Why the study?
Accurately assessing for acute coronary syndromes in the emergency department remains a challenge, and how clinicians perform compared to risk prediction models is unknown.
Does the T-MACS risk prediction model improve diagnostic accuracy for acute coronary syndrome compared to clinician gestalt in adults presenting to the ED with chest pain?
Observational (n=782)
Yes
Does the T-MACS risk prediction model improve diagnostic accuracy for acute coronary syndrome compared to clinician gestalt in adults presenting to the ED with chest pain?
Effect estimate: C-statistic 0.76 (gestalt) vs 0.93 (T-MACS) (95% CI 0.71-0.81 vs 0.90-0.95)
p-value: p=<0.0001
The T-MACS risk prediction model significantly outperforms clinician gestalt in accurately estimating the probability of acute coronary syndrome and stratifying patient risk in the emergency department.
Chest pain is one of the most common reasons for patients attending the Emergency Department (ED). Accurately assessing for Acute Coronary Syndromes (ACS) remains a challenge. There is strong evidence supporting use of the Troponin-only Manchester Acute Coronary Syndrome (T-MACS) risk prediction model. How clinicians perform compared to these models is unknown. We aimed to externally validate the diagnostic accuracy of clinicians’ estimated probability of ACS (gestalt) compared to the T-MACS calculated probability of ACS. The Bedside Evaluation of Sensitive Troponin prospective multi-centre diagnostic accuracy study included adults presenting to the ED with potential ACS. Alongside clinical, ECG and blood sample data, the emergency clinician recorded their estimated probability of ACS (%) following review. The probability of ACS was also calculated using T-MACS. The primary outcome was Major Adverse Cardiac Events (MACE) within 30-days. For this planned secondary analysis, patients from sites using the high-sensitivity cardiac troponin T (Roche Diagnostics Elecsys) were eligible. Of 782 included, 116 (14.8%) had MACE. The C-statistic for clinician gestalt and T-MACS were 0.76 (95% CI 0.71–0.81) and 0.93 (0.90–0.95) respectively (p<0.0001). Compared to T-MACS, clinicians overestimated the probability of ACS (positive bias 18.0%) and were less likely to stratify patients to extremes of probability. For ‘rule out’ of ACS, clinicians identified 72 (9.3%) patients as ‘very low risk’ (<2%) compared to 385 (49.2%) with T-MACS. For ‘rule in’ of ACS, clinicians identified 16 (2.1%) patients as ‘high risk’ of ACS (≥95%) in comparison with 50 (6.4%) with T-MACS. Assessment of model calibration comparing observed against predicted outcomes gave an R square of 0.78 and 0.97 for clinicians and T-MACS respectively. Clinician gestalt has inferior diagnostic accuracy to T-MACS. T-MACS requires a clinician’s skill for appropriate application. Our conclusion is therefore not that computers are better, but that clinician performance can be augmented using T-MACS.
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Oliver et al. (2019) conducted an observational in Potential Acute Coronary Syndromes (ACS) (n=782). Clinician estimated probability of ACS (gestalt) vs. T-MACS calculated probability of ACS was evaluated on Major Adverse Cardiac Events (MACE) within 30-days (C-statistic 0.76 (gestalt) vs 0.93 (T-MACS), 95% CI 0.71-0.81 vs 0.90-0.95, p=<0.0001).
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