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Computed tomography of the acute abdomen in patients with atrial fibrillation.

机译:房颤患者急性腹部的计算机断层扫描。

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OBJECTIVE: To investigate the frequency and basis of clinically relevant computed tomographic (CT) findings in patients with atrial fibrillation and acute abdominal pain. MATERIALS AND METHODS: We retrospectively identified 30 patients with atrial fibrillation referred for enhanced (n = 26) or unenhanced (n = 4) abdominal CT from our emergency department because of acute nontraumatic abdominal pain. All CT images were independently reviewed by a single reader who evaluated the studies for findings that might explain acute pain. Results were correlated with laboratory and surgical findings, clinical course and outcome, and final diagnosis. RESULTS: Eleven (37%) of the 30 patients had CT findings for abdominal pain that related to atrial fibrillation, including end-organ ischemia or infarction (n = 8, 27%) and spontaneous hemorrhage presumably related to anticoagulation (n = 3, 10%). Of the remaining patients, 5 (17%) had nonatrial fibrillation-related CT findings for abdominal pain, and 14 (47%) had no cause for abdominal pain found at CT. CONCLUSIONS: Patients with atrial fibrillation who present to CT with acute abdominal pain have a high likelihood of being found with abdominal pathology relating to their atrial fibrillation, such as arterial embolus or hemorrhage, and a lower likelihood of having more typical causes for abdominal pain. Increased awareness of the high likelihood of atrial fibrillation-related causes for abdominal pain may improve diagnoses and triage for this special and difficult population.
机译:目的:探讨房颤和急性腹痛患者的临床相关计算机体层摄影(CT)检查结果的频率和依据。材料与方法:我们回顾性分析了30例因急性非创伤性腹痛而从急诊科转诊为腹部CT增强(n = 26)或未增强(n = 4)的房颤患者。所有CT图像均由一名读者独立审查,该读者对研究进行了评估,以发现可能解释急性疼痛的发现。结果与实验室和手术结果,临床过程和结果以及最终诊断相关。结果:30例患者中有11例(37%)出现了与房颤有关的腹部疼痛的CT表现,包括终末器官缺血或梗塞(n = 8,27%)以及可能与抗凝有关的自发性出血(n = 3 10%)。在其余患者中,有5例(17%)因腹部疼痛而出现了与非心房颤动相关的CT表现,而14例(47%)没有因CT而发现腹痛的原因。结论:CT表现为急性腹痛的房颤患者极有可能发现与房颤有关的腹部病理,例如动脉栓塞或出血,而出现更典型的腹痛原因的可能性较低。人们越来越认识到与房颤相关的引起腹痛的高可能性,可以改善这一特殊和困难人群的诊断和分类。

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