Beyond the d-dimer-Machine-learning assisted pre-test probability evaluation in patients with suspected pulmonary embolism and elevated d-dimers

Introduction: Acute pulmonary embolism (PE) is a leading cardiovascular cause of death, resembling a common indication for emergency computed tomography (CT). Nonetheless, in clinical routine most CTs performed for suspicion of PE excluded the suspected diagnosis. As patients with low to intermediate risk for PE are triaged according to the d-dimer, its relatively low specifity and widespread elevation among elderly might be an underlying issue. Aim of this study was to find potential predictors based on initial emergency blood tests in patients with elevated d-dimers and suspected PE to further increase pre-test probability. Methods: In this retrospective study all patients at the local university hospital's emergency room from 2009 to 2019 with suspected PE, emergency blood testing and CT were included. Cluster analysis was performed to separate groups with distinct laboratory parameter profiles and PE frequencies were compared. Machine learning algorithms were trained on the groups to predict individual PE probability based on emergency laboratory parameters. Results: Overall, PE frequency among the 2045 analyzed patients was 41%. Three clusters with significant differences (p < 0.05) in PE frequency were identified: C1 showed a PE frequency of 43%, C2 40% and C3 33%. Laboratory parameter profiles (e.g. creatinine) differed significantly between clusters (p < 0.0001). Both logistic regression and support-vector machines were able to predict clusters with an accuracy of over 90%. Discussion: Initial blood parameters seem to enable further differentiation of patients with suspected PE and elevated d-dimers to raise pre-test probability of PE. Machine-learning-based prediction models might help to further narrow down CT indications in the future.