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Erik P. Hess, Judd E. Hollander, Jason T. Schaffer, Jeffrey A. Kline, Carlos A. Torres, Deborah B. Diercks, Russell Jones, Kelly P. Owen, Zachary F. Meisel, Michel Demers, Annie Leblanc, Nilay D. Shah, Jonathan Inselman, Jeph Herrin, Ana Castaneda-Guarderas, and Victor M. Montori.
Author Information and AffiliationsPatients at low risk for acute coronary syndrome (ACS) are frequently admitted for observation and cardiac testing at a substantial burden and cost to the patient and the health care system. We compared the effectiveness of shared decision-making facilitated by the Chest Pain Choice (CPC) decision aid with usual care (UC) in the choice of admission for observation and further cardiac testing or referral for outpatient evaluation in patients with possible ACS.
This was a multicenter pragmatic parallel randomized controlled trial conducted in 6 geographically diverse US emergency departments. Participants included adults (>17 years of age) with a primary complaint of chest pain who were being considered for observation unit admission for cardiac testing. Patients were randomly assigned (1:1) to CPC or to UC. The primary outcome, selected by patient and caregiver representatives, was patient knowledge; secondary outcomes were involvement in the decision to be admitted, proportion of patients admitted for cardiac testing, the 30-day rate of major adverse cardiac events, and 45-day health care utilization. We also conducted a prespecified analysis to assess the heterogeneity of effect of CPC in potentially vulnerable patient groups.
We assessed 3236 patients for eligibility and enrolled 898 patients (451 CPC, 447 UC) from October 2013 to August 2015. Compared with UC, CPC patients had greater knowledge (questions correct: 4.2 CPS vs 3.6 UC; mean difference [MD] 0.66; 95% CI, 0.46-0.86), were more involved in the decision to be admitted (observing patient involvement [OPTION] scores: 18.3 CPC vs 7.9 UC; MD 10.3; 95% CI, 9.1-11.5), and less frequently decided with their clinician to be admitted for cardiac testing (37% CPS vs 52% UC; absolute difference [AD] 15%; P < .001). There was no difference in the emergency department (ED) length of stay (LOS) between the CPC and UC arms, although CPC patients had a significantly shorter median LOS in the ED observation unit (824.5 [SD = 517.5] minutes vs 920.2 [SD = 482.4] minutes; P = .0305) and underwent fewer tests within 45 days (mean [SD] 5.6 [5.4] CPC vs 6.4 [5.8] UC; P = .0465). No major adverse cardiac events occurred due to the intervention. When assessing the effect of the decision aid in potentially vulnerable patient groups, we observed similar effectiveness of the decision aid to the trial population in the elderly and in those with lower levels of education and less income on the outcomes of patient knowledge, decisional conflict, and involvement in the decision (P for interaction = nonsignificant). CPC increased knowledge to a greater degree in White patients compared with non-White patients (11.0% vs 4.8%, AD 6.2%; P for interaction = .018) and in patients with typical numeracy compared with those with low numeracy (10.6% vs 4.7%, AD 5.9%; P for interaction = .025). However, CPC increased physician trust to a greater degree in patients with “low” health literacy compared with those with “typical” literacy (3.7% vs −1.4%, AD 5.1%; P for interaction = .011).
Use of CPC in patients at low risk for ACS increased patient knowledge and engagement and safely decreased health care utilization. All subgroups benefited to a similar extent from use of CPC; White patients and those self-reporting better numeracy had greater knowledge gains, while physician trust increased more in patients with low health literacy.
Chest pain is the second most common reason patients visit emergency departments (EDs) for evaluation, accounting for more than 8 million visits annually.1 Over the past decade, the proportion of patients diagnosed with acute coronary syndrome (ACS) in the emergency setting decreased from 26% to 13%. Despite the decreasing incidence of ACS, advanced cardiac imaging for chest pain has increased nearly 4-fold.2
Current clinical, electrocardiogram (ECG), and laboratory data do not identify all patients who present to the ED with ACS, resulting in a 1.5% miss rate.3 Given the medical, legal, and psychological sequelae associated with missing a diagnosis of ACS, clinicians have a very low threshold to admit patients for prolonged observation and advanced cardiac testing.4 As a consequence, low-risk patients are frequently admitted for observation and cardiac stress testing or coronary computed tomography angiography (CCTA). This results in unnecessary hospital admissions, false-positive test results, and unnecessary invasive downstream investigations, at an estimated cost to the health care system of more than 7 billion US dollars annually.5
Decision aids are patient-centered tools designed to facilitate shared decision-making between a patient and his or her clinician such that patients' values and preferences are incorporated into health care decisions.6 To assist clinicians and patients with possible ACS in making risk-informed decisions about testing and follow-up and to engage patients in the decision-making process, we included validated7,8 45-day risk estimates for ACS into a decision aid, Chest Pain Choice (CPC).9 In a single-center pilot randomized trial of CPC, we observed increased patient knowledge, increased patient engagement, decreased decisional conflict, and a 19% lower rate of observation unit admission for cardiac stress testing in the CPC arm compared with usual care (UC), with no adverse events in either study arm.10 We conducted this pilot randomized trial in a single tertiary care academic ED in the Midwest United States. In order to test the effectiveness of CPC in a broader population of patients with greater socioeconomic diversity and in a variety of clinical contexts, we conducted a multicenter pragmatic11 randomized trial in 6 geographically diverse EDs across the United States.
