Adding a chest X-ray deep learning model to baseline clinical parameters significantly improved the prediction of exercise-induced pulmonary hypertension, increasing the AUC from 0.65 to 0.74.
Observational (n=142)
No
Does a deep learning model based on chest X-ray improve the detection of exercise-induced pulmonary hypertension in patients with scleroderma or mixed connective tissue disease?
A deep learning model applied to standard chest X-rays significantly improves the prediction of exercise-induced pulmonary hypertension in patients with scleroderma when added to baseline clinical parameters.
Absolute Event Rate: 0.74% vs 0.65%
p-value: p=0.046
Background Stress echocardiography is an emerging tool used to detect exercise-induced pulmonary hypertension (EIPH). However, facilities that can perform stress echocardiography are limited by issues such as cost and equipment. Objective We evaluated the usefulness of a deep learning (DL) approach based on a chest X-ray (CXR) to predict EIPH in 6-min walk stress echocardiography. Methods The study enrolled 142 patients with scleroderma or mixed connective tissue disease with scleroderma features who performed a 6-min walk stress echocardiographic test. EIPH was defined by abnormal cardiac output (CO) responses that involved an increase in mean pulmonary artery pressure (mPAP). We used the previously developed AI model to predict PH and calculated PH probability in this cohort. Results EIPH defined as ΔmPAP/ΔCO 3.3 and exercise mPAP 25 mmHg was observed in 52 patients, while non-EIPH was observed in 90 patients. The patients with EIPH had a higher mPAP at rest than those without EIPH. The probability of PH based on the DL model was significantly higher in patients with EIPH than in those without EIPH. Multivariate analysis showed that gender, mean PAP at rest, and the probability of PH based on the DL model were independent predictors of EIPH. A model based on baseline parameters (age, gender, and mPAP at rest) was improved by adding the probability of PH predicted by the DL model (AUC: from 0.65 to 0.74; p = 0.046). Conclusion Applying the DL model based on a CXR may have a potential for detection of EIPH in the clinical setting.
Kusunose et al. (Wed,) conducted a observational in Scleroderma or mixed connective tissue disease (n=142). Deep learning model based on chest X-ray vs. Clinical baseline parameters alone was evaluated on Prediction of exercise-induced pulmonary hypertension (AUC) (p=0.046). Adding a chest X-ray deep learning model to baseline clinical parameters significantly improved the prediction of exercise-induced pulmonary hypertension, increasing the AUC from 0.65 to 0.74.
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