AIIMS Doctors Developed A Model Predicting Shock 12 Hours In Advance

Nearly 30% paediatric ICU patients suffer from sepsis shock and 30% of them end up dying due to multiorgan failure. The machine-learning algorithm to detect shock by thermal imaging has 75% accuracy. Predicting shock (less blood and oxygen supply to major organs, which can lead to death) even 12 hours before it can be clinically recognised by doctors by using the current gold standard (intra-arterial blood pressure) is now possible, thanks to the work by an AIIMS-led multi-institutional team of researchers.

Shock can arise from loss of blood volume, inefficient pumping by the heart or infection (sepsis). Efficient algorithm: The machine-learning algorithm to detect shock at the time a single photo is taken using thermal imaging has an accuracy of 75%. The ability of the algorithm to forecast the probability of a shock happening three, six and 12 hours before clinical recognition done…