Patients seeking emergency care for chest pain, a patient representative (MD), and a caregiver representative (AL) were involved in the design of the study, design of the intervention, submission of the application for funding, monitoring of study conduct, interpretation of the data, review of the manuscript for important intellectual content, and approval of the final manuscript for publication. When designing the trial, the patient representative, the caregiver representative, and the ED patient advisory council at the Saint Mary's Hospital at Mayo Clinic provided input regarding the prioritization and selection of outcomes. As the primary purpose of the study was to educate and empower patients to participate in decisions regarding their emergency care, we prioritized the patient's viewpoint over outcomes of potential interest to other stakeholders. We included outcomes of interest to other key stakeholders as secondary outcomes. When designing the intervention, we sought input from the patient and caregiver representatives, the ED patient advisory council, and patients receiving emergency care for potential ACS regarding the clarity, helpfulness, and usefulness of the information included in the CPC, and the CPC was iteratively refined based on this input. As the patients and patient and caregiver representatives involved in the trial had no prior diagnosis of coronary artery disease and thus no engagement in a heart disease–specific support group or organization, patient representatives were not directly involved in dissemination of the study findings. However, the patient and caregiver representatives were engaged at the highest level possible—partner—and included as coinvestigators on the application for funding, members of the investigative steering committee, and coauthors in the manuscripts generated from this research.
The background and methods of the trial were described in a previous publication.12 This was a pragmatic parallel randomized controlled trial in low-risk patients presenting to the ED with a potential ACS. The trial compared an intervention group receiving a structured risk assessment using a quantitative pretest probability Web tool13 incorporated into a decision aid (CPC) with a control group receiving UC.14 We did not believe cross-contamination would be a problem because access to the quantitative pretest probability instrument was password protected and not easily reproduced by participating clinicians, and we observed in our pilot trial that clinicians reverted to their usual pattern of interacting with patients without the decision aid. For these reasons we opted to randomize at the patient level. The Institutional Review Boards (IRBs) at each of the participating hospitals approved all study procedures. The IRB-approved study protocol is included as a supplementary file with this report.
We enrolled patients and clinicians from the EDs at 6 US sites (University of California Davis, Mayo Clinic Rochester, Indiana University, University of Pennsylvania, Thomas Jefferson University, and Mayo Clinic Florida). Each of the sites, with the exception of Mayo Clinic Florida, had access to an emergency department observation unit in which protocols to provide care for patients with potential acute coronary syndrome existed as part of routine practice. At Mayo Clinic Florida, when a decision is made to admit a patient for observation, the patient's status is changed from an emergency department visit to an observation stay without a physical change in location. Otherwise we collected all data consistently across sites.
We included all physicians, nurse practitioners, and physician assistants caring for patients with chest pain. Eligible patients included adults (>17 years of age) who presented to the ED with a chief complaint of chest pain and were being considered by the treating clinician for observation unit admission for cardiac stress testing or CCTA. Registration staff identified adults with a chief complaint of chest pain on arrival to each participating ED, and study coordinators worked with the treating clinician to assess eligibility. We excluded patients for the following reasons: ischemic changes on the initial ECG (eg, ST-segment depression, T-wave inversion, or new left bundle branch block), initial cardiac troponin of less than the 99th percentile, known coronary artery disease, cocaine use in the past 72 hours (confirmed by history or testing), prior plan for cardiac intervention or admission, barriers to outpatient follow-up, primary institution of care other than the enrolling institution, prisoners, pregnancy, hearing or visual impairment or other inability to use the CPC. We classified as postrandomization exclusions those patients deemed to meet the exclusion criteria after randomization but before the patient–clinician disposition discussion.15
An online password-protected randomization algorithm (Medidata Balance; Medidata Solutions concealed allocation). We randomized patients 1:1 and dynamically stratified16 them by age, gender, and site because of the known associations of age and gender with cardiovascular risk, potential unmeasured differences between sites, and availability of these data at the time of enrollment. We did not randomize clinicians. We did not mask to allocation patients, study coordinators, and treating clinicians. We blinded all other investigators to allocation. We blinded study coordinators to allocation at the time of the 45-day phone call.
We sought to assist patients and clinicians in making a risk-informed shared decision in the emergency setting, in which patients typically do not have the opportunity to learn about their condition prior to the visit and clinicians frequently make decisions unilaterally to facilitate patient safety and rapid treatment of life-threatening conditions. For these contextually specific reasons, we designed CPC for use during the clinical encounter.17 CPC was developed9 in Rochester, Minnesota, through a participatory action research methodology18 in which feedback was intentionally and iteratively sought from patients, clinicians, an expert in health care design, and the investigative team and field-tested until thematic saturation was achieved. Prior to conducting the trial, we refined the CPC to ensure contextual fit with each practice setting. Figure 1 depicts the refined decision aid. At 2 of the sites, CCTA was available and frequently obtained in the evaluation of patients with possible ACS. For these 2 sites, we added the option of CCTA to the CPC. Figure 2 displays the decision aid that includes this option.
For patients randomized to CPC, a study coordinator collected each of the variables needed to populate the quantitative pretest probability Web tool,13 asked the treating clinician to sign off on their accuracy, and calculated the patient's pretest probability of acute coronary syndrome, incorporating the result of the first troponin test but prior to subsequent biomarker testing. Next, the study coordinator selected the decision aid that depicted the level of risk corresponding to the pretest probability generated by the Web tool. The study coordinator then offered to provide the clinician a concise refresher of the content. The treating clinician, after evaluating the patient and receiving the results of the initial ECG and cardiac troponin, then brought the CPC decision aid to the patient's bedside and used it to educate the patient about the results of the 2 tests; the potential need for observation and further cardiac testing; subsequent cardiac troponin testing to definitively rule out acute myocardial infarction, if required; and his or her personalized 45-day risk for ACS. The clinician then engaged the patient to select the management option most closely aligned with his or her values and preferences.
