Abstract
Abstract
Objectives
Acute respiratory distress syndrome (ARDS) constitutes a major factor determining the clinical outcome in polytraumatized patients. Early prediction of ARDS is crucial for timely supportive therapy to reduce morbidity and mortality. The objective of this study was to develop and test a machine learning–based method for the early prediction of ARDS derived from the first computed tomography scan of polytraumatized patients after admission to the hospital.
Materials and methods
One hundred twenty-three patients (86 male and 37 female, age 41.2 ± 16.4) with an injury severity score (ISS) of 16 or higher (31.9 ± 10.9) were prospectively included and received a CT scan within 1 h after the accident. The lungs, including air pockets and pleural effusions, were automatically segmented using a deep learning–based algorithm. Subsequently, we extracted radiomics features from within the lung and trained an ensemble of gradient boosted trees (GBT) to predict future ARDS.
Results
Cross-validated ARDS prediction resulted in an area under the curve (AUC) of 0.79 for the radiomics score compared to 0.66 for ISS, and 0.68 for the abbreviated injury score of the thorax (AIS-thorax). Prediction using the radiomics score yielded an f1-score of 0.70 compared to 0.53 for ISS and 0.57 for AIS-thorax. The radiomics score achieved a sensitivity and specificity of 0.80 and 0.76.
Conclusions
This study proposes a radiomics-based algorithm for the prediction of ARDS in polytraumatized patients at the time of admission to hospital with an accuracy that competes and surpasses conventional scores despite the heterogeneous, and therefore more realistic, scanning protocols.
Key Points
• Early prediction of acute respiratory distress syndrome in polytraumatized patients is possible, even when using heterogenous data.
• Radiomics-based prediction resulted in an area under the curve of 0.79 compared to 0.66 for the injury severity score, and 0.68 for the abbreviated injury score of the thorax.
• Highlighting the most relevant lung regions for prediction facilitates the understanding of machine learning–based prediction.
Funder
Medical University of Vienna
Publisher
Springer Science and Business Media LLC
Subject
Radiology Nuclear Medicine and imaging,General Medicine
Reference52 articles.
1. Elmali M, Baydin A, Nural MS et al (2007) Lung parenchymal injury and its frequency in blunt thoracic trauma: the diagnostic value of chest radiography and thoracic CT. Diagn Interv Radiol 13:179–182
2. Matthay MA, Ware LB, Zimmerman GA (2012) The acute respiratory distress syndrome. J Clin Investig 122:2731–2740
3. Wu J, Sheng L, Ma Y et al (2008) The analysis of risk factors of impacting mortality rate in severe multiple trauma patients with posttraumatic acute respiratory distress syndrome. Am J Emerg Med 26:419–424
4. Alisha C, Gajanan G, Jyothi H (2015) Risk factors affecting the prognosis in patients with pulmonary contusion following chest trauma. J Clin Diagn Res 9:OC17–OC19
5. Neher MD, Weckbach S, Flierl MA et al (2011) Molecular mechanisms of inflammation and tissue injury after major trauma-is complement the “bad guy”? J Biomed Sci 18:90
Cited by
13 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献