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In BMJ health & care informatics

BACKGROUND : Severe sepsis and septic shock are among the leading causes of death in the USA. While early prediction of severe sepsis can reduce adverse patient outcomes, sepsis remains one of the most expensive conditions to diagnose and treat.

OBJECTIVE : The purpose of this study was to evaluate the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay and 30-day readmission.

DESIGN : Prospective clinical outcomes evaluation.

SETTING : Evaluation was performed on a multiyear, multicentre clinical data set of real-world data containing 75 147 patient encounters from nine hospitals across the continental USA, ranging from community hospitals to large academic medical centres.

PARTICIPANTS : Analyses were performed for 17 758 adult patients who met two or more systemic inflammatory response syndrome criteria at any point during their stay ('sepsis-related' patients).

INTERVENTIONS : Machine learning algorithm for severe sepsis prediction.

OUTCOME MEASURES : In-hospital mortality, length of stay and 30-day readmission rates.

RESULTS : Hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay and a 22.7% reduction in 30-day readmission rate for sepsis-related patient stays when using the machine learning algorithm in clinical outcomes analysis.

CONCLUSIONS : Reductions of in-hospital mortality, hospital length of stay and 30-day readmissions were observed in real-world clinical use of the machine learning-based algorithm. The predictive algorithm may be successfully used to improve sepsis-related outcomes in live clinical settings.


Burdick Hoyt, Pino Eduardo, Gabel-Comeau Denise, McCoy Andrea, Gu Carol, Roberts Jonathan, Le Sidney, Slote Joseph, Pellegrini Emily, Green-Saxena Abigail, Hoffman Jana, Das Ritankar


computer methodologies, healthcare, information science, medical informatics