For patients randomized to UC, a study coordinator instructed the clinician to discuss the results of diagnostic investigations and management options in his or her usual way. When clinicians treated patients who were randomized to UC, they did not have access to the quantitative pretest probability Web tool or to CPC. As the trial was intentionally pragmatic in design, UC was not standardized.11
We collected data documenting the screening process, randomization, and outcome assessment in compliance with CONSORT guidelines.19 An immediate post visit survey collected data on patients' knowledge regarding their risk for ACS and the available management options, decisional conflict, and trust in their physician.12 The clinician–patient discussion was video and audio recorded to assess, using the validated OPTION scale, the degree to which the clinician made efforts to engage the patient in the decision-making process.20 Video and audio recordings were time stamped, and we determined the duration of the clinician–patient discussion from these recordings. The recordings were uploaded to a secure server and deleted from the portable devices after upload. The server was protected by 2-step access: (1) password-protected access to all the Mayo clinic's computers, and (2) password-protected access to the secure server. Audio and video files from facilities outside of Mayo Clinic were downloaded onto an encrypted password-protected flash drive, sent securely to the prime site by FedEx, and uploaded to a secure server on receipt. We also collected data on cardiac risk factors, post-ED management, and further cardiac investigations by reviewing the electronic medical record (EMR) at each site.
We collected patient health care utilization data using 2 separate methods to ensure we had the most complete data for each patient. Each patient filled out a health care diary documenting his or her utilization of health care services in the 45 days after the ED visit. Study coordinators, blinded to the arm to which the patient was randomized, contacted patients starting at 45 days after enrollment to query patients about this diary and any additional testing, outpatient or inpatient visits, or procedures at health care facilities other than the original institution. Study coordinators made at least 5 attempts to contact each patient by phone for follow-up during different times of the day and on different days of the week. If the patient was unable to be reached by phone or email and the EMR documented no subsequent visits, mortality status was verified using Accurint, a national database frequently used by banks and other businesses to track individuals and ensure payment collection.21 We included in the analysis all other data collected on patients unable to be reached by phone. In addition, each site provided all the billing data for each of the enrolled patients for the initial ED visit and the 45 days after the initial visit.
To facilitate assessment of the heterogeneity of CPC effect in vulnerable patients, we collected data on patient age, gender, race, annual income, insurance status, education level, health literacy, and numeracy. To collect data on race, we used the categories recommended by the Institute of Medicine (American Indian/Alaska Native, Asian, Black or African American, Native Hawaiian or other Pacific Islander, White/Caucasian, Other).22 To assess health literacy, patients answered 3 questions, each with a 5-point Likert response (Subjective Literacy Scale),23 prior to their encounter with the clinician. For the purposes of this study, we summed the 3 items into a total score after reverse coding 1 item: The higher the summed score, the lower the patient's subjective assessment of his or her general health literacy skills. To assess numeracy, which quantifies the ability to understand and use numbers in daily life,24 patients completed an 8-item questionnaire (Subjective Numeracy Scale).25 We reversed and averaged numeracy responses to all 8 questions, creating an overall score ranging from 1 to 6, where higher scores are indicative of lower levels of numeracy.
Because the goal of patient-centered outcomes research is to provide patients and the public with the information they need to help them make decisions that affect their desired health outcomes,26 we prioritized the perspective of the patient over the perspective of other stakeholders in determining the primary outcome. Because knowledge emerged as the outcome of greatest importance during meetings with patient and caregiver representatives, we selected patient knowledge as the primary outcome. As in our pilot trial10 and in prior work,27 we assessed patient knowledge with an immediate postvisit survey (Figure 3). Each question was classified as true or false, with the exception of Question 9, which was open ended and asked the patient to provide a numerical answer.
Nine Knowledge Questions Included in the Postvisit Patient Questionnaire, Used to Assess Patient Knowledge Immediately After the Emergency Department Encounter.
We measured how informed patients felt about their management options using the Decisional Conflict Scale28 and patients' trust in their clinician using the Trust in Physician Scale.29 The Decisional Conflict Scale includes 16 items scored from 0 to 4; the items are summed, divided by 16, and then multiplied by 25. The scale is from 0 to 100, where higher scores reflect increased patient uncertainty about choice. One study found that for every unit increase in Decisional Conflict Scale scores, patients were 19% more likely to blame their doctor for bad outcomes.30 The Trust in Physician scale consists of 9 items scored from 1 to 5; the items are subtracted by 1, summed, divided by 9, and then multiplied by 25. The scale ranges from 0 to 100, where higher values reflect higher values of patient trust in his or her physician. To the best of our knowledge, a clinically meaningful change in Trust in Physician scale has not been published. We surveyed participating patients and clinicians regarding the clarity and helpfulness of the information shared and the acceptability of the decision aid using a 7-point Likert scale. Finally, 5 trained raters independently viewed videos of the patient–clinician discussion and assessed the degree to which clinicians engaged patients in the decision-making process using the OPTION scale.20 This scale is composed of 12 items with a value of 0 to 4; they are summed, divided by 48, and then multiplied by 100. Scores range from 0 to 100, where higher scores reflect higher levels of patient engagement. Although a clinically meaningful change in OPTION scale score has not been defined, the mean score for outpatient clinicians in the original development investigation was 16.9 (SD, 7.68).31 Given that we conducted the current trial in the emergency setting, in which time pressures and patient acuity often impact the clinician–patient interaction, we anticipated OPTION scale scores in the current investigation to be lower than the originally published mean.
