am to 8 am) had a 24.7% and 24.3% shorter delay time, respectively (P < .001). After multivariable adjustment, the odds ratio of in-hospital mortality for patients with delay times of 0 to 1 hour or less, more than 1 to 2 hours, more than 2 to 3 hours, and more than 3 to 6 hours compared with the reference group (delay time >6 hours) were 1.19 (95% confidence interval [CI], 1.08-1.30), 0.91 (95% CI, 0.83-1.00), 0.77 (95% CI, 0.69-0.88), and 0.90 (95% CI, 0.81-1.00), respectively. CONCLUSIONS: Long delay times are common and have not changed over time for patients with non-STEMI. Because patients cannot differentiate whether symptoms are due to STEMI or non-STEMI, early presentation is desirable in both instances.

Reclassification of cardiovascular risk using integrated clinical and molecular biosignatures: Design of and rationale for the Measurement to Understand the Reclassification of Disease of Cabarrus and Kannapolis (MURDOCK) Horizon 1 Cardiovascular Disease Study.

BACKGROUND: Clinical predictive models leave gaps in our ability to stratify cardiovascular risk. High-throughput molecular profiling promises to improve risk classification. METHODS: Horizon 1 of the Measurement to Understand the Reclassification of Disease of Cabarrus and Kannapolis (MURDOCK) Study was conceived to apply emerging molecular techniques to existing data sets to characterize mechanistic diversity underlying complex human diseases, response to therapy, and prognosis. No previous studies have applied multiple, complementary molecular techniques in combination with well-developed clinical risk models to refine cardiovascular risk prediction. The MURDOCK Cardiovascular Disease Study will assess molecular profiles integrated with clinical data in "clinomic" profiles for cardiovascular risk classification. CONCLUSION: Herein, we describe the design of and rationale for the MURDOCK Cardiovascular Disease Study.

Contemporary mortality risk prediction for percutaneous coronary intervention: results from 588,398 procedures in the National Cardiovascular Data Registry.

OBJECTIVES: We sought to create contemporary models for predicting mortality risk following percutaneous coronary intervention (PCI). BACKGROUND: There is a need to identify PCI risk factors and accurately quantify procedural risks to facilitate comparative effectiveness research, provider comparisons, and informed patient decision making. METHODS: Data from 181,775 procedures performed from January 2004 to March 2006 were used to develop risk models based on pre-procedural and/or angiographic factors using logistic regression. These models were independently evaluated in 2 validation cohorts: contemporary (n = 121,183, January 2004 to March 2006) and prospective (n = 285,440, March 2006 to March 2007). RESULTS: Overall, PCI in-hospital mortality was 1.27%, ranging from 0.65% in elective PCI to 4.81% in ST-segment elevation myocardial infarction patients. Multiple pre-procedural clinical factors were significantly associated with in-hospital mortality. Angiographic variables provided only modest incremental information to pre-procedural risk assessments. The overall National Cardiovascular Data Registry (NCDR) model, as well as a simplified NCDR risk score (based on 8 key pre-procedure factors), had excellent discrimination (c-index: 0.93 and 0.91, respectively). Discrimination and calibration of both risk tools were retained among specific patient subgroups, in the validation samples, and when used to estimate 30-day mortality rates among Medicare patients. CONCLUSIONS: Risks for early mortality following PCI can be accurately predicted in contemporary practice. Incorporation of such risk tools should facilitate research, clinical decisions, and policy applications.

Influence of clinical trial participation on subsequent antithrombin use.

BACKGROUND: Results from the Superior Yield of the New Strategy of Enoxaparin, Revascularization, and Glycoprotein IIb/IIIa Inhibitors (SYNERGY) trial showed that the low-molecular-weight heparin (LMWH) enoxaparin was non-inferior compared with unfractionated heparin (UFH) in patients with non-ST-elevation acute coronary syndromes (NSTE-ACS) managed invasively. HYPOTHESIS: We explored the influence of SYNERGY trial site participation on subsequent patterns of heparin use for NSTE-ACS patients treated in routine practice. METHODS: We examined temporal patterns of LMWH use compared with UFH use among 122 764 patients with NSTE-ACS enrolled in the Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes with Early Implementation of the ACC/AHA Guidelines (CRUSADE) quality improvement initiative between January 1, 2002 and June 30, 2006, to determine whether site participation in SYNERGY influenced the type of heparin used before and after publication of the SYNERGY results in July 2004. RESULTS: A total of 118 out of 388 (30%) U.S. hospitals participating in CRUSADE simultaneously participated in SYNERGY. SYNERGY sites in the CRUSADE registry were more likely to have a teaching affiliation and have more hospital beds than non-SYNERGY centers in the registry. There was no difference in the proportion of patients treated with LMWH at SYNERGY and non-SYNERGY sites prior to July 2004 compared with after July 2004. However, at SYNERGY sites, there was a slight decrease in the proportion of patients treated with both UFH and LMWH within 24 hours of presentation. CONCLUSIONS: The results of the SYNERGY trial did not appear to influence temporal patterns of LMWH use at sites in the CRUSADE registry. Furthermore, site participation in the SYNERGY trial did not alter patterns of LMWH use for NSTE-ACS after publication of the trial results in July 2004.

