25-Predicting Pulmonary Embolism Among Hospitalized Patients With a Machine Learning Algorithm
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Pulmonary, Quality and Patient Safety, 2021
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The Society of Critical Care Medicine's Critical Care Congress features internationally renowned faculty and content sessions highlighting the most up-to-date, evidence-based developments in critical care medicine. This is a presentation from the 2021 Critical Care Congress held virtually from January 31-February 12, 2021.


Anna Siefkas

Introduction/Hypothesis: Pulmonary embolism (PE) is a life-threatening condition that contributes to an estimated 60,000-100,000 annual deaths in the United States. Diagnosis of PE is made difficult due to significant overlap between symptoms of PE and of other conditions, including acute coronary syndrome and heart failure. However, rapid diagnosis of PE is essential to preventing PE-related morbidity and mortality. We hypothesized that a machine learning algorithm could be trained to predict PE before onset.

Methods: We developed the algorithm on 63,841 retrospective patient encounters from a large academic medical center. Patient data including vital signs, laboratory measurements, demographic information, and medical history were used to train a gradient boosted machine learning algorithm. The algorithm was implemented using the XGBoost method for fitting boosted decision trees in Python. The outcome of interest was PE at any point during the hospital stay. The algorithm was compared to the Geneva score for ability to identify patients likely to experience a PE. Algorithm performance was validated on external data from the Medical Information Mart for Intensive Care (MIMIC-III) dataset.

Results: On the development dataset, the algorithm obtained an area under the receiver operating characteristic (AUC) of 0.86, while the Geneva score obtained an AUC of 0.70. On the validation dataset, the machine learning algorithm obtained an AUC of 0.76, while the Geneva score obtained an AUC of 0.63. On both datasets, the algorithm improved sensitivity, specificity, and diagnostic odds ratio as compared to the Geneva score.

Conclusions: A machine learning algorithm demonstrated improved performance as compared to a commonly used scoring system for predicting the onset of PE. Improved risk stratification may lead to improved patient outcomes through earlier diagnosis and treatment of PE.


Meta Tag
Content Type Presentation
Knowledge Area Pulmonary
Knowledge Area Quality and Patient Safety
Knowledge Level Intermediate
Knowledge Level Advanced
Membership Level Select
Tag Pulmonary Embolism
Tag Scoring Systems
Year 2021
Anna Siefkis
Decina Incorporated
machine learning algorithm
pulmonary embolism
risk prediction


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