Abstract
BackgroundDespite decades of research, sepsis remains a leading cause of mortality and morbidity in intensive care units worldwide. The key to effective management and patient outcome is early detection, for which no prospectively validated machine learning prediction algorithm is currently available for clinical use in Europe.ObjectiveWe aimed to develop a high-performance machine learning sepsis prediction algorithm based on routinely collected intensive care unit data, designed to be implemented in European intensive care units.MethodsThe machine learning algorithm was developed using convolutional neural networks, based on Massachusetts Institute of Technology Lab for Computational Physiology MIMIC-III clinical data from intensive care unit patients aged 18 years or older. The model uses 20 variables to produce hourly predictions of onset of sepsis, defined by international Sepsis-3 criteria. Predictive performance was externally validated using hold-out test data.ResultsThe algorithm—NAVOY Sepsis—uses 4 hours of input and can identify patients with high risk of developing sepsis, with high performance (area under the receiver operating characteristics curve 0.90; area under the precision-recall curve 0.62) for predictions up to 3 hours before sepsis onset.ConclusionsThe prediction performance of NAVOY Sepsis was superior to that of existing sepsis early warning scoring systems and comparable with those of other prediction algorithms designed to predict sepsis onset. The algorithm has excellent predictive properties and uses variables that are routinely collected in intensive care units.
Highlights
Sepsis is a life-threatening clinical syndrome caused by dysregulated host response to infection [1]
When comparing the distribution of sepsis predictions made by the algorithm with the actual distribution of sepsis, the algorithm predicted that 28% of patients had sepsis in training data (Table 2) and 27% to 29% of patients had sepsis in test data (Table 3), which is somewhat larger than the prevalence of 20%
Detection is key to effective management and patient outcome, as there is no specific sepsis treatment available
Summary
Sepsis is a life-threatening clinical syndrome caused by dysregulated host response to infection [1]. Sepsis and the inflammatory response that ensues can lead to multiple organ dysfunction syndrome and death. It has been estimated that sepsis is present in 6% of adult hospital admissions [2] and in approximately one-third of intensive care unit (ICU) patients [3]. It affects approximately 49 million people every year [4]. Sepsis remains a leading cause of mortality and morbidity in intensive care units worldwide. The key to effective management and patient outcome is early detection, for which no prospectively validated machine learning prediction algorithm is currently available for clinical use in Europe
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
