Pulmonary Embolism in Acute Asthma Exacerbation: Clinical Characteristics, Prediction Model and Hospital Outcomes.

Abstract

Little is known about the characteristics and impact of acute pulmonary embolism (PE) during episodes of asthma exacerbation. We aimed to characterize patients diagnosed with acute PE in the setting of asthma exacerbation, develop a prediction model to help identify future patients and assess the impact of acute PE on hospital outcomes. We included 758 patients who were treated for asthma exacerbation and underwent a computed tomographic pulmonary angiography (CTA) during the same encounter at a university-based hospital between June 2011 and October 2018. We compared clinical characteristics of patients with and without acute PE and developed a machine learning prediction model to classify the PE status based on the clinical variables. We used multivariable regression analysis to evaluate the impact of acute PE on hospital outcomes. Twenty percent of the asthma exacerbation patients who underwent CTA had an acute PE. Factors associated with acute PE included previous history of PE, high CHA2DS2-VASc score, hyperlipidemia, history of deep vein thrombosis, malignancy, chronic systemic corticosteroids use, high body mass index and atrial fibrillation. Using these factors, we developed a random forest machine learning prediction model which had an 88% accuracy in classifying the acute PE status of the patients (area under the receiver operating characteristic curve = 0.899; 95% confidence interval: 0.885-0.913). Acute PE in asthma exacerbation was associated with longer hospital stay and intensive care unit stay. It is important to consider acute PE, a potentially life-threatening event, in the setting of asthma exacerbation especially when other risk factors are present.