July 21, 2024

Platelet Analysis Method Provides COVID-19 Severity Risk Prediction

Researchers at the Technical University of Munich (TUM) have developed a novel method for assessing the number and structure of aggregated blood platelets, or thrombocytes, which can potentially help quantify the risk of a severe COVID-19 infection. By analyzing platelet aggregates using image-based flow cytometry, the researchers have identified a predictive biomarker for the seriousness of a COVID-19 infection. This breakthrough could allow physicians to adjust treatment at an early stage, potentially improving patient outcomes. The study has been published in the journal Communications Medicine.

When the human body is infected with the SARS-CoV-2 virus, it triggers a series of immune responses. One of these responses involves platelets sticking to immune cells, forming clumps, or aggregates, of cells in the bloodstream. The TUM researchers, led by Professor Oliver Hayden, demonstrated that patients admitted to intensive care with severe COVID-19 infections exhibited a rapid rise in concentrations of platelet aggregates. This increase in platelet aggregates enabled the researchers to identify a biomarker for predicting the risk of severe illness in COVID-19 patients. The optimal interdisciplinary collaborations facilitated by the central TranslaTUM institute allowed TUM engineers to work with medical researchers at Klinikum M√ľnchen rechts der Isar to achieve these results.

The analysis process involves taking a blood sample from the patient and using image-based flow cytometry to count thousands of blood cells and their aggregates within seconds. One of the advantages of this method is that it does not require any treatment or marking of the samples. By using standardized methods, researchers can directly investigate the samples without any aggregation losses caused by high shearing forces. The proximity of patients to the lab when taking samples ensures that the blood can be tested immediately, preventing any effects from sample aging, which can lead to the formation of aggregates.

In the study, the researchers analyzed blood samples from 36 intensive care patients aged 32 to 83 who had been admitted to the hospital with a moderate to severe SARS-CoV-2 infection. The results showed that the number of aggregated thrombocytes in the blood samples of severely ill patients was significantly higher than in moderately ill patients and healthy blood donors. The researchers also found that the number and composition of cell aggregates changed gradually with the severity of the COVID-19 infection, and these changes occurred at an early stage before complications appeared. The aggregates typically consisted of fewer than 10 thrombocytes, but in extreme cases, up to two-thirds of all thrombocytes in a patient were aggregated.

The presence of a high concentration of cell aggregates was observed in all COVID-19 patients admitted to intensive care. This simple diagnostic method based on blood cell aggregates has the potential to identify high-risk patients at an early stage and, consequently, improve their care.

The interdisciplinary team of engineers and medical researchers involved in this study now plan to apply their findings to other diseases. They believe that the method described here could also be effective in assessing the severity and progression of cardiovascular diseases or cancers.


1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it