This study aimed to explore the clinical characteristics of acute cerebral infarction (ACI) patients with thalassemia through the analysis of clinical data. Adult patients with ACI who were admitted to the First Affiliated Hospital of Hainan Medical College, the Second Affiliated Hospital of Hainan Medical College, Hainan Provincial People's Hospital, and the Department of Neurology of Haikou People's Hospital from January 2008 to December 2018 were enrolled. According to the eligibility criteria, 183 ACI patients were examined, of whom there were 33 cases with thalassemia, 50 cases with iron-deficiency anemia (IDA), and 100 non-anemic cases. Laboratory data, including platelet count, homocysteine count, and hemoglobin level, were collected. Besides, the results of auxiliary examinations, such as brain magnetic resonance imaging or computed tomography, carotid ultrasound, electrocardiogram, and cardiac color ultrasound, were collected. Baseline clinical data (e.g., history of smoking and drinking) were acquired. The clinical characteristics were compared and analyzed among the three groups. There were more female ACI patients with thalassemia than male ones. Furthermore, lesions in the thalassemia and IDA groups were mainly located in the region from the corona radiata and the centrum semiovale, in which multiple small infarcts were dominant. In the non-anemia group, patients' lesions were mainly found in the basal ganglia area, and single small infarcts had the highest proportion.
Keyphrases
- magnetic resonance imaging
- end stage renal disease
- chronic kidney disease
- healthcare
- computed tomography
- iron deficiency
- newly diagnosed
- ejection fraction
- sickle cell disease
- electronic health record
- liver failure
- prognostic factors
- big data
- respiratory failure
- patient reported outcomes
- brain injury
- left ventricular
- adverse drug
- multiple sclerosis
- drug induced
- deep learning
- contrast enhanced
- patient reported
- positron emission tomography
- magnetic resonance
- artificial intelligence
- machine learning
- atrial fibrillation