QU study on helping critically ill Covid patients
Dr Mohamed Elrayess, Associate Research Professor at the Center for Biomedical Research – Qatar University (BRC-QU), conducted a study to help critically ill patients with Covid-19, focusing on new biomarkers predictive of duration stay in an intensive care unit (ICU) for better risk management and reduction.
âThe severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2), the cause of the new 2019 coronavirus disease pandemic (Covid-19), has endangered the lives of millions of people around the world. About 20% of Covid-19 patients become seriously ill because they experience respiratory distress requiring immediate oxygen supply, including invasive mechanical ventilation in severe cases. Among critically ill patients, 30% die, âQU said in a statement.
During the peak period, intensivists should predict the duration of invasive mechanical ventilation for better use of ICU resources using a number of laboratory values ââand patient characteristics, also known as the Apache score. , which takes into account both acute and chronic illnesses. However, the accuracy of early clinical prediction of the duration of invasive mechanical ventilation remains limited, especially in patients who require a longer stay and more care.
“Therefore, one of the most difficult aspects of the Covid-19 pandemic is the management of critically ill patients in intensive care, especially during the peak period of illness due to limited capacity and resources. “, notes the press release.
Early detection of metabolic changes in critically ill Covid-19 patients on invasive mechanical ventilation (IMV) in the intensive care unit could help in disease management and recovery. These patients are the real strain on the healthcare system and are susceptible to some of the worst possible outcomes of the disease, making early prediction of their course in the ICU of enormous clinical value, he explains.
In this regard, it has become “very important to identify Covid-19 patients likely to recover more quickly and to predict their recovery time for better management of resuscitation resources”.
Dr Elrayess and a research team from BRC-QU (Dr Asmaa al-Thani, Dr Hadi Yassine, Dr Fatiha Benslimane and Maria Smatti), Hamad Medical Corporation (Dr Ali Ait Hssain) and Hamad Bin Khalifa University (Sara Taleb, Dr Ilhame Diboun and Professor Omar Albagha), studied new biomarkers predictive of length of stay in intensive care for better management of intensive care resources and reduced risk of Covid-19 outcomes.
The new emerging data identified alterations in specific clinical characteristics between admission to intensive care and one week in intensive care. In addition, the data also suggested that certain metabolic changes during the first week of admission may be used to predict the outcome of the Covid-19 ICU.
Previous reports comparing the metabolic profiling of samples infected with Covid-19 and corresponding healthy controls revealed a specific metabolic signature for the severity of the disease. However, most of these studies collected metabolites after patients acquired severe symptoms of the disease. Therefore, the use of these models for the prediction of severity remains limited.
In this study, the researchers investigated whether Covid-19 can trigger specific metabolic changes detectable in the sera of patients undergoing mechanical intubation upon admission to intensive care in order to use them as tools to differentiate those who are likely to have it. to put back. those who would tolerate an extended stay in the ICU. Therefore, targeted metabolic profiling was performed on sera from critically ill Covid-19 patients in intensive care at two specific time points.
The first analysis focused on samples taken within 48 hours of intubation and the second on samples taken one week later. The researchers also confirmed their findings in data published on metabolic markers of the severity of Covid-19 by other investigators. The results of the study identified a model based on two metabolites (hypoxanthine and betaine) measured at ICU admission that was best for predicting whether a patient is likely to have a short or long stay in ICU.
Another model based on five metabolites (kynurenine, 3-methylhistidine, ornithine, p-cresol sulfate and sphingomyelin C24.0), measured one week after ICU admission, was identified to accurately predict invasive mechanical ventilation . Both predictive models outperformed the benchmark Apache II score used in intensive care units around the world and differentiated the severity of Covid-19 in published data.
In summary, the results of the study have shown that it is possible to discriminate on admission between seriously ill Covid-19 patients who are likely to stay for a shorter period of time from those who are likely to undergo a long stay in intensive care. The biomarkers that have been identified and patented are associated with various medical complications of Covid-19 infection such as inflammation, coagulation, kidney damage and the immune response.
The identified models surpassed the predictive power of the Apache II benchmark score, which is typically used to predict mortality and disease severity. The identified predictive biomarkers can also be used as therapeutic targets for an intervention aimed at improving the clinical profile of the patient in the intensive care unit.
Validation of the utility of the biomarker panel for predicting ICU and IMV duration is currently underway for wider use in Qatar and globally.