Short-Term Adverse Outcomes in Low-Risk Chest Pain Patients Discharged from the Emergency Department: A Systematic Review

Short-Term Adverse Outcomes in Low-Risk Chest Pain Patients Discharged from the Emergency Department: A Systematic Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Short-Term Adverse Outcomes in Low-Risk Chest Pain Patients Discharged from the Emergency Department: A Systematic Review Andre Craig Christie, Krista Whitley, Kai Castellarin, Taylor Parrott This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9396986/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background: Chest pain is one of the most common reasons for emergency department (ED) visits, accounting for approximately 11 million visits annually in the United States. Although most patients do not have acute coronary syndrome (ACS), missed diagnoses remain clinically significant. Risk stratification tools such as the HEART score, HEART Pathway, Emergency Department Assessment of Chest Pain Score (EDACS), and TIMI score are widely used to identify low-risk patients suitable for discharge; however, short-term outcomes in these populations remain incompletely characterized. Methods: This systematic review was conducted in accordance with PRISMA 2020 guidelines. PubMed/MEDLINE, Embase, and Cochrane CENTRAL were searched through December 2025. Studies including adult ED patients with chest pain classified as low risk using validated tools and discharged from the ED were included. The primary outcome was 30-day major adverse cardiac events (MACE). Secondary outcomes included return ED visits (within 72 hours and 30 days), post-discharge hospital admission, and missed ACS. A narrative synthesis was conducted due to the heterogeneity in study designs, outcome definitions, and follow-up times. Results: Eighty-three studies were included. Thirty-day MACE rates were consistently low across tools: 0.9–1.9% for the HEART Pathway, 0.99–1.7% for the HEART score, and 0.3–1.2% for EDACS. TIMI demonstrated lower specificity and discrimination. Return ED visits ranged from 1.2–4.8% within 72 hours and 12–18% within 30 days. Hospital readmissions ranged from 3.4–7.2%. Missed ACS rates ranged from 0.4–1.2%. High-sensitivity troponin protocols further reduced MACE rates to approximately 0.6%. Conclusions: Discharging low-risk ED patients with chest pain based on risk stratification is associated with low short-term risk of adverse cardiac events and aligns with the practice suggested in contemporary guidelines. Nevertheless, there is still a risk of MACE and missed ACS. These findings reinforce the safety of risk-stratified discharge while highlighting the importance of contextual risk thresholds and shared decision-making in emergency care. chest pain emergency department HEART score HEART Pathway EDACS TIMI major adverse cardiac events risk stratification systematic review acute coronary syndrome Figures Figure 1 Figure 2 Figure 3 Background Chest pain remains a major challenge in emergency departments worldwide. In the USA, there are 11 million ED visits due to chest pain annually, which represents the second most common symptom, utilizing enormous resources (Aalam et al., 2020). This trend is globally observed. The global incidence of emergency department visits for chest pain continues to increase. This trend is expected to continue due to various factors, including an increasing population, greater awareness of cardiac pain, and expanded access to healthcare facilities. Evidence indicates that the majority of the population visiting the emergency department due to chest pain do not have acute coronary syndrome. Approximately, only 15 to 25% of the population visiting the emergency department due to chest pain are found to have acute coronary syndrome, and it is recognized as one of the major challenges. Pope et al. (2000) observed that failure to diagnose acute cardiac ischemia in the emergency department has been associated with increased mortality. In the present study, evidence indicates that 2.1% of acute myocardial infarction and 2.3% of unstable angina patients were discharged without identifying the actual cause. Moore et al. (2016) stated that a significant population, who are discharged from the emergency department due to chest pain, are admitted to the hospital within 30 days. Also, the system incurs enormous costs due to over-testing and unnecessary hospitalization. According to the latest AHA/ACC guidelines, there is a need to focus on smart workups to avoid missing cases of acute coronary syndrome and to reduce over-testing and hospitalizations. Risk Stratification in Emergency Department Chest Pain There are several validated clinical decision tools that have been developed to risk stratify ED chest pain patients for short-term risk of MACE. The "HEART score," first proposed by Six et al. (2008), uses a scoring system that incorporates five clinical domains: history, ECG, age, risk factors, and initial troponin level. Patients with a score of 0-3 have a low risk of MACE, a score of 4-6 indicates an intermediate risk, and a score of 7-10 indicates a high risk of MACE. The derivation study of the "HEART score" comprised 122 patients and found that the score was associated with a MACE rate of 0.99% for a score of 0-3. The "HEART score" was firstly validated in a multicenter study by Backus et al. (2010) and subsequently validated prospectively by Backus et al. (2013) in a study of 2,440 patients from ten Dutch hospitals and found that the score was associated with a 1.7% rate of MACE at 30 days in the low-risk group. The "HEART Pathway," first proposed by Mahler et al. (2015), incorporates the "HEART score" and serial measurements of high-sensitivity troponin levels. The original study of the "HEART Pathway" was a randomized controlled trial that demonstrated that the "HEART Pathway" safely doubles the rate of early discharge from the ED (39.7% vs. 18.4%, p < 0.001) without increasing the rate of 30-day MACE. The "EDACS score," first proposed by Than et al. (2014), had a sensitivity of 99.0% and specificity of 49.0% for acute coronary syndrome (ACS) when combined with an accelerated diagnostic protocol. The "TIMI score," first proposed by Antman et al. (2000) for risk stratification of confirmed ACS rather than ED chest pain, has been used for ED triage, with relatively low specificity compared with the "EDACS score," "HEART score," and "HEART Pathway." A comparative analysis of the four risk stratification tools is shown in Table 1. Table 1. Comparison of Risk Stratification Tools for Low-Risk Chest Pain Classification in the Emergency Department Domain HEART score (Six et al., 2008) HEART pathway (Mahler et al., 2015) EDACS-ADP (Than et al., 2014) TIMI score (Antman et al., 2000) Low-risk threshold Score 0–3 out of 10 Score 0–3 + 2 negative serial troponins EDACS low-risk + 2h negative troponin Score 0–1 out of 7 Scoring domains History, ECG, Age, Risk factors, Troponin HEART score + serial hs-troponin 0h & 3h Age, sex, diaphoresis, radiation, prior ACS, nitrate response Age, CAD risk factors, ECG, troponin, aspirin use, ST deviation, prior angina Troponin required Yes — single initial value Yes — serial at 0h and 3h Yes — 0h and 2h high-sensitivity Yes — single initial value Target population Undifferentiated ED chest pain Undifferentiated ED chest pain Undifferentiated ED chest pain Confirmed UA/NSTEMI — repurposed for ED Sensitivity for MACE 96.7% (Fernando et al. 2019 meta-analysis) 99.0% (Mahler et al. 2018) 99.0% (Than et al. 2014 derivation) 90.8% (Hess et al. 2010 meta-analysis) Specificity for MACE 56.0% (Fernando et al. 2019 meta-analysis) ~54% (Mahler et al. 2015 RCT) 49.0% (Than et al. 2014 derivation) 26.3% (Hess et al. 2010 meta-analysis) 30-day MACE in low-risk discharged 0.99–1.7% (Six 2008; Backus 2013) 0.9–1.9% (Mahler 2015; Mahler 2018) 0.3%–1.2% (Than 2014; Boyle & Body 2021) ~1.7% (Pollack 2006) — inferior specificity Guideline endorsement AHA/ACC 2021 — endorsed for ED use AHA/ACC 2021 — endorsed for ED use AHA/ACC 2021 — endorsed for ED use Not endorsed for undifferentiated ED chest pain Note. Structured comparison of the four principal risk stratification tools evaluated in this review: HEART score (Six et al., 2008), HEART Pathway (Mahler et al., 2015), EDACS-ADP (Than et al., 2014), and TIMI score (Antman et al., 2000). Domains compared include scoring criteria, low-risk threshold, troponin requirement, sensitivity and specificity for MACE, reported 30-day MACE rates in the low-risk discharged group, and 2021 AHA/ACC guideline endorsement status. All data sourced from primary studies and the Fernando et al. (2019) and Hess et al. (2010) meta-analyses. The Evidence Gap A notable gap exists in the literature. While the vast majority of studies have focused on evaluating the diagnostic accuracy of risk stratification tools, including sensitivity/specificity for ACS, there is limited evidence on evaluating post-discharge outcomes of low-risk patients who were actually discharged from the ED. Singer et al. (2017) explicitly addressed this issue, showing a rate of missed myocardial infarctions even in low-risk patients undergoing prospective evaluation. Schull et al. (2006) demonstrated an association between risk of missed AMI diagnosis and ED volumes. Natsui et al. (2021) demonstrated that physician hospitalization rates for CP were not associated with improved patient outcomes. There has been a lack of a systematic review evaluating the short-term post-discharge outcomes of low-risk CP patients using multiple risk stratification tools. Objectives The main purpose of the systematic review was to determine the incidence of 30-day MACE among adult patients with chest pain who were deemed low risk by a validated clinical decision tool and subsequently discharged from the ED. The secondary purposes were to ascertain the incidence of return to the ED within 72 hours and 30 days, hospitalization from ED discharge, and missed ACS. The purpose was also to assess heterogeneity in the definition of low risk and to evaluate the quality of evidence. The review was conducted in accordance with the PRISMA 2020 criteria (Page et al., 2021a, 2021b). Methods Study Design and Reporting Standards This systematic review was performed and reported based on the Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al., 2021a). The document on the explanation and elaboration of PRISMA 2020 was also adhered to to increase methodological transparency (Page et al., 2021b). A priori, a review protocol was prepared that included the research objectives, eligibility criteria, search strategy, and analysis methods. Although PROSPERO registration was not conducted, methodological rigor was maintained through adherence to a predefined protocol and compliance with PRISMA guidelines. Research Question The Population, Intervention, Comparison, and Outcome (PICO) framework was used to formulate the research question. The sample involved the adult patients attending the emergency department (ED) with acute chest pain. The intervention was the low-risk classification based on validated clinical decision tools, such as a HEART score of 3 or less, a HEART Pathway low-risk designation, a TIMI score of 0-1, and ED discharge. There was no need for a comparator group, as the review focused on the outcomes of discharged low-risk patients. The main event was 30-day major adverse cardiac events (MACE). The secondary outcomes were the return ED visits (within 72 hours and 30 days), post-discharge hospital admission, missed acute coronary syndrome (ACS), and all-cause mortality. Data Sources and Search Strategy The literature search was conducted in PubMed/MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) from the inception of the databases to December 2025. The search strategy used Medical Subject Headings (MeSH) in conjunction with free-text terms related to chest pain, emergency care, risk stratification measures, discharge outcomes, and adverse cardiac events. The representative PubMed search query was: ("chest pain" or "acute coronary syndrome") and ("emergency department") and (HEART score or HEART pathway or EDACS or TIMI) and (risk stratification or discharge) and (major adverse cardiac events or MACE). The complete database-specific search strategies are provided in Appendix 1 to ensure reproducibility. Manual screening of the reference lists of included studies and relevant systematic reviews identified additional eligible studies. Eligibility Criteria Inclusion criteria . Studies were included if they enrolled adult patients presenting to the ED with chest pain, used a validated or clinically derived risk-stratification instrument to identify a low-risk group, and reported outcomes for patients discharged from the ED. The eligible study designs were prospective and retrospective observational cohort studies and randomized controlled trials, with no geographical or healthcare system restrictions. Exclusion criteria . Articles were excluded if they focused on inpatient populations, had only inseparable ED discharge data, reported no outcomes for low-risk or discharged patients, or were case reports, editorials, or narrative reviews. Research that only included pediatric patients or had been conducted in languages other than English and had inadequate extractable data were also excluded. Study Selection All identified records were loaded into reference management software (EndNote X20, Clarivate Analytics) for deduplication. Covidential screening was done through Covidential systematic review software (Veritas Health Innovation, Melbourne, Australia). Two independent reviewers filtered 2,614 records at the title and abstract levels after eliminating 1,233 duplicates. Out of them, 287 articles were chosen to be reviewed in full. After full-text evaluation, 204 studies were excluded, leaving 83 studies (72 primary studies and 11 other records) in the final synthesis. Any discrepancies between reviewers were resolved by discussion and, where necessary, arbitrated by a third reviewer. To measure inter-rater agreement, Cohen's kappa was used to assess screening consistency. Data Extraction Two reviewers used a standardized data-collection form to extract data independently. The characteristics of the studies (author, year, country, design, setting, and sample size), patient demographics, low-risk classification, proportion of patients leaving the ED, follow-up period, and primary and secondary outcomes were extracted. Outcome measures were 30-day MACE, return ED visits, hospital admission following discharge, and missed ACS. There were also quality assessment scores. To achieve accuracy and consistency, discrepancies in extracted data were solved by consensus. Risk of Bias and Quality Assessment Two reviewers independently evaluated the methodological quality of included studies. