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Peter C Austin

Publications and source records attributed to Peter C Austin.

At least 19 recordsLinked to original sources
PubMed2007

The performance of different propensity score methods for estimating marginal odds ratios.

The propensity score which is the probability of exposure to a specific treatment conditional on observed variables. Conditioning on the propensity score results in unbiased estimation of the expected difference in observed responses to two treatments. In the medical literature, propensity score methods are frequently used for estimating odds ratios. The performance of propensity score methods for estimating marginal odds ratios has not been studied. We performed a series of Monte Carlo simulations to assess the performance of propensity score matching, stratifying on the propensity score, and covariate adjustment using the propensity score to estimate marginal odds ratios. We assessed bias, precision, and mean-squared error (MSE) of the propensity score estimators, in addition to the proportion of bias eliminated due to conditioning on the propensity score. When the true marginal odds ratio was one, then matching on the propensity score and covariate adjustment using the propensity score resulted in unbiased estimation of the true treatment effect, whereas stratification on the propensity score resulted in minor bias in estimating the true marginal odds ratio. When the true marginal odds ratio ranged from 2 to 10, then matching on the propensity score resulted in the least bias, with a relative biases ranging from 2.3 to 13.3 per cent. Stratifying on the propensity score resulted in moderate bias, with relative biases ranging from 15.8 to 59.2 per cent. For both methods, relative bias was proportional to the true odds ratio. Finally, matching on the propensity score tended to result in estimators with the lowest MSE.

Bias↗
PubMed2007

A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality.

Clinicians and health service researchers are frequently interested in predicting patient-specific probabilities of adverse events (e.g. death, disease recurrence, post-operative complications, hospital readmission). There is an increasing interest in the use of classification and regression trees (CART) for predicting outcomes in clinical studies. We compared the predictive accuracy of logistic regression with that of regression trees for predicting mortality after hospitalization with an acute myocardial infarction (AMI). We also examined the predictive ability of two other types of data-driven models: generalized additive models (GAMs) and multivariate adaptive regression splines (MARS). We used data on 9484 patients admitted to hospital with an AMI in Ontario. We used repeated split-sample validation: the data were randomly divided into derivation and validation samples. Predictive models were estimated using the derivation sample and the predictive accuracy of the resultant model was assessed using the area under the receiver operating characteristic (ROC) curve in the validation sample. This process was repeated 1000 times-the initial data set was randomly divided into derivation and validation samples 1000 times, and the predictive accuracy of each method was assessed each time. The mean ROC curve area for the regression tree models in the 1000 derivation samples was 0.762, while the mean ROC curve area of a simple logistic regression model was 0.845. The mean ROC curve areas for the other methods ranged from a low of 0.831 to a high of 0.851. Our study shows that regression trees do not perform as well as logistic regression for predicting mortality following AMI. However, the logistic regression model had performance comparable to that of more flexible, data-driven models such as GAMs and MARS.

Data Interpretation, Statistical↗
PubMed2007

A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study.