We assessed management by recording whether patients were admitted to the ED observation unit or hospital, or discharged home; whether cardiac stress testing or CCTA was conducted; the results of testing; and whether the patient underwent percutaneous coronary intervention or coronary artery bypass grafting by review of the EMR at each participating site.
To assess safety, we determined whether a patient experienced a major adverse cardiac event (MACE). Consistent with a consensus document on ACS research in EDs,32 we defined MACE as acute myocardial infarction (AMI),33 death due to a cardiac or unknown cause, emergency revascularization, ventricular arrhythmia, or cardiogenic shock. Potential MACEs were shared with the data safety monitoring board (DSMB), which reviewed the cases in detail and provided its judgment to the investigative team. The entire investigative team also discussed potential MACE cases on monthly conference calls and adjudicated the cases based on consensus among site investigators. We excluded MACEs occurring during the index visit to the ED or hospital, as these events were considered appropriately diagnosed during the index visit. Events that occurred after discharge home, which could have potentially been avoided, were classified as MACE. We collected data on all MACE occurring up to 45 days to be consistent with the follow-up period used in the development of the quantitative pretest probability instrument;34 but we compared 30-day event rates to be consistent with standardized reporting guidelines for ED risk stratification studies of patients with potential ACS.35
For health care utilization, we assessed using both patient-reported data and hospital-level billing data on the following health care services delivered during the ED visit and the subsequent 45 days: hospitalizations, emergency department visits, office visits, testing, imaging, and other services. Patient-reported data provided information on subsequent hospitalizations, emergency department visits, and physician office visits and the number of visits for each. The billing data provided information on testing and imaging during the initial visit and subsequent utilization in the next 45 days. We classified all utilization from the billing data using the Berenson-Eggers Types of Service (BETOS) codes.36 We used BETOS codes to separate out evaluation and management visits, imaging, testing, and procedures. In addition, we separated out inpatient and emergency department visits. Further, we looked at the specific types of testing and imaging, including stress testing and CCTA. Finally, we assessed the amount of time spent in the ED and observation unit in each of the arms. This allowed us to assess whether the intervention increased the amount of time spent in the ED or the observation unit. Although in the original trial protocol12 we anticipated reporting 30-day health care utilization, we opted to report 45-day health care utilization instead given the slightly more robust inferences that can be drawn using a longer follow-up interval to assess utilization.
The primary outcome registered at ClinicalTrials.gov is the phrase “Test if Chest Pain Choice (the decision aid) safely improves validated patient-centered outcome measures,” with the description “Test if the intervention significantly increases patient knowledge.” There is only 1 primary outcome for the study: patient knowledge. The phrase “Test if Chest Pain Choice safely improves validated patient-centered outcome measures” refers to the 5 additional outcome measures (a through e) listed as secondary outcomes at ClinicalTrials.gov. This is documented in the study protocol,12 which was published prior to completion of enrollment for the trial in August 2015.
We estimated that 884 patients would provide 99% power to detect a 16% difference in patient knowledge between intervention and control arms and 90% power to detect a 10% difference in the proportion of patients admitted to an observation unit for cardiac testing.12 To account for an estimated 5% potential loss to follow-up, we planned to enroll 930 patients. We summarized patient characteristics by study group and tested for differences between groups using t-tests and chi-square tests. To test for differences in outcomes, we estimated a series of regression models, each of which included indicators for study group. For continuous outcomes we used linear models, and for categorical outcomes we used multinomial (polytomous) logistic models. To account for nonindependence of outcomes by site, we included indicators for study site in each model. We assessed for additional correlation with clinicians by estimating a hierarchical generalized model for each outcome and calculating the intraclinician correlation coefficient (ICC). Because all ICCs were less than 1%, we chose not to account for this correlation in the final models.
We tested for differences in both self-reported and billing-based utilization between groups using t-tests, chi-square tests, or the Kruskal-Wallis test, as appropriate. To test for differences in outcomes, we estimated a series of regression models, each of which included indicators for study group. As with the primary analysis of the trial,37 we conducted outcome assessments using regression models (linear for continuous outcomes, multinomial for categorical outcomes) that included indicators for study arm assignment and study site. We used appropriate count data models for each of the utilization outcomes. For outcomes that had less than 10% of the patients with zero utilization, we used Poisson or negative binomial models depending on the extent of overdispersion. Among outcomes with more than 10% of patients with no utilization, we used a 2-part model, sometimes referred to as a hurdle model,38 which separates the utilization outcomes into 2 distinct parts: whether there was any utilization of that service, and amount of utilization of that service conditional on any utilization. More specifically, the first hurdle used a logistic model to estimate the probability of having any utilization for a type of service, while the second hurdle used Poisson models to estimate the extent of utilization. We conducted all analyses for the baseline utilization, utilization in the 45 days after the index visit, and total utilization. To account for nonindependence of outcomes by site, we included indicators for study site in each model.
In a previously published patient-level meta-analysis of 7 randomized trials assessing the efficacy of patient decision aids (DAs) used during the clinical encounter,39 we observed efficacy estimates similar to the trial population when DAs were used with vulnerable patients such as the elderly and those with less income and less formal education. We also observed, however, a greater increase in knowledge of risk among patients with higher education compared with those with lower levels of education. Subgroup effects based on race yielded imprecise results with wide confidence intervals. Based on the findings in the meta-analysis, we hypothesized that use of CPC would increase knowledge, decrease decisional conflict, and increase patient engagement across all sociodemographic patient groups but would also increase knowledge to a relatively greater degree in patients with higher levels of education, health literacy, and numeracy. Moreover, given empiric data demonstrating high levels of physician trust in a cohort consisting largely of Caucasian patients10 and less positive perceptions of physicians in patients from racial and ethnic minority groups,40 we hypothesized that use of CPC would increase physician trust to a greater degree in patients from racial and ethnic minority groups. We designed the heterogeneity of CPC effect analyses to test these prespecified hypotheses.
