AI algorithm more accurately predicts when patients have developed sepsis

Sepsis affects more than 30 million people worldwide, causing an estimated six million deaths. It is an extreme response from the human body to an infection and is often life-threatening.

Every hour of delayed treatment can increase the odds of death by 4-8 per cent, so timely and accurate predictions of sepsis are crucial to reduce morbidity and mortality. Various healthcare organisations are already deploying predictive analytics to help identify patients with sepsis by using electronic medical record (EMR) data.

But the new AI, developed by a team of international researchers from McMaster University and St Joseph’s Healthcare Hamilton, both in Canada, could greatly improve the timeliness and accuracy of data-driven sepsis predictions.

“Sepsis can be predicted very accurately and very early using AI with clinical data, but the key questions to the clinician and data scientists are how much historical data these algorithms need to make accurate predictions and how far ahead they can predict sepsis accurately,” said Manaf Zargoush, study co-author.

To predict sepsis in clinical care settings, some systems use EMR data with disease scoring tools to determine sepsis risk scores – essentially acting as digital, automated assessment tools. More advanced systems employ predictive analytics, such as AI algorithms, to go beyond risk assessment and identify sepsis itself.

The latest AI uses an algorithm called the Bidirectional Long Short-Term Memory (BiLSTM). It examines several variables such as the length of stay in an intensive care unit, hours between hospital and ICU admission, vital signs, demographics and laboratory tests.

Compared to other algorithms, the BiLSTM is a more complex subset of machine learning – called deep learning – that uses neural networks to increase its predictive power, the researchers said.

The study compared the BiLSTM with six other machine-learning algorithms and found it was superior to the others in terms of accuracy. Improving accuracy by reducing false positives is key to a successful algorithm, since these errors not only waste medical resources, but they also erode physicians’ confidence in the algorithm.

Interestingly, the study found that predictive accuracy may be increased through algorithms that focus more heavily on a patient’s recent data points, instead of looking back further to include as many data points as possible.

Researchers noted that it is understandable that clinicians would be inclined to populate the algorithm with as many data points as possible over a long timeframe. However, their findings suggest that when the purpose of prediction is being accurate and timely regarding sepsis predictions, physicians with long prediction horizons should rely more on the fewer yet more recent clinical data of the patient.