The propensity score--the probability of exposure to a specific treatment conditional on observed variables--is increasingly being used in observational studies. Creating strata in which subjects are matched on the propensity score allows one to balance measured variables between treated and untreated subjects. There is an ongoing controversy in the literature as to which variables to include in the propensity score model. Some advocate including those variables that predict treatment assignment, while others suggest including all variables potentially related to the outcome, and still others advocate including only variables that are associated with both treatment and outcome. We provide a case study of the association between drug exposure and mortality to show that including a variable that is related to treatment, but not outcome, does not improve balance and reduces the number of matched pairs available for analysis. In order to investigate this issue more comprehensively, we conducted a series of Monte Carlo simulations of the performance of propensity score models that contained variables related to treatment allocation, or variables that were confounders for the treatment-outcome pair, or variables related to outcome or all variables related to either outcome or treatment or neither. We compared the use of these different propensity scores models in matching and stratification in terms of the extent to which they balanced variables. We demonstrated that all propensity scores models balanced measured confounders between treated and untreated subjects in a propensity-score matched sample. However, including only the true confounders or the variables predictive of the outcome in the propensity score model resulted in a substantially larger number of matched pairs than did using the treatment-allocation model. Stratifying on the quintiles of any propensity score model resulted in residual imbalance between treated and untreated subjects in the upper and lower quintiles. Greater balance between treated and untreated subjects was obtained after matching on the propensity score than after stratifying on the quintiles of the propensity score. When a confounding variable was omitted from any of the propensity score models, then matching or stratifying on the propensity score resulted in residual imbalance in prognostically important variables between treated and untreated subjects. We considered four propensity score models for estimating treatment effects: the model that included only true confounders; the model that included all variables associated with the outcome; the model that included all measured variables; and the model that included all variables associated with treatment selection. Reduction in bias when estimating a null treatment effect was equivalent for all four propensity score models when propensity score matching was used. Reduction in bias was marginally greater for the first two propensity score models than for the last two propensity score models when stratification on the quintiles of the propensity score model was employed. Furthermore, omitting a confounding variable from the propensity score model resulted in biased estimation of the treatment effect. Finally, the mean squared error for estimating a null treatment effect was lower when either of the first two propensity scores was used compared to when either of the last two propensity score models was used.

Aged↗
PubMed2007

Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study.

Propensity score methods are increasingly being used to estimate causal treatment effects in the medical literature. Conditioning on the propensity score results in unbiased estimation of the expected difference in observed responses to two treatments. The degree to which conditioning on the propensity score introduces bias into the estimation of the conditional odds ratio or conditional hazard ratio, which are frequently used as measures of treatment effect in observational studies, has not been extensively studied. We conducted Monte Carlo simulations to determine the degree to which propensity score matching, stratification on the quintiles of the propensity score, and covariate adjustment using the propensity score result in biased estimation of conditional odds ratios, hazard ratios, and rate ratios. We found that conditioning on the propensity score resulted in biased estimation of the true conditional odds ratio and the true conditional hazard ratio. In all scenarios examined, treatment effects were biased towards the null treatment effect. However, conditioning on the propensity score did not result in biased estimation of the true conditional rate ratio. In contrast, conventional regression methods allowed unbiased estimation of the true conditional treatment effect when all variables associated with the outcome were included in the regression model. The observed bias in propensity score methods is due to the fact that regression models allow one to estimate conditional treatment effects, whereas propensity score methods allow one to estimate marginal treatment effects. In several settings with non-linear treatment effects, marginal and conditional treatment effects do not coincide.

Bias↗
PubMed2006

Effect of provider continuity on test repetition.

BACKGROUND: Provider continuity (PC) occurs when a patient is treated by the same physician over time. A perceived benefit of PC is decreased test repetition. Repeat tests make up a significant proportion of overall laboratory utilization. This study determined whether test repetition increases when PC decreases. METHODS: Cohort study of adults in eastern Ontario, Canada between September 1999 and September 2000 using population-based clinical databases. The primary study outcome was the probability that 7 common laboratory tests (hemoglobin, sodium, creatinine, thyrotropin, total cholesterol, ferritin, and hemoglobin A1C) were repeated at physician visits subsequent to the index test. We determined whether the probability of test repetition changed if the follow-up physician ordered the index test. We adjusted for multiple factors regarding the patient (age, sex, days in hospital, and number of physician visits in previous year), index test (normality and location), follow-up visit (location and time from index test), and follow-up physician (age and specialty). RESULTS: The study included 881,353 patients, 1,419,438 index laboratory tests, and 7,622,938 follow-up physician visits. After adjusting for other important factors, we found that tests were significantly more likely to be repeated if the follow-up physician ordered the index test (adjusted odds ratio range 2.5-5.9). This association was consistent in most subgroups. CONCLUSIONS: For these common laboratory investigations, PC was associated with increased, not decreased, test repetition. This suggests that increased PC alone will likely not decrease test utilization.

