Objectives The aim of this study was to develop machine learning (ML) models to explore the relationship between chronic pulmonary embolism (PE) burden and severe pulmonary hypertension (PH) in surgical chronic thromboembolic pulmonary hypertension (CTEPH). Materials and methods CTEPH patients with a preoperative CT pulmonary angiogram and pulmonary endarterectomy between 01/2017 and 06/2022 were included. A mean pulmonary artery pressure of > 50 mmHg was classified as severe. CTs were scored by a blinded radiologist who recorded chronic pulmonary embolism extent in detail, and measured the right ventricle (RV), left ventricle (LV), main pulmonary artery (PA) and ascending aorta (Ao) diameters. XGBoost models were developed to identify CTEPH feature importance and compared to a logistic regression model. Results There were 184 patients included; 54.9% were female, and 21.7% had severe PH. The average age was 57 ± 15 years. PE burden alone was not helpful in identifying severe PH. The RV/LV ratio logistic regression model performed well (AUC 0.76) with a cutoff of 1.4. A baseline ML model (Model 1) including only the RV, LV, Pa and Ao measures and their ratios yielded an average AUC of 0.66 ± 0.10. The addition of demographics and statistics summarizing the CT findings raised the AUC to 0.75 ± 0.08 (F1 score 0.41). Conclusions While measures of PE burden had little bearing on PH severity independently, the RV/LV ratio, extent of disease in various segments, total webs observed, and patient demographics improved performance of machine learning models in identifying severe PH. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Can machine learning methods applied to CT-based cardiac measurements and detailed maps of chronic thromboembolism type and distribution predict pulmonary hypertension (PH) severity? <Emphasis Type="BoldItalic">Findings</Emphasis> The right-to-left ventricle (RV/LV) ratio was predictive of PH severity with an optimal cutoff of 1.4, and detailed accounts of chronic thromboembolic burden improved model performance. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> The identification of a CT-based RV/LV ratio cutoff of 1.4 gives radiologists, clinicians, and patients a point of reference for chronic thromboembolic PH severity. Detailed chronic thromboembolic burden data are useful but cannot be used alone to predict PH severity. Graphical Abstract

Utility of machine learning for predicting severe chronic thromboembolic pulmonary hypertension based on CT metrics in a surgical cohort

期刊论文
发表日期：

2025年8月23日

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Micah Grubert Van Iderstine,
Sangwook Kim,
Gauri Rani Karur,
John Granton,
Marc de Perrot,
Chris McIntosh,
Micheal McInnis

摘要

Objectives

The aim of this study was to develop machine learning (ML) models to explore the relationship between chronic pulmonary embolism (PE) burden and severe pulmonary hypertension (PH) in surgical chronic thromboembolic pulmonary hypertension (CTEPH).

Materials and methods

CTEPH patients with a preoperative CT pulmonary angiogram and pulmonary endarterectomy between 01/2017 and 06/2022 were included. A mean pulmonary artery pressure of > 50 mmHg was classified as severe. CTs were scored by a blinded radiologist who recorded chronic pulmonary embolism extent in detail, and measured the right ventricle (RV), left ventricle (LV), main pulmonary artery (PA) and ascending aorta (Ao) diameters. XGBoost models were developed to identify CTEPH feature importance and compared to a logistic regression model.

Results

There were 184 patients included; 54.9% were female, and 21.7% had severe PH. The average age was 57 ± 15 years. PE burden alone was not helpful in identifying severe PH. The RV/LV ratio logistic regression model performed well (AUC 0.76) with a cutoff of 1.4. A baseline ML model (Model 1) including only the RV, LV, Pa and Ao measures and their ratios yielded an average AUC of 0.66 ± 0.10. The addition of demographics and statistics summarizing the CT findings raised the AUC to 0.75 ± 0.08 (F1 score 0.41).

Conclusions

While measures of PE burden had little bearing on PH severity independently, the RV/LV ratio, extent of disease in various segments, total webs observed, and patient demographics improved performance of machine learning models in identifying severe PH.

Key Points

Question Can machine learning methods applied to CT-based cardiac measurements and detailed maps of chronic thromboembolism type and distribution predict pulmonary hypertension (PH) severity?

Findings The right-to-left ventricle (RV/LV) ratio was predictive of PH severity with an optimal cutoff of 1.4, and detailed accounts of chronic thromboembolic burden improved model performance.

Clinical relevance The identification of a CT-based RV/LV ratio cutoff of 1.4 gives radiologists, clinicians, and patients a point of reference for chronic thromboembolic PH severity. Detailed chronic thromboembolic burden data are useful but cannot be used alone to predict PH severity.

Graphical Abstract