Best Clinical Practice: Current Controversies in the Evaluation of Low-Risk Chest Pain with Risk Stratification Aids. Part 2

Utilization of Chest Pain Decision Aids in a Community Hospital Emergency Department: A Mixed-methods Implementation Study

INTRODUCTION

Chest pain is a common reason for emergency department (ED) visits. Evidence-based decision aids assessing risk for an adverse cardiac event are underused in community hospital emergency care. This study explored the acceptability, barriers, facilitators, and potential strategies for implementation of the HEART Score risk stratification tool, accelerated diagnostic pathway, and shared decision-making visual aid with physicians and chest pain patients ages >45 in a community hospital ED.

METHODS

Single center, mixed-methods study. (1) Physician semistructured interviews using The Consolidated Framework for Implementation Research for systematic analysis. (2) Patient and physician surveys. (3) 16-week intervention of physician training and pilot testing of decision aids with ED patients.

RESULTS

Physician interviews (n = 19); key facilitators: electronic medical record decision support, ease of use, risk stratification and disposition support, and shared decision-making training. Key barriers: time constraints, patient ability, and/or willingness to participate in shared decision-making, lack of integration with medical record and change in practice workflow. Patient study participants (n = 184) with a survey response rate of 92% (n = 170). Most patients (85%) were satisfied with the shared decision-making visual aid. Physicians surveyed (n = 84) with a response rate of 50% (n = 42). Most physicians, 95% (n = 40), support use of the HEART Score, with limited acceptance of the shared decision-making visual aid of 57% (n = 24).

CONCLUSIONS

Using evidence-based chest pain decision aids in a community hospital ED is feasible and acceptable. Key barriers and facilitators for implementation were identified. Further research in community hospitals is needed to verify findings, examine generalizability, and test implementation strategies.

Utilizing machine learning dimensionality reduction for risk stratification of chest pain patients in the emergency department

BACKGROUND

Chest pain is among the most common presenting complaints in the emergency department (ED). Swift and accurate risk stratification of chest pain patients in the ED may improve patient outcomes and reduce unnecessary costs. Traditional logistic regression with stepwise variable selection has been used to build risk prediction models for ED chest pain patients. In this study, we aimed to investigate if machine learning dimensionality reduction methods can improve performance in deriving risk stratification models.

METHODS

A retrospective analysis was conducted on the data of patients > 20 years old who presented to the ED of Singapore General Hospital with chest pain between September 2010 and July 2015. Variables used included demographics, medical history, laboratory findings, heart rate variability (HRV), and heart rate n-variability (HRnV) parameters calculated from five to six-minute electrocardiograms (ECGs). The primary outcome was 30-day major adverse cardiac events (MACE), which included death, acute myocardial infarction, and revascularization within 30 days of ED presentation. We used eight machine learning dimensionality reduction methods and logistic regression to create different prediction models. We further excluded cardiac troponin from candidate variables and derived a separate set of models to evaluate the performance of models without using laboratory tests. Receiver operating characteristic (ROC) and calibration analysis was used to compare model performance.

RESULTS

Seven hundred ninety-five patients were included in the analysis, of which 247 (31%) met the primary outcome of 30-day MACE. Patients with MACE were older and more likely to be male. All eight dimensionality reduction methods achieved comparable performance with the traditional stepwise variable selection; The multidimensional scaling algorithm performed the best with an area under the curve of 0.901. All prediction models generated in this study outperformed several existing clinical scores in ROC analysis.

CONCLUSIONS

Dimensionality reduction models showed marginal value in improving the prediction of 30-day MACE for ED chest pain patients. Moreover, they are black box models, making them difficult to explain and interpret in clinical practice.

Impact of Education-based HEART Score Pathway on Coronary Computed Tomography Angiography Utilization and Yield in the Emergency Department

OBJECTIVE

There is a growing consensus to reduce unnecessary testing among low-risk chest pain patients. The objective of this study was to evaluate the impact of implementing an education-based HEART score pathway in the emergency department on coronary computed tomography angiography (CCTA) utilization and yield.

METHODS

A retrospective before and after intervention study was conducted at a single site. Adult emergency department patients undergoing CCTA for suspected acute coronary syndrome were included. Primary outcomes were CCTA utilization and yield. Utilization was defined as the percentage of patients evaluated with CCTA and yield was calculated as the percentage of patients with a diagnosis of obstructive coronary artery disease, defined as ≥50% stenosis in any one coronary artery due to atherosclerosis.

