Study of Biochemical and Radiological Findings among COVID-19 Patients in a Tertiary Care Hospital

Background: The COVID-19 pandemic has resulted in severe illness for many patients globally. Leveraging biochemical and radiological data, the research seeks to optimize patient management, ultimately leading to fewer deaths and disease-related complications. Methods: This observational study has been carried out among 50 patients suffering either from COVID-19 aged between 16-90 years. The place of study was East west Medical College and Hospital, Uttara, Dhaka. The duration of this study was 6 months and was conducted from July 01, 2020 to December 31, 2020. The data are collected from biochemical and radiological investigations with the help of medical equipment for obtaining blood samples and CT scan. Quality of data is strictly maintained and ethical issues are properly maintained in all the steps of this study. The data was then analyzed by SPSS software (version 26.0) and then presented in tables and charts. Results: The cohort included 29 (58.0%) men and 21 (42.0%) women, and the mean age was 56·9 years (SD ±7.3). The biochemical profile revealed that 39 (78.0%) patients had lymphocytosis and 44 (88.0%) had increased D-Dimer levels. In the CT chest findings, 35 (70.0%) had ground-glass opacity and 32 (64.0%) were present with consolidation. Conclusion: The study provides information by examining biomarkers and chest CT scans aiding in the disease diagnosis, severity assessment, and outcome prediction. J. Dhaka National Med. Coll. Hos. 2024; 30 (02): 28-32

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Publication Details

Journal
Journal of Dhaka National Medical College & Hospital
Published
2024-09-30
DOI
https://doi.org/10.3329/jdnmch.v30i2.81380
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Study of Biochemical and Radiological Findings among COVID-19 Patients in a Tertiary Care Hospital

Sujit Saha, Nusrat Jahan, Musomi Khandaker, Bedanta Roy et al.
Journal of Dhaka National Medical College & Hospital
Radiomics and Machine Learning in Medical Imaging
article

Study of Biochemical and Radiological Findings among COVID-19 Patients in a Tertiary Care Hospital

Sujit Saha, Nusrat Jahan, Musomi Khandaker, Bedanta Roy, Asma Khatun, S. Ferdous, Jannatul Nayim
article en

Abstract

Background: The COVID-19 pandemic has resulted in severe illness for many patients globally. Leveraging biochemical and radiological data, the research seeks to optimize patient management, ultimately leading to fewer deaths and disease-related complications. Methods: This observational study has been carried out among 50 patients suffering either from COVID-19 aged between 16-90 years. The place of study was East west Medical College and Hospital, Uttara, Dhaka. The duration of this study was 6 months and was conducted from July 01, 2020 to December 31, 2020. The data are collected from biochemical and radiological investigations with the help of medical equipment for obtaining blood samples and CT scan. Quality of data is strictly maintained and ethical issues are properly maintained in all the steps of this study. The data was then analyzed by SPSS software (version 26.0) and then presented in tables and charts. Results: The cohort included 29 (58.0%) men and 21 (42.0%) women, and the mean age was 56·9 years (SD ±7.3). The biochemical profile revealed that 39 (78.0%) patients had lymphocytosis and 44 (88.0%) had increased D-Dimer levels. In the CT chest findings, 35 (70.0%) had ground-glass opacity and 32 (64.0%) were present with consolidation. Conclusion: The study provides information by examining biomarkers and chest CT scans aiding in the disease diagnosis, severity assessment, and outcome prediction. J. Dhaka National Med. Coll. Hos. 2024; 30 (02): 28-32

Journal of Dhaka National Medical College & HospitalVol. 30(2)
Bangladesh University (BD), University of Dhaka (BD), Bangladesh Open University (BD), Women Medical College (PK), Shaheed Suhrawardy Medical College (BD), Dhaka Medical College and Hospital (BD)
Openalex Percentile: Top 55%
Radiomics and Machine Learning in Medical Imaging
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