For the analysis, we dichotomized each of the following variables to evaluate the differential effect of CPC across patient groups: age, gender, race, annual income, insurance status, level of education, health literacy, and numeracy. We chose to dichotomize for 2 reasons: (1) Given the overall enrollment of 898 patients, more than 2 classifications would have resulted in many subgroups that were too small to analyze; and (2) binary classifications made the heterogeneity of CPC effect simpler to analyze and interpret by way of interactions.41 We dichotomized gender naturally. Other classifications were as follows: race as White vs non-White (to facilitate comparison of CPC effects between minorities and the largest nonminority group); annual income as less than $40 000 vs greater than or equal to $40 000; insurance status as uninsured vs insured; education as less than or equal to high school/Graduate Education Diploma (GED) vs greater than high school/GED; literacy as typical (≤3) vs low (>3); and numeracy as typical (≤4) vs low (>4). We based these classifications on both the distribution of the values and conceptual considerations regarding the most likely contrasts to show heterogeneity of effect. We excluded patients missing a subgroup variable from the analysis for that subgroup. For race, we included the “other” group with “non-White,” as we interpreted “other” as implying not White. For education, we excluded the “other” category from the dichotomous groups, as we could not assume it to indicate either of the 2 categories. As with the primary analysis of the trial,37 we conducted outcome assessments for this study using regression models (linear for continuous outcomes, multinomial for categorical outcomes) that included indicators for study arm assignment and study site. In addition, to assess for heterogeneity of effect across each of the subgroups, we included an interaction term for group assignment and subgroup. To improve interpretation, we also replicated the main trial analysis (ie, with no interaction term) within each subgroup, and reported the group effect and whether the effect differed significantly from zero. We reported this group effect as a coefficient for continuous outcomes and odds ratios for dichotomous or multinomial outcomes.
Our statistical approach to subgroup analysis was informed by publication guidelines for reporting subgroup analyses.41 We prespecified interaction testing between patient characteristics and the outcomes of patient knowledge, decisional conflict, involvement in decision-making, and physician trust, and we used a significance level of 5% to identify significant interactions for these subgroup effects. Given that a total of 80 comparisons were performed, we anticipated up to 0.05 multiplied by 80, or 4, significant interactions based on chance alone. As such, we considered subgroup analyses that were not prespecified to be hypothesis generating. We performed all analyses using Stata Version 14.1 (Stata Corporation; 2016). We followed the principle of intention to treat in the conduct of the trial and in all analyses.
We assessed 3236 patients for eligibility from October 2013 to August 2015 (Figure 4), and 361 clinicians participated in the study. We randomized 913 patients, with 13 postrandomization exclusions and 2 patients who withdrew consent, leaving 898 patients (451 CPC, 447 UC) in the final analysis. In all 13 postrandomization exclusions, additional information became available after randomization, but before the patient–clinician disposition discussion, indicating that the patient was not eligible. We audio or video recorded the patient–clinician disposition discussion in 536 (59.7%) encounters. The main reasons recordings were not obtained were clinician and patient refusal and technical difficulties with recording equipment. We contacted 828 (92.2%, 413 CPC) patients by telephone and/or email for follow-up. Of the 70 (7.8%) remaining patients, 68 had mortality data available in the EMR and/or Accurint,21 which confirmed that none of these 68 patients died within 45 days. The 2 patients with missing mortality data were in the UC arm.
Participant Flow Diagram.
Table 1 summarizes patient characteristics. Mean age (SD) was 50.3 (14.5) years, and 534 (59.5%) were women. Most patients were White (531, 59.1%) or Black (309, 34.4%). There were 285 (31.7%) patients whose highest level of education was high school, GED, or less. The mean (SD) pretest probability of ACS in the usual care arm was 3.8 (4.3) and 3.6 (3.7) in the DA arm (P = .46). There were no significant differences in baseline characteristics between the study arms.
Baseline Characteristics.
As Table 2 depicts, patients randomized to CPC had greater knowledge (questions correct: 4.2 CPC vs 3.6 UC; mean difference [MD], 0.66; 95% CI, 0.46-0.86). A greater proportion of patients in the intervention arm correctly reported their exact pretest probability of ACS and their risk within 10% of the correct value (65.0% CPC vs 18.1% UC; absolute difference [AD], 46.8%; 95% CI, 41.2-52.5). Patients in the intervention arm reported significantly less decisional conflict (Decisional Conflict Scale: 43.5 [15.3] CPC vs 46.4 [14.8] UC; MD −2.9; 95% CI, −4.8 to −0.90). Use of CPC did not significantly impact patients' trust in their physician. The proportion of patients who were “strongly satisfied” with the information shared with them by their clinician did not significantly differ between the study arms (49% CPC vs 43% UC; absolute difference 6%; P = .06).
Effect of Decision Aid on Patient Knowledge, Decisional Conflict, Trust in the Physician, Patient Involvement in the Decision, and Decision Aid Acceptability.
Interobserver agreement (Cohen's κ) between raters for OPTION scale assessments was 0.89 (95% CI, 0.84-0.93). Patients randomized to CPC were more engaged in the decision-making process as indicated by higher OPTION scores (18.3 [9.4] CPC vs 7.9 [5.4] UC; MD 10.3, 95% CI, 9.1-11.5; see Table 2). Patients randomized to CPC found the information discussed to be of greater clarity, and a greater proportion (88.0% CPC vs 79.9% UC; AD 8.1%; P = .004) would recommend to others the way they discussed management options with their clinician.