RESULTS

1540 patients undergoing CCTAs were included. CCTA utilization before and after were 2.2% [95% confidence interval (CI) 2.0-2.3] and 2.0% (95% CI 1.9-2.2), respectively; mean difference 0.1% (95% CI -0.1 to 0.3; P = 0.21). The mean age was 53 years (SD = 11) and females were 52%. Of 1477 patients included in CCTA yield analysis, patients diagnosed with obstructive coronary artery disease before and after were 15.0% (95% CI 12.6-17.7) and 16.2% (95% CI 13.6-19.1), respectively; mean difference 1.2% (95% CI -2.6 to 5.1; P = 0.53).

CONCLUSIONS

There was no significant change in the CCTA utilization or yield after the implementation of an education-based HEART pathway in a large academic center. Our findings suggest adopting a more comprehensive approach for deploying such evidence-based protocols to increase institutional compliance.

Heart rate n-variability (HRnV) and its application to risk stratification of chest pain patients in the emergency department

Background

Chest pain is one of the most common complaints among patients presenting to the emergency department (ED). Causes of chest pain can be benign or life threatening, making accurate risk stratification a critical issue in the ED. In addition to the use of established clinical scores, prior studies have attempted to create predictive models with heart rate variability (HRV). In this study, we proposed heart rate n-variability (HRnV), an alternative representation of beat-to-beat variation in electrocardiogram (ECG), and investigated its association with major adverse cardiac events (MACE) in ED patients with chest pain.

Methods

We conducted a retrospective analysis of data collected from the ED of a tertiary hospital in Singapore between September 2010 and July 2015. Patients > 20 years old who presented to the ED with chief complaint of chest pain were conveniently recruited. Five to six-minute single-lead ECGs, demographics, medical history, troponin, and other required variables were collected. We developed the HRnV-Calc software to calculate HRnV parameters. The primary outcome was 30-day MACE, which included all-cause death, acute myocardial infarction, and revascularization. Univariable and multivariable logistic regression analyses were conducted to investigate the association between individual risk factors and the outcome. Receiver operating characteristic (ROC) analysis was performed to compare the HRnV model (based on leave-one-out cross-validation) against other clinical scores in predicting 30-day MACE.

Results

A total of 795 patients were included in the analysis, of which 247 (31%) had MACE within 30 days. The MACE group was older, with a higher proportion being male patients. Twenty-one conventional HRV and 115 HRnV parameters were calculated. In univariable analysis, eleven HRV and 48 HRnV parameters were significantly associated with 30-day MACE. The multivariable stepwise logistic regression identified 16 predictors that were strongly associated with MACE outcome; these predictors consisted of one HRV, seven HRnV parameters, troponin, ST segment changes, and several other factors. The HRnV model outperformed several clinical scores in the ROC analysis.

Conclusions

The novel HRnV representation demonstrated its value of augmenting HRV and traditional risk factors in designing a robust risk stratification tool for patients with chest pain in the ED.

Diagnostic and Prognostic Role of the Modified Diamond-Forrester Model in Combination With Coronary Calcium Score in Acute Chest Pain Patients

BACKGROUND

The aim of this study was to evaluate whether pretest probability (PTP) assessment using the Diamond-Forrester Model (DFM) combined with coronary calcium scoring (CCS) can safely rule out obstructive coronary artery disease (CAD) and 30-day major adverse cardiovascular events (MACE) in acute chest pain patients.

METHODS

We retrospectively evaluated consecutive patients, age ≥18 years, with no known CAD, negative initial electrocardiogram, and troponin level. All patients had coronary computed tomographic angiography (CCTA) with CCS, and our final cohort consisted of 1988 patients. Obstructive CAD was defined as luminal narrowing of ≥50% in 1 or more vessels by CCTA. Patients were classified according to PTP as low (<10%), intermediate (10%-90%), or high (>90%).

RESULTS

The DFM classified 293 (14.7%), 1445 (72.7%), and 250 (12.6%) of patients as low, intermediate, and high risk, respectively, with corresponding 30-day MACE rates of 0.0%, 2.35%, and 14.8%. For patients with intermediate PTP and CCS ≤10, the negative predictive value was 99.2% (95% confidence interval: 98.7-99.8) for 30-day MACE while it was 92.62% (95% confidence interval: 87.9-97.3) for patients with high PTP. Among patients with a high PTP and CCS of zero, the prevalence of 30-day MACE and obstructive CAD remained high (7.07% and 10.1%, respectively).

CONCLUSIONS

In acute chest pain patients without evidence of ischemia on initial electrocardiogram and cardiac troponin, low PTP by DFM or the combination of intermediate PTP and CCS ≤10 had excellent negative predictive values to rule out 30-day MACE. CCS is not sufficient to exclude obstructive CAD and 30-day MACE in patients with high PTP.