Adult↗
PubMed2006

Outcome of heart failure with preserved ejection fraction in a population-based study.

BACKGROUND: The importance of heart failure with preserved ejection fraction is increasingly recognized. We conducted a study to evaluate the epidemiologic features and outcomes of patients with heart failure with preserved ejection fraction and to compare the findings with those from patients who had heart failure with reduced ejection fraction. METHODS: From April 1, 1999, through March 31, 2001, we studied 2802 patients admitted to 103 hospitals in the province of Ontario, Canada, with a discharge diagnosis of heart failure whose ejection fraction had also been assessed. The patients were categorized in three groups: those with an ejection fraction of less than 40 percent (heart failure with reduced ejection fraction), those with an ejection fraction of 40 to 50 percent (heart failure with borderline ejection fraction), and those with an ejection fraction of more than 50 percent (heart failure with preserved ejection fraction). Two groups were studied in detail: those with an ejection fraction of less than 40 percent and those with an ejection fraction of more than 50 percent. The main outcome measures were death within one year and readmission to the hospital for heart failure. RESULTS: Thirty-one percent of the patients had an ejection fraction of more than 50 percent. Patients with heart failure with preserved ejection fraction were more likely to be older and female and to have a history of hypertension and atrial fibrillation. The presenting history and clinical examination findings were similar for the two groups. The unadjusted mortality rates for patients with an ejection fraction of more than 50 percent were not significantly different from those for patients with an ejection fraction of less than 40 percent at 30 days (5 percent vs. 7 percent, P=0.08) and at 1 year (22 percent vs. 26 percent, P=0.07); the adjusted one-year mortality rates were also not significantly different in the two groups (hazard ratio, 1.13; 95 percent confidence interval, 0.94 to 1.36; P=0.18). The rates of readmission for heart failure and of in-hospital complications did not differ between the two groups. CONCLUSIONS: Among patients presenting with new-onset heart failure, a substantial proportion had an ejection fraction of more than 50 percent. The survival of patients with heart failure with preserved ejection fraction was similar to that of patients with reduced ejection fraction.

Aged↗
PubMed2006

The effect of hospitalization on oral anticoagulation control: a population-based study.

BACKGROUND: For patients taking oral anticoagulants (OAC), the proportion of time spent in the therapeutic range is strongly associated with bleeding and thromboembolic risk. Previous studies examining OAC control may not generalize because the patient population was select or INR capture was incomplete. OBJECTIVES: Measure OAC control for an entire population of elderly people and determine patient factors associated with OAC control. PATIENTS: People in Eastern Ontario without valve replacement aged 65 years or greater who were treated with warfarin between 1 September 1999 and 1 September 2000. DESIGN: Retrospective cohort study using population-based administrative databases. OAC control was measured as the proportion of days in therapeutic range (PDTR), defined as the number days with the INR between 2 and 3 divided by total number of days observation. Linear interpolation was used to determine INR levels between measures. Negative binomial regression was used to identify patient factors independently associated with PDTR. We also determined which factors were associated with proportion of days with a critically low (<1.5) or critically high (>/=5) INR. RESULTS: 7179 people were followed for a total of 3238 years. 15% of people were hospitalized during the study. Overall, PDTR was 59.2% (95% CI 59.1%-59.2%). Independent of all other significant factors, hospitalization was associated with a 15% decrease in the PDTR 15% (rate ratio 0.85, 95% CI 0.83-0.87). Hospitalization was also independently associated with greater proportion of time with a critically low INR (rate ratio 1.68, 95% CI 1.51-1.88) and a critically high INR (1.70, 95% CI 1.38-2.08). CONCLUSIONS: Elderly people in eastern Ontario taking warfarin were therapeutic 59.2% of the time. Independent of other patient factors, patients who are hospitalized have the greatest risk of poor anticoagulation control. Control for anticoagulated patients who get hospitalized should be reviewed to determine if and how it could be improved.