A greater proportion of clinicians in the intervention arm reported the information shared with their patient about the decision to be admitted for further observation and testing to be extremely helpful (235 [54.1] CPC vs 141 [33.7] UC; AD 20.4%; P < .001; see Table 2). Most (62.7%) clinicians would recommend using the CPC to others, and 62.9% would want to use a CPC for other decisions. The mean (SD) length of the clinician–patient discussion was 1.3 minutes longer in the intervention arm (4.4 [0.40] minutes CPC vs 3.1 [0.29] minutes UC; MD 1.3; P = .008).
A significantly lower proportion of patients randomized to CPC decided, with their clinician, to be admitted to the ED observation unit for cardiac stress testing or CCTA (37.3% CPC vs 52.1% UC; AD 14.8%; 95% CI, 1.1-13.9; see Table 3). A significantly lower proportion of patients in the CPC arm underwent cardiac stress testing within 30 days, including tests obtained during the index ED visit and the subsequent 30 days (38.1% CPC vs 45.6% UC; AD 7.5%; 95% CI, 1.1-13.9). Of those who underwent cardiac stress testing, a significantly greater proportion of patients randomized to CPC had testing performed in the outpatient setting (30.2% CPC vs 17.2% UC; AD 13.1%; 95% CI, 4.5-21.7). There was no difference in the rate of coronary angiography, coronary revascularization, hospitalization, repeat hospitalization, repeat ED visits, or outpatient clinic visits between study arms.
Management and 30-Day Outcomes.
A total of 5 patients had AMIs, 4 in the CPC arm and 1 in the UC arm (P for difference = 1.0). The 1 patient in the UC arm with AMI had the AMI diagnosed during the index ED visit on serial cardiac troponins and was admitted to the hospital. Four of the 5 patients with AMI and all cardiac interventions occurred during the index visit. Two of the 4 patients with acute myocardial infarction in the CPC arm had an initial troponin less than the 99th percentile, no acute ischemic changes on the initial ECG, and a subsequent increased troponin level detected on serial cardiac biomarker testing. These patients were admitted to the hospital for further evaluation and management and received a diagnosis of non-ST segment elevation myocardial infarction. A third case of myocardial infarction in the CPC arm occurred in a patient who had negative serial cardiac troponin results and no acute ischemic changes on the ECG but symptoms suggestive of acute coronary syndrome. The patient was admitted to the hospital, underwent percutaneous coronary intervention, and subsequently developed in-stent thrombosis. This in-stent thrombosis, which occurred in the hospital, was accompanied by increased troponin levels and ST-segment elevation on ECG. The patient underwent a second percutaneous coronary intervention and recovered uneventfully. There were no deaths of cardiac or unknown cause in either arm. The fourth patient in the CPC arm with acute myocardial infarction was classified as a MACE. This patient decided with her clinician to be admitted to the hospital, she underwent nuclear perfusion stress testing as an inpatient, and the test was interpreted as negative. She was discharged from the hospital. Subsequently, she developed recurrent chest pain and returned to the ED within 30 days of hospital discharge with a non-ST segment myocardial infarction and underwent percutaneous coronary intervention. This was the only patient who had a cardiac intervention performed after the index ED admission; all the remaining cardiac interventions occurred during the index ED admission. The DSMB classified this MACE as not related to the intervention.
Of the 898 patients, patient-reported health care utilization data were available for all 834 (92.9%) patients who were reached by phone for 45-day follow-up. Based on the 8 patient-reported utilization questions in the health care diary, there was no significant difference in 45-day health care utilization between the study arms (Table 4).
Health Care Utilization Within 45 Days of the Emergency Department Visit Collected by Patient Report By Use of a Health Care Diary.
Hospital-level billing data were available for all 898 (100%) enrolled patients. Overall, including the date of the ED visit and the subsequent 45 days, there was no difference in physician evaluation and management codes (level of physician services), number of imaging tests obtained, or the number of procedures performed between arms of the study. However, the mean (SD) number of tests was lower in the CPC arm (13.3 [6.9] CPC vs 14.7 [7.7] UC; P = .0432; see Table 5). During the index ED visit, the mean (SD) number of imaging tests was lower in the CPC arm (1.4 [1.4] CPC vs 1.6 [1.2] UC; P = .02), and there was no difference in the median length of stay (LOS). There was no difference in the frequency of observation unit admission between arms; however, for patients who were admitted to the ED observation unit, the median LOS was 95 minutes shorter in the CPC arm (824.8 minutes vs 920.2 minutes; P = .03; see Table 6). In the 45 days following the ED visit (not including the ED visit), patients randomized to CPC underwent fewer tests overall (mean [SD], 5.6 [5.4] CPC vs 6.4 [5.8] UC; P = .0465; see Table 5).
Health Care Utilization Data Obtained From Hospital-Level Billing Data: Index Visit, Subsequent 45 Days, and Total.
Chest Pain–Specific Utilization.
