Author:
Li Xiang,Duan Lijun,Mu Shengnan,Dong Xin,Lu Xiaoqian,Cao Dianbo
Abstract
Abstract
Background
Pulmonary arteriovenous malformation (PAVM), also known as pulmonary arteriovenous fistula, is a rare vascular developmental anomaly. Most cases of PAVM are associated with hereditary hemorrhagic telangiectasia (HHT). Hemothorax associated with PAVM is even rarer, and management concerning this complication still challenges.
Case presentation
A 55-year-old man with sudden onset of dyspnea and chest pain was admitted to our hospital. He had a medical history of epistaxis, intraperitoneal germ cell tumor and PAVM. Chest unenhanced CT revealed the left-sided pleural effusion together with partial passive atelectasis and gradual increase at the interval of six days. Diagnostic thoracocentesis further revealed hemorrhagic effusion. CT angiography (CTA) showed tortuously dilated lumen of the left lower pulmonary artery and PAVM with the formation of aneurysm. Due to his family's refusal of surgery, the patient underwent transcatheter embolization therapy. However, the left pleural effusion did not significantly reduce and there was a slow drop in hemoglobin value even after interventional treatment, indicating the possibility of ongoing active bleeding. Eventually, the patient received lobectomy of the left lower lobe with a satisfactory outcome.
Conclusions
Massive hemothorax resulting from PAVM rupture into the pleural space can lead to fatal outcomes. CTA can accurately diagnose this pathologic condition. Transcatheter embolization is frequently used in the treatment of PAVM, but it may be challenging to achieve the desirable effect in patients with hemothorax. Combined with our case and literature review, direct radical surgery can lead to a successful outcome when PAVM complicated with hemothorax and a large diameter of the draining vein.
Funder
Predicting pathologic response for those resectable stage III non-small cell lung cancer patients with neoadjuvant therapy through artifcial intelligence method
Classification and prediction for pulmonary nodules through AI-based analysis of CT images
Publisher
Springer Science and Business Media LLC
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