Assessing the economic attractiveness of coronary artery revascularization in chronic kidney disease patients.

Chronic kidney disease (CKD) is associated with increased morbidity and mortality in coronary artery disease (CAD) patients. We compared the economic attractiveness of CAD revascularization procedures in patients with and without CKD. Our population included 6218 patients with significant CAD undergoing cardiac catheterization at Duke University between 1996 and 2001, with follow-up through 2002. We investigated the influence of CKD (creatinine clearance < 60 mL/min) upon 3-year survival and medical costs in our CAD population. Coronary artery bypass graft (CABG) surgery was an economically attractive alternative vs. percutaneous coronary intervention (PCI) or medical therapy for all patients with left main disease, three-vessel CAD patients without CKD, and two-vessel CAD patients with CKD. Medical therapy was an economically attractive strategy vs. CABG surgery or PCI for three-vessel CAD patients with CKD, two-vessel CAD patients without CKD, and all single-vessel CAD patients.

Hypercholesterolemia paradox in relation to mortality in acute coronary syndrome.

BACKGROUND: Hypercholesterolemia is a risk factor for coronary artery disease, yet is associated with lower risk of adverse outcomes in patients with acute coronary syndromes (ACS). HYPOTHESIS: We explored this paradox in 84,429 patients with non-ST-segment elevation ACS in the Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes with Early Implementation of the American College of Cardiology/American Heart Association Guidelines registry. METHODS: We examined the association between a history of hypercholesterolemia and in-hospital mortality after adjusting for clinical covariates. After excluding patients with previously diagnosed hypercholesterolemia, we repeated the analysis, examining the association between newly diagnosed hypercholesterolemia (in-hospital low-density lipoprotein cholesterol [LDL-C] > or = 100 mg/dL) and mortality. RESULTS: A history of hypercholesterolemia was associated with lower in-hospital mortality (unadjusted odds ratio [OR]: 0.58; 95% confidence interval [CI]: 0.55, 0.62). This protective association persisted after adjusting for baseline characteristics (OR: 0.71; 95% CI: 0.66, 0.76) and prior statin use (OR: 0.74; 95% CI: 0.68, 0.80). Among 22,711 patients with no history of hypercholesterolemia, 12,809 had a new in-hospital diagnosis of hypercholesterolemia. Unadjusted mortality in these patients was lower than among those with normal LDL levels (OR: 0.58; 95% CI: 0.50, 0.67); however, this difference was not significant after multivariable adjustment (OR: 0.86; 95% CI: 0.73, 1.01). CONCLUSIONS: The association of hypercholesterolemia with better outcomes highlights a major challenge in observational analyses. Our results suggest this paradox may result from confounding due to other clinical characteristics, impact of statin treatment, and perhaps most importantly, the fact that previously diagnosed hypercholesterolemia is a marker for patients with more prior medical contact.

The Society of Thoracic Surgeons 2008 cardiac surgery risk models: part 1--coronary artery bypass grafting surgery.

BACKGROUND: The first version of The Society of Thoracic Surgeons National Adult Cardiac Surgery Database (STS NCD) was developed nearly 2 decades ago. Since its inception, the number of participants has grown dramatically, patient acuity has increased, and overall outcomes have consistently improved. To adjust for these and other changes, all STS risk models have undergone periodic revisions. This report provides a detailed description of the 2008 STS risk model for coronary artery bypass grafting surgery (CABG). METHODS: The study population consisted of 774,881 isolated CABG procedures performed on adult patients aged 20 to 100 years between January 1, 2002, and December 31, 2006, at 819 STS NCD participating centers. This cohort was randomly divided into a 60% training (development) sample and a 40% test (validation) sample. The development sample was used to identify predictor variables and estimate model coefficients. The validation sample was used to assess model calibration and discrimination. Model outcomes included operative mortality, renal failure, stroke, reoperation for any cause, prolonged ventilation, deep sternal wound infection, composite major morbidity or mortality, prolonged length of stay (> 14 days), and short length of stay (< 6 days and alive). Candidate predictor variables were selected based on their availability in versions 2.35, 2.41, and 2.52.1 of the STS NCD and their presence in (or ability to be mapped to) version 2.61. Potential predictor variables were screened for overall prevalence in the study population, missing data frequency, coding concerns, bivariate relationships with outcomes, and their presence in previous STS or other CABG risk models. Supervised backwards selection was then performed with input from an expert panel of cardiac surgeons and biostatisticians. After successfully validating the fit of the models, the development and validation samples were subsequently combined, and the final regression coefficients were estimated using the overall combined (development plus validation) sample. RESULTS: The c-index for the mortality model was 0.812, and the c-indices for other endpoints ranged from 0.653 for reoperation to 0.793 for renal failure in the validation sample. Plots of observed versus predicted event rates revealed acceptable calibration in the overall population and in numerous subgroups. When patients were grouped into categories of predicted risk, the absolute difference between the observed and expected event rates was less than 1.5% for each endpoint. The final model intercept and coefficients are provided. CONCLUSIONS: New STS risk models have been developed for CABG mortality and eight other endpoints. Detailed descriptions of model development and testing are provided, together with the final algorithm. Overall model performance is excellent.

