Acute Pulmonary Embolism and Chronic Thromboembolic Pulmonary Hypertension: Clinical and Serial CT Pulmonary Angiographic Features

Factors affecting the probability of pulmonary embolism in lung ventilation/perfusion scintigraphy

BACKGROUND

Probabilities for Pulmonary Embolism (PE) are reported on V/Q scintigraphy, though the clinical implications of these probabilities remain unclear. This study examined the factors influencing PE probability groups determined by V/Q scintigraphy.

METHODS

Demographic and radiologic data, V/Q scintigraphy results, echocardiographic findings, and pulse oxygen saturation (SpO2) were recorded for 95 patients admitted to the Nuclear Medicine Clinic between January 2022-2023. Patients were categorized into normal, low, intermediate, and high probability groups for PE based on V/Q scintigraphy findings.

RESULTS

The median age of the group with normal perfusion scintigraphy was 63 years (range: 19-85 years), which was significantly lower compared to other groups (p = 0.003). There was a weak positive correlation between the probability of PE and an increased right ventricle/left ventricle (RV/LV) ratio on CT (r = 0.256, p = 0.034), while no correlation was found with pulmonary artery (PA) diameter. Although systolic pulmonary artery pressure (sPAP) was not correlated with increased PE probability, a positive correlation was found with the presence of tricuspid regurgitation (TR) (r = 0.241, p = 0.037). A strong positive correlation was observed between PE probability and perfusion defect size (r = 0.758, p < 0.001), while a weak negative correlation was found with SpO2 (r = -0.330, p = 0.014). Multivariable regression analysis revealed a significant relationship only with SpO2.

CONCLUSION

An increased probability of PE on V/Q scintigraphy is associated with a higher RV/LV ratio on thorax CT, TR detection on echocardiography, hypoxemia, and larger perfusion defect size. These findings suggest that V/Q scintigraphy could also be useful in determining the prognosis of PE.

Diagnostic value of systematic compression ultrasonography for the detection of unrecognized venous thromboembolism in patients admitted to an internal medicine ward for dyspnea

The diagnosis of venous thromboembolism (VTE) is complex, and many cases of pulmonary embolism (PE) and deep vein thrombosis (DVT) go undetected despite validated diagnostic algorithms. This study evaluated the diagnostic performance of compression ultrasound (CUS) when systematically performed in patients admitted to an internal medicine department for dyspnea and/or respiratory failure. We conducted a prospective observational cohort study of consecutive adult hospitalized patients admitted for dyspnea and/or respiratory failure with at least one of the following: tachycardia (> 100 bpm), tachypnea (> 20/min), chest pain, cough, syncope, or hemoptysis. Patients with a previous diagnosis of VTE or who underwent computed tomography pulmonary angiography (CTPA) or CUS during evaluation in the emergency department were excluded. The study included 263 patients (50.2% women, average age 84 years). CUS was positive in 31 patients (11.8%); Bilateral DVT was diagnosed in two patients and unilateral DVT in 29 patients. Of these, 10 underwent CT scan, with PE confirmed in 9 cases. Using the Wells score for DVT (cut-off ≥ 2), only 8 patients (25.8%) were at high risk. The accuracy of the Wells score in identifying PE was suboptimal, as 5 of 9 patients (55.5%) with confirmed PE were in the low-risk group (three-level interpretation) and 8 (89.9%) were in the "EP unlikely" group (two-level interpretation). The systematic use of CUS as a point-of-care tool can improve the diagnostic accuracy for VTE in patients admitted to internal medicine departments with dyspnea/respiratory failure.

CT-Derived Features as Predictors of Clot Burden and Resolution

Objectives: To evaluate the prognostic utility of CT-imaging-derived biomarkers in distinguishing acute pulmonary embolism (PE) resolution and its progression to chronic PE, as well as their association with clot burden. Materials and Methods: We utilized a cohort of 45 patients (19 male (42.2%)) and 96 corresponding CT scans with exertional dyspnea following an acute PE. These patients were referred for invasive cardiopulmonary exercise testing (CPET) at the University of Pittsburgh Medical Center from 2018 to 2022, for whom we have ground truth classification of chronic PE, as well as CT-derived features related to body composition, cardiopulmonary vasculature, and PE clot burden using artificial intelligence (AI) algorithms. We applied Lasso regularization to select parameters, followed by (1) Ordinary Least Squares (OLS) regressions to analyze the relationship between clot burden and the selected parameters and (2) logistic regressions to differentiate between chronic and resolved patients. Results: Several body composition and cardiopulmonary factors showed statistically significant association with clot burden. A multivariate model based on cardiopulmonary features demonstrated superior performance in predicting PE resolution (AUC: 0.83, 95% CI: 0.71-0.95), indicating significant associations between airway ratio (negative correlation), aorta diameter, and heart volume (positive correlation) with PE resolution. Other multivariate models integrating demographic features showed comparable performance, while models solely based on body composition and baseline clot burden demonstrated inferior performance. Conclusions: Our analysis suggests that cardiopulmonary and demographic features hold prognostic value for predicting PE resolution, whereas body composition and baseline clot burden do not. Clinical Relevance: Our identified prognostic factors may facilitate the follow-up procedures for patients diagnosed with acute PE.

Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection

PURPOSE

Diagnosing pulmonary embolism (PE) is still challenging due to other conditions that can mimic its appearance, leading to incomplete or delayed management and several inter-observer variabilities. This study evaluated the performance and clinical utility of an artificial intelligence (AI)-based application designed to assist clinicians in the detection of PE on CT pulmonary angiography (CTPA).

PATIENTS AND METHODS

CTPAs from 230 US cities acquired on 57 scanner models from 6 different vendors were retrospectively collected. Three US board certified expert radiologists defined the ground truth by majority agreement. The same cases were analyzed by CINA-PE, an AI-driven algorithm capable of detecting and highlighting suspected PE locations. The algorithm's performance at a per-case and per-finding level was evaluated. Furthermore, cases with PE not mentioned in the clinical report but correctly detected by the algorithm were analyzed.

RESULTS

A total of 1204 CTPAs (mean age 62.1 years ± 16.6[SD], 44.4 % female, 14.9 % positive) were included in the study. Per-case sensitivity and specificity were 93.9 % (95%CI: 89.3 %-96.9 %) and 94.8 % (95%CI: 93.3 %-96.1 %), respectively. Per-finding positive predictive value was 89.5 % (95%CI: 86.7 %-91.9 %). Among the 196 positive cases, 29 (15.6 %) were not mentioned in the clinical report. The algorithm detected 22/29 (76 %) of these cases, leading to a reduction in the miss rate from 15.6 % to 3.8 % (7/186).

CONCLUSIONS

The AI-based application may improve diagnostic accuracy in detecting PE and enhance patient outcomes through timely intervention. Integrating AI tools in clinical workflows can reduce missed or delayed diagnoses, and positively impact healthcare delivery and patient care.

Exploring varied radiologic appearance in pulmonary embolism with CT pulmonary angiography: Case series with literature review

Pulmonary embolism (PE) is a life-threatening condition caused by a sudden blockage of pulmonary arteries. Nonspecific and extremely variable clinical presentation frequently leads to undetected cases, making computed tomography pulmonary angiography (CTPA) hold a crucial role in the diagnosis of PE. This case series presents numerous types and findings of PE in CTPA among patients with different initial presentations followed by a literature review. We presented 3 cases with different initial presentations such as dyspnea with wheezing, productive cough, and hematemesis. All patients were consequently evaluated for D-dimer due to suspicion of PE from cardiac ultrasonography, electrocardiography (ECG), and persistent symptoms. Large to subsegmental PE can be found with various secondary findings such as pleural effusion and Hampton's hump. All patient's conditions were improved after anticoagulant treatment. This case series highlights the significance of CTPA as an imaging modality in the diagnosis of PE, as well as the necessity of evaluating the main to subsegmental pulmonary artery through an in-depth understanding of the images that can be assessed on CTPA.

Clot ratio, new clot burden score with deep learning, correlates with the risk stratification of patients with acute pulmonary embolism

Background

Risk stratification for patients with acute pulmonary embolism (APE) is significantly important for treatment and prognosis evaluation. We aimed to develop a novel clot burden score on computed tomography pulmonary angiography (CTPA) based on deep learning (DL) algorithm for risk stratification of APE.

Methods

The study retrospectively enrolled patients newly diagnosed with APE in China-Japan Friendship Hospital consecutively. We collected baseline data and CTPA parameters, and calculated four different clot burden scores, including Qanadli score, Mastora score, clot volume and clot ratio. The former two were calculated by two radiologists separately, while clot volume and clot ratio were based on the DL algorithm. The area under the curve (AUC) of four clot burden scores were analyzed.

Results

Seventy patients were enrolled, including 17 in high-/intermediate-high risk and 53 in low-/intermediate-low risk. Clot burden was related to the risk stratification of APE. Among four clot burden scores, clot ratio had the highest AUC (0.719, 95% CI: 0.569-0.868) to predict patients with higher risk. In the patients with hemodynamically stable APE, only clot ratio presented statistical difference (P=0.046).

Conclusions

Clot ratio is a new imaging marker of clot burden which correlates with the risk stratification of patients with APE. Higher clot ratio may indicate higher risk and acute right ventricular dysfunction in patients with hemodynamically stable status.