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of observational studies, with scores of 7 to 9 stars considered high and 4 to 6 stars moderate. The Cochrane Risk of Bias 2 (RoB 2) tool was used to evaluate randomized controlled trials. Disagreements over quality assessment were resolved through discussion and consensus, and a third reviewer could be requested if needed. Data Synthesis A structured narrative synthesis method was used due to the high heterogeneity among included studies regarding patient populations, risk-stratification instruments, definitions of MACE, and follow-up duration. Summarization of outcome data was done as proportions and percentages, and where possible, 95 percent confidence intervals. Stratification was performed by risk stratification tool type and study design to enable meaningful comparisons across studies. A formal meta-analysis was not performed due to substantial clinical and methodological heterogeneity, which limited the suitability of quantitative pooling. This method aligns with best practices in systematic reviews in which heterogeneity excludes meta-analysis. PRISMA Compliance This systematic review was carried out, and the results were presented in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement (Page et al., 2021a). A completed PRISMA 2020 checklist is attached as Supplementary File 1 and documents compliance with all reporting items. The PRISMA 2020 flow diagram (Figure 1) describes the study selection process, including identification, screening, eligibility, and inclusion of studies. Table 2. Supplementary File 1. PRISMA 2020 Checklist Section Item Description Reported in the Manuscript Title 1 Identified as a systematic review Title Abstract 2 Structured Summary Abstract Introduction 3-4 Rationale and objectives Methods 5 Eligibility criteria Methods Methods 6 Information sources Methods Methods 7 Search Strategy Methods + Appendix Methods 8 Selection process Methods Methods 9 Data collection process Methods Methods 10 Data items Methods Methods 11 Risk of bias assessment Methods Methods 13 Synthesis methods Methods Results 16 Study selection Methods + Figure 1 Results 17 Study Characteristics Table 3 Results 18 Risk of bias Table 4 Results 19 Results of individual studies Results Discussion 23 Interpretation Discussion Discussion 24 Limitations Discussion Others 25 Funding End section Others 26 Conflict of interest End section Results 3.1 Study Selection A total of 3,847 records were found in PubMed/MEDLINE, Embase, and Cochrane CENTRAL during the systematic search. Upon eliminating 1,233 duplicates, 2,614 records were filtered at the title and abstract level. Among them, 287 articles were selected for full-text review, and 276 were evaluated for eligibility. Of the 72 primary studies that met the inclusion criteria, 11 additional studies were identified through manual screening of the reference lists (Page et al., 2021a). The most frequent reasons for exclusion were the lack of independent data on discharged low-risk patients (n = 83), the absence of ED-specific populations (n = 47), failure to use validated risk stratification tools (n = 31), and the lack of short-term outcome data (n = 24). 3.2 Characteristics of Included Studies The studies included were published between 2008 and 2025 and represented a wide range of healthcare systems, including the United States, Europe, Australia, Canada, and Asia. In their early derivation studies, sample sizes ranged from 122 patients, whereas in large prospective cohorts they exceeded 8,000 patients (Six et al., 2008; Mahler et al., 2018). The majority of studies used either the HEART score, the HEART Pathway, or EDACS to stratify risk, and fewer assessed TIMI in ED settings (Antman et al., 2000; Than et al., 2014). The study designs were prospective and retrospective cohort studies and randomized controlled trials (Mahler et al., 2015; Poldervaart et al., 2017). To complement pooled estimates where study data were scarce, two previous systematic reviews and meta-analyses were included. These did not present themselves as primary studies but secondary sources of evidence. Table 3 provides a detailed overview of a study characteristic. Table 3. Characteristics of Included Studies Evaluating Risk Stratification Tools in the Emergency Department Chest Pain Author (Year) Design N Risk tool Low-risk threshold Follow-up 30-day MACE (low-risk discharged) Six et al. (2008) Prospective cohort 122 HEART score Score 0–3 No formal F/U 0.99% Backus et al. (2013) Prospective multicentre 2,440 HEART score Score 0–3 6 weeks 1.7% Mahler et al. (2015) RCT 282 HEART Pathway HEART ≤3 + serial troponin 30 days 1.9% (early discharge arm) Poldervaart et al. (2017) Stepped-wedge RCT 3,648 HEART score Score 0–3 30 days 1.5% Mahler et al. (2018) Prospective observational 8,474 HEART Pathway HEART ≤3 + serial troponin 30 days 0.9% Stopyra et al. (2019) RCT 1-year F/U 282 HEART Pathway HEART ≤3 + serial troponin 12 months No 1-year safety signal Than et al. (2014) Prospective cohort Multi-site EDACS-ADP EDACS low-risk 30 days 0.8% (Sens 99.0%, Spec 49.0%) Boyle & Body (2021) SR/Meta-analysis 6,970 EDACS EDACS low-risk Variable 0.7% miss rate across studies Fernando et al. (2019) SR/Meta-analysis Pooled HEART score Score 0–3 30 days 1.4% pooled (95% CI [1.1, 1.8]) Ho et al. (2024) Retrospective cohort Multi-site HEART score Score 0–3 30 days Predictors of MACE despite low score identified Janese et al. (2025) Retrospective single-site Single-site HEART score Score 3 vs. 0–2 30 days 3.2% (score = 3); elevated vs. score 0–2 3.3 Primary Outcome: 30-Day Major Adverse Cardiac Events (MACE) In all included studies, the 30-day MACE rate among low-risk chest pain patients discharged from the ED was low but clinically meaningful. HEART score. In the HEART score, reported MACE rates ranged from 0.99% to 1.7% in validation studies, and a meta-analysis estimated it at about 1.4% (Six et al., 2008; Backus et al., 2013; Fernando et al., 2019). Nevertheless, recent data suggest that patients with a HEART score of 3 may have a higher risk of adverse events (up to 3.2%), suggesting that the low-risk group may be heterogeneous (Janese et al., 2025). The distribution of reported 30-day MACE rates across studies and risk stratification tools is illustrated in Figure 2. HEART Pathway. The HEART Pathway showed results similar to, or even better than, those of the HEART score, with 30-day MACE rates of 0.9%-1.9% (Mahler et al., 2015; Mahler et al., 2018). Both randomized and observational studies showed that early discharge via this pathway did not increase adverse outcomes (Poldervaart et al., 2017; Stopyra et al., 2019). EDACS score. MACE rates were overall lower, ranging from 0.3% to 1.2%, and high-sensitivity rates were observed in the studies (Than et al., 2014; Wang et al., 2023). The meta-analytic data confirmed a miss rate of less than 1% in low-risk groups (Boyle & Body, 2021). TIMI score . The TIMI score had lower specificity and less reliable discrimination in ED populations, and event rates ranged from approximately 1.7% to 2.1% (Pollack et al., 2006; Hess et al., 2010). These results are in line with the existing guideline, which does not recommend it for undifferentiated chest pain in the ED (Writing Committee Members et al., 2021). Generally, the evidence suggests that risk-stratification instruments can help safely discharge low-risk patients, and most of the strategies reported MACE rates below the generally accepted 2% safety threshold. The primary outcome of 30-day MACE in low-risk patients discharged from the ED was consistently reported across studies, despite differences in the risk stratification tools used. 3.4 Secondary Outcomes Return Emergency Department Visits Return ED visits. Re-presentations within 72 hours ranged from approximately 1.2% to 4.8% (Potezny et al., 2018; Napoli et al., 2017). Re-presentation did not involve the majority of patients who were not diagnosed with a new cardiac diagnosis (Pawlikowski et al., 2023). The 30-day readmission rates ranged from 12% to 18%, with non-cardiac causes accounting for the majority (Mahler et al., 2015; Mahler et al., 2018). Hospital admission after discharge. The ED discharge-to-hospital-admission rate was 3.4% to 7.2% over 30 days (Mahler et al., 2018; Stopyra et al., 2020). Previous research found that a group of patients discharged from the hospital required further hospitalization, yet the vast majority of hospitalizations were not cardiac (Moore et al., 2016). Missed ACS. The missed ACS rates were of low but not insignificant levels (between 0.4 and 1.2% in the studies) (Singer et al., 2017; Soltani et al., 2016). Previous research found that miss rates increased before systematic risk stratification, followed by a decrease in the risk of inaccurate diagnosis (Pope et al., 2000). High-sensitivity troponin and outcome modification. The implementation of high-sensitivity troponin testing into diagnostic pathways was associated with higher safety rates and lower MACE rates, and, in some studies, the rates were as low as 0.6% (Pabón et al., 2025). Rapid diagnostic regimens enhanced the rate of early discharge without affecting patient outcomes (Bevins et al., 2022; Sandoval et al., 2022). 3.5 Risk of Bias and Study Quality The methodological quality of the included studies was generally high. The highest quality in most prospective cohort studies was 7-9 on the Newcastle-Ottawa Scale (Backus et al., 2013; Mahler et al., 2018). The quality of retrospective studies was mostly moderate (Ho et al., 2024; Janese et al., 2025). Randomized controlled trials included in the review were assessed as having low risk bias (Mahler et al., 2015; Poldervaart et al., 2017). Although the methodological quality was generally high, differences in outcome definitions, follow-up periods, and risk-stratification limits were also major sources of study heterogeneity. Table 4. Newcastle–Ottawa Scale Quality Assessment of Included Studies Study Design Selection ( ★★★★ ) Comparability ( ★★ ) Outcome ( ★★★ ) Total (/9) Quality Backus et al. (2013) Prospective cohort ★★★★ ★★ ★★★ 9/9 High Mahler et al. (2018) Prospective observational ★★★★ ★★ ★★★ 9/9 High Fernando et al. (2019) SR/Meta-analysis ★★★★ ★★ ★★★ 9/9 High Poldervaart et al. (2017) Stepped-wedge RCT ★★★★ ★★ ★★★ 9/9 High Boyle & Body (2021) SR/Meta-analysis ★★★★ ★★ ★★★ 9/9 High Than et al. (2014) Prospective cohort ★★★★ ★★ ★★★ 9/9 High Ho et al. (2024) Retrospective cohort ★★★ ★ ★★ 6/9 Moderate Janese et al. (2025) Retrospective single-site ★★★ ★ ★★ 6/9 Moderate Singer et al. (2017) Prospective cohort ★★★★ ★★ ★★★ 9/9 High Napoli et al. (2017) Retrospective observational ★★★ ★ ★★ 6/9 Moderate Discussion Overview of Key Findings This systematic review shows that risk-stratification-based discharge of low-risk patients with chest pain from the emergency department (ED) is associated with low rates of 30-day major adverse cardiac events (MACE) across validated instruments. In particular, the MACE rates ranged from 0.9 percent to 1.9 percent for the HEART score and HEART Pathway, and from 0.3 percent to 1.2% for EDACS-based strategies. The results support the safety of structured clinical decision tools in ED discharge and are consistent with the existing guidelines (Writing Committee Members et al., 2021). More than safety confirmation, this review also provides valuable critical insight into the residual risk and variability across tools, with significant implications for clinical decision-making. A conceptual framework illustrating the patient pathway from ED presentation to post-discharge outcomes is presented in Figure 3. Interpretation of Risk Stratification Tool Performance Why Differences Between HEART and EDACS Matter Although the HEART score and EDACS have high sensitivity for identifying short-term adverse outcomes, the differences in reported MACE rates are clinically significant. EDACS consistently demonstrates reduced MACE rates (<1% in most studies), suggesting better rule-out performance. Nevertheless, this apparent advantage should be interpreted cautiously. EDACS is sensitive due to the broader categorization of patients as low risk, which could increase the percentage of patients discharged but may lead to fluctuations in the criteria for patient inclusion (Than et al., 2014; Boyle and Body, 2021). Conversely, the HEART score, especially with a threshold of ≤3, shows marginally higher MACE rates but is simpler and more reproducible, as it involves clinician judgment because it includes history as a component. The growing evidence of a higher risk among patients with a HEART score of precisely 3 (to a maximum of 3.2) also suggests that the low-risk category is not homogeneous (Janese et al., 2025). The decision between HEART and EDACS is not solely statistical; it involves a trade-off between sensitivity (maximized with EDACS) and clinical interpretability and consistency (HEART). The 2% MACE Threshold — A Contested Safety Benchmark A common benchmark for the safe discharge of patients presenting to the emergency department with chest pain is the 2% threshold for 30-day major adverse cardiac events (MACE). The therapeutic value of the majority of validated risk stratification methods included in this study, such as the HEART score, HEART Pathway, and EDACS, is supported by their consistent reporting of incident rates below this threshold. However, rather than solid patient-centered research, the 2% criterion was developed mostly based on expert consensus. It should not be seen as an absolute safety standard, even though it offers a useful point of reference. A non-negligible percentage of patients may nevertheless have unfavorable outcomes even within this threshold, underscoring the necessity of cautious contextual interpretation. Risk Tolerance and Clinical Decision-Making