Graded Coronary Risk Stratification for Emergency Department Patients With Chest Pain: A Controlled Cohort Study.

Background Resource utilization among emergency department (ED) patients with possible coronary chest pain is highly variable. Methods and Results Controlled cohort study amongst 21 EDs of an integrated healthcare system examining the implementation of a graded coronary risk stratification algorithm (RISTRA-ACS [risk stratification for acute coronary syndrome]). Thirteen EDs had access to RISTRA-ACS within the electronic health record (RISTRA sites) beginning in month 24 of a 48-month study period (January 2016 to December 2019); the remaining 8 EDs served as contemporaneous controls. Study participants had a chief complaint of chest pain and serum troponin measurement in the ED. The primary outcome was index visit resource utilization (observation unit or hospital admission, or 7-day objective cardiac testing). Secondary outcomes were 30-day objective cardiac testing, 60-day major adverse cardiac events (MACE), and 60-day MACE-CR (MACE excluding coronary revascularization). Difference-in-differences analyses controlled for secular trends with stratification by estimated risk and adjustment for risk factors, ED physician and facility. A total of 154 914 encounters were included. Relative to control sites, 30-day objective cardiac testing decreased at RISTRA sites among patients with low (≤2%) estimated 60-day MACE risk (-2.5%, 95% CI -3.7 to -1.2%, P<0.001) and increased among patients with non-low (>2%) estimated risk (+2.8%, 95% CI +0.6 to +4.9%, P=0.014), without significant overall change (-1.0%, 95% CI -2.1 to 0.1%, P=0.079). There were no statistically significant differences in index visit resource utilization, 60-day MACE or 60-day MACE-CR. Conclusions Implementation of RISTRA-ACS was associated with better allocation of 30-day objective cardiac testing and no change in index visit resource utilization or 60-day MACE. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03286179.

Prospective Validation and Comparative Analysis of Coronary Risk Stratification Strategies Among Emergency Department Patients With Chest Pain.

Background Coronary risk stratification is recommended for emergency department patients with chest pain. Many protocols are designed as "rule-out" binary classification strategies, while others use graded-risk stratification. The comparative performance of competing approaches at varying levels of risk tolerance has not been widely reported. Methods and Results This is a prospective cohort study of adult patients with chest pain presenting between January 2018 and December 2019 to 13 medical center emergency departments within an integrated healthcare delivery system. Using an electronic clinical decision support interface, we externally validated and assessed the net benefit (at varying risk thresholds) of several coronary risk scores (History, ECG, Age, Risk Factors, and Troponin [HEART] score, HEART pathway, Emergency Department Assessment of Chest Pain Score Accelerated Diagnostic Protocol), troponin-only strategies (fourth-generation assay), unstructured physician gestalt, and a novel risk algorithm (RISTRA-ACS). The primary outcome was 60-day major adverse cardiac event defined as myocardial infarction, cardiac arrest, cardiogenic shock, coronary revascularization, or all-cause mortality. There were 13 192 patient encounters included with a 60-day major adverse cardiac event incidence of 3.7%. RISTRA-ACS and HEART pathway had the lowest negative likelihood ratios (0.06, 95% CI, 0.03-0.10 and 0.07, 95% CI, 0.04-0.11, respectively) and the greatest net benefit across a range of low-risk thresholds. RISTRA-ACS demonstrated the highest discrimination for 60-day major adverse cardiac event (area under the receiver operating characteristic curve 0.92, 95% CI, 0.91-0.94, P<0.0001). Conclusions RISTRA-ACS and HEART pathway were the optimal rule-out approaches, while RISTRA-ACS was the best-performing graded-risk approach. RISTRA-ACS offers promise as a versatile single approach to emergency department coronary risk stratification. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03286179.

Automated Retrospective Calculation of the EDACS and HEART Scores in a Multicenter Prospective Cohort of Emergency Department Chest Pain Patients.

OBJECTIVES: Coronary risk scores are commonly applied to emergency department patients with undifferentiated chest pain. Two prominent risk score-based protocols are the Emergency Department Assessment of Chest pain Score Accelerated Diagnostic Protocol (EDACS-ADP) and the History, ECG, Age, Risk factors, and Troponin (HEART) pathway. Since prospective documentation of these risk determinations can be challenging to obtain, quality improvement projects could benefit from automated retrospective risk score classification methodologies. METHODS: EDACS-ADP and HEART pathway data elements were prospectively collected using a Web-based electronic clinical decision support (eCDS) tool over a 24-month period (2018-2019) among patients presenting with chest pain to 13 EDs within an integrated health system. Data elements were also extracted and processed electronically (retrospectively) from the electronic health record (EHR) for the same patients. The primary outcome was agreement between the prospective/eCDS and retrospective/EHR data sets on dichotomous risk protocol classification, as assessed by kappa statistics (ĸ). RESULTS: There were 12,110 eligible eCDS uses during the study period, of which 66 and 47% were low-risk encounters by EDACS-ADP and HEART pathway, respectively. Agreement on low-risk status was acceptable for EDACS-ADP (ĸ = 0.73, 95% confidence interval [CI] = 0.72 to 0.75) and HEART pathway (ĸ = 0.69, 95% CI = 0.68 to 0.70) and for the continuous scores (interclass correlation coefficients = 0.87 and 0.84 for EDACS and HEART, respectively). CONCLUSIONS: Automated retrospective determination of low risk status by either the EDACS-ADP or the HEART pathway provides acceptable agreement compared to prospective score calculations, providing a feasible risk adjustment option for use in large data set analyses.