Diagnostic value of transthoracic echocardiography and computerized tomography for surgically confirmed late tamponade after cardiac surgery

Variations in current clinical practice of postoperative pericardial effusion: a questionnaire study

OBJECTIVE

Postoperative pericardial effusion (PPE) occurs frequently after cardiac surgery, potentially leading to life-threatening cardiac tamponade. Specific treatment guidelines are currently lacking, possibly leading to variations in clinical practice. Our goal was to assess clinical PPE management and evaluate variation between centres and clinicians.

METHODS

A nationwide survey was sent to all interventional cardiologists and cardiothoracic surgeons in the Netherlands, regarding their preferred diagnostic and treatment modality of PPE. Clinical preferences were explored utilising four patient scenarios, each with a high/low echocardiographic and clinical suspicion of cardiac tamponade. Scenarios were also stratified by three PPE sizes (<1 cm, 1-2 cm, >2 cm).

RESULTS

In total, 46/140 interventional cardiologists and 48/120 cardiothoracic surgeons responded (27/31 contacted centres). Cardiologists favoured routine postoperative echocardiography in all patients (44%), whereas cardiothoracic surgeons preferred routine imaging after specific procedures, especially mitral (85%) and tricuspid (79%) valve surgery. Overall, pericardiocentesis (83%) was preferred over surgical evacuation (17%). Regarding all patient scenarios, cardiothoracic surgeons significantly preferred evacuation compared with cardiologists (51% vs 37%, p<0.001). This was also observed with cardiologists employed in surgical centres compared with non-surgical centres (43% vs 31%, p=0.02). Inter-rater analysis varied from poor to near-excellent (к 0.22-0.67), suggesting varying PPE treatment preferences within one centre.

CONCLUSION

There is significant variation in the preferred management of PPE between hospitals and clinicians, even within the same centre, possibly due to the lack of specific guidelines. Therefore, robust results of a systematic approach to PPE diagnosis and treatment are needed to formulate evidence-based recommendations and optimise patient outcome.

Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network

Pericardial effusions (PEFs) are often missed on Computed Tomography (CT), which particularly affects the outcome of patients presenting with hemodynamic compromise. An automatic PEF detection, segmentation, and classification tool would expedite and improve CT based PEF diagnosis; 258 CTs with (206 with simple PEF, 52 with hemopericardium) and without PEF (each 134 with contrast, 124 non-enhanced) were identified using the radiology report (01/2016−01/2021). PEF were manually 3D-segmented. A deep convolutional neural network (nnU-Net) was trained on 316 cases and separately tested on the remaining 200 and 22 external post-mortem CTs. Inter-reader variability was tested on 40 CTs. PEF classification utilized the median Hounsfield unit from each prediction. The sensitivity and specificity for PEF detection was 97% (95% CI 91.48−99.38%) and 100.00% (95% CI 96.38−100.00%) and 89.74% and 83.61% for diagnosing hemopericardium (AUC 0.944, 95% CI 0.904−0.984). Model performance (Dice coefficient: 0.75 ± 0.01) was non-inferior to inter-reader (0.69 ± 0.02) and was unaffected by contrast administration nor alternative chest pathology (p > 0.05). External dataset testing yielded similar results. Our model reliably detects, segments, and classifies PEF on CT in a complex dataset, potentially serving as an alert tool whilst enhancing report quality. The model and corresponding datasets are publicly available.