Aged↗
PubMed2006

Testing multiple statistical hypotheses resulted in spurious associations: a study of astrological signs and health.

OBJECTIVES: To illustrate how multiple hypotheses testing can produce associations with no clinical plausibility. STUDY DESIGN AND SETTING: We conducted a study of all 10,674,945 residents of Ontario aged between 18 and 100 years in 2000. Residents were randomly assigned to equally sized derivation and validation cohorts and classified according to their astrological sign. Using the derivation cohort, we searched through 223 of the most common diagnoses for hospitalization until we identified two for which subjects born under one astrological sign had a significantly higher probability of hospitalization compared to subjects born under the remaining signs combined (P<0.05). RESULTS: We tested these 24 associations in the independent validation cohort. Residents born under Leo had a higher probability of gastrointestinal hemorrhage (P=0.0447), while Sagittarians had a higher probability of humerus fracture (P=0.0123) compared to all other signs combined. After adjusting the significance level to account for multiple comparisons, none of the identified associations remained significant in either the derivation or validation cohort. CONCLUSIONS: Our analyses illustrate how the testing of multiple, non-prespecified hypotheses increases the likelihood of detecting implausible associations. Our findings have important implications for the analysis and interpretation of clinical studies.

Adolescent↗
PubMed2006

A comparison of propensity score methods: a case-study estimating the effectiveness of post-AMI statin use.

There is an increasing interest in the use of propensity score methods to estimate causal effects in observational studies. However, recent systematic reviews have demonstrated that propensity score methods are inconsistently used and frequently poorly applied in the medical literature. In this study, we compared the following propensity score methods for estimating the reduction in all-cause mortality due to statin therapy for patients hospitalized with acute myocardial infarction: propensity-score matching, stratification using the propensity score, covariate adjustment using the propensity score, and weighting using the propensity score. We used propensity score methods to estimate both adjusted treated effects and the absolute and relative risk reduction in all-cause mortality. We also examined the use of statistical hypothesis testing, standardized differences, box plots, non-parametric density estimates, and quantile-quantile plots to assess residual confounding that remained after stratification or matching on the propensity score. Estimates of the absolute reduction in 3-year mortality ranged from 2.1 to 4.5 per cent, while estimates of the relative risk reduction ranged from 13.3 to 17.0 per cent. Adjusted estimates of the reduction in the odds of 3-year death varied from 15 to 24 per cent across the different propensity score methods.

Acute Disease↗
PubMed2006

How many "Me-Too" drugs are enough? The case of physician preferences for specific statins.

BACKGROUND: The increasing availability of "Me-Too" drugs has provided considerable treatment options for clinicians. However, the number of such drugs within a class that are actually used by clinicians has not been well studied. OBJECTIVE: To determine the number of different statins that individual physicians use in practice. METHODS: The Ontario Drug Benefit database was used to identify physicians who issued at least 10 incident statin prescriptions between October 2001 and May 2003 for patients aged 66 years and older. A preferred statin was defined for each physician, and the proportion of each physician's incident prescriptions written for that agent was determined. We then determined the number of different statins required to fill each physician's incident prescribing needs. RESULTS: A total of 3426 physicians wrote 73,571 incident statin prescriptions. The mean percentage of prescriptions written for each physician's preferred statin formulation was 73.7%. Repeat analysis to examine the proportion of prescriptions filled using each physician's top 2 statin formulations found that the average physician wrote the vast majority of his or her incident prescriptions (94.9%) for only 1 or 2 statins. Half of all physicians used, at most, 2 different statins for all incident prescribing, while 91.3% of physicians used, at most, 3 different statins for all of their incident prescribing. CONCLUSIONS: A high proportion of Ontario physicians issued the majority of their incident statin prescriptions for the same statin formulation. Most physicians required, at most, 3 different statins for all incident statin prescribing.

