Machine-based algorithm: a revolution we need for early sepsis diagnosis in hospitals
Sepsis is a life-threatening medical emergency and one of the fundamental causes of early mortality within hospitals. WHO report of 2020 shows that nearly 11 million people worldwide die from sepsis yearly (1). It commonly affects neonates and children; however, pregnant women, the elderly, cancer patients, and bed-bound individuals are particularly at high risk of developing sepsis as well due to the immunosuppressant state of their condition. Sepsis is a clinical diagnosis; thus majorly depends upon a multitude of symptoms, clinical examinations, baseline investigations, and the expertise of the clinicians. Unfortunately, all these methods are highly non-specific, making the diagnosis difficult.
Owing to its high mortality, the key to survival lies in the timely initiation of the therapy. Therefore, sepsis requires early diagnosis to help save the life of an affected individual. In this search, multiple systems and methods have been developed previously and used in various hospitals. It includes evaluating by SIRS criteria, detecting the levels of pro-inflammatory markers in the serum, and by newer techniques like diagnosing using electrochemical sensors like PCT (procalcitonin), optical and fluorometric sensors, and microfluidic sensors. These detect different biomarkers in the serum of the patients and thus help in rapid diagnosis. Regardless, the sensitivity and specificity of these systems remain low.
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