CRP, PCT, and D-dimer as Biomarkers for Disease Severity in COVID-19 Patients: A Retrospective Study in Kinshasa, Democratic Republic of Congo
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Abstract
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.
2. Sarah C, Amy C, Rebecca C, Mariah F, Anthea F, Elizabeth G, et al. Making the case for cross-border public health stratagies: a compartivie assessment of Covid-19 epidemiological trends in the Balkan countries across 17 months. Journal of Biostatistics and Epidemiology. 2022; 8(2).
3. Savy N, Moodie EE, Drouet I, Chambaz A, Falissard B, Kosorok MR, et al. Statistics, philosophy, and health: the SMAC 2021 webconference. The International Journal of Biostatistics. 2022.
4. Suo-wen X, Iqra I, Weng , Jian-ping. Endothelial dysfunction in COVID-19: an overview of evidence, biomarkers, mechanisms and potential therapies. Acta Pharmacologica Sinica. 2023; 44(4): p. 695-709.
5. Giovanni P, Monia M, Cristel R, Aldo T, Tomris O. Biomarkers associated with COVID-19 disease progression. Critical reviews in clinical laboratory sciences. 2020; 57(6): p. 389-399.
6. Karimollah HT, Zahra G, Vahid N. Statistical Considerations in Combining Multiple Biomarkers for Diagnostic Classification: Logistic Regression Risk Score Versus Discriminant Function Score. Journal of Biostatistics and Epidemiology. 2022.
7. Maria CF, Giampiero F, Marco L, Antonio A, Enea B, Carla P, et al. Investigating biomarkers for COVID-19 morbidity and mortality. Current Topics in Medicinal Chemistry. 2023.
8. Marc V, Dmitry S, Marie-Christine B, Frédérique D, François M, Florence H, et al. Prognostic value of cellular population data in patients with COVID-19. Informatics in Medicine Unlocked. 2023; 38: p. 101-207.
9. Marin BG, Aghagoli G, Lavine K, Yang L, Siff EJ, Chiang SS, et al. Predictors of COVID‐19 severity: a literature review. Reviews in medical virology. 2021; 31(1).
10. Siddharth S, Kuldeep S, Patel , B S, S PF, Mohammed O, et al. Elevated D-dimer levels are associated with increased risk of mortality in coronavirus disease 2019: a systematic review and meta-analysis. Cardiology in review. 2020; 28(6): p. 295-302.
11. Amit K, Era K, Kiran T, Pramod K, Ganesh C, Aradhana K, et al. Procalcitonin as a predictive marker in COVID-19: A systematic review and meta-analysis. Plos one. 2022; 17(9).
12. Takayuki Y, Mako W, Takahiro Y, Nitin C, Takahisa M, Hirotaka M, et al. Value of leukocytosis and elevated C-reactive protein in predicting severe coronavirus 2019 (COVID-19): A systematic review and meta-analysis. Clinica chimica acta. 2020; 509.
13. Chaochao T, Ying H, Fengxia S, Tan K, Ma Q, Chen Y, et al. C-reactive protein correlates with computed tomographic findings and predicts severe COVID-19 early. Journal of medical virology. 2020; 92(7).
14. OpenAI. ChatGPT: Language Generation Model. [Online].; 2023 [cited 2023 07 08. Available from: https://chat.openai.com/.
15. Jingyuan L, Yao L, Pan X, Lin P, Haofeng X, Chuansheng L, et al. Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage. Journal of translational medicine. 2020; 18(1).
16. Fei Z, Ting Y, Ronghui D, Guohui F, Ying L, Zhibo L, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The lancet. 2020; 395(10229).
17. Benkeser D, Mertens A, Colford J, Hubbard A, J M. A machine learning-based approach for estimating and testing associations with multivariate outcomes. The international journal of biostatistics. 2020; 17(1).
18. Griesbach C, Safken B, Waldmann E. Gradient boosting for linear mixed models. The International Journal of Biostatistics. 2021; 17(2).
| Files | ||
| Issue | Vol 9 No 4 (2023) | |
| Section | Articles | |
| DOI | https://doi.org/10.18502/jbe.v9i4.16668 | |
| Keywords | ||
| Disease severity biomarkers Prediction Machine learning. COVID-19 | ||
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