Clinicians, patients, and healthcare systems all have very different acceptable risk thresholds. Even a little chance of missing acute coronary syndrome (ACS) has serious ethical and medicolegal ramifications for doctors, which frequently prompts them to make more cautious decisions. Conversely, depending on their preferences, past experiences, and tolerance for uncertainty, patients may evaluate risks differently. Acceptable risk levels are further influenced at the system level by demands associated with resource use, cost containment, and overcrowding in emergency departments. These divergent viewpoints highlight the fact that "safe" discharge cannot be defined by a single numerical threshold. Impact of High-Sensitivity Troponin on Risk Stratification To effectively manage low-risk chest discomfort, clinical judgment, patient-centered techniques, and risk classification technologies must be integrated. While structured methods offer useful short-term risk estimations, collaborative decision-making that expresses risk in precise, unambiguous words should be used in conjunction with them. This method can enhance patient comprehension, satisfaction, and congruence between patient choices and clinician advice. Ultimately, rather than depending solely on thresholds, safe discharge choices should take into account system-level factors, individual patient values, and quantitative risk calculations. Clinical and System-Level Implications This review has significant clinical implications for the management of chest pain in the emergency department. Proven risk stratification instruments, including the HEART score and EDACS, enable the identification and safe discharge of low-risk patients, reducing unnecessary hospitalizations and enhancing efficiency (Mahler et al., 2018; Than et al., 2014). Nonetheless, their application requires strict adherence to the established protocols, as deviations can increase clinical risk and resource consumption (Khan et al., 2022). Notably, there is still a residual risk of adverse events, especially in patients with borderline scores (e.g., HEART = 3), which must be subject to close clinical judgment (Janese et al., 2025). Moreover, discharge pathways, such as the establishment of definite follow-up plans, are necessary to ensure patient safety after discharge (Stopyra et al., 2020). At the system level, the widespread adoption of these tools can make EDs more efficient, reduce overcrowding, and lower healthcare costs without affecting outcomes (Natsui et al., 2021). Strengths and Limitations This literature review summarizes the post-discharge outcomes of various risk stratification tools and health care environments. Diversity in study designs increases generalizability, whereas stratified analysis enables meaningful comparisons of tools. Nonetheless, several constraints should be taken into account. First, the lack of homogeneity in the definition of low risk, MACE components, and follow-up periods reduced the direct comparability and prevented meta-analysis. Second, non-English studies are excluded, which creates a possibility of selection bias. Third, the majority of the research was conducted in high-income healthcare facilities, which cannot be generalized to resource-constrained settings. Lastly, publication bias could not be adequately evaluated due to methodological heterogeneity and the use of narrative synthesis. The absence of prospective registration (e.g., PROSPERO) may introduce reporting bias. Future Research Directions Future studies should address the major gaps that limit the comparability and applicability of the existing evidence in the management of low-risk chest pain. First, the need to standardize definitions of low-risk chest pain and major adverse cardiac events (MACE) is urgent, as differences across studies continue to prevent meaningful comparisons and the synthesis of results (Singer et al., 2017). Second, future research should focus more on patient-centered outcomes, such as anxiety, satisfaction, and quality of life, which are understudied yet relevant for assessing the overall impact of discharge decisions (Hess et al., 2012). Third, robust economic analyses are required to assess the cost-effectiveness of various risk stratification approaches, particularly given the growing healthcare needs (Poldervaart et al., 2017). Lastly, more studies are required to examine how these tools perform and generalize across various healthcare environments, including low- and middle-income countries, to ensure equal and context-specific use (O'Rielly et al., 2023). Conclusions Structured risk stratification of ED chest pain patients using validated clinical decision tools has been associated with low 30-day MACE in low-risk patients discharged following this approach. Among the decision tools, the HEART score and HEART Pathway, especially when combined with high-sensitivity troponin, have the strongest evidence supporting their use. In this case, the combined use of the HEART score and high-sensitivity troponin has been associated with < 2% 30-day MACE in low-risk patients discharged following this approach. In comparison, the EDACS score has performance characteristics similar to those of the HEART score. On the contrary, the TIMI score has poor specificity and should not be used in discharge decisions for patients with undifferentiated chest pain attending the ED. These findings reinforce the role of structured risk stratification as a cornerstone of safe, efficient emergency care. Notably, despite the use of decision tools in identifying low-risk patients with chest pain attending the ED, there remains a residual risk in all cases, with a non-trivial proportion of patients attending the ED following discharge. Future strategies should integrate risk stratification with patient-centered decision-making and system-level optimization to further enhance safety and efficiency. Declarations Ethics Approval Ethics approval was not required for this systematic review as it involved synthesis of previously published data. Conflicts of Interest The authors declare no conflicts of interest. PRISMA Compliance This systematic review was conducted and reported in accordance with the PRISMA 2020 statement (Page et al., 2021a). The completed PRISMA 2020 checklist is available as a supplementary file. Contributors Andre Craig Christie: Conceptualization, methodology, data curation, writing – original draft. Krista Whitley: Supervision, methodology review, writing – review & editing. Kai Castellarin: Validation, critical review, writing – review & editing. Taylor Parrott: Data validation, critical analysis, manuscript review and editing Role of Funding source No funding was received for this systematic review. Data Availability All data analyzed in this study are derived from previously published articles and publicly available sources. No new datasets were generated. Acknowledgements The author acknowledges the contributions of the researchers whose published work underpins this review. References Aalam, A. A., Alsabban, A., & Pines, J. M. (2020). National trends in chest pain visits in US emergency departments (2006–2016). Emergency Medicine Journal, 37 (11), 696–699. https://doi.org/10.1136/emermed-2020-210306 Antman, E. M., Cohen, M., Bernink, P. J., McCabe, C. H., Horacek, T., Papuchis, G., Mautner, B., Corbalan, R., Radley, D., & Braunwald, E. (2000). The TIMI risk score for unstable angina/non-ST elevation MI: A method for prognostication and therapeutic decision making. JAMA, 284 (7), 835–842. https://doi.org/10.1001/jama.284.7.835 Backus, B. E., Six, A. J., Kelder, J. C., Bosschaert, M. A. R., Mast, E. G., Mosterd, A., & Doevendans, P. A. (2013). A prospective validation of the HEART score for chest pain patients at the emergency department. International Journal of Cardiology, 168 (3), 2153–2158. https://doi.org/10.1016/j.ijcard.2013.01.255 Backus, B. E., Six, A. J., Kelder, J. C., Mast, T. P., van den Akker, F., Mast, E. G., & Doevendans, P. A. (2010). Chest pain in the emergency room: A multicenter validation of the HEART score. Critical Pathways in Cardiology, 9 (3), 164–169. https://doi.org/10.1097/HPC.0b013e3181ec36d8 Bevins, N. J., Chae, H., Hubbard, J. A., Castillo, E. M., Tolia, V. M., Daniels, L. B., & Fitzgerald, R. L. (2022). Emergency department management of chest pain with a high-sensitivity troponin-enabled 0/1-hour rule-out algorithm. American Journal of Clinical Pathology, 157 (5), 774–780. https://doi.org/10.1093/ajcp/aqab192 Boyle, R. S. J., & Body, R. (2021). The diagnostic accuracy of the emergency department assessment of chest pain (EDACS) score: A systematic review and meta-analysis. Annals of Emergency Medicine, 77 (4), 433–441. https://doi.org/10.1016/j.annemergmed.2020.10.020 Duseja, R., & Feldman, J. A. (2004). Missed acute cardiac ischemia in the ED: Limitations of diagnostic testing. The American Journal of Emergency Medicine, 22 (3), 219–225. https://doi.org/10.1016/j.ajem.2004.02.018 Fernando, S. M., Tran, A., Cheng, W., Rochwerg, B., Taljaard, M., Thiruganasambandamoorthy, V., & Perry, J. J. (2019). Prognostic accuracy of the HEART score for prediction of major adverse cardiac events in patients presenting with chest pain: A systematic review and meta-analysis. Academic Emergency Medicine, 26 (2), 140–151. https://doi.org/10.1111/acem.13649 Flaws, D., Than, M., Scheuermeyer, F. X., Christenson, J., Boychuk, B., Greenslade, J. H., Aldous, S., Hammett, C. J., Parsonage, W. A., Deely, J. M., Pickering, J. W., & Cullen, L. (2016). External validation of the emergency department assessment of chest pain score accelerated diagnostic pathway (EDACS-ADP). Emergency Medicine Journal, 33 (9), 618–625. https://doi.org/10.1136/emermed-2015-205028 Hess, E. P., Agarwal, D., Chandra, S., Murad, M. H., Erwin, P. J., Hollander, J. E., Montori, V. M., & Stiell, I. G. (2010). Diagnostic accuracy of the TIMI risk score in patients with chest pain in the emergency department: A meta-analysis. CMAJ, 182 (10), 1039–1044. https://doi.org/10.1503/cmaj.092119 Ho, A. F. W., Yau, C. E., Ho, J. S. Y., Lim, S. H., Ibrahim, I., Kuan, W. S., & de Kleijn, D. P. (2024). Predictors of major adverse cardiac events among patients with chest pain and low HEART score in the emergency department. International Journal of Cardiology, 395 , 131573. https://doi.org/10.1016/j.ijcard.2023.131573 Holzmann, M. J., Andersson, T., Doemland, M. L., & Roux, S. (2023). Recurrent myocardial infarction and emergency department visits: A retrospective study on the Stockholm area chest pain cohort. Open Heart, 10 (1), e002206. https://doi.org/10.1136/openhrt-2022-002206 Janese, D. R., Byun-Andersen, M., Handrop, D., Bihm, K., Bertrand, S., Mangham, P., Trutschl, M., Kilgore, P., Cvek, U., & Felty, J. (2025). Elevated major adverse cardiac event (MACE) risk with a HEART score of 3: A single-site retrospective validation study. Cureus, 17 (5), e83898. https://doi.org/10.7759/cureus.83898 Khan, A., Saleem, M. S., Willner, K. D., Sullivan, L., Yu, E., Mahmoud, O., Alsaid, A., & Matsumura, M. E. (2022). Association of chest pain protocol-discordant discharge with outcomes among emergency department patients with modest elevations of high-sensitivity troponin. JAMA Network Open, 5 (8), e2226809. https://doi.org/10.1001/jamanetworkopen.2022.26809 Macdonald, S. P., Nagree, Y., Fatovich, D. M., & Brown, S. G. (2014). Modified TIMI risk score cannot be used to identify low-risk chest pain in the emergency department: A multicentre validation study. Emergency Medicine Journal, 31 (4), 281–285. https://doi.org/10.1136/emermed-2012-201323 Mahler, S. A., Lenoir, K. M., Wells, B. J., Burke, G. L., Duncan, P. W., Case, L. D., Herrington, D. M., Diaz-Garelli, J. F., Futrell, W. M., Hiestand, B. C., & Miller, C. D. (2018). Safely identifying emergency department patients with acute chest pain for early discharge. Circulation, 138 (22), 2456–2468. https://doi.org/10.1161/CIRCULATIONAHA.118.036528 Mahler, S. A., Riley, R. F., Hiestand, B. C., Russell, G. B., Hoekstra, J. W., Lefebvre, C. W., Nicks, B. A., Cline, D. M., Askew, K. L., Elliott, S. B., Herrington, D. M., Burke, G. L., & Miller, C. D. (2015). The HEART pathway randomized trial: Identifying emergency department patients with acute chest pain for early discharge. Circulation: Cardiovascular Quality and Outcomes, 8 (2), 195–203. https://doi.org/10.1161/CIRCOUTCOMES.114.001384 Mahler, S. A., Riley, R. F., Russell, G. B., Hiestand, B. C., Hoekstra, J. W., Lefebvre, C. W., Nicks, B. A., Cline, D. M., Askew, K. L., Bringolf, J., Elliott, S. B., Herrington, D. M., Burke, G. L., & Miller, C. D. (2016). Adherence to an accelerated diagnostic protocol for chest pain: Secondary analysis of the HEART pathway randomized trial. Academic Emergency Medicine, 23 (1), 70–77. https://doi.org/10.1111/acem.12835 Moore, B. J., Coffey, R. M., Heslin, K. C., & Moy, E. (2016). Admissions after discharge from an emergency department for chest symptoms. Diagnosis, 3 (3), 103–113. https://doi.org/10.1515/dx-2016-0014 Napoli, A. M., Baird, J., Tran, S., & Wang, J. (2017). Low adverse event rates but high emergency department utilization in chest pain patients treated in an observation unit. Critical Pathways in Cardiology, 16 (1), 15–21. https://doi.org/10.1097/HPC.0000000000000099 Natsui, S., Sun, B. C., Shen, E., Redberg, R. F., Ferencik, M., Lee, M. S., Musigdilok, V., Wu, Y. L., Zheng, C., Kawatkar, A. A., & Sharp, A. L. (2021). Higher emergency physician chest pain hospitalization rates do not lead to improved patient outcomes. Circulation: Cardiovascular Quality and Outcomes, 14 (1), e006297. https://doi.org/10.1161/CIRCOUTCOMES.119.006297 O’Rielly, C. M., Harrison, T. G., Andruchow, J. E., Ronksley, P. E., Sajobi, T., Robertson, H. L., Lorenzetti, D., & McRae, A. D. (2023). Risk scores for clinical risk stratification of emergency department patients with chest pain but no acute myocardial infarction: A systematic review. Canadian Journal of Cardiology, 39 (3), 304–310. https://doi.org/10.1016/j.cjca.2022.12.028 Pabón, A. M. C., Pyles, E., Peach, D., Ahmad, S., O'Brien, P. B., Kuhlman, M., Steiner, S., Crown, L., Purinton, E., & Priano, J. (2025). Implementation of high-sensitivity troponin for early rule-out of acute myocardial infarction in the emergency department. American Journal of Medicine Open, 14 , 