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Crit Care Med. 2019 Nov;47(11):1485-1492. doi: 10.1097/CCM.0000000000003891.

A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

Author information

1
Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA.
2
University of Pennsylvania Health System, Philadelphia, PA.
3
Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.
4
Department of Nursing, Hospital of the University of Pennsylvania, Philadelphia, PA.
5
Department of Clinical Informatics, Pennsylvania Hospital, Philadelphia, PA.
6
Penn Presbyterian Medical Center, Philadelphia, PA.
7
Division of General Internal Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.
8
Center for Evidence-based Practice, University of Pennsylvania Health System, Philadelphia, PA.

Abstract

OBJECTIVES:

Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.

DESIGN:

Retrospective cohort for algorithm derivation and validation, pre-post impact evaluation.

SETTING:

Tertiary teaching hospital system in Philadelphia, PA.

PATIENTS:

All non-ICU admissions; algorithm derivation July 2011 to June 2014 (n = 162,212); algorithm validation October to December 2015 (n = 10,448); silent versus alert comparison January 2016 to February 2017 (silent n = 22,280; alert n = 32,184).

INTERVENTIONS:

A random-forest classifier, derived and validated using electronic health record data, was deployed both silently and later with an alert to notify clinical teams of sepsis prediction.

MEASUREMENT AND MAIN RESULT:

Patients identified for training the algorithm were required to have International Classification of Diseases, 9th Edition codes for severe sepsis or septic shock and a positive blood culture during their hospital encounter with either a lactate greater than 2.2 mmol/L or a systolic blood pressure less than 90 mm Hg. The algorithm demonstrated a sensitivity of 26% and specificity of 98%, with a positive predictive value of 29% and positive likelihood ratio of 13. The alert resulted in a small statistically significant increase in lactate testing and IV fluid administration. There was no significant difference in mortality, discharge disposition, or transfer to ICU, although there was a reduction in time-to-ICU transfer.

CONCLUSIONS:

Our machine learning algorithm can predict, with low sensitivity but high specificity, the impending occurrence of severe sepsis and septic shock. Algorithm-generated predictive alerts modestly impacted clinical measures. Next steps include describing clinical perception of this tool and optimizing algorithm design and delivery.

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