Adult↗
PubMed2006

Gender differences in outcomes after hospital discharge from coronary artery bypass grafting.

BACKGROUND: There are few comparative data regarding long-term nonfatal outcomes for women versus men after coronary artery bypass grafting (CABG). This study compares gender differences in cardiac events in a population of hospital survivors up to 11 years after isolated CABG surgery in Ontario, Canada. METHODS AND RESULTS: A population-based cohort study (n=68,774 patients, 15,043 women) between September 1, 1991, and April 1, 2002, was assembled with linked clinical and administrative databases. Cox modeling and propensity score matching were used to compare death, cardiac readmission (angina, heart failure, myocardial infarction), repeat revascularization (angioplasty or CABG), and stroke readmission between men and women. Women were older (65+/-17 versus 62+/-13 years), more likely to present with urgent or emergent status (64% versus 56%), and less likely to receive arterial grafts (70% versus 78%). Women had a higher rate of cardiac readmission in the first year after surgery (hazard ratio [HR] of 1.5, 95% confidence interval [CI] 1.36 to 1.56), and this increased risk persisted after 1 year (HR 1.2, 95% CI 1.14 to 1.31). This was primarily due to readmissions for unstable angina (HR 1.3, 95% CI 1.24 to 1.38) and congestive heart failure (HR 1.1, 95% CI 1.06 to 1.21). Propensity-matched women had similar rates of death (HR 0.9, 95% CI 0.83 to 0.98) and repeat revascularization (HR 1.0, 95% CI 0.91 to 1.06). CONCLUSIONS: Women have a more complex clinical preoperative presentation and are more likely to be readmitted with unstable angina and congestive heart failure after CABG but experience survival similar to those seen in men. Gender differences in outcomes may be improved through durable revascularization strategies and close postoperative follow-up care targeted to women.

Aged↗
PubMed2006

Socioeconomic status and mortality after acute myocardial infarction.

BACKGROUND: Gradients that link socioeconomic status and cardiovascular mortality have been observed in many populations, including those of countries that provide publicly funded comprehensive medical coverage. The intermediary causes of such gradients remain poorly elucidated. OBJECTIVE: To examine the relationships among socioeconomic status, other health factors, and 2-year mortality rates after acute myocardial infarction (MI). DESIGN: Prospective cohort study. SETTING: Ontario, Canada. PATIENTS: 3407 patients who were hospitalized for acute MI in 53 large-volume hospitals in Canada from December 1999 to February 2003. MEASUREMENTS: The authors obtained self-reported measures of income and education and developed profiles of the patients' prehospitalization cardiac risks and comorbid conditions. To create these profiles, the authors used the patients' self-reports and retrospectively linked no less than 12 years' worth of previous hospitalization data. Mortality rates 2 years after acute MI were examined with and without sequential risk adjustment for age, sex, ethnicity, social support, cardiovascular history and risk, comorbid conditions, and selected in-hospital process factors. RESULTS: Income was strongly and inversely correlated with 2-year mortality rate (crude hazard ratio for high-income vs. low-income tertile, 0.45 [95% CI, 0.35 to 0.57]; P < 0.001). However, after adjustment for age and preexisting cardiovascular events or conventional vascular risk factors, the effect of income was greatly attenuated (adjusted hazard ratio for high-income vs. low-income tertile, 0.77 [CI, 0.54 to 1.10]; P = 0.150). Noncardiovascular comorbid conditions and in-hospital process factors had negligible explanatory effect. LIMITATIONS: Previous cardiovascular risks were ascertained through self-report or retrospectively through the longitudinal tracking of the hospitals' administrative databases. The study began with a cohort of patients who had an index cardiac event rather than with asymptomatic individuals. CONCLUSIONS: Age, past cardiovascular events, and current vascular risk factors accounted for most of the income-mortality gradient after acute MI. This observation suggests that the "wealth-health gradient" in cardiovascular mortality may be partially ameliorated by more rigorous management of known risk factors among less affluent persons. *For a list of members of the SESAMI Study Group, see the Appendix.

