Patients who arrive at the ED with acute chest pain (ACP) syndrome end up receiving a series of often-negative tests, but a new MGB-led study suggests that CXR AI might make ACP triage more accurate and efficient.
The researchers trained three ACP triage models using data from 23k MGH patients to predict acute coronary syndrome, pulmonary embolism, aortic dissection, and all-cause mortality within 30 days.
- Model 1: Patient age and sex
- Model 2: Patient age, sex, and troponin or D-dimer positivity
- Model 3: CXR AI predictions plus Model 2
In internal testing with 5.7k MGH patients, Model 3 predicted which patients would experience any of the ACP outcomes far more accurately than Models 2 and 1 (AUCs: 0.85 vs. 0.76 vs. 0.62), while maintaining performance across patient demographic groups.
- At a 99% sensitivity threshold, Model 3 would have allowed 14% of the patients to skip additional cardiovascular or pulmonary testing (vs. Model 2’s 2%).
In external validation with 22.8k Brigham and Women’s patients, poor AI generalizability caused Model 3’s performance to drop dramatically, while Models 2 and 1 maintained their performance (AUCs: 0.77 vs. 0.76 vs. 0.64). However, fine-tuning with BWH’s own images significantly improved the performance of the CXR AI model (from 0.67 to 0.74 AUCs) and Model 3 (from 0.77 to 0.81 AUCs).
- At a 99% sensitivity threshold, the fine-tuned Model 3 would have allowed 8% of BWH patients to skip additional cardiovascular or pulmonary testing (vs. Model 2’s 2%).
Acute chest pain is among the most common reasons for ED visits, but it’s also a major driver of wasted ED time and resources. Considering that most ACP patients undergo CXR exams early in the triage process, this proof-of-concept study suggests that adding CXR AI could improve ACP diagnosis and significantly reduce downstream testing.