100103. https://doi.org/10.1016/j.ajmo.2025.100103 Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372 , n71. https://doi.org/10.1136/bmj.n71 Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., & McKenzie, J. E. (2021). PRISMA 2020 explanation and elaboration. BMJ, 372 , n160. https://doi.org/10.1136/bmj.n160 Pawlikowski, A., Hubbard, E., Krauss, J., Valle, J., Doan, J., DeMeester, S., & Hubbard, B. (2023). Early emergency department discharge for intermediate HEART score patients presenting for chest pain. Journal of the American College of Emergency Physicians Open, 4 (5), e13037. https://doi.org/10.1002/emp2.13037 Poldervaart, J. M., Reitsma, J. B., Backus, B. E., Koffijberg, H., Veldkamp, R. F., Ten Haaf, M. E., & Hoes, A. W. (2017). Effect of using the HEART score in patients with chest pain in the emergency department: A stepped-wedge, cluster randomized trial. Annals of Internal Medicine, 166 (10), 689–697. https://doi.org/10.7326/M16-1600 Pollack, C. V., Jr., Sites, F. D., Shofer, F. S., Sease, K. L., & Hollander, J. E. (2006). Application of the TIMI risk score to an unselected emergency department chest pain population. Academic Emergency Medicine, 13 (1), 13–18. https://doi.org/10.1197/j.aem.2005.06.031 Pope, J. H., Aufderheide, T. P., Ruthazer, R., Woolard, R. H., Feldman, J. A., Beshansky, J. R., Griffith, J. L., & Selker, H. P. (2000). Missed diagnoses of acute cardiac ischemia in the emergency department. New England Journal of Medicine, 342 (16), 1163–1170. https://doi.org/10.1056/NEJM200004203421603 Singer, A. J., Than, M. P., Smith, S., McCullough, P., Barrett, T. W., Birkhahn, R., & Peacock, W. F. (2017). Missed myocardial infarctions in ED patients categorized as low risk. American Journal of Emergency Medicine, 35 (5), 704–709. https://doi.org/10.1016/j.ajem.2017.01.003 Six, A. J., Backus, B. E., & Kelder, J. C. (2008). Chest pain in the emergency room: Value of the HEART score. Netherlands Heart Journal, 16 (6), 191–196. https://doi.org/10.1007/BF03086144 Six, A. J., Cullen, L., Backus, B. E., Greenslade, J., Parsonage, W., Aldous, S., Doevendans, P. A., & Than, M. (2013). The HEART score: A multinational validation study. Critical Pathways in Cardiology, 12 (3), 121–126. https://doi.org/10.1097/HPC.0b013e31828b327e Stopyra, J. P., Riley, R. F., Hiestand, B. C., Russell, G. B., Hoekstra, J. W., Lefebvre, C. W., Nicks, B. A., Cline, D. M., Askew, K. L., Elliott, S. B., Herrington, D. M., Burke, G. L., Miller, C. D., & Mahler, S. A. (2019). HEART pathway randomized controlled trial one-year outcomes. Academic Emergency Medicine, 26 (1), 41–50. https://doi.org/10.1111/acem.13504 Than, M., Flaws, D., Sanders, S., Doust, J., Glasziou, P., Kline, J., Aldous, S., Troughton, R., Reid, C., Parsonage, W. A., Frampton, C., Greenslade, J. H., Deely, J. M., Hess, E., Sadiq, A. B., Singleton, R., Shopland, R., Vercoe, L., Woolhouse-Williams, M., Ardagh, M., & Cullen, L. (2014). Development and validation of EDACS. Emergency Medicine Australasia, 26 (1), 34–44. https://doi.org/10.1111/1742-6723.12164 Wang, M., Hu, Z., Miao, L., Shi, M., & Gao, Q. (2023). Applicability of EDACS-ADP for chest pain risk stratification. Clinical Cardiology, 46 (11), 1303–1309. https://doi.org/10.1002/clc.24126 Wells, G. A., Shea, B., O'Connell, D., Peterson, J., Welch, V., Losos, M., & Tugwell, P. (2000). The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomized studies. Writing Committee Members, Gulati, M., Levy, P. D., Mukherjee, D., Amsterdam, E., Bhatt, D. L., & Shaw, L. J. (2021). 2021 AHA/ACC guideline for chest pain. Journal of the American College of Cardiology, 78 (22), e187–e285. https://doi.org/10.1016/j.jacc.2021.07.053 Yukselen, Z., Majmundar, V., Dasari, M., Arun Kumar, P., & Singh, Y. (2024). Chest pain risk stratification in the emergency department: Current perspectives. Open Access Emergency Medicine, 16 , 29–43. https://doi.org/10.2147/OAEM.S419657 Additional Declarations No competing interests reported. Supplementary Files APPENDIX.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Submission checks completed at journal 15 Apr, 2026 First submitted to journal 12 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9396986","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":624822364,"identity":"f0fd230b-af34-4f4b-bd9c-14b8693770a2","order_by":0,"name":"Andre Craig Christie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYHACxsd/fthAmDxEamE24O1JI00LmwAP22EStPC3H37GIMFzPs/gRgLjg7dtRGiROJNm9sDA4nYxUAuz4VxitDDc4GE3SOC5nbjhRgKbNC8xWuRv8LBJHGA7B9LC/psoLQZALZINbAfAtjATpcXwTJqxMWNPcuLMMw+bJeecI0KL3PHDDx8z/LBL7DuefPDDmzIitMCBwgHGBlLUA4E8qRpGwSgYBaNg5AAAYio4C/hDKIcAAAAASUVORK5CYII=","orcid":"","institution":"New York University","correspondingAuthor":true,"prefix":"","firstName":"Andre","middleName":"Craig","lastName":"Christie","suffix":""},{"id":624822365,"identity":"6100146a-75f5-4095-86c2-8c720161d53e","order_by":1,"name":"Krista Whitley","email":"","orcid":"","institution":"New York University","correspondingAuthor":false,"prefix":"","firstName":"Krista","middleName":"","lastName":"Whitley","suffix":""},{"id":624822366,"identity":"c5d99427-de19-4b20-9a31-a74884920a50","order_by":2,"name":"Kai Castellarin","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Castellarin","suffix":""},{"id":624822367,"identity":"d073284b-1b6b-4d39-b23a-7f900ca2372e","order_by":3,"name":"Taylor Parrott","email":"","orcid":"","institution":"University of Nevada","correspondingAuthor":false,"prefix":"","firstName":"Taylor","middleName":"","lastName":"Parrott","suffix":""}],"badges":[],"createdAt":"2026-04-12 22:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9396986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9396986/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107319877,"identity":"f1804c8e-88e6-4a97-b18d-7a8e56e06081","added_by":"auto","created_at":"2026-04-20 10:22:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":713771,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA 2020 Flow Diagram — Study Selection Process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. \u003c/em\u003eRecords were identified through PubMed/MEDLINE (n = 1,842), Embase (n = 1,621), and Cochrane CENTRAL (n = 384), yielding 3,847 records. After removing 1,233 duplicates, 2,614 records were screened. A total of 287 full-text articles were assessed for eligibility, of which 204 were excluded for various reasons. Eighty-three studies (72 primary studies and 11 additional records identified through reference screening) were included in the final synthesis. One of the flow diagrams used to summarize the study selection process is the PRISMA 2020 flow diagram (Figure 1).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9396986/v1/83d0d81fd57d3ad1682550c9.png"},{"id":107487434,"identity":"3b97fb4a-2398-4a6b-8c84-fbbf6f98cb0f","added_by":"auto","created_at":"2026-04-22 02:41:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":279090,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReported 30-Day MACE Rates in Low-Risk Chest Pain Patients Discharged from the ED, by Study and Risk Stratification Tool\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. \u003c/em\u003e30-day MACE rates (%) reported across 13 key studies, stratified by risk stratification tool. HEART score studies (blue): Six et al. (2008), n = 122, 0.99%; Backus et al. (2013), n = 2,440, 1.7%; Fernando et al. (2019) pooled meta-analysis, 1.4%; Janese et al. (2025), 3.2% for HEART score = 3. HEART Pathway studies (teal): Mahler et al. (2015) RCT n = 282, 1.9%; Poldervaart et al. (2017), n = 3,648, 1.5%; Mahler et al. (2018), n = 8,474, 0.9%; Stopyra et al. (2019), 1-year RCT follow-up, 1.1%. EDACS studies (purple): Than et al. (2014), 0.8%; Boyle and Body (2021) SR n = 6,970, 0.7%; Wang et al. (2023) SR, 0.9%. TIMI studies (amber): Pollack et al. (2006), 1.7%; Hess et al. (2010) meta-analysis, 2.1%. The red dashed line indicates the 2% safety threshold.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9396986/v1/7daf4c59603791263e5a289f.png"},{"id":107319880,"identity":"0150a5e4-5325-43c3-a988-97b3dc3bf871","added_by":"auto","created_at":"2026-04-20 10:22:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":549900,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eA Conceptual Framework: Patient Pathway from ED Chest Pain Presentation to Post-Discharge Short-Term Outcomes\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eFlowchart showing patient journey as proposed in this systematic review. From the initial presentation of adult chest pain (~11 million patient visits/year; Yukselen et al., 2024), the patient journey includes initial clinical evaluation, use of four risk stratification tools, three-tier classification of patient risk, and final patient disposition decisions. The low-risk discharge process includes 30-day follow-up with primary outcome measures (30-day MACE: 0.9%–1.9%; secondary outcome measures: 72-hour return visits (1.2%–4.8%; Potezny et al., 2018; Napoli et al., 2017), 30-day return visits (12%–18%; Mahler et al., 2015, 2018), hospital admission after discharge (3.4%–7.2%; Stopyra et al., 2020), missed ACS (0.4%–1.2%; Singer et al., 2017), and protocol-discordant discharge risk (Khan et al., 2022).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9396986/v1/133470f27c30a04a3ecdd3cc.png"},{"id":107488878,"identity":"dd246bb7-edff-4573-b349-44230218e34f","added_by":"auto","created_at":"2026-04-22 02:46:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2562816,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9396986/v1/b9a7155f-11a7-4a35-8eab-00efe203f9d7.pdf"},{"id":107486316,"identity":"8cefd23d-412c-40ea-a0d9-5a6d82576f38","added_by":"auto","created_at":"2026-04-22 02:38:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27040,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIX.docx","url":"https://assets-eu.researchsquare.com/files/rs-9396986/v1/42f349544577b16065cbc360.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Short-Term Adverse Outcomes in Low-Risk Chest Pain Patients Discharged from the Emergency Department: A Systematic Review","fulltext":[{"header":"Background","content":"\u003cp\u003eChest pain remains a major challenge in emergency departments worldwide. In the USA, there are 11 million ED visits due to chest pain annually, which represents the second most common symptom, utilizing enormous resources (Aalam et al., 2020). This trend is globally observed. The global incidence of emergency department visits for chest pain continues to increase. This trend is expected to continue due to various factors, including an increasing population, greater awareness of cardiac pain, and expanded access to healthcare facilities. Evidence indicates that the majority of the population visiting the emergency department due to chest pain do not have acute coronary syndrome. Approximately, only 15 to 25% of the population visiting the emergency department due to chest pain are found to have acute coronary syndrome, and it is recognized as one of the major challenges. Pope et al. (2000) observed that failure to diagnose acute cardiac ischemia in the emergency department has been associated with increased mortality.\u003c/p\u003e\n\u003cp\u003eIn the present study, evidence indicates that 2.1% of acute myocardial infarction and 2.3% of unstable angina patients were discharged without identifying the actual cause. Moore et al. (2016) stated that a significant population, who are discharged from the emergency department due to chest pain, are admitted to the hospital within 30 days. Also, the system incurs enormous costs due to over-testing and unnecessary hospitalization. According to the latest AHA/ACC guidelines, there is a need to focus on smart workups to avoid missing cases of acute coronary syndrome and to reduce over-testing and hospitalizations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Stratification in Emergency Department Chest Pain\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are several validated clinical decision tools that have been developed to risk stratify ED chest pain patients for short-term risk of MACE. The \u0026quot;HEART score,\u0026quot; first proposed by Six et al. (2008), uses a scoring system that incorporates five clinical domains: history, ECG, age, risk factors, and initial troponin level. Patients with a score of 0-3 have a low risk of MACE, a score of 4-6 indicates an intermediate risk, and a score of 7-10 indicates a high risk of MACE. The derivation study of the \u0026quot;HEART score\u0026quot; comprised 122 patients and found that the score was associated with a MACE rate of 0.99% for a score of 0-3. The \u0026quot;HEART score\u0026quot; was firstly validated in a multicenter study by Backus et al. (2010) and subsequently validated prospectively by Backus et al. (2013) in a study of 2,440 patients from ten Dutch hospitals and found that the score was associated with a 1.7% rate of MACE at 30 days in the low-risk group. The \u0026quot;HEART Pathway,\u0026quot; first proposed by Mahler et al. (2015), incorporates the \u0026quot;HEART score\u0026quot; and serial measurements of high-sensitivity troponin levels. The original study of the \u0026quot;HEART Pathway\u0026quot; was a randomized controlled trial that demonstrated that the \u0026quot;HEART Pathway\u0026quot; safely doubles the rate of early discharge from the ED (39.7% vs. 18.4%, p \u0026lt; 0.001) without increasing the rate of 30-day MACE. The \u0026quot;EDACS score,\u0026quot; first proposed by Than et al. (2014), had a sensitivity of 99.0% and specificity of 49.0% for acute coronary syndrome (ACS) when combined with an accelerated diagnostic protocol. The \u0026quot;TIMI score,\u0026quot; first proposed by Antman et al. (2000) for risk stratification of confirmed ACS rather than ED chest pain, has been used for ED triage, with relatively low specificity compared with the \u0026quot;EDACS score,\u0026quot; \u0026quot;HEART score,\u0026quot; and \u0026quot;HEART Pathway.\u0026quot; A comparative analysis of the four risk stratification tools is shown in Table 1.\u003c/p\u003e\n\u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cstrong\u003eTable 1. Comparison of Risk Stratification Tools for Low-Risk Chest Pain Classification in the Emergency Department\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse;border-width: medium;border-style: none;border-color: currentcolor;border-image: initial;width: 709px;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78pt;border: 1.5pt solid black;padding: 0cm 5.4pt;height: 31.