There were significant interactions between patient characteristics and the outcomes of knowledge (percentage of questions correct), knowledge of ACS risk, and trust in the physician (Table 7). For patient knowledge (%), there were significant interactions based on race (P = .018) and numeracy (P = .025) (Figure 5). Use of CPC increased knowledge in both the White and non-White subgroups, but to a greater degree in the White subgroup (11.0%) compared with the non-White subgroup (4.8%), with the increase in both subgroups reaching significance compared with UC. For numeracy, CPC increased knowledge 10.6% in the “typical” subgroup compared with 4.7% in the “low” subgroup, with both increases reaching significance compared with UC. For knowledge of ACS risk, there was a significant interaction with patient race (P = .018) (Table 7). Use of CPC increased patient knowledge of ACS risk more in the White subgroup (11.4%) compared with the non-White subgroup (7.2%), with the increase in the White subgroup reaching significance compared with UC. Finally, for trust in the physician, we observed a significant interaction with health literacy (P = .011) (Figure 6). Use of CPC increased trust in the physician in patients with “low” health literacy (3.7%) while decreasing trust in the physician for patients with “typical” literacy (−1.4%), with the increased trust reaching significance in the “low” subgroup compared with UC. There were no significant interactions on based on decisional conflict or patient involvement in the decision-making process (OPTION score).
Differential Effect of the DA on Patient Knowledge, Decisional Conflict, Trust in the Physician, and Involvement in the Decision (OPTION Score) Based on Patient Sociodemographic Characteristics.
Knowledge (%) Subgroup Effects.
Trust in Physician Scale Subgroup Effects.
Regarding management decisions, there were significant interactions between patient race (P = .004) and annual income (P = .028) and the decision to have a stress test (Table 8). White patients had 52% lower odds of having a stress test, while non-White patients had 19% greater odds of having a stress test, with the decreased odds of having a stress test reaching significance in the White subgroup compared with UC (Figure 7). For annual income, the group with income <$40 000 had 8% greater odds of having a stress test, and those with income ≥$40 000 or more had 46% lower odds of having a stress test, with the decrease in the ≥$40 000 income group reaching significance compared with UC. There were no significant interactions on the decision to have a CT angiogram or to undergo revascularization.
Differential Effect of the DA on Use of Stress Testing, Coronary CT Angiography, and Revascularization Based on Patient Sociodemographic Characteristics.
Stress Testing by Subgroups.
In patients with chest pain who were otherwise being considered for observation unit admission and advanced cardiac testing, use of the DA increased patient knowledge, increased patient engagement, decreased decisional conflict, and did not significantly affect physician trust. The DA was found to be acceptable to both patients and physicians, and its use, which took an average of 1 additional minute of clinician time, decreased the proportion of patients who decided, with their clinician, to be admitted to the observation unit for advanced cardiac testing. Use of the DA also decreased cardiac stress testing within 30 days of the ED visit and the 45-day rate of testing overall. Although there was no difference in the ED length of stay between study arms, patients randomized to the DA who were admitted to the ED observation unit had a 90-minute-shorter median length of observation unit stay. When assessing heterogeneity of effect, use of the DA increased patient knowledge, decreased decisional conflict, and increased involvement in the decision similar to the trial population in the elderly and in patients with less formal education and lower income. However, the DA increased knowledge to a greater degree in White patients compared with non-White patients and in patients with greater numeracy. However, the DA increased physician trust to a greater degree in patients with low self-reported health literacy. We do not think the 1 patient with a MACE in the DA arm was related to the intervention, as the patient was admitted to the hospital during the index ED admission.
Findings from this trial suggest that patients can be effectively educated and engaged in the emergency care setting in decisions regarding testing and follow-up and that it is feasible to do so in the flow of clinical care. In addition, when risk estimates from validated prediction models were shared with patients, and patients were invited to apply their informed values and preferences to decisions, rates of admission and testing did not increase. Rather, patient-centered interventions such as those tested in this trial indicate that patients, when educated and informed of their risk, may choose with their clinician to undergo less extensive evaluation more closely tailored to their personalized risk. Use of an encounter-level CPC to facilitate risk-informed diagnosis in the emergency care setting also has potential to decrease downstream health care utilization related to diagnostic testing.
When assessing the effect of the DA in potentially vulnerable patient groups, we observed similar effectiveness of the DA to the trial population in the elderly and in those with lower levels of education and less income on the outcomes of patient knowledge, decisional conflict, and involvement in the decision. However, knowledge increased to a greater degree in patients with “high” numeracy compared with those with “low” numeracy. Given that communication of risk in numerical terms is frequently involved in shared decision-making conversations, it is important for clinicians to follow best practices when communicating risk with patients, such as use of natural frequencies, estimates of absolute risk, and a consistent denominator,42 and anticipate that patients with low numeracy may have difficulty comprehending and applying numerical concepts to the decision. It is also important for shared decision-making researchers to involve patients with limited numeracy in the DA development process and test the effect of the DA in this subgroup of patients. We also observed greater increases in knowledge in White compared with non-White patients. Given that knowledge was the primary outcome of the study, this interaction is of potential importance. Racial and ethnic differences between patients and physicians may impact knowledge transfer. However, we did not prespecify an interaction between patient race and knowledge, and this observation could be due to chance alone and should be interpreted with caution. We also observed a significant interaction between patients with “low” health literacy and physician trust. Although this interaction is interesting and supports the hypothesis that use of the DA increased physician trust to a greater degree in potentially vulnerable patients with low health literacy, this interaction was not prespecified and should be considered hypothesis generating. Finally, White patients and patients with annual incomes of >$40 000 were less likely to undergo stress testing than non-White patients and those with annual incomes of ≤$40 000. This is a potentially important finding, as shared decision-making may decrease utilization when the knowledge of risk is effectively transferred, the patient understands the available management options, and there is ready access to outpatient follow-up. However, these findings were not prespecified and should be confirmed in future shared decision-making trials.
