The researchers tested the model using a second independent cohort of 11,430 outpatients (mean age 60.1 years; 42.9% male) who had a routine outpatient chest X-ray at Mass General Brigham and were potentially eligible for statin therapy.
Of 11,430 patients, 1,096, or 9.6%, suffered a major adverse cardiac event over the median follow-up of 10.3 years. There was a significant association between the risk predicted by the CXR-CVD risk deep learning model and observed major cardiac events.
The researchers also compared the prognostic value of the model to the established clinical standard for deciding statin eligibility. This could be calculated in only 2,401 patients (21%) due to missing data (e.g., blood pressure, cholesterol) in the electronic record. For this subset of patients, the CXR-CVD risk model performed similarly to the established clinical standard and even provided incremental value.
"The beauty of this approach is you only need an X-ray, which is acquired millions of times a day across the world," Dr. Weiss said. "Based on a single existing chest X-ray image, our deep learning model predicts future major adverse cardiovascular events with similar performance and incremental value to the established clinical standard."
Dr. Weiss said additional research, including a controlled, randomized trial, is necessary to validate the deep learning model, which could ultimately serve as a decision-support tool for treating physicians.
"What we've shown is a chest X-ray is more than a chest X-ray," Dr. Weiss said. "With an approach like this, we get a quantitative measure, which allows us to provide both diagnostic and prognostic information that helps the clinician and the patient."
Co-authors are Vineet Raghu, Ph.D., Kaavya Paruchuri, M.D., Pradeep Natarajan, M.D., M.M.S.C., Hugo Aerts, Ph.D., and Michael T. Lu, M.D., M.P.H. Investigators were supported in part by funding from the National Academy of Medicine and the American Heart Association.
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