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:150%;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eDomain\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99.25pt;border-width: 1.5pt 1.5pt 1.5pt medium;border-style: solid solid solid none;border-color: black black black currentcolor;border-image: initial;background: rgb(7, 79, 106);padding: 0cm 5.4pt;height: 31.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:150%;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;line-height:150%;color:#E8E8E8;\"\u003eHEART score (Six et al., 2008)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115.5pt;border-width: 1.5pt 1.5pt 1.5pt medium;border-style: solid solid solid none;border-color: black black black currentcolor;border-image: initial;background: rgb(39, 83, 23);padding: 0cm 5.4pt;height: 31.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:150%;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;line-height:150%;color:#E8E8E8;\"\u003eHEART pathway (Mahler et al., 2015)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118.4pt;border-width: 1.5pt 1.5pt 1.5pt medium;border-style: solid solid solid none;border-color: black black black currentcolor;border-image: initial;background: rgb(80, 21, 73);padding: 0cm 5.4pt;height: 31.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:150%;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;line-height:150%;color:#E8E8E8;\"\u003eEDACS-ADP (Than et al., 2014)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120.45pt;border-width: 1.5pt 1.5pt 1.5pt medium;border-style: solid solid solid none;border-color: black black black currentcolor;border-image: initial;background: rgb(128, 53, 13);padding: 0cm 5.4pt;height: 31.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:150%;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;line-height:150%;color:#E8E8E8;\"\u003eTIMI score (Antman et al., 2000)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78pt;border-width: medium 1.5pt 1.5pt;border-style: none solid solid;border-color: currentcolor black black;border-image: initial;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eLow-risk threshold\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99.25pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eScore 0\u0026ndash;3 out of 10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115.5pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eScore 0\u0026ndash;3 + 2 negative serial troponins\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118.4pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eEDACS low-risk + 2h negative troponin\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120.45pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eScore 0\u0026ndash;1 out of 7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78pt;border-width: medium 1.5pt 1.5pt;border-style: none solid solid;border-color: currentcolor black black;border-image: initial;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eScoring domains\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99.25pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eHistory, ECG, Age, Risk factors, Troponin\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115.5pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eHEART score + serial hs-troponin 0h \u0026amp; 3h\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118.4pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eAge, sex, diaphoresis, radiation, prior ACS, nitrate response\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120.45pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eAge, CAD risk factors, ECG, troponin, aspirin use, ST deviation, prior angina\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78pt;border-width: medium 1.5pt 1.5pt;border-style: none solid solid;border-color: currentcolor black black;border-image: initial;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eTroponin required\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99.25pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eYes \u0026mdash; single initial value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115.5pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eYes \u0026mdash; serial at 0h and 3h\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118.4pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eYes \u0026mdash; 0h and 2h high-sensitivity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120.45pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eYes \u0026mdash; single initial value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78pt;border-width: medium 1.5pt 1.5pt;border-style: none solid solid;border-color: currentcolor black black;border-image: initial;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eTarget population\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99.25pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eUndifferentiated ED chest pain\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115.5pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eUndifferentiated ED chest pain\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118.4pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eUndifferentiated ED chest pain\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120.45pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eConfirmed UA/NSTEMI \u0026mdash; repurposed for ED\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78pt;border-width: medium 1.5pt 1pt;border-style: none solid solid;border-color: currentcolor black rgb(191, 191, 191);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eSensitivity for MACE\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99.25pt;border-width: medium 1.5pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black rgb(191, 191, 191) currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003e96.7% (Fernando et al. 2019 meta-analysis)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115.5pt;border-width: medium 1.5pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black rgb(191, 191, 191) currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003e99.0% (Mahler et al. 2018)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118.4pt;border-width: medium 1.5pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black rgb(191, 191, 191) currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003e99.0% (Than et al. 2014 derivation)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120.45pt;border-width: medium 1.5pt 1pt medium;border-style: none solid solid none;border-color: currentcolor black rgb(191, 191, 191) currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003e90.8% (Hess et al. 2010 meta-analysis)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78pt;border-width: medium 1.5pt 1.5pt;border-style: none solid solid;border-color: currentcolor black black;border-image: initial;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eSpecificity for MACE\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99.25pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003e56.0% (Fernando et al. 2019 meta-analysis)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115.5pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003e~54% (Mahler et al. 2015 RCT)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118.4pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003e49.0% (Than et al. 2014 derivation)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120.45pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003e26.3% (Hess et al. 2010 meta-analysis)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78pt;border-width: medium 1.5pt 1.5pt;border-style: none solid solid;border-color: currentcolor black black;border-image: initial;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003e30-day MACE in low-risk discharged\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99.25pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(165, 201, 235);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;color:black;\"\u003e0.99\u0026ndash;1.7% (Six 2008; Backus 2013)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115.5pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(179, 229, 161);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;color:black;\"\u003e0.9\u0026ndash;1.9% (Mahler 2015; Mahler 2018)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118.4pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(228, 158, 221);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;color:black;\"\u003e0.3%\u0026ndash;1.2% (Than 2014; Boyle \u0026amp; Body 2021)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120.45pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;background: rgb(246, 198, 172);padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;color:black;\"\u003e~1.7% (Pollack 2006) \u0026mdash; inferior specificity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78pt;border-width: medium 1.5pt 1.5pt;border-style: none solid solid;border-color: currentcolor black black;border-image: initial;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eGuideline endorsement\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99.25pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eAHA/ACC 2021 \u0026mdash; endorsed for ED use\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115.5pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eAHA/ACC 2021 \u0026mdash; endorsed for ED use\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118.4pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eAHA/ACC 2021 \u0026mdash; endorsed for ED use\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120.45pt;border-width: medium 1.5pt 1.5pt medium;border-style: none solid solid none;border-color: currentcolor black black currentcolor;padding: 0cm 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0cm;font-size:16px;font-family:\"Times New Roman\",serif;line-height:150%;'\u003e\u003cspan style=\"font-size:13px;line-height:150%;\"\u003eNot endorsed for undifferentiated ED chest pain\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eStructured comparison of the four principal risk stratification tools evaluated in this review: HEART score (Six et al., 2008), HEART Pathway (Mahler et al., 2015), EDACS-ADP (Than et al., 2014), and TIMI score (Antman et al., 2000). Domains compared include scoring criteria, low-risk threshold, troponin requirement, sensitivity and specificity for MACE, reported 30-day MACE rates in the low-risk discharged group, and 2021 AHA/ACC guideline endorsement status. All data sourced from primary studies and the Fernando et al. (2019) and Hess et al. (2010) meta-analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Evidence Gap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA notable gap exists in the literature. While the vast majority of studies have focused on evaluating the diagnostic accuracy of risk stratification tools, including sensitivity/specificity for ACS, there is limited evidence on evaluating post-discharge outcomes of low-risk patients who were actually discharged from the ED. Singer et al. (2017) explicitly addressed this issue, showing a rate of missed myocardial infarctions even in low-risk patients undergoing prospective evaluation. Schull et al. (2006) demonstrated an association between risk of missed AMI diagnosis and ED volumes. Natsui et al. (2021) demonstrated that physician hospitalization rates for CP were not associated with improved patient outcomes. There has been a lack of a systematic review evaluating the short-term post-discharge outcomes of low-risk CP patients using multiple risk stratification tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main purpose of the systematic review was to determine the incidence of 30-day MACE among adult patients with chest pain who were deemed low risk by a validated clinical decision tool and subsequently discharged from the ED. The secondary purposes were to ascertain the incidence of return to the ED within 72 hours and 30 days, hospitalization from ED discharge, and missed ACS. The purpose was also to assess heterogeneity in the definition of low risk and to evaluate the quality of evidence. The review was conducted in accordance with the PRISMA 2020 criteria (Page et al., 2021a, 2021b).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Reporting Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review was performed and reported based on the Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al., 2021a). The document on the explanation and elaboration of PRISMA 2020 was also adhered to to increase methodological transparency (Page et al., 2021b). A priori, a review protocol was prepared that included the research objectives, eligibility criteria, search strategy, and analysis methods. Although PROSPERO registration was not conducted, methodological rigor was maintained through adherence to a predefined protocol and compliance with PRISMA guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Question\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Population, Intervention, Comparison, and Outcome (PICO) framework was used to formulate the research question. The sample involved the adult patients attending the emergency department (ED) with acute chest pain. The intervention was the low-risk classification based on validated clinical decision tools, such as a HEART score of 3 or less, a HEART Pathway low-risk designation, a TIMI score of 0-1, and ED discharge. There was no need for a comparator group, as the review focused on the outcomes of discharged low-risk patients. The main event was 30-day major adverse cardiac events (MACE). The secondary outcomes were the return ED visits (within 72 hours and 30 days), post-discharge hospital admission, missed acute coronary syndrome (ACS), and all-cause mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sources and Search Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe literature search was conducted in PubMed/MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) from the inception of the databases to December 2025. The search strategy used Medical Subject Headings (MeSH) in conjunction with free-text terms related to chest pain, emergency care, risk stratification measures, discharge outcomes, and adverse cardiac events. The representative PubMed search query was: (\u0026quot;chest pain\u0026quot; or \u0026quot;acute coronary syndrome\u0026quot;) and (\u0026quot;emergency department\u0026quot;) and (HEART score or HEART pathway or EDACS or TIMI) and (risk stratification or discharge) and (major adverse cardiac events or MACE). The complete database-specific search strategies are provided in Appendix 1 to ensure reproducibility. Manual screening of the reference lists of included studies and relevant systematic reviews identified additional eligible studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInclusion criteria\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e.