The difference between deep learning-estimated X-ray age and chronological age was independently associated with the composite of all-cause mortality and heart failure re-hospitalization (HR 1.019 per 1-year increase).
Observational (n=1,562)
Yes
Does deep learning-based age estimation from chest X-rays predict clinical outcomes in patients with heart failure and cardiovascular disease?
Deep learning-derived age estimation from chest X-rays strongly correlates with chronological age and serves as an independent prognostic indicator for adverse cardiovascular outcomes.
Effect estimate: HR 1.019 (95% CI 1.005-1.032)
p-value: p=6.69 × 10^-3
BACKGROUND: In recent years, there has been considerable research on the use of artificial intelligence to estimate age and disease status from medical images. However, age estimation from chest X-ray (CXR) images has not been well studied and the clinical significance of estimated age has not been fully determined. METHODS: To address this, we trained a deep neural network (DNN) model using more than 100,000 CXRs to estimate the patients' age solely from CXRs. We applied our DNN to CXRs of 1562 consecutive hospitalized heart failure patients, and 3586 patients admitted to the intensive care unit with cardiovascular disease. RESULTS: The DNN's estimated age (X-ray age) showed a strong significant correlation with chronological age on the hold-out test data and independent test data. Elevated X-ray age is associated with worse clinical outcomes (heart failure readmission and all-cause death) for heart failure. Additionally, elevated X-ray age was associated with a worse prognosis in 3586 patients admitted to the intensive care unit with cardiovascular disease. CONCLUSIONS: Our results suggest that X-ray age can serve as a useful indicator of cardiovascular abnormalities, which will help clinicians to predict, prevent and manage cardiovascular diseases.
Ieki et al. (Fri,) conducted a observational in Heart Failure (n=1,562). Deep learning-based X-ray age estimation (age discrepancy) vs. Chronological age was evaluated on Composite of all-cause mortality and heart failure re-hospitalization (HR 1.019, 95% CI 1.005-1.032, p=6.69 × 10^-3). The difference between deep learning-estimated X-ray age and chronological age was independently associated with the composite of all-cause mortality and heart failure re-hospitalization (HR 1.019 per 1-year increase).