The Society of Thoracic Surgeons 2008 cardiac surgery risk models: part 2--isolated valve surgery.

BACKGROUND: Adjustment for case-mix is essential when using observational data to compare surgical techniques or providers. That is most often accomplished through the use of risk models that account for preoperative patient factors that may impact outcomes. The Society of Thoracic Surgeons (STS) uses such risk models to create risk-adjusted performance reports for participants in the STS National Adult Cardiac Surgery Database (NCD). Although risk models were initially developed for coronary artery bypass surgery, similar models have now been developed for use with heart valve surgery, particularly as the proportion of such procedures has increased. The last published STS model for isolated valve surgery was based on data from 1994 to 1997 and did not include patients undergoing mitral valve repair. STS has developed new valve surgery models using contemporary data that include both valve repair as well as replacement. Expanding upon existing valve models, the new STS models include several nonfatal complications in addition to mortality. METHODS: Using STS data from 2002 to 2006, isolated valve surgery risk models were developed for operative mortality, permanent stroke, renal failure, prolonged ventilation (> 24 hours), deep sternal wound infection, reoperation for any reason, a major morbidity or mortality composite endpoint, prolonged postoperative length of stay, and short postoperative length of stay. The study population consisted of adult patients who underwent one of three types of valve surgery: isolated aortic valve replacement (n = 67,292), isolated mitral valve replacement (n = 21,229), or isolated mitral valve repair (n = 21,238). The population was divided into a 60% development sample and a 40% validation sample. After an initial empirical investigation, the three surgery groups were combined into a single logistic regression model with numerous interactions to allow the covariate effects to differ across these groups. Variables were selected based on a combination of automated stepwise selection and expert panel review. RESULTS: Unadjusted operative mortality (in-hospital regardless of timing, and 30-day regardless of venue) for all isolated valve procedures was 3.4%, and unadjusted in-hospital morbidity rates ranged from 0.3% for deep sternal wound infection to 11.8% for prolonged ventilation. The number of predictors in each model ranged from 10 covariates in the sternal infection model to 24 covariates in the composite mortality plus morbidity model. Discrimination as measured by the c-index ranged from 0.639 for reoperation to 0.799 for mortality. When patients in the validation sample were grouped into 10 categories based on deciles of predicted risk, the average absolute difference between observed versus predicted events within these groups ranged from 0.06% for deep sternal wound infection to 1.06% for prolonged postoperative stay. CONCLUSIONS: The new STS risk models for valve surgery include mitral valve repair as well as multiple endpoints other than mortality. Model coefficients are provided and an online risk calculator is publicly available from The Society of Thoracic Surgeons website.

The Society of Thoracic Surgeons 2008 cardiac surgery risk models: part 3--valve plus coronary artery bypass grafting surgery.

