Outcomes of cardiac surgery in patients age ≥80 years: results from the National Cardiovascular Network

AbstractOBJECTIVESThe purpose of this study was to evaluate characteristics and outcomes of patients age ≥80 undergoing cardiac surgery.BACKGROUNDPrior single-institution series have found high mortality rates in octogenarians after cardiac surgery. However, the major preoperative risk factors in this age group have not been identified. In addition, the additive risks in the elderly of valve replacement surgery at the time of bypass are unknown.METHODSWe report in-hospital morbidity and mortality in 67,764 patients (4,743 octogenarians) undergoing cardiac surgery at 22 centers in the National Cardiovascular Network. We examine the predictors of in-hospital mortality in octogenarians compared with those predictors in younger patients.RESULTSOctogenarians undergoing cardiac surgery had fewer comorbid illnesses but higher disease severity and surgical urgency than younger patients. Octogenarians had significantly higher in-hospital mortality after cardiac surgery than younger patients: coronary artery bypass grafting (CABG) only (8.1% vs. 3.0%), CABG/aortic valve (10.1% vs. 7.9%), CABG/mitral valve (19.6% vs. 12.2%). In addition, they had twice the incidence of postoperative stroke and renal failure. The preoperative clinical factors predicting CABG mortality in the very elderly were quite similar to those for younger patients with age, emergency surgery and prior CABG being the powerful predictors of outcome in both age categories. Of note, elderly patients without significant comorbidity had in-hospital mortality rates of 4.2% after CABG, 7% after CABG with aortic valve replacement (CABG/AVR), and 18.2% after CABG with mitral valve replacement (CABG/MVR).CONCLUSIONSRisks for octogenarians undergoing cardiac surgery are less than previously reported, especially for CABG only or CABG/AVR. In selected octogenarians without significant comorbidity, mortality approaches that seen in younger patients

The impact of statistical adjustment on economic profiles of interventional cardiologists

AbstractOBJECTIVESThe objective of this study was to identify preprocedure patient factors associated with percutaneous intervention costs and to examine the impact of these patient factors on economic profiles of interventional cardiologists.BACKGROUNDThere is increasing demand for information about comparative resource use patterns of interventional cardiologists. Economic provider profiles, however, often fail to account for patient characteristics.METHODSData were obtained from Duke Medical Center cost and clinical information systems for 1,949 procedures performed by 13 providers between July 1, 1997, and December 31, 1998. Patient factors that influenced cost were identified using multiple regression analysis. After assessing interprovider variation in unadjusted cost, mixed linear models were used to examine how much cost variability was associated with the provider when patient characteristics were taken into account.RESULTSTotal hospital costs averaged $15,643 (median,$13,809), $6,515 of which represented catheterization laboratory costs. Disease severity, acuity, comorbid illness and lesion type influenced total costs (R2= 38%), whereas catheterization costs were affected by lesion type and acuity (R2= 32%). Patient characteristics varied significantly among providers. Unadjusted total costs were weakly associated with provider, and this association disappeared after accounting for patient factors. The provider influence on catheterization costs persisted after adjusting for patient characteristics. Furthermore, the pattern of variation changed: the adjusted analysis identified three new outliers, and two providers lost their outlier status. Only one provider was consistently identified as an outlier in the unadjusted and adjusted analyses.CONCLUSIONSEconomic profiles of interventional cardiologists may be misleading if they do not adequately adjust for patient characteristics before procedure

Clinical judgement and therapeutic decision making

AbstractClinical decision making is under increased scrutiny due to concerns about the cost and quality of medical care. Variability in physician decision making is common, in part because of deficiencies in the knowledge base, but also due to the difference in physicians' approaches to clinical problem solving. Evaluation of patient prognosis is a critical factor in the selection of therapy, and careful attention to methodology is essential to provide reliable information.Randomized controlled clinical trials provide the most solid basis for the establishment of broad therapeutic principles. Because randomized studies cannot be performed to address every question, observational studies will continue to play a complementary role in the evaluation of therapy. Randomized studies in progress, meta analyses of existing data, and increased use of administrative and collaborative clinical data bases will improve the knowledge base for decision making in the future

920-52 Are Provider Profiles Affected by Risk-adjustment Methodology? Results from the Cooperative Cardiovascular Project

