Effect of Using the HEART Score in Patients With Chest Pain in the Emergency Department: A Stepped-Wedge, Cluster Randomized Trial.

BACKGROUND: The HEART (History, Electrocardiogram, Age, Risk factors, and initial Troponin) score is an easy-to-apply instrument to stratify patients with chest pain according to their short-term risk for major adverse cardiac events (MACEs), but its effect on daily practice is unknown. OBJECTIVE: To measure the effect of use of the HEART score on patient outcomes and use of health care resources. DESIGN: Stepped-wedge, cluster randomized trial. (ClinicalTrials.gov: NCT01756846). SETTING: Emergency departments in 9 Dutch hospitals. PATIENTS: Unselected patients with chest pain presenting at emergency departments in 2013 and 2014. INTERVENTION: All hospitals started with usual care. Every 6 weeks, 1 hospital was randomly assigned to switch to "HEART care," during which physicians calculated the HEART score to guide patient management. MEASUREMENTS: For safety, a noninferiority margin of a 3.0% absolute increase in MACEs within 6 weeks was set. Other outcomes included use of health care resources, quality of life, and cost-effectiveness. RESULTS: A total of 3648 patients were included (1827 receiving usual care and 1821 receiving HEART care). Six-week incidence of MACEs during HEART care was 1.3% lower than during usual care (upper limit of the 1-sided 95% CI, 2.1% [within the noninferiority margin of 3.0%]). In low-risk patients, incidence of MACEs was 2.0% (95% CI, 1.2% to 3.3%). No statistically significant differences in early discharge, readmissions, recurrent emergency department visits, outpatient visits, or visits to general practitioners were observed. LIMITATION: Physicians were hesitant to refrain from admission and diagnostic tests in patients classified as low risk by the HEART score. CONCLUSION: Using the HEART score during initial assessment of patients with chest pain is safe, but the effect on health care resources is limited, possibly due to nonadherence to management recommendations. PRIMARY FUNDING SOURCE: Netherlands Organisation for Health Research and Development.

Decision analytic modeling was useful to assess the impact of a prediction model on health outcomes before a randomized trial.

OBJECTIVE: To demonstrate how decision analytic models (DAMs) can be used to quantify impact of using a (diagnostic or prognostic) prediction model in clinical practice and provide general guidance on how to perform such assessments. STUDY DESIGN AND SETTING: A DAM was developed to assess the impact of using the HEART score for predicting major adverse cardiac events (MACE). Impact on patient health outcomes and health care costs was assessed in scenarios by varying compliance with and informed deviation (ID) (using additional clinical knowledge) from HEART score management recommendations. Probabilistic sensitivity analysis was used to assess estimated impact robustness. RESULTS: Impact of using the HEART score on health outcomes and health care costs was influenced by an interplay of compliance with and ID from HEART score management recommendations. Compliance of 50% (with 0% ID) resulted in increased missed MACE and costs compared with usual care. Any compliance combined with at least 50% ID reduced both costs and missed MACE. Other scenarios yielded a reduction in missed MACE at higher costs. CONCLUSION: Decision analytic modeling is a useful approach to assess impact of using a prediction model in practice on health outcomes and health care costs. This approach is recommended before conducting an impact trial to improve its design and conduct.

Predicted burden could replace predicted risk in preventive strategies for cardiovascular disease.

OBJECTIVES: The objective of this study was to explore the extent of the differences in definitions of composite end points and assess how these differences influence estimates of cardiovascular disease (CVD) burden. STUDY DESIGN AND SETTINGS: Data from a Dutch cohort study (n = 19,484) was used to calculate 10-year risks according to four CVD risk prediction models: Adult Treatment Panel (ATP) III, Framingham Global Risk Score (FRS), Pooled Cohort Equations (PCE), and SCORE. Health loss was estimated based on the impact of event types included in the corresponding composite end points. Finally, each prediction model was used to estimate the expected CVD burden in high-risk individuals, expressed as Quality-Adjusted Life Years (QALYs) lost. RESULTS: The definition of the composite end points varied widely across the four models. FRS predicted the highest CVD risks, and the composite end point used in SCORE was associated with the highest health burden. The predicted CVD burden in high-risk individuals was 0.23, 0.74, 0.43, and 0.39 QALYs lost per individual when using ATP, FRS, PCE, and SCORE, respectively. CONCLUSION: The investigated CVD risk prediction models showed huge variation in definition of composite end points and associated health burden. Therefore, health consequences related to predicted risks cannot be readily compared across prediction models, and estimates of burden of disease depend crucially on the prediction model used.

A new selection method to increase the health benefits of CVD prevention strategies.

Background Cardiovascular disease (CVD) prevention is commonly focused on providing individuals at high predicted CVD risk with preventive medication. Whereas CVD risk increases rapidly with age, current risk-based selection of individuals mainly targets the elderly. However, the lifelong (preventable) consequences of CVD events may be larger in younger individuals. The purpose of this paper is to investigate if health benefits from preventive treatment may increase when the selection strategy is further optimised. Methods Data from three Dutch cohorts were combined ( n = 47469, men:women 1:1.92) and classified into subgroups based on age and gender. The Framingham global risk score was used to estimate 10-year CVD risk. The associated lifelong burden of CVD events according to this 10-year CVD risk was expressed as quality-adjusted life years lost. Based on this approach, the additional health benefits from preventive treatment, reducing this 10-year CVD risk, from selecting individuals based on their expected CVD burden rather than their expected CVD risk were estimated. These benefits were expressed as quality-adjusted life years gained over lifetime. Results When using the current selection strategy (10% risk threshold), 32% of the individuals were selected for preventive treatment. When the same proportion was selected based on burden, more younger and fewer older individuals would receive treatment. Across all individuals, the gain in quality-adjusted life years was 217 between the two strategies, over a 10-year time horizon. In addition, when combining the strategies 5% extra eligible individuals were selected resulting in a gain of 628 quality-adjusted life years. Conclusion Improvement of the selection approach of individuals can help to reduce further the CVD burden. Selecting individuals for preventive treatment based on their expected CVD burden will provide more younger and fewer older individuals with treatment, and will reduce the overall CVD burden.