BACKGROUND: Since 1999, The Society of Thoracic Surgeons (STS) has published two risk models that can be used to adjust the results of valve surgery combined with coronary artery bypass graft surgery (CABG). The most recent was developed from data for patients who had surgery between 1994 and 1997 using operative mortality as the only endpoint. Furthermore, this model did not specifically consider mitral valve repair plus CABG, an increasingly common procedure. Consistent with STS policy of periodically updating and improving its risk models, new models for valve surgery combined with CABG have been developed. These models specifically address both perioperative morbidity and mitral valve repair, and they are based on contemporary data. METHODS: The final study population consisted of 101,661 procedures, including aortic valve replacement (AVR) plus CABG, mitral valve replacement (MVR) plus CABG, or mitral valve repair (MVRepair) plus CABG between January 1, 2002, and December 31, 2006. Model outcomes included operative mortality, stroke, deep sternal wound infection, reoperation, prolonged ventilation, renal failure, composite major morbidity or mortality, prolonged postoperative length of stay, and short postoperative length of stay. Candidate variables were screened for frequency of missing data, and imputation techniques were used where appropriate. Stepwise variable selection was employed, supplemented by advice from an expert panel of cardiac surgeons and biostatisticians. Several variables were forced into models to insure face validity (eg, atrial fibrillation for the permanent stroke model, sex for all models). Based on preliminary analyses of the data, a single model was employed for valve plus CABG, with indicator variables for the specific type of procedure. Interaction terms were included to allow for differential impact of predictor variables depending on procedure type. After validating the model in the 40% validation sample, the development and validation samples were then combined, and the final model coefficients were estimated using the overall 100% combined sample. The final logistic regression model was estimated using generalized estimating equations to account for clustering of patients within institutions. RESULTS: The c-index for mortality prediction for the overall valve plus CABG population was 0.75. Morbidity model c-indices for specific complications (permanent stroke, renal failure, prolonged ventilation > 24 hours, deep sternal wound infection, reoperation for any reason, major morbidity or mortality composite, and prolonged postoperative length of stay) for the overall group of valve plus CABG procedures ranged from 0.622 to 0.724, and calibration was excellent. CONCLUSIONS: New STS risk models have been developed for heart valve surgery combined with CABG. These are the first valve plus CABG models that also include risk prediction for individual major morbidities, composite major morbidity or mortality, and short and prolonged length of stay.

Strategies for analyzing multilevel cluster-randomized studies with binary outcomes collected at varying intervals of time.

Frequently, studies are conducted in a real clinic setting. When the outcome of interest is collected longitudinally over a specified period of time, this design can lead to unequally spaced intervals and varying numbers of assessments. In our study, these features were embedded in a randomized, factorial design in which interventions to improve blood pressure control were delivered to both patients and providers. We examine the effect of the intervention and compare methods of estimation of both fixed effects and variance components in the multilevel generalized linear mixed model. Methods of comparison include penalized quasi-likelihood (PQL), adaptive quadrature, and Bayesian Monte Carlo methods. We also investigate the implications of reducing the data and analysis to baseline and final measurements. In the full analysis, the PQL fixed-effects estimates were closest to zero and confidence intervals were generally narrower than those of the other methods. The adaptive quadrature and Bayesian fixed-effects estimates were similar, but the Bayesian credible intervals were consistently wider. Variance component estimation was markedly different across methods, particularly for the patient-level random effects. In the baseline and final measurement analysis, we found that estimates and corresponding confidence intervals for the adaptive quadrature and Bayesian methods were very similar. However, the time effect was diminished and other factors also failed to reach statistical significance, most likely due to decreased power. When analyzing data from this type of design, we recommend using either adaptive quadrature or Bayesian methods to fit a multilevel generalized linear mixed model including all available measurements.

Patterns of transfer for patients with non-ST-segment elevation acute coronary syndrome from community to tertiary care hospitals.

BACKGROUND: Practice guidelines for non-ST-segment elevation acute coronary syndromes (NSTE ACS) recommend early invasive management (cardiac catheterization and revascularization within 48 hours of hospital presentation) for high-risk patients, but interhospital transfer is necessary to provide rapid access to revascularization procedures for patients who present to community hospitals without revascularization capabilities. METHODS: We analyzed patterns and factors associated with interhospital transfer among 19,238 patients with NSTE ACS (positive cardiac markers and/or ischemic ST-segment changes) from 124 community hospitals without revascularization capabilities in the Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA Guidelines quality improvement initiative from January 2001 through June 2004. RESULTS: Less than half of the patients (46.3%) admitted to community hospitals were transferred to tertiary hospitals, and fewer (20%) were transferred early (within 48 hours of presentation). Early transfer rates increased by 16% over 10 quarters in patients with a predicted low or moderate risk of inhospital mortality, compared with 5% in high-risk patients. By the last quarter of the analysis, 41.4% of low-risk patients were transferred early versus 12.5% of high-risk patients. Factors significantly associated with early transfer included younger age, lack of prior heart failure, cardiology inpatient care, and ischemic ST-segment electrocardiographic changes. Among patients who were not transferred, 29% had no further risk stratification performed with stress testing, ejection fraction measurement, or diagnostic cardiac catheterization (at hospitals with catheterization laboratories). CONCLUSIONS: Most patients with NSTE ACS presenting to community hospitals without revascularization capabilities are not rapidly transferred to tertiary hospitals, and lower-risk patients appear to be preferentially transferred early. Further investigation is needed to determine if improved risk-based triage at community hospitals can optimize transfer decision making for high-risk patients with NSTE ACS.