Infrared spectra of blood plasma from 160 COVID patients in Mumbai, was gathered and studied.
Researchers at IIT Bombay and Australia's QIMR Berghofer Medical Research Institute, with the support of Agilent Technologies, have formulated a quick screening method to gauge the expected severity of COVID-infected citizens. This process will differentiate patients based on their probability of experiencing severe or mild symptoms. With an accuracy of 85% in the pilot study of COVID sufferers, this test could be used to triage patients at the hotspots of the deadly virus, in the future.
A quick test to predict high-risk COVID patients
With the infection affecting more than 200 million people in 220 countries in just 18 months, a lot of medical management facilities have been overwhelmed by COVID. While severely ill patients are at high risks, not everyone might experience symptoms requiring intensive care. With the help of this research, early spotting and triage of patients based on severity is possible, which in turn can help smoothen the resource supply, refine patient outcomes and support frontline warriors overlooking critical resource decisions.
Slight differences were observed between severe & non-severe COVID patients
Infrared spectra of blood plasma from 160 COVID patients (130 as a training set for model development and 30 as a blind test set for model validation) in Mumbai, was gathered and studied. Slight observable differences between severe and non-severe COVID-19 patient samples were disclosed from the spectra collected by the researchers. This classification algorithm based on the study was published in the journal Analytical Chemistry.
One of the lead scientists of the study and the head of QIMR Berghofer's Precision and Systems Biomedicine Research Group, Michelle Hill explained, "We found there were measurable differences in the infra-red spectra in the patients who became severely unwell. In particular, there were differences in two infra-red regions that correspond to sugar and phosphate chemical groups, as well as primary amines, which occur in specific types of proteins". According to Mr. Hill, a multivariate model was developed and tested on the basis of these differences.
Increase in testing likely to reduce 'false positives': Professor Sanjeeva
IIT-Bombay's Professor Sanjeeva Srivastava said, "We also found that having diabetes was a key predictor of becoming severely unwell in this group of patients, so we fed clinical parameters such as age, sex, diabetes mellitus, and hypertension into the algorithm. We then tested the algorithm on blood samples from a separate group of 30 patients from Mumbai and found it was 69.2% specific and 94.1% sensitive in predicting which patients would become severely ill."
"However, it did result in more 'false positives' than predictions that were based solely on the clinical risk factors of age, sex, hypertension, and diabetes. We hope that with more testing, we can reduce these false positives," added Professor Srivastava.
The Agilent Cary 630 FTIR spectrometer
The Agilent Cary 630 FTIR spectrometer is deployed by researchers in crucial studies all over the world, owing to its compact form, and ease of use. Paired with powerful multivariate statistical analysis, this versatile instrument is best suited for the examination of biological samples meant for research of infectious disease, allowing researchers to link spectral information with evaluative and macroscopic properties.