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Talebi A, Borumandnia N, Jafari R, Pourhoseingholi MA, Jafari NJ, Ashtari S, Roozpeykar S, RahimiBashar F, Karimi L, Guest PC, Jamialahmadi T, Vahedian-Azimi A, Gohari-Moghadam K, Sahebkar A. Predicting the COVID-19 Patients Status Using Chest CT Scan Findings: A Risk Assessment Model Based on Decision Tree Analysis. Adv Exp Med Biol 2023; 1412:237-250. [PMID: 37378771 DOI: 10.1007/978-3-031-28012-2_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
BACKGROUND The role of chest computed tomography (CT) to diagnose coronavirus disease 2019 (COVID-19) is still an open field to be explored. The aim of this study was to apply the decision tree (DT) model to predict critical or non-critical status of patients infected with COVID-19 based on available information on non-contrast CT scans. METHODS This retrospective study was performed on patients with COVID-19 who underwent chest CT scans. Medical records of 1078 patients with COVID-19 were evaluated. The classification and regression tree (CART) of decision tree model and k-fold cross-validation were used to predict the status of patients using sensitivity, specificity, and area under the curve (AUC) assessments. RESULTS The subjects comprised of 169 critical cases and 909 non-critical cases. The bilateral distribution and multifocal lung involvement were 165 (97.6%) and 766 (84.3%) in critical patients, respectively. According to the DT model, total opacity score, age, lesion types, and gender were statistically significant predictors for critical outcomes. Moreover, the results showed that the accuracy, sensitivity and specificity of the DT model were 93.3%, 72.8%, and 97.1%, respectively. CONCLUSIONS The presented algorithm demonstrates the factors affecting health conditions in COVID-19 disease patients. This model has the potential characteristics for clinical applications and can identify high-risk subpopulations that need specific prevention. Further developments including integration of blood biomarkers are underway to increase the performance of the model.
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Affiliation(s)
- Atefeh Talebi
- Colorectal Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Nasrin Borumandnia
- Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ramezan Jafari
- Department of Radiology, Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Mohamad Amin Pourhoseingholi
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nematollah Jonaidi Jafari
- Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Sara Ashtari
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Saeid Roozpeykar
- Department of Radiology, Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Farshid RahimiBashar
- Anesthesia and Critical Care Department, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Leila Karimi
- Behavioral Sciences Research Center, LifeStyle Institute, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Paul C Guest
- Department of Psychiatry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
- Laboratory of Translational Psychiatry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Tannaz Jamialahmadi
- Surgical Oncology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Applied Biomedical Research Center, Mashhad University of Medical Sciences, Vakilabad blvd., Mashhad, Iran
| | - Amir Vahedian-Azimi
- Trauma Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Keivan Gohari-Moghadam
- Medical ICU and Pulmonary unit, Shariati hospital, Tehran University of Medical Sciences, Tehran, Iran.
| | - Amirhossein Sahebkar
- Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Biotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
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Roozpeykar S, Azizian M, Zamani Z, Farzan MR, Veshnavei HA, Tavoosi N, Toghyani A, Sadeghian A, Afzali M. Contrast-enhanced weighted-T1 and FLAIR sequences in MRI of meningeal lesions. Am J Nucl Med Mol Imaging 2022; 12:63-70. [PMID: 35535121 PMCID: PMC9077169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/24/2022] [Indexed: 06/14/2023]
Abstract
Magnetic resonance imaging (MRI) is widely used in meningeal lesions due to rapid and accurate diagnosis and prevention of serious complications. The aim of the present study was to compare these two sequences after injection of a contrast agent into meningeal lesions. This is a descriptive-analytical study that was performed in 2018-2020 on patients referred to the radiology ward with detection of any meningeal involvements in the MRI images. In addition to T1-W, FLAIR sequence imaging was also performed. Images were initially evaluated by two expert radiologists and a neurologist. The diagnostic values of the sequences were compared. Overall, a total number of 147 patients with meningeal lesions in their brain MRI entered the study. 57.1% of cases (84 patients) had an infectious etiology and 42.9% (63 patients) had a tumoral etiology. T1-W images without contrast were able to diagnose 78 cases of meningitis (92.8% of them), and FLAIR sequences could diagnose 82 patients (97.6% of them). Without contrast injection on MRI, the diagnostic value of T1-W sequence was higher than FLAIR sequence for tumoral lesions (P < 0.01). The enhancement degree of T1-W was higher for tumoral findings (P < 0.01). In contrast, the enhancement degree of the FLAIR sequence was higher for infectious findings, which was also statistically significant (P = 0.015). FLAIR sequences had 92% sensitivity and 85% specificity for diagnosis of brain inflammatory diseases. Similar analysis showed that T1 sequence had 82% sensitivity and 73% specificity for diagnosis of brain inflammatory diseases.
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Affiliation(s)
- Saeid Roozpeykar
- Department of Radiology and Health Research Center, Baqiyatallah University of Medical SciencesTehran, Iran
| | - Maryam Azizian
- School of Medicine, Kerman University of Medical SciencesKerman, Iran
| | - Zohreh Zamani
- Department of Neurology, Firooz Abadi Hospital, Iran University of Medical SciencesTehran, Iran
| | - Marjan Rahimi Farzan
- Department of Neurology, Firoozgar Hospital, Iran University of Medical SciencesTehran, Iran
| | - Hossein Abdollahi Veshnavei
- Department of Midwifery, School of Nursing and Midwifery, Islamic Azad University Shahrekord BranchShahrekord, Iran
| | - Nooshin Tavoosi
- Department of Midwifery, School of Nursing and Midwifery, Islamic Azad University Shahrekord BranchShahrekord, Iran
| | - Arash Toghyani
- School of Medicine, Isfahan University of Medical SciencesIsfahan, Iran
| | | | - Mahdieh Afzali
- Department of Neurology, School of Medicine, Yas Hospital, Tehran University of Medical SciencesTehran, Iran
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