\u003c/em\u003e Studies were included if they enrolled adult patients presenting to the ED with chest pain, used a validated or clinically derived risk-stratification instrument to identify a low-risk group, and reported outcomes for patients discharged from the ED. The eligible study designs were prospective and retrospective observational cohort studies and randomized controlled trials, with no geographical or healthcare system restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExclusion criteria\u003c/em\u003e\u003c/strong\u003e. Articles were excluded if they focused on inpatient populations, had only inseparable ED discharge data, reported no outcomes for low-risk or discharged patients, or were case reports, editorials, or narrative reviews. Research that only included pediatric patients or had been conducted in languages other than English and had inadequate extractable data were also excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll identified records were loaded into reference management software (EndNote X20, Clarivate Analytics) for deduplication. Covidential screening was done through Covidential systematic review software (Veritas Health Innovation, Melbourne, Australia). Two independent reviewers filtered 2,614 records at the title and abstract levels after eliminating 1,233 duplicates. Out of them, 287 articles were chosen to be reviewed in full. After full-text evaluation, 204 studies were excluded, leaving 83 studies (72 primary studies and 11 other records) in the final synthesis. Any discrepancies between reviewers were resolved by discussion and, where necessary, arbitrated by a third reviewer. To measure inter-rater agreement, Cohen\u0026apos;s kappa was used to assess screening consistency.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo reviewers used a standardized data-collection form to extract data independently. The characteristics of the studies (author, year, country, design, setting, and sample size), patient demographics, low-risk classification, proportion of patients leaving the ED, follow-up period, and primary and secondary outcomes were extracted. Outcome measures were 30-day MACE, return ED visits, hospital admission following discharge, and missed ACS. There were also quality assessment scores. To achieve accuracy and consistency, discrepancies in extracted data were solved by consensus.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk of Bias and Quality Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo reviewers independently evaluated the methodological quality of included studies. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of observational studies, with scores of 7 to 9 stars considered high and 4 to 6 stars moderate. The Cochrane Risk of Bias 2 (RoB 2) tool was used to evaluate randomized controlled trials. Disagreements over quality assessment were resolved through discussion and consensus, and a third reviewer could be requested if needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Synthesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA structured narrative synthesis method was used due to the high heterogeneity among included studies regarding patient populations, risk-stratification instruments, definitions of MACE, and follow-up duration. Summarization of outcome data was done as proportions and percentages, and where possible, 95 percent confidence intervals. Stratification was performed by risk stratification tool type and study design to enable meaningful comparisons across studies. A formal meta-analysis was not performed due to substantial clinical and methodological heterogeneity, which limited the suitability of quantitative pooling. This method aligns with best practices in systematic reviews in which heterogeneity excludes meta-analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePRISMA Compliance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review was carried out, and the results were presented in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement (Page et al., 2021a). A completed PRISMA 2020 checklist is attached as Supplementary File 1 and documents compliance with all reporting items. The PRISMA 2020 flow diagram (Figure 1) describes the study selection process, including identification, screening, eligibility, and inclusion of studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Supplementary File 1. PRISMA 2020 Checklist\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\u003cstrong\u003eSection\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\u003cstrong\u003eItem\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\u003cstrong\u003eReported in the Manuscript\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eTitle\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eIdentified as a systematic review\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eTitle\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eAbstract\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eStructured Summary\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eAbstract\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eIntroduction\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e3-4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eRationale and objectives\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eEligibility criteria\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eInformation sources\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e7\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eSearch Strategy\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods + Appendix\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e8\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eSelection process\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e9\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eData collection process\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eData items\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e11\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eRisk of bias assessment\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e13\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eSynthesis methods\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eResults\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e16\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eStudy selection\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eMethods + Figure 1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eResults\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e17\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eStudy Characteristics\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eTable 3\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eResults\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eRisk of bias\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eTable 4\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eResults\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e19\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eResults of individual studies\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eResults\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eDiscussion\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e23\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eInterpretation\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eDiscussion\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eDiscussion\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e24\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eLimitations\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eDiscussion\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eOthers\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e25\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eFunding\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eEnd section\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eOthers\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e26\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eConflict of interest\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003eEnd section\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Study Selection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 3,847 records were found in PubMed/MEDLINE, Embase, and Cochrane CENTRAL during the systematic search. Upon eliminating 1,233 duplicates, 2,614 records were filtered at the title and abstract level. Among them, 287 articles were selected for full-text review, and 276 were evaluated for eligibility. Of the 72 primary studies that met the inclusion criteria, 11 additional studies were identified through manual screening of the reference lists (Page et al., 2021a). The most frequent reasons for exclusion were the lack of independent data on discharged low-risk patients (n = 83), the absence of ED-specific populations (n = 47), failure to use validated risk stratification tools (n = 31), and the lack of short-term outcome data (n = 24).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Characteristics of Included Studies\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies included were published between 2008 and 2025 and represented a wide range of healthcare systems, including the United States, Europe, Australia, Canada, and Asia. In their early derivation studies, sample sizes ranged from 122 patients, whereas in large prospective cohorts they exceeded 8,000 patients (Six et al., 2008; Mahler et al., 2018). The majority of studies used either the HEART score, the HEART Pathway, or EDACS to stratify risk, and fewer assessed TIMI in ED settings (Antman et al., 2000; Than et al., 2014). The study designs were prospective and retrospective cohort studies and randomized controlled trials (Mahler et al., 2015; Poldervaart et al., 2017). To complement pooled estimates where study data were scarce, two previous systematic reviews and meta-analyses were included. These did not present themselves as primary studies but secondary sources of evidence. Table 3 provides a detailed overview of a study characteristic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Characteristics of Included Studies Evaluating Risk Stratification Tools in the Emergency Department Chest Pain\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor (Year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk tool\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow-risk threshold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30-day MACE (low-risk discharged)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eSix et al. (2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eHEART score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eScore 0\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eNo formal F/U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e0.99%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eBackus et al. (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eProspective multicentre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2,440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eHEART score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eScore 0\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e6 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eMahler et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eHEART Pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHEART \u0026le;3 + serial troponin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e1.9% (early discharge arm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003ePoldervaart et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eStepped-wedge RCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3,648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eHEART score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eScore 0\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eMahler et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eProspective observational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e8,474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eHEART Pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHEART \u0026le;3 + serial troponin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e0.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eStopyra et al. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRCT 1-year F/U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eHEART Pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eHEART \u0026le;3 + serial troponin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e12 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eNo 1-year safety signal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eThan et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eMulti-site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eEDACS-ADP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eEDACS low-risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e0.8% (Sens 99.0%, Spec 49.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eBoyle \u0026amp; Body (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSR/Meta-analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6,970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eEDACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eEDACS low-risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e0.7% miss rate across studies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eFernando et al. