|
|
Journal of Biostatistics and Epidemiology، جلد ۹، شماره ۴، صفحات ۴۲۶-۴۳۶
|
|
|
| عنوان فارسی |
|
|
| چکیده فارسی مقاله |
|
|
| کلیدواژه‌های فارسی مقاله |
|
|
| عنوان انگلیسی |
CRP, PCT, and D-dimer as Biomarkers for Disease Severity in COVID-19 Patients: A Retrospective Study in Kinshasa, Democratic Republic of Congo |
|
| چکیده انگلیسی مقاله |
Abstract
Background: The COVID-19 pandemic has had a significant impact on global health, resulting in more than 6 million reported deaths worldwide as of April 2023. This study aimed to investigate the potential of C-reactive protein (CRP), procalcitonin (PCT), and D-dimer as biomarkers for assessing disease severity in COVID-19 patients in Kinshasa, Democratic Republic of Congo.
Methods: A retrospective examination was conducted involving 339 COVID-19 patients admitted to Kinshasa hospitals between January 2021 and March 2022. CRP, PCT, and D-dimer levels were measured in all patients and compared between those with severe and non-severe illnesses.
Results: Our findings revealed significantly higher CRP, PCT, and D-dimer levels in severe cases compared to non-severe cases. Specifically, the median CRP level was 120.6 mg/L in severe cases, 47.3 mg/L in mild cases, and 13.5 mg/L in moderate cases. The median PCT levels were 0.26 ng/mL in severe cases, 0.08 ng/mL in mild cases, and 0.07 ng/L in moderate cases. Additionally, the median D-dimer level was 1836.9 µg/L in severe cases and 597.6 µg/L in mild cases, with a value of 481.1 µg/L in moderate cases. System learning techniques were also employed to predict disease severity based on these biomarkers, achieving an accuracy of 97%.
Conclusion: Our findings suggest that CRP, PCT, and D-dimer serve as valuable biomarkers for identifying severe COVID-19 cases in Kinshasa. Furthermore, the application of machine learning methods can yield accurate predictions of disease severity based on these biomarkers. These biomarkers hold the potential to assist clinicians in informed decision-making regarding patient management and contribute to improved clinical outcomes for COVID-19 patients. |
|
| کلیدواژه‌های انگلیسی مقاله |
|
|
| نویسندگان مقاله |
| Tasnime Hamdeni
Statistics' Departement, Higher School of Statistics and Information Analysis (ESSAI), University of Carthage, Laboratory of Mathematical Modeling, Harmonic Analysis, and Potential Theory (LR18ES09) Tunis, Tunisia
| Frederick Tshibasu
Division of Diagnostic Imaging, University Hospital of Kinshasa, School of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| Asma Kerkeni
Department of Mathematics, National Higher Engineering School of Tunis (ENSIT), University of Tunis, Tunis, Tunisia
| Tasnime Hamdeni
Department of Mathematics, National Higher Engineering School of Tunis (ENSIT), University of Tunis, Tunis, Tunisia
|
|
| نشانی اینترنتی |
https://jbe.tums.ac.ir/index.php/jbe/article/view/1268 |
| فایل مقاله |
فایلی برای مقاله ذخیره نشده است |
| کد مقاله (doi) |
|
| زبان مقاله منتشر شده |
|
| موضوعات مقاله منتشر شده |
|
| نوع مقاله منتشر شده |
|
|
|
|
برگشت به:
صفحه اول پایگاه |
نسخه مرتبط |
نشریه مرتبط |
فهرست نشریات
|