Health care payors and consumers have a growing interest in risk-adjusted provider profiles. Using chart-abstracted clinical data from the Cooperative Cardiovascular Project, we ranked 28 hospitals performing bypass surgery in Alabama and Iowa by their risk-adjusted surgical mortality rates using three published risk-adjustment methodologies: Parsonnet (PI, O’Connor (a) and Hannan (H). In total. 3653 bypass surgery cases performed from 6/92 to 3/93 were reviewed (mean 130 cases/hospital). The discriminatory abilities of each method for predicting surgical mortality were quite similar (area under ROC curves 0.72–0.75). Below, we display the risk-adjusted hospital rankings (comparing observed with expected mortality) by these three riskadjustment techniques:In terms of hospital rankings, there was generally close correlation between any two of the methods (Spearman's R=0.87,0.88, and 0.93, comparing P-O, P-H, and H-O). Rankings for an individual hospital varied, however, an average of ±3.3 ranks (range 0–12 ranks) depending on which riskadjustment methodology was used.ConclusionIn general. published methods of risk-adjustment for bypass surgery accurately identify institutions with low, moderate and high adjusted mortality outcomes. The precise ranking of an individual hospital. however, may vary depending on the risk adjustment method applied

Effect of clinical factors on length of stay after coronary artery bypass surgery: results of the cooperative cardiovascular project

BACKGROUND: Rising health care costs have prompted careful review of comparative hospital resource use. Length of stay after bypass surgery has received particular attention. However, many providers assert that these variations are caused by differences in the clinical mix of patients treated. Our goals were to identify the major clinical predictors of postoperative length of stay (PLOS) after coronary artery bypass graft surgery (CABG), document variations in PLOS among 28 hospitals, and assess the degree to which patient characteristics account for hospital variations in PLOS. METHODS: Detailed clinical data on 3605 Medicare patients undergoing CABG in 28 Alabama and Iowa hospitals were analyzed by stepwise linear regression to identify significant clinical predictors of PLOS. Analysis of variance was used to compare hospitals\u27 PLOS while controlling for significant patient risk factors. RESULTS: The mean age was 72.1 years, 34.7% were female, and the in-hospital mortality rate was 5.6%. The median and mean PLOS were 8 and 11.1 days, respectively. Significant predictors of longer PLOS included increasing age, female sex, history of chronic obstructive pulmonary disease, cerebrovascular disease, or mitral valve disease, elevated admission blood urea nitrogen, and preoperative placement of an intraaortic balloon pump. Hospitals varied significantly (P =.0001) in their unadjusted PLOS. These hospital-level variations persisted despite adjustment for both preoperative patient characteristics (P =.0001) and postoperative complications and death (P =.0001). CONCLUSIONS: This study found significant between-hospital variations in PLOS that were not explained by patient factors. This finding suggests the potential for increased efficiency in the care of patients undergoing CABG at many institutions. Further research is needed to determine the practice patterns contributing to variations in length of stay after bypass surgery

Challenges in comparing risk-adjusted bypass surgery mortality results: results from the Cooperative Cardiovascular Project

OBJECTIVES: We sought to evaluate the predictive accuracy of four bypass surgery mortality clinical risk models and to examine the extent to which hospitals\u27 risk-adjusted surgical outcomes vary depending on which risk-adjustment method is applied. BACKGROUND: Cardiovascular report cards often compare risk-adjusted surgical outcomes; however, it is unclear to what extent the risk-adjustment process itself may affect these metrics. METHODS: As part of the Cooperative Cardiovascular Project\u27s Pilot Revascularization Study, we compared the predictive accuracy of four bypass clinical risk models among 3,654 Medicare patients undergoing surgery at 28 hospitals in Alabama and Iowa. We also compared the agreement in hospital-level risk-adjusted bypass outcome performance ratings depending on which of the four risk models was applied. RESULTS: Although the four risk models had similar discriminatory abilities (C-index, 0.71 to 0.74), certain models tended to overpredict mortality in higher-risk patients. There was high correlation between a hospital\u27s risk-adjusted mortality rates regardless of which of the four models was used (correlation between risk-adjusted rating, 0.93 to 0.97). In contrast, there was limited agreement in which hospitals were identified as performance outliers depending on which risk-adjustment model was used and how outlier status was defined. CONCLUSIONS: A hospital\u27s risk-adjusted bypass surgery mortality rating, relative to its peers, was consistent regardless of the risk-adjustment model applied, supporting their use as a means of provider performance feedback. Designation of performance outliers, however, can vary significantly depending on the benchmark and methods used for this determination