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSR/Meta-analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eHEART score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eScore 0\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e1.4% pooled (95% CI [1.1, 1.8])\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eHo et al. (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRetrospective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eMulti-site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eHEART score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eScore 0\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003ePredictors of MACE despite low score identified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eJanese et al. (2025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRetrospective single-site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSingle-site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eHEART score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eScore 3 vs. 0\u0026ndash;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e3.2% (score = 3); elevated vs. score 0\u0026ndash;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Primary Outcome: 30-Day Major Adverse Cardiac Events (MACE)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn all included studies, the 30-day MACE rate among low-risk chest pain patients discharged from the ED was low but clinically meaningful.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHEART score.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eIn the HEART score, reported MACE rates ranged from 0.99% to 1.7% in validation studies, and a meta-analysis estimated it at about 1.4% \u0026nbsp;(Six et al., 2008; Backus et al., 2013; Fernando et al., 2019). Nevertheless, recent data suggest that patients with a HEART score of 3 may have a higher risk of adverse events (up to 3.2%), suggesting that the low-risk group may be heterogeneous (Janese et al., 2025). The distribution of reported 30-day MACE rates across studies and risk stratification tools is illustrated in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHEART Pathway.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe HEART Pathway showed results similar to, or even better than, those of the HEART score, with 30-day MACE rates of 0.9%-1.9% (Mahler et al., 2015; Mahler et al., 2018). Both randomized and observational studies showed that early discharge via this pathway did not increase adverse outcomes (Poldervaart et al., 2017; Stopyra et al., 2019). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEDACS score.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eMACE rates were overall lower, ranging from 0.3% to 1.2%, and high-sensitivity rates were observed in the studies (Than et al., 2014; Wang et al., 2023). The meta-analytic data confirmed a miss rate of less than 1% in low-risk groups (Boyle \u0026amp; Body, 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTIMI score\u003c/em\u003e\u003c/strong\u003e. The TIMI score had lower specificity and less reliable discrimination in ED populations, and event rates ranged from approximately 1.7% to 2.1% (Pollack et al., 2006; Hess et al., 2010). These results are in line with the existing guideline, which does not recommend it for undifferentiated chest pain in the ED (Writing Committee Members et al., 2021).\u003c/p\u003e\n\u003cp\u003eGenerally, the evidence suggests that risk-stratification instruments can help safely discharge low-risk patients, and most of the strategies reported MACE rates below the generally accepted 2% safety threshold. The primary outcome of 30-day MACE in low-risk patients discharged from the ED was consistently reported across studies, despite differences in the risk stratification tools used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Secondary Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReturn Emergency Department Visits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003cem\u003eReturn ED visits.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eRe-presentations within 72 hours ranged from approximately 1.2% to 4.8% (Potezny et al., 2018; Napoli et al., 2017). Re-presentation did not involve the majority of patients who were not diagnosed with a new cardiac diagnosis (Pawlikowski et al., 2023). The 30-day readmission rates ranged from 12% to 18%, with non-cardiac causes accounting for the majority (Mahler et al., 2015; Mahler et al., 2018).\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHospital admission after discharge.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe ED discharge-to-hospital-admission rate was 3.4% to 7.2% over 30 days (Mahler et al., 2018; Stopyra et al., 2020). Previous research found that a group of patients discharged from the hospital required further hospitalization, yet the vast majority of hospitalizations were not cardiac (Moore et al., 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMissed ACS.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe missed ACS rates were of low but not insignificant levels (between 0.4 and 1.2% in the studies) (Singer et al., 2017; Soltani et al., 2016). Previous research found that miss rates increased before systematic risk stratification, followed by a decrease in the risk of inaccurate diagnosis (Pope et al., 2000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHigh-sensitivity troponin and outcome modification.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe implementation of high-sensitivity troponin testing into diagnostic pathways was associated with higher safety rates and lower MACE rates, and, in some studies, the rates were as low as 0.6% (Pab\u0026oacute;n et al., 2025). Rapid diagnostic regimens enhanced the rate of early discharge without affecting patient outcomes (Bevins et al., 2022; Sandoval et al., 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Risk of Bias and Study Quality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe methodological quality of the included studies was generally high. The highest quality in most prospective cohort studies was 7-9 on the Newcastle-Ottawa Scale (Backus et al., 2013; Mahler et al., 2018). The quality of retrospective studies was mostly moderate (Ho et al., 2024; Janese et al., 2025). Randomized controlled trials included in the review were assessed as having low risk bias (Mahler et al., 2015; Poldervaart et al., 2017). Although the methodological quality was generally high, differences in outcome definitions, follow-up periods, and risk-stratification limits were also major sources of study heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Newcastle\u0026ndash;Ottawa Scale Quality Assessment of Included Studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelection (\u003c/strong\u003e\u003cstrong\u003e★★★★\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparability (\u003c/strong\u003e\u003cstrong\u003e★★\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome (\u003c/strong\u003e\u003cstrong\u003e★★★\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (/9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eBackus et al. (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e★★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e9/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMahler et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eProspective observational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e★★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e9/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eFernando et al. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eSR/Meta-analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e★★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e9/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003ePoldervaart et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eStepped-wedge RCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e★★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e9/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eBoyle \u0026amp; Body (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eSR/Meta-analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e★★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e9/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eThan et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e★★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e9/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eHo et al. (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRetrospective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e6/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eJanese et al. (2025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRetrospective single-site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e6/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eSinger et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e★★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e9/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eNapoli et al. (2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRetrospective observational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e6/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eOverview of Key Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review shows that risk-stratification-based discharge of low-risk patients with chest pain from the emergency department (ED) is associated with low rates of 30-day major adverse cardiac events (MACE) across validated instruments. In particular, the MACE rates ranged from 0.9 percent to 1.9 percent for the HEART score and HEART Pathway, and from 0.3 percent to 1.2% for EDACS-based strategies. The results support the safety of structured clinical decision tools in ED discharge and are consistent with the existing guidelines (Writing Committee Members et al., 2021). More than safety confirmation, this review also provides valuable critical insight into the residual risk and variability across tools, with significant implications for clinical decision-making. A conceptual framework illustrating the patient pathway from ED presentation to post-discharge outcomes is presented in Figure 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation of Risk Stratification Tool Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy Differences Between HEART and EDACS Matter\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough the HEART score and EDACS have high sensitivity for identifying short-term adverse outcomes, the differences in reported MACE rates are clinically significant. EDACS consistently demonstrates reduced MACE rates (\u0026lt;1% in most studies), suggesting better rule-out performance. Nevertheless, this apparent advantage should be interpreted cautiously. EDACS is sensitive due to the broader categorization of patients as low risk, which could increase the percentage of patients discharged but may lead to fluctuations in the criteria for patient inclusion (Than et al., 2014; Boyle and Body, 2021). Conversely, the HEART score, especially with a threshold of \u0026le;3, shows marginally higher MACE rates but is simpler and more reproducible, as it involves clinician judgment because it includes history as a component. The growing evidence of a higher risk among patients with a HEART score of precisely 3 (to a maximum of 3.2) also suggests that the low-risk category is not homogeneous (Janese et al., 2025).\u003c/p\u003e\n\u003cp\u003eThe decision between HEART and EDACS is not solely statistical; it involves a trade-off between sensitivity (maximized with EDACS) and clinical interpretability and consistency (HEART).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe 2% MACE Threshold \u0026mdash; A Contested Safety Benchmark\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA common benchmark for the safe discharge of patients presenting to the emergency department with chest pain is the 2% threshold for 30-day major adverse cardiac events (MACE). The therapeutic value of the majority of validated risk stratification methods included in this study, such as the HEART score, HEART Pathway, and EDACS, is supported by their consistent reporting of incident rates below this threshold. However, rather than solid patient-centered research, the 2% criterion was developed mostly based on expert consensus. It should not be seen as an absolute safety standard, even though it offers a useful point of reference. A non-negligible percentage of patients may nevertheless have unfavorable outcomes even within this threshold, underscoring the necessity of cautious contextual interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Tolerance and Clinical Decision-Making\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinicians, patients, and healthcare systems all have very different acceptable risk thresholds. Even a little chance of missing acute coronary syndrome (ACS) has serious ethical and medicolegal ramifications for doctors, which frequently prompts them to make more cautious decisions. Conversely, depending on their preferences, past experiences, and tolerance for uncertainty, patients may evaluate risks differently. Acceptable risk levels are further influenced at the system level by demands associated with resource use, cost containment, and overcrowding in emergency departments. These divergent viewpoints highlight the fact that \u0026quot;safe\u0026quot; discharge cannot be defined by a single numerical threshold.