Adult↗
PubMed2006

Missed opportunities in the secondary prevention of myocardial infarction: an assessment of the effects of statin underprescribing on mortality.

BACKGROUND: The benefits of statins for the secondary prevention of coronary heart disease are well established. Previous research indicates that patients at the greatest risk of cardiovascular events are the least likely to receive statins. We explored the potential reduction in mortality at the population level that could result from improving statin prescribing among patients least likely to be prescribed a statin after acute myocardial infarction (AMI). METHODS: Simulation analysis of detailed clinical data for a population-based sample of 7285 AMI survivors discharged from 102 hospitals between April 1, 1999, and March 31, 2001 in Ontario, Canada, was done. Using estimates obtained from randomized controlled trials, we estimated the reduction in 3-year all-cause mortality associated with improved statin prescribing at hospital discharge. RESULTS: Overall, 35.6% of patients received a statin prescription at hospital discharge. We estimate that increasing statin prescribing among patients least likely to receive them (ie, the lowest quintile of propensity to receive a prescription at discharge) from the current rate of 7.8% to the rate among all patients (35.6%) could decrease AMI mortality by 83 deaths in Ontario per year (2.1% of all post-AMI deaths within 3 years of discharge). Increasing statin prescribing to 70% among all patients with AMI could avert 312 deaths per year in Ontario. Factoring in low rates of adherence to statin therapy would reduce these estimates to 33 and 126, respectively. CONCLUSIONS: Modest increases in statin prescribing for patients least likely to receive one could decrease post-AMI mortality at the population level.

Aged↗
PubMed2006

Public versus private institutional performance reporting: what is mandatory for quality improvement?

BACKGROUND: In the past 11 years, Ontario has generated institution-level performance report cards on outcomes of coronary artery bypass graft (CABG) surgery. The objective of this study was to evaluate the differences in patient characteristics and outcomes observed during the transition from no reporting to confidential, and ultimately public performance report cards for CABG surgery in a public health system. METHODS: We used clinical and administrative data to assess crude, expected, and risk-adjusted 30-day mortality rates after isolated CABG surgery in Ontario for 67693 patients from September 1, 1991, to March 31, 2002. Confidence intervals on relative mortality reductions were determined by bootstrapping. We compared 30-day mortality trends to a control outcome (risk-adjusted 30-day all-cause readmission). We analyzed inhospital mortality trends for Ontario compared with the rest of Canada for the period from 1992 to 1998. RESULTS: The risk-adjusted 30-day mortality rate decreased 29% (95% CI 21-39) from the era of no reporting (1991-1993) to confidential reporting (1994-1998). There was no further decrease with public reporting (1999-2001). The control outcome of 30-day readmission did not decrease across reporting eras. Inhospital mortality fell significantly faster in Ontario during the period of confidential reporting than in other parts of Canada. CONCLUSION: Ontario CABG mortality outcomes improved sharply after provider results were confidentially disclosed at an institutional level. No such changes were seen for nondisclosed outcomes or regions outside Ontario. Further public reporting of outcomes had no discernible impact on performance. These results are consistent with the hypothesis that confidential disclosure of outcomes was sufficient to accelerate quality improvement in a public system with little competition for patients between hospitals.

Aged↗
PubMed2006

Quantifying the impact of survivor treatment bias in observational studies.

