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048 Computer beats doctor? Estimating the probability of acute coronary syndrome for individual patients
  1. Govind Oliver,
  2. Charles Reynard,
  3. Niall Morris,
  4. Richard Body
  1. Manchester University NHS Foundation Trust/University of Manchester


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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