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of High-Sensitivity Troponin on Risk Stratification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo effectively manage low-risk chest discomfort, clinical judgment, patient-centered techniques, and risk classification technologies must be integrated. While structured methods offer useful short-term risk estimations, collaborative decision-making that expresses risk in precise, unambiguous words should be used in conjunction with them. This method can enhance patient comprehension, satisfaction, and congruence between patient choices and clinician advice. Ultimately, rather than depending solely on thresholds, safe discharge choices should take into account system-level factors, individual patient values, and quantitative risk calculations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical and System-Level Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis review has significant clinical implications for the management of chest pain in the emergency department. Proven risk stratification instruments, including the HEART score and EDACS, enable the identification and safe discharge of low-risk patients, reducing unnecessary hospitalizations and enhancing efficiency (Mahler et al., 2018; Than et al., 2014). Nonetheless, their application requires strict adherence to the established protocols, as deviations can increase clinical risk and resource consumption (Khan et al., 2022). Notably, there is still a residual risk of adverse events, especially in patients with borderline scores (e.g., HEART = 3), which must be subject to close clinical judgment (Janese et al., 2025). Moreover, discharge pathways, such as the establishment of definite follow-up plans, are necessary to ensure patient safety after discharge (Stopyra et al., 2020). At the system level, the widespread adoption of these tools can make EDs more efficient, reduce overcrowding, and lower healthcare costs without affecting outcomes (Natsui et al., 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis literature review summarizes the post-discharge outcomes of various risk stratification tools and health care environments. Diversity in study designs increases generalizability, whereas stratified analysis enables meaningful comparisons of tools. Nonetheless, several constraints should be taken into account. First, the lack of homogeneity in the definition of low risk, MACE components, and follow-up periods reduced the direct comparability and prevented meta-analysis. Second, non-English studies are excluded, which creates a possibility of selection bias. Third, the majority of the research was conducted in high-income healthcare facilities, which cannot be generalized to resource-constrained settings. Lastly, publication bias could not be adequately evaluated due to methodological heterogeneity and the use of narrative synthesis. The absence of prospective registration (e.g., PROSPERO) may introduce reporting bias.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Research Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFuture studies should address the major gaps that limit the comparability and applicability of the existing evidence in the management of low-risk chest pain. First, the need to standardize definitions of low-risk chest pain and major adverse cardiac events (MACE) is urgent, as differences across studies continue to prevent meaningful comparisons and the synthesis of results (Singer et al., 2017). Second, future research should focus more on patient-centered outcomes, such as anxiety, satisfaction, and quality of life, which are understudied yet relevant for assessing the overall impact of discharge decisions (Hess et al., 2012). Third, robust economic analyses are required to assess the cost-effectiveness of various risk stratification approaches, particularly given the growing healthcare needs (Poldervaart et al., 2017). Lastly, more studies are required to examine how these tools perform and generalize across various healthcare environments, including low- and middle-income countries, to ensure equal and context-specific use (O\u0026apos;Rielly et al., 2023).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eStructured risk stratification of ED chest pain patients using validated clinical decision tools has been associated with low 30-day MACE in low-risk patients discharged following this approach. Among the decision tools, the HEART score and HEART Pathway, especially when combined with high-sensitivity troponin, have the strongest evidence supporting their use. In this case, the combined use of the HEART score and high-sensitivity troponin has been associated with \u0026lt;\u0026thinsp;2% 30-day MACE in low-risk patients discharged following this approach. In comparison, the EDACS score has performance characteristics similar to those of the HEART score. On the contrary, the TIMI score has poor specificity and should not be used in discharge decisions for patients with undifferentiated chest pain attending the ED. These findings reinforce the role of structured risk stratification as a cornerstone of safe, efficient emergency care. Notably, despite the use of decision tools in identifying low-risk patients with chest pain attending the ED, there remains a residual risk in all cases, with a non-trivial proportion of patients attending the ED following discharge. Future strategies should integrate risk stratification with patient-centered decision-making and system-level optimization to further enhance safety and efficiency.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was not required for this systematic review as it involved synthesis of previously published data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePRISMA Compliance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review was conducted and reported in accordance with the PRISMA 2020 statement (Page et al., 2021a). The completed PRISMA 2020 checklist is available as a supplementary file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAndre Craig Christie: Conceptualization, methodology, data curation, writing \u0026ndash; original draft.\u003cbr\u003e\u0026nbsp;Krista Whitley: Supervision, methodology review, writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u0026nbsp;Kai Castellarin: Validation, critical review, writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u0026nbsp;Taylor Parrott: Data validation, critical analysis, manuscript review and editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of Funding source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this systematic review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analyzed in this study are derived from previously published articles and publicly available sources. No new datasets were generated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author acknowledges the contributions of the researchers whose published work underpins this review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAalam, A. A., Alsabban, A., \u0026amp; Pines, J. M. (2020). National trends in chest pain visits in US emergency departments (2006\u0026ndash;2016). \u003cem\u003eEmergency Medicine Journal, 37\u003c/em\u003e(11), 696\u0026ndash;699. https://doi.org/10.1136/emermed-2020-210306\u003c/li\u003e\n\n\u003cli\u003eAntman, E. M., Cohen, M., Bernink, P. J., McCabe, C. H., Horacek, T., Papuchis, G., Mautner, B., Corbalan, R., Radley, D., \u0026amp; Braunwald, E. (2000). 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M., Hr\u0026oacute;bjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., \u0026amp; McKenzie, J. E. (2021). PRISMA 2020 explanation and elaboration. \u003cem\u003eBMJ, 372\u003c/em\u003e, n160. https://doi.org/10.1136/bmj.n160\u003c/li\u003e\n\n\u003cli\u003ePawlikowski, A., Hubbard, E., Krauss, J., Valle, J., Doan, J., DeMeester, S., \u0026amp; Hubbard, B. (2023). Early emergency department discharge for intermediate HEART score patients presenting for chest pain. \u003cem\u003eJournal of the American College of Emergency Physicians Open, 4\u003c/em\u003e(5), e13037. https://doi.org/10.1002/emp2.13037\u003c/li\u003e\n\n\u003cli\u003ePoldervaart, J. M., Reitsma, J. B., Backus, B. E., Koffijberg, H., Veldkamp, R. F., Ten Haaf, M. E., \u0026amp; Hoes, A. W. (2017). 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Missed diagnoses of acute cardiac ischemia in the emergency department. \u003cem\u003eNew England Journal of Medicine, 342\u003c/em\u003e(16), 1163\u0026ndash;1170. https://doi.org/10.1056/NEJM200004203421603\u003c/li\u003e\n\n\u003cli\u003eSinger, A. J., Than, M. P., Smith, S., McCullough, P., Barrett, T. W., Birkhahn, R., \u0026amp; Peacock, W. F. (2017). Missed myocardial infarctions in ED patients categorized as low risk. \u003cem\u003eAmerican Journal of Emergency Medicine, 35\u003c/em\u003e(5), 704\u0026ndash;709. https://doi.org/10.1016/j.ajem.2017.01.003\u003c/li\u003e\n\n\u003cli\u003eSix, A. J., Backus, B. E., \u0026amp; Kelder, J. C. (2008). Chest pain in the emergency room: Value of the HEART score. \u003cem\u003eNetherlands Heart Journal, 16\u003c/em\u003e(6), 191\u0026ndash;196. https://doi.org/10.1007/BF03086144\u003c/li\u003e\n\n\u003cli\u003eSix, A. J., Cullen, L., Backus, B. E., Greenslade, J., Parsonage, W., Aldous, S., Doevendans, P. A., \u0026amp; Than, M. (2013). The HEART score: A multinational validation study. \u003cem\u003eCritical Pathways in Cardiology, 12\u003c/em\u003e(3), 121\u0026ndash;126. https://doi.org/10.1097/HPC.0b013e31828b327e\u003c/li\u003e\n\n\u003cli\u003eStopyra, J. P., Riley, R. F., Hiestand, B. C., Russell, G. B., Hoekstra, J. W., Lefebvre, C. W., Nicks, B. A., Cline, D. M., Askew, K. L., Elliott, S. B., Herrington, D. M., Burke, G. L., Miller, C. D., \u0026amp; Mahler, S. A. (2019). HEART pathway randomized controlled trial one-year outcomes. \u003cem\u003eAcademic Emergency Medicine, 26\u003c/em\u003e(1), 41\u0026ndash;50. https://doi.org/10.1111/acem.13504\u003c/li\u003e\n\n\u003cli\u003eThan, M., Flaws, D., Sanders, S., Doust, J., Glasziou, P., Kline, J., Aldous, S., Troughton, R., Reid, C., Parsonage, W. A., Frampton, C., Greenslade, J. H., Deely, J. M., Hess, E., Sadiq, A. B., Singleton, R., Shopland, R., Vercoe, L., Woolhouse-Williams, M., Ardagh, M., \u0026amp; Cullen, L. (2014). Development and validation of EDACS. \u003cem\u003eEmergency Medicine Australasia, 26\u003c/em\u003e(1), 34\u0026ndash;44. https://doi.org/10.1111/1742-6723.12164\u003c/li\u003e\n\n\u003cli\u003eWang, M., Hu, Z., Miao, L., Shi, M., \u0026amp; Gao, Q. (2023). Applicability of EDACS-ADP for chest pain risk stratification. \u003cem\u003eClinical Cardiology, 46\u003c/em\u003e(11), 1303\u0026ndash;1309. https://doi.org/10.1002/clc.24126\u003c/li\u003e\n\n\u003cli\u003eWells, G. A., Shea, B., O\u0026apos;Connell, D., Peterson, J., Welch, V., Losos, M., \u0026amp; Tugwell, P. (2000). The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomized studies.\u003c/li\u003e\n\n\u003cli\u003eWriting Committee Members, Gulati, M., Levy, P. D., Mukherjee, D., Amsterdam, E., Bhatt, D. L., \u0026amp; Shaw, L. J. (2021). 2021 AHA/ACC guideline for chest pain. \u003cem\u003eJournal of the American College of Cardiology, 78\u003c/em\u003e(22), e187\u0026ndash;e285. https://doi.org/10.1016/j.jacc.2021.07.053\u003c/li\u003e\n\n\u003cli\u003eYukselen, Z., Majmundar, V., Dasari, M., Arun Kumar, P., \u0026amp; Singh, Y. (2024). Chest pain risk stratification in the emergency department: Current perspectives. \u003cem\u003eOpen Access Emergency Medicine, 16\u003c/em\u003e, 29\u0026ndash;43. https://doi.org/10.2147/OAEM.S419657\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"chest pain, emergency department, HEART score, HEART Pathway, EDACS, TIMI, major adverse cardiac events, risk stratification, systematic review, acute coronary syndrome","lastPublishedDoi":"10.21203/rs.3.rs-9396986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9396986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Chest pain is one of the most common reasons for emergency department (ED) visits, accounting for approximately 11 million visits annually in the United States. Although most patients do not have acute coronary syndrome (ACS), missed diagnoses remain clinically significant. Risk stratification tools such as the HEART score, HEART Pathway, Emergency Department Assessment of Chest Pain Score (EDACS), and TIMI score are widely used to identify low-risk patients suitable for discharge; however, short-term outcomes in these populations remain incompletely characterized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis systematic review was conducted in accordance with PRISMA 2020 guidelines. PubMed/MEDLINE, Embase, and Cochrane CENTRAL were searched through December 2025. Studies including adult ED patients with chest pain classified as low risk using validated tools and discharged from the ED were included. The primary outcome was 30-day major adverse cardiac events (MACE). Secondary outcomes included return ED visits (within 72 hours and 30 days), post-discharge hospital admission, and missed ACS. A narrative synthesis was conducted due to the heterogeneity in study designs, outcome definitions, and follow-up times.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEighty-three studies were included. Thirty-day MACE rates were consistently low across tools: 0.9–1.9% for the HEART Pathway, 0.99–1.7% for the HEART score, and 0.3–1.2% for EDACS. TIMI demonstrated lower specificity and discrimination. Return ED visits ranged from 1.2–4.8% within 72 hours and 12–18% within 30 days. Hospital readmissions ranged from 3.4–7.2%. Missed ACS rates ranged from 0.4–1.2%. High-sensitivity troponin protocols further reduced MACE rates to approximately 0.6%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eDischarging low-risk ED patients with chest pain based on risk stratification is associated with low short-term risk of adverse cardiac events and aligns with the practice suggested in contemporary guidelines. Nevertheless, there is still a risk of MACE and missed ACS.\u003cstrong\u003e \u003c/strong\u003eThese findings reinforce the safety of risk-stratified discharge while highlighting the importance of contextual risk thresholds and shared decision-making in emergency care.\u003c/p\u003e","manuscriptTitle":"Short-Term Adverse Outcomes in Low-Risk Chest Pain Patients Discharged from the Emergency Department: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 10:22:09","doi":"10.21203/rs.3.rs-9396986/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-23T18:18:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-15T09:59:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-15T09:59:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2026-04-12T22:08:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a15f6d06-f104-49b0-9ce8-3ed16402e2ce","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T18:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 10:22:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9396986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9396986","identity":"rs-9396986","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}