RATIONALE: Observational cohort studies are frequently used to measure the impact of therapies on the time to a particular outcome. Treatment often has a time-variant nature since it is frequently initiated at varying times during a patient's follow-up. Studies in the medical literature frequently ignore the time-dependent nature of treatment exposure. Survivor treatment bias can arise when the time dependent nature of treatment exposure is ignored since patients who survived to receive treatment may be healthier than patients who died prior to receipt of treatment. AIMS AND OBJECTIVES: The objective of the current study was to explicitly quantify the magnitude of survivor-treatment bias. METHODS: Monte Carlo simulations using parameters obtained from an analysis of patients admitted to hospital with a diagnosis of acute myocardial infarction in Ontario, Canada. RESULTS AND CONCLUSIONS: When the true treatment was null (hazard ratio of 1), estimated treatment effects varied from a 4% reduction in mortality to a reduction in mortality of 27% when the time varying nature of the treatment was ignored. Furthermore, survivor-treatment bias increased as the time required foe exposed patients to receive treatment increased. Similarly, survivor treatment bias was amplified as exposure was defined to be exposure at any time prior to mortality compared to exposure within a fixed time interval starting at the time origin. Ignoring the time-dependent nature of treatment results in overly optimistic estimates of treatment effects. Depending on the period required for patients to initiate therapy, treatments with no effect on survival can appear to be strongly associated with improved survival. The current study is the first to explicitly quantify the magnitude of bias that results from ignoring the time-varying nature of treatment exposure in survival studies.

Adrenergic beta-Antagonists↗
PubMed2005

Risk-treatment mismatch in the pharmacotherapy of heart failure.

CONTEXT: Patients with heart failure have a wide spectrum of mortality risks. To maximize the benefit of available pharmacotherapies, patients with high mortality risk should receive high rates of drug therapy. OBJECTIVE: To examine patterns of drug therapy and underlying mortality risk in patients with heart failure. DESIGN, SETTING, AND PATIENTS: In the Enhanced Feedback for Effective Cardiac Treatment (EFFECT) population-based cohort (1999-2001) of 9942 patients with heart failure hospitalized in Ontario, Canada, we evaluated 1418 patients with documented left ventricular ejection fraction of 40% or less and aged 79 years or younger with low-, average-, and high-predicted risk of death within 1 year; all patients survived to hospital discharge. Administration of angiotensin-converting enzyme (ACE) inhibitors, ACE inhibitors or angiotensin II receptor blockers (ARBs), and beta-adrenoreceptor antagonists was evaluated according to predicted risk of death. MAIN OUTCOME MEASURE: Heart failure drug administration rates at time of discharge and 90 days after hospital discharge. RESULTS: At hospital discharge, prescription rates for patients in the low-, average-, and high-risk groups were 81%, 73%, 60%, respectively, for ACE inhibitors; 86%, 80%, 65%, respectively, for ACE inhibitors or ARBs; and 40%, 33%, 24%, respectively, for beta-adrenoreceptor antagonists (all P<.001 for trend). Within 90 days following hospital discharge, the rates were 83%, 76%, and 61% for ACE inhibitors; 89%, 83%, and 67% for ACE inhibitors or ARBs; and 43%, 36%, and 28% for beta-adrenoreceptor antagonists for the 3 risk groups, respectively (all P<.001 for trend). The pattern of lower rates of drug administration in those patients at increasing risk was maintained up to 1 year postdischarge (P<.001). After accounting for varying survival time and potential contraindications to therapy, low-risk patients were more likely to receive ACE inhibitors or ARBs (adjusted hazard ratio [HR], 1.61; 95% confidence interval [CI], 1.49-1.74) and beta-adrenoreceptor antagonists (HR, 1.80; 95% CI, 1.60-2.01) compared with high-risk patients (both P<.001). CONCLUSIONS: Patients with heart failure at greatest risk of death are least likely to receive ACE inhibitors, ACE inhibitors or ARBs, and beta-adrenoreceptor antagonists. Understanding the reasons underlying this mismatch may facilitate improvements in care and outcomes for patients with heart failure.

Adrenergic beta-Antagonists↗