Several limitations of this trial should be taken into consideration. The quantitative pretest probability Web tool7,8 applies only to patients with chest pain. As such, the DA cannot be used in patients with potential ACS who present with non–chest pain syndromes (eg, shortness of breath and/or diaphoresis). We used 2 different versions of the DA in the trial—1 that included the option of CCTA and 1 that included only cardiac stress testing. Although this introduced a degree of heterogeneity in the intervention, the trial was intentionally pragmatic in design, and contextual fit of the DA to facilitate clinician–patient discussions relevant to the clinical settings enrolling patients in the trial was essential. In addition, there is now evidence to support applying the shared decision-making tool in a greater variety of clinical care contexts. We randomized at the patient level, increasing the risk of contamination between intervention and control groups. To limit the risk of contamination, the quantitative pretest probability Web tool was password protected, and coordinators did not provide clinicians access to the DA. However, even if contamination were to occur, this would bias the results of the trial toward the null, and we observed a positive effect of the intervention despite the potential for contamination. We were unable to contact 70 patients (8%) for assessment of a secondary outcome. Of these, 68 were confirmed alive at 45 days. The 92% phone follow-up rate supplemented by mortality review from a national database, however, is robust and comparable to other ED high-quality studies of patients with potential ACS. We were unable obtain video recordings in 40% of the encounters. However, the 536 video recordings that were obtained exceeded the required sample size of 221 needed to meet power estimates. The study had 78% power to detect a 5% difference in MACE rate between study arms, using a 1-sided noninferiority test with an α of .05. Although this was substantially greater power than the initial cohort of patients recruited in our single-center pilot trial, greater power and precision would be optimal.
For the health care utilization analysis, utilization measures were defined prospectively as secondary outcomes that would be evaluated, but the study design was not necessarily powered to detect a difference in these measures.12 Although we reviewed the EMR and attempted to contact all enrolled patients, we were unable to collect self-reported health care utilization data in the 70 (8%) patients who were unable to be contacted by phone for follow-up. To increase the rigor of the health care utilization analysis, we collected hospital-level billing data to help evaluate utilization. It is possible that patients could have undergone further testing outside of these institutions that may not be captured in these data. However, all of the patients identified the institution where they were enrolled as their primary institution.
The primary limitations of the heterogeneity of DA effect analysis relate to issues of multiple testing and imprecision around the estimates of subgroup effects. Given that a total of 80 comparisons were performed, 1 could estimate that up to 4 interactions (80 x 0.05) could be observed based on chance alone. To limit the risk of bias associated with multiple testing, we prespecified hypotheses based on prior observations in Shared Decision-Making (SDM) trials39 and prior literature demonstrating lower levels of physician trust in racial and ethnic minority groups compared with White patients.40 We also followed guideline recommendations for reporting subgroup analyses in clinical trials41 by presenting only those subgroup analyses that were prespecified or based on a primary study outcome in the abstract, distinguishing subgroup analyses of special interest in the methods, basing analyses of the heterogeneity of effect on tests for interaction, and exercising caution in interpreting subgroup differences. Our analyses often yielded imprecise results of potentially important subgroup effects. However, this limitation is inherent in subgroup analyses of clinical trials, and, to the best of the investigators' knowledge, the current investigation represents the largest cohort of patients enrolled in a shared decision-making trial to date and has potential to reveal important insights related to the effect of a DA in vulnerable patients.
To date, no shared decision-making interventions have been routinized and incorporated into clinical protocols and emergency care delivery. While the findings from this multicenter trial suggest that the DA may be effective across a variety of clinical settings, further implementation studies are needed to determine how best to incorporate the DA in care pathways; how emergency clinicians, cardiologists, and primary care clinicians can best work together to ensure incorporation and implementation of risk-informed patient preferences into admission, testing, and follow-up decisions; and how to ensure patient preferences guide decision-making both during and after the ED encounter. In addition, given the limited time available for clinician–patient interaction in the emergency setting and variable levels of health care literacy between patients, time-efficient, vulnerable population–targeted approaches to patient activation that involve education and preparation for engagement in shared decisions with clinicians—such as a brief, standardized video–should be explored. Interventions designed to ensure communication of the rationale for care decisions to family members who were not present during the ED encounter are also needed to ensure effective implementation of the care decisions made. Finally, a large-scale implementation trial may be needed to more definitively test the safety of the intervention.
Use of a DA in patients with low-risk chest pain who were otherwise being considered for observation unit admission for cardiac stress testing or CCTA increased patient knowledge, increased patient engagement, and decreased decisional conflict. Shared decision-making facilitated by the DA was perceived to be acceptable to both patients and physicians, and its use decreased the median length of stay for patients admitted to the ED observation unit, the 30-day rate of stress testing, and the 45-day rate of all cause testing with no adverse events related to the intervention. All subgroups benefited to a similar extent from use of the DA; White patients and those self-reporting better numeracy had greater knowledge gains, while physician trust increased more in patients with low health literacy. Future SDM studies using encounter-level DAs should take into consideration tailored approaches to empowering and engaging vulnerable patients in decisions regarding their care.
Research reported in this report was [partially] funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Award (#952). Further information available at: https://www.pcori.org/research-results/2012/testing-decision-aid-patients-low-risk-chest-pain-emergency-room-chest-pain-choice-trial
Hess EP, Hollander JE, Schaffer JT, et al. (2018). Testing a Decision Aid for Patients with Low-Risk Chest Pain in the Emergency Room – The Chest Pain Choice Trial. Patient-Centered Outcomes Research Institute (PCORI). http://doi.org/10.25302/3.2018.CER.952
The [views, statements, opinions] presented in this report are solely the responsibility of the author(s) and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute® (PCORI®), its Board of Governors or Methodology Committee.
This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License which permits noncommercial use and distribution provided the original author(s) and source are credited. (See https://creativecommons.org/licenses/by-nc-nd/4.0/
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