Predicting Short-Term Mortality in Patients With Acute Pulmonary Embolism With Deep Learning.

Autor:	Cicek V; Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University., Orhan AL; Sultan II Abdülhamid Han Training and Research Hospital, Department of Cardiology, Health Sciences University., Saylik F; Van Training and Research Hospital, Department of Cardiology, Health Sciences University., Sharma V; Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University., Tur Y; Department of Computer Science, Stanford University., Erdem A; Sultan II Abdülhamid Han Training and Research Hospital, Department of Cardiology, Health Sciences University., Babaoglu M; Sultan II Abdülhamid Han Training and Research Hospital, Department of Cardiology, Health Sciences University., Ayten O; Sultan II Abdülhamid Han Training and Research Hospital, Department of Pulmonary Medicine, Health Sciences University., Taslicukur S; Department of Cardiology, Istanbul Education and Research Hospital., Oz A; Department of Cardiology, Istanbul Education and Research Hospital., Uzun M; Sultan II Abdülhamid Han Training and Research Hospital, Department of Cardiology, Health Sciences University., Keser N; Sultan II Abdülhamid Han Training and Research Hospital, Department of Cardiology, Health Sciences University., Hayiroglu MI; Department of Cardiology, Dr. Siyami Ersek Cardiovascular and Thoracic Surgery Research and Training Hospital., Cinar T; Department of Medicine, University of Maryland., Bagci U; Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University.
Jazyk:	angličtina
Zdroj:	Circulation journal : official journal of the Japanese Circulation Society [Circ J] 2024 Nov 30. Date of Electronic Publication: 2024 Nov 30.
DOI:	10.1253/circj.CJ-24-0630
Abstrakt:	Background: Accurate prediction of short-term mortality in patients with acute pulmonary embolism (PE) is critical for optimizing treatment strategies and improving patient outcomes. The Pulmonary Embolism Severity Index (PESI) is the current reference score used for this purpose, but it has limitations regarding predictive accuracy. Our aim was to develop a new short-term mortality prediction model for PE patients based on deep learning (DL) with multimodal data, including imaging and clinical/demographic data. Methods and Results: We developed a novel multimodal deep learning (mmDL) model using contrast-enhanced multidetector computed tomography scans combined with clinical and demographic data to predict short-term mortality in patients with acute PE. We benchmarked various machine learning architectures, including XGBoost, convolutional neural networks (CNNs), and Transformers. Our cohort included 207 acute PE patients, of whom 53 died during their hospital stay. The mmDL model achieved an area under the receiver operating characteristic curve (AUC) of 0.98 (P<0.001), significantly outperforming the PESI score, which had an AUC of 0.86 (P<0.001). Statistical analysis confirmed that the mmDL model was superior to PESI in predicting short-term mortality (P<0.001). Conclusions: Our proposed mmDL model predicts short-term mortality in patients with acute PE with high accuracy and significantly outperforms the current standard PESI score.
Databáze:	MEDLINE
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