The Positive Rate of Pulmonary Embolism by CT Pulmonary Angiography Is High in an Emergency Department, Even in Low-Risk or Young Patients

ECG Features of Pulmonary Embolism in a Patient With Normal D-Dimer and Hypoxia

Pulmonary embolism is a life-threatening condition that requires urgent treatment. We present the case of a 76-year-old male referred to our medical team with dyspnoea, shortness of breath on exertion, and chest pain. Upon further questioning, the patient reported a two-week history of right-sided parasternal pleuritic chest pain without radiation. He denied any history of haemoptysis, calf swelling or pain, recent surgery, and reduced mobility. The patient had a medical history of bilateral cataracts, glaucoma, and hypertension. Clinical examination was unremarkable except for requiring 2L/minute supplemental oxygen to maintain an oxygen saturation of 94%, and blood tests were unremarkable, including a normal D-dimer. Chest radiography revealed no obvious pathological findings. However, the electrocardiogram showed a right bundle branch, sinus tachycardia, and an S1Q3T3 pattern. A computed tomography pulmonary angiogram confirmed pulmonary emboli within the right lower lobe segmental artery, extending into the bilateral basal segmental branch and posterior basal segmental branch. The patient was commenced on low molecular weight heparin initially followed by rivaroxaban 20 mg once daily. This case highlights the importance of having a high degree of suspicion for pulmonary embolism, and D-dimer is an important screening test that can be normal.

Assessment of the Utilization of Validated Diagnostic Predictive Tools and D-Dimer in the Evaluation of Pulmonary Embolism: A Single-Center Retrospective Cohort Study from a Public Hospital in New York City

INTRODUCTION

A significant increase in the use of computed tomography with pulmonary angiography (CTPA) for the diagnosis of pulmonary embolism (PE) has been observed in the past twenty years. We aimed to investigate whether the validated diagnostic predictive tools and D-dimers were adequately utilized in a large public hospital in New York City.

METHODS

We conducted a retrospective review of patients who underwent CTPA for the specific indication of ruling out PE over a period of one year. Two independent reviewers, blinded to each other and to the CTPA and D-dimer results, estimated the clinical probability (CP) of PE using Well's score, the YEARS algorithm, and the revised Geneva score. Patients were classified based on the presence or absence of PE in the CTPA.

RESULTS

A total of 917 patients were included in the analysis (median age: 57 years, female: 59%). The clinical probability of PE was considered low by both independent reviewers in 563 (61.4%), 487 (55%), and 184 (20.1%) patients based on Well's score, the YEARS algorithm, and the revised Geneva score, respectively. D-dimer testing was conducted in less than half of the patients who were deemed to have low CP for PE by both independent reviewers. Using a D-dimer cut-off of <500 ng/mL or the age-adjusted cut-off in patients with a low CP of PE would have missed only a small number of mainly subsegmental PE. All three tools, when combined with D-dimer < 500 ng/mL or <age-adjusted cut-off, yielded a NPV of > 95%.

CONCLUSION

All three validated diagnostic predictive tools were found to have significant diagnostic value in ruling out PE when combined with a D-dimer cut-off of <500 ng/mL or the age-adjusted cut-off. Excessive use of CTPA was likely secondary to suboptimal use of diagnostic predictive tools.

Raising the D-dimer threshold for ruling out pulmonary embolism: A single-site, observational study with a historical comparison

OBJECTIVE

The objective of this study was to assess the impact of introduction of a new pulmonary embolism (PE) diagnostic guideline with a raised D-dimer threshold.

METHODS

This is a single-site, observational, cohort study with a historical comparison. The new guideline raised the D-dimer threshold to 1000 ng/mL for most patients with a Wells' score of 4 or less. Patients investigated for PE with a D-dimer level and/or definitive imaging in 6-month periods before and after the introduction of the guideline were eligible. Patients with D-dimers of 500-1000 ng/mL were prospectively followed up at 3 months for missed PE.

RESULTS

During the pre-intervention period, 688 patients were investigated for PE, 366 (53.2%) received definitive imaging and 39 PE were diagnosed (5.7% overall, 10.7% of those imaged). For the 121 patients with D-dimers ≥500 and <1000 ng/mL, 87 (71.9%) were imaged with 7 (5.8%) having a PE diagnosed. Post intervention there were 930 patients, of which 426 (45.8%) received definitive chest imaging and there were 50 patients with PE diagnosed (5.4% overall, 11.7% of those imaged). For the 185 patients with D-dimers ≥500 and <1000 ng/mL, 60 (32.4%) were imaged with 5 (2.7%) having PE diagnosed. No cases of missed PE were identified at 3 months.

CONCLUSION

The introduction of the new guideline was associated with a reduction in overall imaging rates without evidence of missed PE. Further evaluation in other settings is recommended.

Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients

Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from those among elderly people. Therefore, the current prediction models for VTE are not applicable to young-middle-aged inpatients. The aim of this study was to develop and externally validate a new prediction model for young-middle-aged people using machine learning methods. The clinical data sets linked with 167 inpatients with deep venous thrombosis (DVT) and/or pulmonary embolism (PE) and 406 patients without DVT or PE were compared and analysed with machine learning techniques. Five algorithms, including logistic regression, decision tree, feed-forward neural network, support vector machine, and random forest, were used for training and preparing the models. The support vector machine model had the best performance, with AUC values of 0.806-0.944 for 95% CI, 59% sensitivity and 99% specificity, and an accuracy of 87%. Although different top predictors of adverse outcomes appeared in the different models, life-threatening illness, fibrinogen, RBCs, and PT appeared to be more consistently featured by the different models as top predictors of adverse outcomes. Clinical data sets of young and middle-aged inpatients can be used to accurately predict the risk of VTE with a support vector machine model.