Pre-discharge stress echocardiography and exercise ECG for risk stratification after uncomplicated acute myocardial infarction: results of the COSTAMI-II (cost of strategies after myocardial infarction) trial

Accrual monitoring in cardiovascular trials

Objective

To provide brief guidance on how to design accrual monitoring activities in a clinical trial protocol.

Setting

Two completed clinical trials that did not achieve the planned sample size, the Cost of Strategies After Myocardial Infarction (COSTAMI) trial and the Biventricular Pacing After Cardiac Surgery (BiPACS) trial.

Design

A Bayesian monitoring tool, the constant accrual model, is applied retrospectively to accrual data from each case study to illustrate how the tool could be used to identify problems with accrual early in the trial period and to frame the conditions in which the approach can be used in practice.

Results

After 312 days and 155 patients enrolled in the COSTAMI trial, accrual could be classified as ‘off target’ on the basis of statistical criteria outlined in the protocol. As for the BiPACS trial, after 2 years, it was already evident that the accrual was ‘considerably off target’.

Conclusions

Prompt awareness of a high risk of accrual failure could trigger different interventions to overcome protocol-related, patient-related or investigator-related barriers to recruitment or ultimately contribute to an early stopping decision due to recruitment futility.

Accrual prediction models should be included as standard tools for routine monitoring activities in cardiovascular research. Among them, methods relying on the Bayesian approach are particularly attractive, as they can naturally update past evidence when actual accrual data becomes available.

Among Unstable Angina and Non-ST-Elevation Myocardial Infarction Patients, Transient Myocardial Ischemia and Early Invasive Treatment Are Predictors of Major In-hospital Complications

BACKGROUND

Treatment for unstable angina (UA) or non-ST-elevation myocardial infarction (NSTEMI) is aimed at plaque stabilization to prevent infarction. Two treatment strategies are (1) invasive (ie, cardiac catheterization laboratory <24 hours after admission) or (2) selectively invasive (ie, medications with cardiac catheterization laboratory >24 hours for recurrent symptoms). However, it is not known if the frequency of transient myocardial ischemia (TMI) or complications during hospitalization varies by treatment.

PURPOSE

We aimed to (1) examine occurrence of TMI in UA/NSTEMI, (2) compare frequency of TMI by treatment pathway, and (3) determine predictors of in-hospital complications (ie, death, myocardial infarction [MI], pulmonary edema, shock, dysrhythmia with intervention).

METHODS

Hospitalized patients with coronary artery disease (ie, history of MI, percutaneous coronary intervention/stent, coronary artery bypass graft, >50% lesion via angiogram, or positive troponin) were recruited, and 12-lead electrocardiogram Holter initiated. Clinicians, blinded to Holter data, decided treatment strategy; offline analysis was done after discharge. Transient myocardial ischemia was defined as more than 1-mm ST segment ↑ or ↓, in more than 1 electrocardiographic lead, more than 1 minute.

RESULTS

Of 291 patients, 91% were white, 66% were male, 44% had prior MI, and 59% had prior percutaneous coronary intervention/stent or coronary artery bypass graft. Treatment pathway was early in 123 (42%) and selective in 168 (58%). Forty-nine (17%) had TMI: 19 (15%) early invasive, 30 (18%) selective (P = .637). Acute MI after admission was higher in patients with TMI regardless of treatment strategy (early: no TMI 4% vs yes TMI 21%; P = .020; selective: no TMI 1% vs yes TMI 13%; P = .0004). Predictors of major in-hospital complication were TMI (odds ratio, 9.9; 95% confidence interval, 3.84-25.78) and early invasive treatment (odds ratio 3.5; 95% confidence interval, 1.23-10.20).

CONCLUSIONS

In UA/NSTEMI patients treated with contemporary therapies, TMI is not uncommon. The presence of TMI and early invasive treatment are predictors of major in-hospital complications.

Regression models for analyzing costs and their determinants in health care: an introductory review

OBJECTIVE

This article aims to describe the various approaches in multivariable modelling of healthcare costs data and to synthesize the respective criticisms as proposed in the literature.

METHODS

We present regression methods suitable for the analysis of healthcare costs and then apply them to an experimental setting in cardiovascular treatment (COSTAMI study) and an observational setting in diabetes hospital care.

RESULTS

We show how methods can produce different results depending on the degree of matching between the underlying assumptions of each method and the specific characteristics of the healthcare problem.

CONCLUSIONS

The matching of healthcare cost models to the analytic objectives and characteristics of the data available to a study requires caution. The study results and interpretation can be heavily dependent on the choice of model with a real risk of spurious results and conclusions.

Extreme regression models for characterizing high-cost patients

OBJECTIVE

Healthcare cost distribution generally presents a high level of skewness, with a relatively small number of subjects accounting for a large portion of healthcare expenditures. Information on factors that predict high expenditures is of interest in healthcare planning. The aim of this paper was to inspect the behaviour of extreme regression (ER) models.

METHODS

We performed a simple simulation study, based on the LogNormal distribution, to assess the performance of ER in the special cases of heterogeneity and strong asymmetry of the cost variable. We then discussed the application of ER models to the analysis of three data sets of diabetes, lung cancer and myocardial infarction patients.

RESULTS

The ER showed to be able to cope fairly well with skewed distribution but under the condition that all observations have strictly positive costs.

CONCLUSION

The main advantage of the ER model is to unify these approaches in a unique framework, where the estimation of the cut-offs and the production of the prediction rules are performed simultaneously for a continuous response variable. The final model can thus be analysed at any desiderate quantile of the cost distribution, avoiding the need of pre-specifying any cut-off.

Proper modeling strategies selection for the assessment of post-infarction costs

BACKGROUND

The evaluation of the economic impact of ischemic disease has gained increasing interest. Such field of investigation is suffering however of the heterogeneity of methods used in evaluating costs, limiting the comparison of study results.

OBJECTIVE

The aim of the study is to show how estimates of 1-year costs of treatment of patients with uncomplicated acute myocardial infarction can vary significantly in relation to the statistical method adopted in the analysis.

RESEARCH DESIGN AND METHODS

The study analyses post-IMA costs as a function of demographic and clinical covariates, by applying the following statistical survival models: the parametric survival model assuming Weibull distribution, the Cox proportional hazard (PH) model and the Aalen additive regression for modelling costs. The Aalen approach is robust both for the non-proportionality in hazard and for departures from normality. In addition it is able to easily model the effect of covariates on the extreme costs. This cost analysis is based on data collected in the two COSTAMI trials (N=487).

RESULTS

There is agreement in all models with the effects of the considered covariates (age, sex, duration of disease and presence of other pathologies). There is a clear tendency of both the Aalen and the Cox model to provide a lower mean cost estimate than the other model, but with the additional feature for the Aalen model to be able to cope with all the other models in furnishing unbiased estimates with the advantage of a greater flexibility in representing the covariates' effect on the cost process.

CONCLUSIONS

An appropriate choice of the model is crucial in avoiding mis-interpretation of cost determinants of IMA patients. For our data set the Aalen model proved itself to be a realistic and informative way to characterize the effect of covariates on costs.